ALBERT

Description: Sensors, Vol. 18, Pages 2532: fPADnet: Small and Efficient Convolutional Neural Network for Presentation Attack Detection Sensors doi: 10.3390/s18082532 Authors: Thi Nguyen Eunsoo Park Xuenan Cui Van Nguyen Hakil Kim The rapid growth of fingerprint authentication-based applications makes presentation attack detection, which is the detection of fake fingerprints, become a crucial problem. There have been numerous attempts to deal with this problem; however, the existing algorithms have a significant trade-off between accuracy and computational complexity. This paper proposes a presentation attack detection method using Convolutional Neural Networks (CNN), named fPADnet (fingerprint Presentation Attack Detection network), which consists of Fire and Gram-K modules. Fire modules of fPADnet are designed following the structure of the SqueezeNet Fire module. Gram-K modules, which are derived from the Gram matrix, are used to extract texture information since texture can provide useful features in distinguishing between real and fake fingerprints. Combining Fire and Gram-K modules results in a compact and efficient network for fake fingerprint detection. Experimental results on three public databases, including LivDet 2011, 2013 and 2015, show that fPADnet can achieve an average detection error rate of 2.61%, which is comparable to the state-of-the-art accuracy, while the network size and processing time are significantly reduced.

Description: Sensors, Vol. 18, Pages 2520: High-Efficiency Output Pressure Performance Using Capacitive Micromachined Ultrasonic Transducers with Substrate-Embedded Springs Sensors doi: 10.3390/s18082520 Authors: Byung Chul Lee Amin Nikoozadeh Kwan Kyu Park Butrus T. Khuri-Yakub Capacitive micromachined ultrasonic transducers (CMUTs) with substrate-embedded springs offer highly efficient output pressure performance over conventional CMUTs, owing to their nonflexural parallel plate movement. The embedded silicon springs support thick Si piston plates, creating a large nonflexural average volume displacement efficiency in the operating frequency range from 1–3 MHz. Static and dynamic volume displacements of the nonflexural parallel plates were examined using white light interferometry and laser Doppler vibrometry. In addition, an output pressure measurement in immersion was performed using a hydrophone. The device showed a maximum transmission efficiency of 21 kPa/V, and an average volume displacement efficiency of 1.1 nm/V at 1.85 MHz with a low DC bias voltage of 55 V. The device element outperformed the lead zirconate titanate (PZT) ceramic HD3203, in the maximum transmission efficiency or the average volume displacement efficiency by 1.35 times. Furthermore, its average volume displacement efficiency reached almost 80% of the ideal state-of-the-art single-crystal relaxor ferroelectric materials PMN-0.33PT. Additionally, we confirmed that high-efficiency output pressure could be generated from the CMUT device, by quantitatively comparing the hydrophone measurement of a commercial PZT transducer.

Description: Sensors, Vol. 18, Pages 2527: Self-Sensing Nonlinear Ultrasonic Fatigue Crack Detection under Temperature Variation † Sensors doi: 10.3390/s18082527 Authors: Namgyu Kim Keunyoung Jang Yun-Kyu An This paper proposes a self-sensing nonlinear ultrasonic technique for fatigue crack detection under temperature variations. Fatigue cracks are identified from linear (α) and nonlinear (β) ultrasonic parameters recorded by a self-sensing piezoelectric transducer (PZT). The self-sensing PZT scheme minimizes the data acquisition system’s inherent nonlinearity, which often prevents the identification of fatigue cracks. Also, temperature-dependent false alarms are prevented based on the different behaviors of α and β. The proposed technique was numerically pre-validated with finite element method simulations to confirm the trends of α and β with changing temperature, and then was experimentally validated using an aluminum plate with an artificially induced fatigue crack. These validation tests reveal that fatigue cracks can be detected successfully in realistic conditions of unpredictable temperature and that positive false alarms of 0.12% occur.

Description: Sensors, Vol. 18, Pages 2528: Pendulum-Type Hetero-Core Fiber Optic Accelerometer for Low-Frequency Vibration Monitoring Sensors doi: 10.3390/s18082528 Authors: Hiroshi Yamazaki Ichiro Kurose Michiko Nishiyama Kazuhiro Watanabe In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes, in addition to immunity to electromagnetic interference and chemical corrosions. Thus, the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within (−6°, 4°), and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.

Description: Sensors, Vol. 18, Pages 2521: A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors Sensors doi: 10.3390/s18082521 Authors: Jake Cowton Ilias Kyriazakis Thomas Plötz Jaume Bacardit We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of “healthy environmental data” had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1–7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.

Description: Sensors, Vol. 18, Pages 2525: A Simple and Effective Colorimetric Assay for Glucose Based on MnO2 Nanosheets Sensors doi: 10.3390/s18082525 Authors: Zhengjun Huang Linlin Zheng Feng Feng Yuyuan Chen Zhenzhen Wang Zhen Lin Xinhua Lin Shaohuang Weng Simple and effective methods for the detection of the level of blood glucose are closely linked to the monitoring of people’s health. In the study, MnO2 nanosheets with absorption range of 300 nm~500 nm and obvious yellow color were easily prepared and applied to detect glucose through their absorbance and color. The proposed method is based on the fact that a specific concentration of glucose can be quantitatively transformed into hydrogen peroxide (H2O2) under the catalytic effect of glucose oxidase. Based on the redox reaction of MnO2 with H2O2, yellow MnO2 can be converted into colorless Mn2+ to monitor the concentration of glucose. Under optimal conditions, a simple and effective visual assay for the sensitive and reliable detection of glucose was developed. The linear range was estimated to the range from 0 μM to 100 μM, with a detection limit of 12.8 μM. Furthermore, the proposed colorimetric assay based on MnO2 nanosheets can effectively detect blood glucose of clinical serum samples with accuracy and convenience.

Description: Sensors, Vol. 18, Pages 2549: Accurate Indoor Localization Based on CSI and Visibility Graph Sensors doi: 10.3390/s18082549 Authors: Zhefu Wu Lei Jiang Zhuangzhuang Jiang Bin Chen Kai Liu Qi Xuan Yun Xiang Passive indoor localization techniques can have many important applications. They are nonintrusive and do not require users carrying measuring devices. Therefore, indoor localization techniques are widely used in many critical areas, such as security, logistics, healthcare, etc. However, because of the unpredictable indoor environment dynamics, the existing nonintrusive indoor localization techniques can be quite inaccurate, which greatly limits their real-world applications. To address those problems, in this work, we develop a channel state information (CSI) based indoor localization technique. Unlike the existing methods, we employ both the intra-subcarrier statistics features and the inter-subcarrier network features. Specifically, we make the following contributions: (1) we design a novel passive indoor localization algorithm which combines the statistics and network features; (2) we modify the visibility graph (VG) technique to build complex networks for the indoor localization applications; and (3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve about 96% accuracy on average and is more than 9% better than the state-of-the-art techniques.

Description: Sensors, Vol. 18, Pages 2546: Scoping Review of Systems to Train Psychomotor Skills in Hearing Impaired Children Sensors doi: 10.3390/s18082546 Authors: Victor M. Peñeñory Cristina Manresa-Yee Inmaculada Riquelme Cesar A. Collazos Habib M. Fardoun Objectives: The aim of this work is to provide a scoping review to compile and classify the systems helping train and enhance psychomotor skills in hearing impaired (HI) children. Methods: Based on an exhaustive review on psychomotor deficits in HI children, the procedure used to carry out a scoping review was: select keywords and identify synonyms, select databases and prepare the queries using keywords, analyze the quality of the works found using the PEDro Scale, classify the works based on psychomotor competences, analyze the interactive systems (e.g., sensors), and the achieved results. Results: Thirteen works were found. These works used a variety of sensors and input devices such as cameras, contact sensors, touch screens, mouse and keyboard, tangible objects, haptic and virtual reality (VR) devices. Conclusions: From the research it was possible to contextualize the deficits and psychomotor problems of HI children that prevent their normal development. Additionally, from the analysis of different proposals of interactive systems addressed to this population, it was possible to establish the current state of the use of different technologies and how they contribute to psychomotor rehabilitation.

Description: Sensors, Vol. 18, Pages 2571: Towards a Meaningful 3D Map Using a 3D Lidar and a Camera Sensors doi: 10.3390/s18082571 Authors: Jongmin Jeong Tae Sung Yoon Jin Bae Park Semantic 3D maps are required for various applications including robot navigation and surveying, and their importance has significantly increased. Generally, existing studies on semantic mapping were camera-based approaches that could not be operated in large-scale environments owing to their computational burden. Recently, a method of combining a 3D Lidar with a camera was introduced to address this problem, and a 3D Lidar and a camera were also utilized for semantic 3D mapping. In this study, our algorithm consists of semantic mapping and map refinement. In the semantic mapping, a GPS and an IMU are integrated to estimate the odometry of the system, and subsequently, the point clouds measured from a 3D Lidar are registered by using this information. Furthermore, we use the latest CNN-based semantic segmentation to obtain semantic information on the surrounding environment. To integrate the point cloud with semantic information, we developed incremental semantic labeling including coordinate alignment, error minimization, and semantic information fusion. Additionally, to improve the quality of the generated semantic map, the map refinement is processed in a batch. It enhances the spatial distribution of labels and removes traces produced by moving vehicles effectively. We conduct experiments on challenging sequences to demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and intersection over union.

Description: Sensors, Vol. 18, Pages 2569: An X-band Bi-Directional Transmit/Receive Module for a Phased Array System in 65-nm CMOS Sensors doi: 10.3390/s18082569 Authors: Van-Viet Nguyen Hyohyun Nam Young Joe Choe Bok-Hyung Lee Jung-Dong Park We present an X-band bi-directional transmit/receive module (TRM) for a phased array system utilized in radar-based sensor systems. The proposed module, comprising a 6-bit phase shifter, a 6-bit digital step attenuator, and bi-directional gain amplifiers, is fabricated using 65-nm CMOS technology. By constructing passive networks in the phase-shifter and the variable attenuator, the implemented TRM provides amplitude and phase control with 360° phase coverage and 5.625° as the minimum step size while the attenuation range varies from 0 to 31.5 dB with a step size of 0.5 dB. The fabricated T/R module in all of the phase shift states had RMS phase errors of less than 4° and an RMS amplitude error of less than 0.93 dB at 9–11 GHz. The output 1dB gain compression point (OP1dB) of the chip was 5.13 dBm at 10 GHz. The circuit occupies 3.92 × 2.44 mm2 of the chip area and consumes 170 mW of DC power.

Description: Sensors, Vol. 18, Pages 2561: Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network Sensors doi: 10.3390/s18082561 Authors: Ahmed M. M. Almassri Wan Zuha Wan Hasan Siti Anom Ahmad Suhaidi Shafie Chikamune Wada Keiichi Horio This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model’s capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.

Description: Sensors, Vol. 18, Pages 2583: Q3: A Compact Device for Quick, High Precision qPCR Sensors doi: 10.3390/s18082583 Authors: Marco Cereda Alessandro Cocci Davide Cucchi Lillo Raia Danilo Pirola Lorenzo Bruno Pietro Ferrari Valentina Pavanati Giorgia Calisti Francesco Ferrara Alessandro P. Bramanti Marco A. Bianchessi An accurate and easy-to-use Q3 system for on-chip quantitative real-time Polymerase Chain Reaction (qPCR) is hereby demonstrated, and described in detail. The qPCR reactions take place inside a single-use Lab-on-a-Chip with multiple wells, each with 5 to 15 µL capacity. The same chip hosts a printed metal heater coupled with a calibrated sensor, for rapid and accurate temperature control inside the reaction mixture. The rest of the system is non-disposable and encased in a 7 × 14 × 8.5 (height) cm plastic shell weighing 300 g. Included in the non-disposable part is a fluorescence read-out system featuring up to four channels and a self-contained control and data storage system, interfacing with an external user-friendly software suite. Hereby, we illustrate the engineering details of the Q3 system and benchmark it with seamlessly ported testing protocols, showing that Q3 equals the performance of standard commercial systems. Overall, to the best of our knowledge, this is one of the most mature general-purpose systems for on-chip qPCR currently available.

Description: Sensors, Vol. 18, Pages 2591: A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks Sensors doi: 10.3390/s18082591 Authors: Yixiong Feng Yong Wang Hao Zheng Shanghua Mi Jianrong Tan Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

Description: Sensors, Vol. 18, Pages 2588: Physical Extraction and Feature Fusion for Multi-Mode Signals in a Measurement System for Patients in Rehabilitation Exoskeleton Sensors doi: 10.3390/s18082588 Authors: Canjun Yang Qianxiao Wei Xin Wu Zhangyi Ma Qiaoling Chen Xin Wang Hansong Wang Wu Fan Measurement system of exoskeleton robots can reflect the state of the patient. In this study, we combined an inertial measurement unit and a visual measurement unit to obtain a repeatable fusion measurement system to compensate for the deficiencies of the single data acquisition mode used by exoskeletons. Inertial measurement unit is comprised four distributed angle sensors. Triaxial acceleration and angular velocity information were transmitted to an upper computer by Bluetooth. The data sent to the control center were processed by a Kalman filter to eliminate any noise. Visual measurement unit uses camera to acquire real time images and related data information. The two data acquisition methods were fused and have its weight. Comparisons of the fusion results with individual measurement results demonstrated that the data fusion method could effectively improve the accuracy of system. It provides a set of accurate real-time measurements for patients in rehabilitation exoskeleton and data support for effective control of exoskeleton robot.

Description: Sensors, Vol. 18, Pages 2585: Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor Sensors doi: 10.3390/s18082585 Authors: Anh Vu Le Veerajagadheswar Prabakaran Vinu Sivanantham Rajesh Elara Mohan Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Description: Sensors, Vol. 18, Pages 2597: Distributed Deformation Monitoring for a Single-Cell Box Girder Based on Distributed Long-Gage Fiber Bragg Grating Sensors Sensors doi: 10.3390/s18082597 Authors: Sheng Shen Shao-Fei Jiang Distributed deformation based on fiber Bragg grating sensors or other kinds of strain sensors can be used to monitor bridges during operation. However, most research on distributed deformation monitoring has focused on solid rectangular beams rather than box girders—a kind of typical hollow beam widely employed in actual bridges. The deformation of a single-cell box girder contains bending deflection and also two additional deformations respectively caused by shear lag and shearing action. This paper revises the improved conjugated beam method (ICBM) based on the long-gage fiber Bragg grating (LFBG) sensors to satisfy the requirements for monitoring the two additional deformations in a single-cell box girder. This paper also proposes a suitable LFBG sensor placement in a box girder to overcome the influence of strain fluctuation on the flange caused by the shear lag effect. Results from numerical simulations show that the theoretical monitoring errors of the revised ICBM are typically 0.3–1.5%, and the maximum error is 2.4%. A loading experiment for a single-cell box gilder monitored by LFBG sensors shows that most of the practical monitoring errors are 6–8% and the maximum error is 11%.

Description: Sensors, Vol. 18, Pages 2596: GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing Sensors doi: 10.3390/s18082596 Authors: Willian Zamora Elsa Vera Carlos T. Calafate Juan-Carlos Cano Pietro Manzoni Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client–server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.

Description: Sensors, Vol. 18, Pages 2602: High-Dimensional Probabilistic Fingerprinting in Wireless Sensor Networks Based on a Multivariate Gaussian Mixture Model Sensors doi: 10.3390/s18082602 Authors: Yan Li Simon Williams Bill Moran Allison Kealy Guenther Retscher The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment in which three typical problems arise. Firstly, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Secondly, heterogeneous devices record different received signal strength (RSS) levels because of the variations in chip-set and antenna attenuation. Thirdly, APs are not necessarily visible in every scanning cycle leading to missing data issue. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. To account for spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) was fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP was investigated in this research, and demonstrated the efficiency as a beneficial information to differentiate between cells. The proposed system is able to achieve comparable localisation performance. Filed test results achieve a reliable 97% localisation room level accuracy of multiple mobile users in a real university campus Wi-Fi network.

Description: Sensors, Vol. 18, Pages 2593: Experiments on Temperature Changes of Microbolometer under Blackbody Radiation and Predictions Using Thermal Modeling by COMSOL Multiphysics Simulator Sensors doi: 10.3390/s18082593 Authors: Yu-Zhen Deng Shiang-Feng Tang Hong-Yuan Zeng Zheng-Yuan Wu Der-Kuo Tung In this study, we study a heat transfer model, with the surface of the microbolometer device receiving radiation from blackbody constructed using a COMSOL Multiphysics simulator. We have proposed three kinds of L-type 2-leg and 4-leg with the pixel pitch of 35 μm based on vanadium oxide absorbent membrane sandwiched with top passivated and bottom Si3N4 supporting films, respectively. Under the blackbody radiation, the surface temperature changes and distributions of these samples are simulated and analyzed in detail. The trend of change of the temperature dependent resistance of the four kinds of bolometer devices using the proposed heat transfer model is consistent with the actual results of the change of resistance of 4 samples irradiated with 325 K blackbody located in the front distance of 5 cm. In this paper, ΔT indicates the averaged differences of the top temperature on the suspended membrane and the lowest temperature on the post of legs of the microbolometers. It is shown that ΔT ≈ 17 mK is larger in nominal 2-leg microbolometer device than that of 4-leg one and of 2-leg with 2 μm × 2 μm central square hole and two 7.5 μm × 2 μm slits in suspended films. Additionally, only ΔT ≈ 5 mK with 4-leg microbolometer device under the same radiated energy of 325 K blackbody results from the larger total thermal conductance.

Description: Sensors, Vol. 18, Pages 2616: A Fusion Method for Combining Low-Cost IMU/Magnetometer Outputs for Use in Applications on Mobile Devices Sensors doi: 10.3390/s18082616 Authors: Photis Patonis Petros Patias Ilias N. Tziavos Dimitrios Rossikopoulos Konstantinos G. Margaritis This paper presents a fusion method for combining outputs acquired by low-cost inertial measurement units and electronic magnetic compasses. Specifically, measurements of inertial accelerometer and gyroscope sensors are combined with no-inertial magnetometer sensor measurements to provide the optimal three-dimensional (3D) orientation of the sensors’ axis systems in real time. The method combines Euler–Cardan angles and rotation matrix for attitude and heading representation estimation and deals with the “gimbal lock” problem. The mathematical formulation of the method is based on Kalman filter and takes into account the computational cost required for operation on mobile devices as well as the characteristics of the low-cost microelectromechanical sensors. The method was implemented, debugged, and evaluated in a desktop software utility by using a low-cost sensor system, and it was tested in an augmented reality application on an Android mobile device, while its efficiency was evaluated experimentally.

Description: Sensors, Vol. 18, Pages 2615: Humidity Measurement in Carbon Dioxide with Capacitive Humidity Sensors at Low Temperature and Pressure Sensors doi: 10.3390/s18082615 Authors: Andreas Lorek Jacek Majewski In experimental chambers for simulating the atmospheric near-surface conditions of Mars, or in situ measurements on Mars, the measurement of the humidity in carbon dioxide gas at low temperature and under low pressure is needed. For this purpose, polymer-based capacitive humidity sensors are used; however, these sensors are designed for measuring the humidity in the air on the Earth. The manufacturers provide only the generic calibration equation for standard environmental conditions in air, and temperature corrections of humidity signal. Because of the lack of freely available information regarding the behavior of the sensors in CO2, the range of reliable results is limited. For these reasons, capacitive humidity sensors (Sensirion SHT75) were tested at the German Aerospace Center (DLR) in its Martian Simulation Facility (MSF). The sensors were investigated in cells with a continuously humidified carbon dioxide flow, for temperatures between −70 °C and 10 °C, and pressures between 10 hPa and 1000 hPa. For 28 temperature–pressure combinations, the sensor calibration equations were calculated together with temperature–dependent formulas for the coefficients of the equations. The characteristic curves obtained from the tests in CO2 and in air were compared for selected temperature–pressure combinations. The results document a strong cross-sensitivity of the sensors to CO2 and, compared with air, a strong pressure sensitivity as well. The reason could be an interaction of the molecules of CO2 with the adsorption sites on the thin polymeric sensing layer. In these circumstances, an individual calibration for each pressure with respect to temperature is required. The performed experiments have shown that this kind of sensor can be a suitable, lightweight, and relatively inexpensive choice for applications in harsh environments such as on Mars.

Description: Sensors, Vol. 18, Pages 2612: Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems Sensors doi: 10.3390/s18082612 Authors: Andrés C. Jiménez Vicente García-Díaz Rubén González-Crespo Sandro Bolaños Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system.

Description: Sensors, Vol. 18, Pages 2613: Application of Electronic-Nose Technologies and VOC-Biomarkers for the Noninvasive Early Diagnosis of Gastrointestinal Diseases † Sensors doi: 10.3390/s18082613 Authors: Alphus Dan Wilson Conventional methods utilized for clinical diagnosis of gastrointestinal (GI) diseases have employed invasive medical procedures that cause stress, anxiety and pain to patients. These methods are often expensive, time-consuming, and require sophisticated chemical-analysis instruments and advanced modeling procedures to achieve diagnostic interpretations. This paper reviews recent applications of simpler, electronic-nose (e-nose) devices for the noninvasive early diagnosis of a wide range of GI diseases by collective analysis of headspace volatile organic compound (VOC)-metabolites from clinical samples to produce disease-specific aroma signatures (VOC profiles). A different “metabolomics” approach to GI disease diagnostics, involving identifications and quantifications of disease VOC-metabolites, are compared to the electronic-nose approach based on diagnostic costs, accuracy, advantages and disadvantages. The importance of changes in gut microbiome composition that result from disease are discussed relative to effects on disease detection. A new diagnostic approach, which combines the use of e-nose instruments for early rapid prophylactic disease-screenings with targeted identification of known disease biomarkers, is proposed to yield cheaper, quicker and more dependable diagnostic results. Some priority future research needs and coordination for bringing e-nose instruments into routine clinical practice are summarized.

Description: Sensors, Vol. 18, Pages 2611: Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks Sensors doi: 10.3390/s18082611 Authors: Theofanis P. Raptis Andrea Passarella Marco Conti Maintaining critical data access latency requirements is an important challenge of Industry 4.0. The traditional, centralized industrial networks, which transfer the data to a central network controller prior to delivery, might be incapable of meeting such strict requirements. In this paper, we exploit distributed data management to overcome this issue. Given a set of data, the set of consumer nodes and the maximum access latency that consumers can tolerate, we consider a method for identifying and selecting a limited set of proxies in the network where data needed by the consumer nodes can be cached. The method targets at balancing two requirements; data access latency within the given constraints and low numbers of selected proxies. We implement the method and evaluate its performance using a network of WSN430 IEEE 802.15.4-enabled open nodes. Additionally, we validate a simulation model and use it for performance evaluation in larger scales and more general topologies. We demonstrate that the proposed method (i) guarantees average access latency below the given threshold and (ii) outperforms traditional centralized and even distributed approaches.

Description: Sensors, Vol. 18, Pages 2620: Design and Implementation of a Novel Polarimetric Active Radar Calibrator for Gaofen-3 SAR Sensors doi: 10.3390/s18082620 Authors: Liang Li Yongtao Zhu Jun Hong Feng Ming Yu Wang The Chinese first fully polarimetric space-borne synthetic aperture radar (SAR)-Gaofen-3 (GF-3) was launched in August 2016, which operates at the C-band and the resolution can reach 1 m. Polarimetric SAR calibration is a procedure that corrects the polarization distortion of a measured scattering matrix by referring to the scattering matrix of a known target. The present paper describes the principle, design, manufacture, and measurement results of a novel polarimetric active radar calibrator (PARC) designed for GF-3. A new design method for PARC was presented and two dual-polarized antennas with very high polarization purity were used. The internal calibration technique was introduced to ensure balance in the amplitude and phase, which ensures the precision of the PARC’s scattering matrices. The results we obtained through measurement in the microwave anechoic chamber and experiments in in-orbit calibration agree well with the theoretical predictions, and the novel PARC presented is proved to be well suited for polarization and radiometric calibration of GF-3.

Description: Sensors, Vol. 18, Pages 2631: A Novel and Safe Approach to Simulate Cutting Movements Using Ground Reaction Forces Sensors doi: 10.3390/s18082631 Authors: Amelia S. Lanier Brian A. Knarr Nicholas Stergiou Thomas S. Buchanan Control of shear ground reaction forces (sGRF) is important in performing running and cutting tasks as poor sGRF control has implications for those with knee injuries, such as anterior cruciate ligament (ACL) ruptures. The goal of this study was to develop a novel and safe task to evaluate control or accurate modulation of shear ground reaction forces related to those generated during cutting. Our approach utilized a force control task using real-time visual feedback of a subject’s force production and evaluated control capabilities through accuracy and divergence measurements. Ten healthy recreational athletes completed the force control task while force control via accuracy measures and divergence calculations was investigated. Participants were able to accurately control sGRF in multiple directions based on error measurements. Forces generated during the task were equal to or greater than those measured during a number of functional activities. We found no significant difference in the divergence of the force profiles using the Lyapunov Exponent of the sGRF trajectories. Participants using our approach produced high accuracy and low divergence force profiles and functional force magnitudes. Moving forward, we will utilize this task in at-risk populations who are unable to complete a cutting maneuver in early stages of rehabilitation, such as ACL deficient and newly reconstructed individuals, allowing insight into force control not obtainable otherwise.

Description: Sensors, Vol. 18, Pages 2633: PHAROS—PHysical Assistant RObot System Sensors doi: 10.3390/s18082633 Authors: Angelo Costa Ester Martinez-Martin Miguel Cazorla Vicente Julian The great demographic change leading to an ageing society demands technological solutions to satisfy the increasing varied elderly needs. This paper presents PHAROS, an interactive robot system that recommends and monitors physical exercises designed for the elderly. The aim of PHAROS is to be a friendly elderly companion that periodically suggests personalised physical activities, promoting healthy living and active ageing. Here, it is presented the PHAROS architecture, components and experimental results. The architecture has three main strands: a Pepper robot, that interacts with the users and records their exercises performance; the Human Exercise Recognition, that uses the Pepper recorded information to classify the exercise performed using Deep Leaning methods; and the Recommender, a smart-decision maker that schedules periodically personalised physical exercises in the users’ agenda. The experimental results show a high accuracy in terms of detecting and classifying the physical exercises (97.35%) done by 7 persons. Furthermore, we have implemented a novel procedure of rating exercises on the recommendation algorithm. It closely follows the users’ health status (poor performance may reveal health problems) and adapts the suggestions to it. The history may be used to access the physical condition of the user, revealing underlying problems that may be impossible to see otherwise.

Description: Sensors, Vol. 18, Pages 2634: A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals Sensors doi: 10.3390/s18082634 Authors: Caleb Vununu Kwang-Seok Moon Suk-Hwan Lee Ki-Ryong Kwon Machine fault diagnosis (MFD) has gained an important enthusiasm since the unfolding of the pattern recognition techniques in the last three decades. It refers to all of the studies that aim to automatically detect the faults on the machines using various kinds of signals that they can generate. The present work proposes a MFD system for the drilling machines that is based on the sounds they produce. The first key contribution of this paper is to present a system specifically designed for the drills, by attempting not only to detect the faulty drills but also to detect whether the sounds were generated during the active or the idling stage of the whole machinery system, in order to provide a complete remote control. The second key contribution of the work is to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. The created images, the so-called power spectrum density (PSD)-images, are then given to a deep convolutional autoencoder (DCAE) for a high-level feature extraction process. The final step of the scheme consists of adopting the proposed PSD-images + DCAE features as the final representation of the original sounds and utilize them as the inputs of a nonlinear classifier whose outputs will represent the final diagnosis decision. The results of the experiments demonstrate the high discrimination potential afforded by the proposed PSD-images + DCAE features. They were also tested on a noisy dataset and the results show their robustness against noises.

Description: Sensors, Vol. 18, Pages 2641: Salivary Detection of Dengue Virus NS1 Protein with a Label-Free Immunosensor for Early Dengue Diagnosis Sensors doi: 10.3390/s18082641 Authors: Daniel Wasik Ashok Mulchandani Marylynn V. Yates Dengue virus (DENV) is a highly pathogenic, arthropod-borne virus transmitted between people by Aedes mosquitoes. Despite efforts to prevent global spread, the potential for DENV epidemics is increasing world-wide. Annually, 3.6 billion people are at risk of infection. With no licensed vaccine, early diagnosis of dengue infection is critical for clinical management and patient survival. Detection of DENV non-structural protein 1 (NS1) is a clinically accepted biomarker for the early detection of DENV infection. Unfortunately, virtually all of the laboratory and commercial DENV NS1 diagnostic methods require a blood draw for sample analysis, limiting point-of-care diagnostics and decreases patient willingness. Alternatively, NS1 in human saliva has been identified for the potential early diagnosis of DENV infection. The collection of saliva is simple, non-invasive, painless, and inexpensive, even by minimally trained personnel. In this study, we present a label-free chemiresistive immunosensor for the detection of the DENV NS1 protein utilizing a network of single-walled carbon nanotubes functionalized with anti-dengue NS1 monoclonal antibodies. NS1 was successfully detected in adulterated artificial human saliva over the range of clinically relevant concentrations with high sensitivity and selectivity. It has potential application in clinical diagnosis and the ease of collection allows for self-testing, even within the home.

Description: Sensors, Vol. 18, Pages 2643: Galfenol Thin Films and Nanowires Sensors doi: 10.3390/s18082643 Authors: Bethanie J. H. Stadler Madhukar Reddy Rajneeta Basantkumar Patrick McGary Eliot Estrine Xiaobo Huang Sang Yeob Sung Liwen Tan Jia Zou Mazin Maqableh Daniel Shore Thomas Gage Joseph Um Matthew Hein Anirudh Sharma Galfenol (Fe1−xGax, 10 < x < 40) may be the only smart material that can be made by electrochemical deposition which enables thick film and nanowire structures. This article reviews the deposition, characterization, and applications of Galfenol thin films and nanowires. Galfenol films have been made by sputter deposition as well as by electrochemical deposition, which can be difficult due to the insolubility of gallium. However, a stable process has been developed, using citrate complexing, a rotating disk electrode, Cu seed layers, and pulsed deposition. Galfenol thin films and nanowires have been characterized for crystal structures and magnetostriction both by our group and by collaborators. Films and nanowires have been shown to be largely polycrystalline, with magnetostrictions that are on the same order of magnitude as textured bulk Galfenol. Electrodeposited Galfenol films were made with epitaxial texture on GaAs. Galfenol nanowires have been made by electrodeposition into anodic aluminum oxide templates using similar parameters defined for films. Segmented nanowires of Galfenol/Cu have been made to provide engineered magnetic properties. Applications of Galfenol and other magnetic nanowires include microfluidic sensors, magnetic separation, cellular radio-frequency identification (RFID) tags, magnetic resonance imaging (MRI) contrast, and hyperthermia.

Description: Sensors, Vol. 18, Pages 2642: Adaptive Discrete Vector Field in Sensor Networks Sensors doi: 10.3390/s18082642 Authors: Mengyi Zhang Alban Goupil Homology groups are a prime tool for measuring the connectivity of a network, and their computation in a distributed and adaptive way is mandatory for their use in sensor networks. In this paper, we propose a solution based on the construction of an adaptive discrete vector field from where, thanks to the discrete Morse theory, the generators of the homology groups are extracted. The efficiency and the adaptability of our approach are tested against two applications: the detection and the localization of the holes in the coverage, and the selection of active sensors ensuring complete coverage.

Description: Sensors, Vol. 18, Pages 2673: One-Step Laser Encapsulation of Nano-Cracking Strain Sensors Sensors doi: 10.3390/s18082673 Authors: Chan Park Hyunsuk Jung Hyunwoo Lee Sunguk Hong Hyonguk Kim Seong J. Cho Development of flexible strain sensors that can be attached directly onto the skin, such as skin-mountable or wearable electronic devices, has recently attracted attention. However, such flexible sensors are generally exposed to various harsh environments, such as sweat, humidity, or dust, which cause noise and shorten the sensor lifetimes. This study reports the development of a nano-crack-based flexible sensor with mechanically, thermally, and chemically stable electrical characteristics in external environments using a novel one-step laser encapsulation (OLE) method optimized for thin films. The OLE process allows simultaneous patterning, cutting, and encapsulating of a device using laser cutting and thermoplastic polymers. The processes are simplified for economical and rapid production (one sensor in 8 s). Unlike other encapsulation methods, OLE does not degrade the performance of the sensor because the sensing layers remain unaffected. Sensors protected with OLE exhibit mechanical, thermal, and chemical stability under water-, heat-, dust-, and detergent-exposed conditions. Finally, a waterproof, flexible strain sensor is developed to detect motions around the eye, where oil and sweat are generated. OLE-based sensors can be used in several applications that are exposed to a large amount of foreign matter, such as humid or sweaty environments.

Description: Sensors, Vol. 18, Pages 2672: A Forward Collision Warning System for Smartphones Using Image Processing and V2V Communication Sensors doi: 10.3390/s18082672 Authors: Subhadeep Patra Peter Veelaert Carlos T. Calafate Juan-Carlos Cano Willian Zamora Pietro Manzoni Fabio González In this paper, we present a forward collision warning application for smartphones that uses license plate recognition and vehicular communication to generate warnings for notifying the drivers of the vehicle behind and the one ahead, of a probable collision when the vehicle behind does not maintain an established safe distance between itself and the vehicle ahead. The application was tested in both static and mobile scenarios, from which we confirmed the working of our application, even though its performance is affected by the hardware limitations of the smartphones.

Description: Sensors, Vol. 18, Pages 2669: A Fiber Bragg Grating Based Torsional Vibration Sensor for Rotating Machinery Sensors doi: 10.3390/s18082669 Authors: Jingjing Wang Li Wei Ruiya Li Qin Liu Lingling Yu This paper proposes a new type of torsional vibration sensor based on fiber Bragg grating (FBG). The sensor has two mass ball optical fiber systems. The optical fiber is directly treated as an elastomer and a mass ball is fixed in the middle of the fiber in each mass ball fiber system, which is advantageously small, lightweight, and has anti-electromagnetic interference properties. The torsional vibration signal can be calculated by the four FBGs’ wavelength shifts, which are caused by mass balls. The difference in the two sets of mass ball optical fiber systems achieves anti-horizontal vibration and anti-temperature interference. The principle and model of the sensor, as well as numerical analysis and structural parameter design, are introduced. The experimental conclusions show that the minimum torsional natural frequency of the sensor is 27.35 Hz and the torsional vibration measurement sensitivity is 0.3603 pm/(rad/s2).

Description: Sensors, Vol. 18, Pages 2670: Tracking Personal Health-Environment Interaction with Novel Mobile Sensing Devices Sensors doi: 10.3390/s18082670 Authors: Yue Deng Nai-Yuan Liu Francis Tsow Xiaojun Xian Rosa Krajmalnik-Brown Nongjian Tao Erica Forzani The development of connected health devices has allowed for a more accurate assessment of a person’s state under free-living conditions. In this work, we use two mobile sensing devices and investigate the correlation between individual’s resting metabolic rate (RMR) and volatile organic compounds (VOCs) exposure levels. A total of 17 healthy, young, and sedentary office workers were recruited, measured for RMR with a mobile indirect calorimetry (IC) device, and compared with their corresponding predicted RMR values from the Academy of Nutrition and Dietetics’ recommended epidemiological equation, the Mifflin–St Jeor equation (MSJE). Individual differences in the RMR values from the IC device and the epidemiological equation were found, and the subjects’ RMRs were classified as normal, high, or low based on a cut-off of ±200 kcal/day difference with respect to the predicted value. To study the cause of the difference, VOCs exposure levels of each participant’s daytime working environment and nighttime resting environment were assessed using a second mobile sensing device for VOCs exposure detection. The results showed that all sedentary office workers had a low VOCs exposure level (<2 ppmC), and there was no obvious correlation between VOCs exposure and the RMR difference. However, an additional participant who was a worker in an auto repair shop, showed high VOCs exposure with respect to the sedentary office worker population and a significant difference between measured and predicted RMR, with a low RMR of 500 kcal/day difference. The mobile sensing devices have been demonstrated to be suitable for the assessment of direct information of human health–environment interactions at free-living conditions.

Description: Sensors, Vol. 18, Pages 2668: Biosensors for the Detection of Interaction between Legionella pneumophila Collagen-Like Protein and Glycosaminoglycans Sensors doi: 10.3390/s18082668 Authors: Han Su Shaopei Li Mauricio Terebiznik Cyril Guyard Kagan Kerman The adhesin Legionella collagen-like (Lcl) protein can bind to extracellular matrix components and mediate the binding of Legionella pneumophila to host cells. In this study, electrochemical impedance spectroscopy (EIS) and surface plasmon resonance (SPR)-based biosensors were employed to characterize these interactions between glycosaminoglycans (GAGs) and the adhesin Lcl protein. Fucoidan displayed a high affinity (KD 18 nM) for Lcl protein. Chondroitin sulfate A and dermatan sulfate differ in the position of a carboxyl group replacing D-glucuronate with D-iduronate. Our results indicated that the presence of D-iduronate in dermatan sulfate strongly hindered its interaction with Lcl. These biophysical studies provided valuable information in our understanding of adhesin-ligand interactions related to Legionella pneumophila infections.

Description: Sensors, Vol. 18, Pages 2666: Fine-Grained Face Annotation Using Deep Multi-Task CNN Sensors doi: 10.3390/s18082666 Authors: Luigi Celona Simone Bianco Raimondo Schettini We present a multi-task learning-based convolutional neural network (MTL-CNN) able to estimate multiple tags describing face images simultaneously. In total, the model is able to estimate up to 74 different face attributes belonging to three distinct recognition tasks: age group, gender and visual attributes (such as hair color, face shape and the presence of makeup). The proposed model shares all the CNN’s parameters among tasks and deals with task-specific estimation through the introduction of two components: (i) a gating mechanism to control activations’ sharing and to adaptively route them across different face attributes; (ii) a module to post-process the predictions in order to take into account the correlation among face attributes. The model is trained by fusing multiple databases for increasing the number of face attributes that can be estimated and using a center loss for disentangling representations among face attributes in the embedding space. Extensive experiments validate the effectiveness of the proposed approach.

Description: Sensors, Vol. 18, Pages 2664: Device-Based Security to Improve User Privacy in the Internet of Things † Sensors doi: 10.3390/s18082664 Authors: Luis Alberto Belem Pacheco Eduardo Adilio Pelinson Alchieri Priscila América Solís Mendez Barreto The use of Internet of Things (IoT) is rapidly growing and a huge amount of data is being generated by IoT devices. Cloud computing is a natural candidate to handle this data since it has enough power and capacity to process, store and control data access. Moreover, this approach brings several benefits to the IoT, such as the aggregation of all IoT data in a common place and the use of cloud services to consume this data and provide useful applications. However, enforcing user privacy when sending sensitive information to the cloud is a challenge. This work presents and evaluates an architecture to provide privacy in the integration of IoT and cloud computing. The proposed architecture, called PROTeCt—Privacy aRquitecture for integratiOn of internet of Things and Cloud computing, improves user privacy by implementing privacy enforcement at the IoT devices instead of at the gateway, as is usually done. Consequently, the proposed approach improves both system security and fault tolerance, since it removes the single point of failure (gateway). The proposed architecture is evaluated through an analytical analysis and simulations with severely constrained devices, where delay and energy consumption are evaluated and compared to other architectures. The obtained results show the practical feasibility of the proposed solutions and demonstrate that the overheads introduced in the IoT devices are worthwhile considering the increased level of privacy and security.

Description: Sensors, Vol. 18, Pages 2698: Functionalized Silver Nano-Sensor for Colorimetric Detection of Hg2+ Ions: Facile Synthesis and Docking Studies Sensors doi: 10.3390/s18082698 Authors: Kollur Shiva Prasad Govindaraju Shruthi Chandan Shivamallu In the present study, we describe the facile synthesis of silver nanoparticles (AgNPs) and their nanostructures functionalized with 2-aminopyrimidine-4,6-diol (APD-AgNPs) for Hg2+ ion detection. The promising colorimetric response of APD-AgNPs to detect Hg2+ ions was visible with naked eyes and spectroscopic changes were examined by using a UV-Visible spectrophotometer. The aggregation of APD-AgNPs upon addition of Hg2+ ions was due to the chelation effect of the functionalized nanostructures and results in a color change from pale brown to deep yellow color. The probing sensitivity was observed within five minutes with a detection limit of about 0.35 µM/L. The TEM images of APD-AgNPs showed polydispersed morphologies with hexagonal, heptagonal and spherical nanostructures with an average size between 10 to 40 nm. Furthermore, the sensing behavior of APD-AgNPs towards Hg2+ ions detection was investigated using docking and interaction studies.

Description: Sensors, Vol. 18, Pages 2699: Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations Sensors doi: 10.3390/s18082699 Authors: Jian Li Liqiao Tian Qingjun Song Zhaohua Sun Hongjing Yu Qianguo Xing Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

Description: Sensors, Vol. 18, Pages 2695: Analysis of Water, Ethanol, and Fructose Mixtures Using Nondestructive Resonant Spectroscopy of Mechanical Vibrations and a Grouping Genetic Algorithm Sensors doi: 10.3390/s18082695 Authors: Pilar García Díaz Juan Antonio Martínez Rojas Manuel Utrilla Manso Leticia Monasterio Expósito A new haptic sensor that is based on vibration produced by mechanical excitation from a clock coupled to a resonant cavity is presented. This sensor is intended to determine the chemical composition of liquid mixtures in a completely non-destructive method. In this case, a set of 23 samples of water, ethanol, and fructose mixtures has been used to simulate different kinds of alcoholic beverage. The spectral information from the vibrational absorption bands of liquid samples is analyzed by a Grouping Genetic Algorithm. An Extreme Learning Machine implements the fitness function that is able to classify the mixtures according to the concentration of ethanol and fructose. The 23 samples range from 0%–13% by volume of ethanol and from 0–3 g/L of fructose, all of them with different concentration. The new technique achieves an average classification accuracy of 96%.

Description: Sensors, Vol. 18, Pages 2694: Modification of the AFM Sensor by a Precisely Regulated Air Stream to Increase Imaging Speed and Accuracy in the Contact Mode Sensors doi: 10.3390/s18082694 Authors: Andrius Dzedzickis Vytautas Bucinskas Darius Viržonis Nikolaj Sesok Arturas Ulcinas Igor Iljin Ernestas Sutinys Sigitas Petkevicius Justinas Gargasas Inga Morkvenaite-Vilkonciene Increasing the imaging rate of atomic force microscopy (AFM) without impairing of the imaging quality is a challenging task, since the increase in the scanning speed leads to a number of artifacts related to the limited mechanical bandwidth of the AFM components. One of these artifacts is the loss of contact between the probe tip and the sample. We propose to apply an additional nonlinear force on the upper surface of a cantilever, which will help to keep the tip and surface in contact. In practice, this force can be produced by the precisely regulated airflow. Such an improvement affects the AFM system dynamics, which were evaluated using a mathematical model that is presented in this paper. The model defines the relationships between the additional nonlinear force, the pressure of the applied air stream, and the initial air gap between the upper surface of the cantilever and the end of the air duct. It was found that the nonlinear force created by the stream of compressed air (aerodynamic force) prevents the contact loss caused by the high scanning speed or the higher surface roughness, thus maintaining stable contact between the probe and the surface. This improvement allows us to effectively increase the scanning speed by at least 10 times using a soft (spring constant of 0.2 N/m) cantilever by applying the air pressure of 40 Pa. If a stiff cantilever (spring constant of 40 N/m) is used, the potential of vertical deviation improvement is twice is large. This method is suitable for use with different types of AFM sensors and it can be implemented practically without essential changes in AFM sensor design.

Description: Sensors, Vol. 18, Pages 2696: Comparative Computational and Experimental Detection of Adenosine Using Ultrasensitive Surface-Enhanced Raman Spectroscopy Sensors doi: 10.3390/s18082696 Authors: Emma M. Sundin John D. Ciubuc Kevin E. Bennet Katia Ochoa Felicia S. Manciu To better understand detection and monitoring of the important neurotransmitter adenosine at physiological levels, this study combines quantum chemical density functional modeling and ultrasensitive surface-enhanced Raman spectroscopic (SERS) measurements. Combined simulation results and experimental data for an analyte concentration of about 10−11 molar indicate the presence of all known molecular forms resulting from adenosine’s complex redox-reaction. Detailed analysis presented here, besides assessing potential Raman signatures of these adenosinic forms, also sheds light on the analytic redox process and voltammetric detection. Examples of adenosine Raman fingerprints for different molecular orientations with respect to the SERS substrate are the vibrational line around 920 ± 10 cm−1 for analyte physisorption through the carbinol moiety and around 1600 ± 20 cm−1 for its fully oxidized form. However, both hydroxyl/oxygen sites and NH2/nitrogen sites contribute to molecule’s interaction with the SERS environment. Our results also reveal that contributions of partially oxidized adenosine forms and of the standard form are more likely to be detected with the first recorded voltammetric oxidation peak. The fully oxidized adenosine form contributes mostly to the second peak. Thus, this comparative theoretical–experimental investigation of adenosine’s vibrational signatures provides significant insights for advancing its detection, and for future development of opto-voltammetric biosensors.

Description: Sensors, Vol. 18, Pages 2688: High Temperature High Sensitivity Multipoint Sensing System Based on Three Cascade Mach–Zehnder Interferometers Sensors doi: 10.3390/s18082688 Authors: Na Zhao Qijing Lin Zhuangde Jiang Kun Yao Bian Tian Xudong Fang Peng Shi Zhongkai Zhang A temperature multipoint sensing system based on three cascade Mach–Zehnder interferometers (MZIs) is introduced. The MZIs with different lengths are fabricated based on waist-enlarged fiber bitapers. The fast Fourier transformation is applied to the overlapping transmission spectrum and the corresponding interference spectra can be obtained via the cascaded frequency spectrum based on the inverse Fourier transformation. By analyzing the drift of interference spectra, the temperature response sensitivities of 0.063 nm/°C, 0.071 nm/°C, and 0.059 nm/°C in different furnaces can be detected from room temperature up to 1000 °C, and the temperature response at different regions can be measured through the sensitivity matrix equation. These results demonstrate feasibility of multipoint measurement, which also support that the temperature sensing system provides new solution to the MZI cascade problem.

Description: Sensors, Vol. 18, Pages 2687: Robust Channel Allocation with Heterogeneous Requirements for Wireless Mesh Backbone Networks Sensors doi: 10.3390/s18082687 Authors: Pangun Park Bang Jung Hyungjoo Lee Dae-Jin Jung When multiple mobile sensors and actuators share a common wireless mesh backbone network of defence systems, the channel allocation mechanism must guarantee the heterogeneous link requirements under conditions of uncertainty. In this paper, a robust channel allocation mechanism is proposed by exploiting partially overlapped channels for directional multi-channel wireless mesh networks. The approach relies on a chance-constrained optimization problem, in which the objective is to minimize the spectrum usage of the network, and the constraints are the signal-to-interference-plus-noise ratio requirements of links with uncertainty. We convert the proposed integer non-linear optimization problem into a mixed-integer convex problem by using efficient transition and approximation. The optimal channel allocation is obtained by solving the proposed optimization problem which adapts to the heterogeneous link and robustness requirements. The simulation results show that the proposed method ensures the heterogeneous link requirements under uncertain conditions while minimizing the spectrum usage of the network.

Description: Sensors, Vol. 18, Pages 2720: Sensitivity-Based Fault Detection and Isolation Algorithm for Road Vehicle Chassis Sensors Sensors doi: 10.3390/s18082720 Authors: Na Park Lee Yu Lee Vehicle control systems such as ESC (electronic stability control), MDPS (motor-driven power steering), and ECS (electronically controlled suspension) improve vehicle stability, driver comfort, and safety. Vehicle control systems such as ACC (adaptive cruise control), LKA (lane-keeping assistance), and AEB (autonomous emergency braking) have also been actively studied in recent years as functions that assist drivers to a higher level. These DASs (driver assistance systems) are implemented using vehicle sensors that observe vehicle status and send signals to the ECU (electronic control unit). Therefore, the failure of each system sensor affects the function of the system, which not only causes discomfort to the driver but also increases the risk of accidents. In this paper, we propose a new method to detect and isolate faults in a vehicle control system. The proposed method calculates the constraints and residuals of 12 systems by applying the model-based fault diagnosis method to the sensor of the chassis system. To solve the inaccuracy in detecting and isolating sensor failure, we applied residual sensitivity to a threshold that determines whether faults occur. Moreover, we applied a sensitivity analysis to the parameters semi-correlation table to derive a fault isolation table. To validate the FDI (fault detection and isolation) algorithm developed in this study, fault signals were injected and verified in the HILS (hardware-in-the-loop simulation) environment using an RCP (rapid control prototyping) device.

Description: Sensors, Vol. 18, Pages 2715: A Contactless Sensor for Pacemaker Pulse Detection: Design Hints and Performance Assessment Sensors doi: 10.3390/s18082715 Authors: Emilio Andreozzi Gaetano D. Gargiulo Antonio Fratini Daniele Esposito Paolo Bifulco Continuous monitoring of pacemaker activity can provide valuable information to improve patients’ follow-up. Concise information is stored in some types of pacemakers, whereas ECG can provide more detailed information, but requires electrodes and cannot be used for continuous monitoring. This study highlights the possibility of a continuous monitoring of pacemaker pulses by sensing magnetic field variations due to the current pulses. This can be achieved by means of a sensor coil positioned near the patient’s thorax without any need for physical contact. A simplified model of coil response to pacemaker pulses is presented in this paper, along with circuits suitable for pulse detection. In vitro tests were carried out using real pacemakers immersed in saline solution; experimental data were used to assess the accuracy of the model and to evaluate the sensor performance. It was found that the coil signal amplitude decreases with increasing distance from the pacemaker lead wire. The sensor was able to easily perform pacemaker spike detection up to a distance of 12 cm from the pacemaker leads. The stimulation rate can be measured in real time with high accuracy. Since any electromagnetic pulse triggers the same coil response, EMI may corrupt sensor measurements and thus should be discriminated.

Description: Sensors, Vol. 18, Pages 2716: Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films Sensors doi: 10.3390/s18082716 Authors: Coral Salvo-Comino Celia García-Hernández Cristina García-Cabezón Maria Luz Rodríguez-Méndez A nanostructured electrochemical bi-sensor system for the analysis of milks has been developed using the layer-by-layer technique. The non-enzymatic sensor [CHI+IL/CuPcS]2, is a layered material containing a negative film of the anionic sulfonated copper phthalocyanine (CuPcS) acting as electrocatalytic material, and a cationic layer containing a mixture of an ionic liquid (IL) (1-butyl-3-methylimidazolium tetrafluoroborate) that enhances the conductivity, and chitosan (CHI), that facilitates the enzyme immobilization. The biosensor ([CHI+IL/CuPcS]2-GAO) results from the immobilization of galactose oxidase on the top of the LbL layers. FTIR, UV–vis, and AFM have confirmed the proposed structure and cyclic voltammetry has demonstrated the amplification caused by the combination of materials in the film. Sensors have been combined to form an electronic tongue for milk analysis. Principal component analysis has revealed the ability of the sensor system to discriminate between milk samples with different lactose content. Using a PLS-1 calibration models, correlations have been found between the voltammetric signals and chemical parameters measured by classical methods. PLS-1 models provide excellent correlations with lactose content. Additional information about other components, such as fats, proteins, and acidity, can also be obtained. The method developed is simple, and the short response time permits its use in assaying milk samples online.

Description: Sensors, Vol. 18, Pages 2717: Bragg-Grating-Based Photonic Strain and Temperature Sensor Foils Realized Using Imprinting and Operating at Very Near Infrared Wavelengths Sensors doi: 10.3390/s18082717 Authors: Jeroen Missinne Nuria Teigell Benéitez Marie-Aline Mattelin Alfredo Lamberti Geert Luyckx Wim Van Paepegem Geert Van Steenberge Thin and flexible sensor foils are very suitable for unobtrusive integration with mechanical structures and allow monitoring for example strain and temperature while minimally interfering with the operation of those structures. Electrical strain gages have long been used for this purpose, but optical strain sensors based on Bragg gratings are gaining importance because of their improved accuracy, insusceptibility to electromagnetic interference, and multiplexing capability, thereby drastically reducing the amount of interconnection cables required. This paper reports on thin polymer sensor foils that can be used as photonic strain gage or temperature sensors, using several Bragg grating sensors multiplexed in a single polymer waveguide. Compared to commercially available optical fibers with Bragg grating sensors, our planar approach allows fabricating multiple, closely spaced sensors in well-defined directions in the same plane realizing photonic strain gage rosettes. While most of the reported Bragg grating sensors operate around a wavelength of 1550 nm, the sensors in the current paper operate around a wavelength of 850 nm, where the material losses are the lowest. This was accomplished by imprinting gratings with pitches 280 nm, 285 nm, and 290 nm at the core-cladding interface of an imprinted single mode waveguide with cross-sectional dimensions 3 × 3 µm2. We show that it is possible to realize high-quality imprinted single mode waveguides, with gratings, having only a very thin residual layer which is important to limit bend losses or cross-talk with neighboring waveguides. The strain and temperature sensitivity of the Bragg grating sensors was found to be 0.85 pm/µε and −150 pm/°C, respectively. These values correspond well with those of previously reported sensors based on the same materials but operating around 1550 nm, taking into account that sensitivity scales with the wavelength.

Description: Sensors, Vol. 18, Pages 2726: Direct Detection of Toxic Contaminants in Minimally Processed Food Products Using Dendritic Surface-Enhanced Raman Scattering Substrates Sensors doi: 10.3390/s18082726 Authors: Hannah Dies Maria Siampani Carlos Escobedo Aristides Docoslis We present a method for the surface-enhanced Raman scattering (SERS)-based detection of toxic contaminants in minimally processed liquid food products, through the use of a dendritic silver nanostructure, produced through electrokinetic assembly of nanoparticles from solution. The dendritic nanostructure is produced on the surface of a microelectrode chip, connected to an AC field with an imposed DC bias. We apply this chip for the detection of thiram, a toxic fruit pesticide, in apple juice, to a limit of detection of 115 ppb, with no sample preprocessing. We also apply the chip for the detection of melamine, a toxic contaminant/food additive, to a limit of detection of 1.5 ppm in milk and 105 ppb in infant formula. All the reported limits of detection are below the recommended safe limits in food products, rendering this technique useful as a screening method to identify liquid food with hazardous amounts of toxic contaminants.

Description: Sensors, Vol. 18, Pages 2724: Optimal Deployment of FiWi Networks Using Heuristic Method for Integration Microgrids with Smart Metering Sensors doi: 10.3390/s18082724 Authors: Esteban Inga Miguel Campaña Roberto Hincapié Oswaldo Moscoso-Zea The unpredictable increase in electrical demand affects the quality of the energy throughout the network. A solution to the problem is the increase of distributed generation units, which burn fossil fuels. While this is an immediate solution to the problem, the ecosystem is affected by the emission of CO2. A promising solution is the integration of Distributed Renewable Energy Sources (DRES) with the conventional electrical system, thus introducing the concept of Smart Microgrids (SMG). These SMGs require a safe, reliable and technically planned two-way communication system. This paper presents a heuristic based on planning capable of providing a bidirectional communication that is near optimal. The model follows the structure of a hybrid Fiber-Wireless (FiWi) network with the purpose of obtaining information of electrical parameters that help us to manage the use of energy by integrating conventional electrical system with SMG. The optimization model is based on clustering techniques, through the construction of balanced conglomerates. The method is used for the development of the clusters along with the Nearest-Neighbor Spanning Tree algorithm (N-NST). Additionally, the Optimal Delay Balancing (ODB) model will be used to minimize the end to end delay of each grouping. In addition, the heuristic observes real design parameters such as: capacity and coverage. Using the Dijkstra algorithm, the routes are built following the shortest path. Therefore, this paper presents a heuristic able to plan the deployment of Smart Meters (SMs) through a tree-like hierarchical topology for the integration of SMG at the lowest cost.

Description: Sensors, Vol. 18, Pages 2705: Smart Data-Driven Optimization of Powered Prosthetic Ankles Using Surface Electromyography Sensors doi: 10.3390/s18082705 Authors: Roozbeh Atri J. Sebastian Marquez Connie Leung Masudur R. Siddiquee Douglas P. Murphy Ashraf S. Gorgey William T. Lovegreen Ding-Yu Fei Ou Bai The advent of powered prosthetic ankles provided more balance and optimal energy expenditure to lower amputee gait. However, these types of systems require an extensive setup where the parameters of the ankle, such as the amount of positive power and the stiffness of the ankle, need to be setup. Currently, calibrations are performed by experts, who base the inputs on subjective observations and experience. In this study, a novel evidence-based tuning method was presented using multi-channel electromyogram data from the residual limb, and a model for muscle activity was built. Tuning using this model requires an exhaustive search over all the possible combinations of parameters, leading to computationally inefficient system. Various data-driven optimization methods were investigated and a modified Nelder–Mead algorithm using a Latin Hypercube Sampling method was introduced to tune the powered prosthetic. The results of the modified Nelder–Mead optimization were compared to the Exhaustive search, Genetic Algorithm, and conventional Nelder–Mead method, and the results showed the feasibility of using the presented method, to objectively calibrate the parameters in a time-efficient way using biological evidence.

Description: Sensors, Vol. 18, Pages 2706: Semi-Supervised Generative Adversarial Nets with Multiple Generators for SAR Image Recognition Sensors doi: 10.3390/s18082706 Authors: Fei Gao Fei Ma Jun Wang Jinping Sun Erfu Yang Huiyu Zhou As an important model of deep learning, semi-supervised learning models are based on Generative Adversarial Nets (GANs) and have achieved a competitive performance on standard optical images. However, the training of GANs becomes unstable when they are applied to SAR images, which reduces the feature extraction capability of the discriminator in GANs. This paper presents a new semi-supervised GANs with Multiple generators and a classifier (MCGAN). This model improves the stability of training for SAR images by employing multiple generators. A multi-classifier is introduced to the new GANs to utilize the labeled images during the training of the GANs, which shares the low level layers with the discriminator. Then, the layers of the trained discriminator and the classifier construct the recognition network for SAR images after having been finely tuned using a small number of the labeled images. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) databases show that the proposed recognition network achieves a better and more stable recognition performance than several traditional semi-supervised methods as well as other GANs-based semi-supervised methods.

Description: Sensors, Vol. 18, Pages 2707: A Uniform Linear Multi-Coil Array-Based Borehole Transient Electromagnetic System for Non-Destructive Evaluations of Downhole Casings Sensors doi: 10.3390/s18082707 Authors: Bo Dang Ling Yang Changzan Liu Yahong Zheng Hui Li Ruirong Dang Baoquan Sun Borehole transient electromagnetic (TEM) techniques have been proven to be efficient for nondestructive evaluations (NDEs) of metal casings using eddy-current properties. However, physical limitations and bad borehole conditions restrict the use of eddy-current sensors, which makes downhole casing inspections very different from those of conventional NDE systems. In this paper, we present a uniform linear multi-coil array-based borehole TEM system for NDEs of downhole casings. On the basis of the borehole TEM signal model, a numerical multi-coil array approach using the Gauss–Legendre quadrature is derived. The TEM response can be divided into two independent parts related to the transmitting-receiving distance (TRD) and the observation time and casing thickness. Using this property, the signal received by the multi-coil array is weighted to cancel the influence of the TRDs of the different array elements to obtain the optimal response according to the linearly constrained minimum variance criterion, which can be shown to be identical to that of achieving the maximum signal-to-noise ratio. The effectiveness of the proposed method was verified by applying the uniform linear multi-coil array to a borehole TEM system for NDEs of oil-well casings. Field experiments were conducted, and the results demonstrate the effectiveness of the proposed method.

Description: Sensors, Vol. 18, Pages 2703: Opportunistic and Location-Based Collaboration Architecture among Mobile Assets and Fixed Manufacturing Processes Sensors doi: 10.3390/s18082703 Authors: Dae Han Wi Hyo Jun Kwon Jung Kwang Park Soon Ju Kang Jae Duck Lee Research into integrating the concept of the internet of things (IoT) into smart factories has accelerated, leading to the emergence of various smart factory solutions. Most ideas, however, focus on the automation and integration of processes in factory, rather than organic cooperation among mobile assets (e.g., the workers and manufactured products) and fixed manufacturing equipment (e.g., press molds, computer numerical controls, painting). Additionally, it is difficult to apply smart factory and IoT designs to analog factories, because such a factory would require the integration of mobile assets and smart manufacturing processes. Thus, existing analog factories remain intact and smart factories are newly constructed. To overcome this disparity and to make analog factories compatible with smart technologies and IoT, we propose the opportunistic and location-based collaboration architecture (OLCA) platform, which allows for smart devices to be attached to workers, products, and facilities to enable the collaboration of location and event information in devices. Using this system, we can monitor workers’ positions and production processes in real-time to help prevent dangerous situations and better understand product movement. We evaluate the proposed OLCA platform’s performance while using a simple smart factory scenario, thus confirming its suitability.

Description: Sensors, Vol. 18, Pages 2700: Continuous Monitoring of Respiratory Rate in Emergency Admissions: Evaluation of the RespiraSense™ Sensor in Acute Care Compared to the Industry Standard and Gold Standard Sensors doi: 10.3390/s18082700 Authors: Christian Peter Subbe Sean Kinsella Respiratory Rate (RR) is the best marker to indicate deterioration but measurement are often inaccurate. The RespiraSense™ is a non-invasive, wireless, body worn, motion-tolerant and continuous respiratory rate monitor. We aimed to determine whether the performance of RespiraSense™ was equivalent to that of a gold standard measurement technique of capnography and the industry standard of manual counts. RespiraSense™ measures respiratory rate and transmit signals wirelessly to a tablet device. We measured respiratory rate in 24 emergency admissions to an Acute Medical Unit in the UK. Patients were observed for two hours. Manual counts were undertaken every 15 min and compared to measurements with capnography and RespiraSense™. Data from 17 patients admitted as medical emergencies was evaluated. For measurements obtained at rest a mean RR of 19.3 (SD 4.89) for manual measurements compared to mean RR of 20.2 (SD 4.54) for measurements obtained with capnography and mean RR of 19.8 (SD 4.52) with RespiraSense™. At rest, RespiraSense™ has a bias of 0.38 and limits of agreement of 1.0 to 1.8 bpm, when compared to the capnography derived RR. Measurements were within pre-defined limits of error at rest. Continuous measurement of RR with RespiraSense™ in patients admitted as acute emergencies is both feasible and reliable.

Description: Sensors, Vol. 18, Pages 2702: Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images Sensors doi: 10.3390/s18082702 Authors: Shuxin Li Zhilong Zhang Biao Li Chuwei Li Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.

Description: Sensors, Vol. 18, Pages 2704: Development of a Dew/Frost Point Temperature Sensor Based on Tunable Diode Laser Absorption Spectroscopy and Its Application in a Cryogenic Wind Tunnel Sensors doi: 10.3390/s18082704 Authors: Wei Nie Zhenyu Xu Ruifeng Kan Jun Ruan Lu Yao Bin Wang Yabai He We have proposed a sensor for real-time and online measurement of dew/frost point temperature using tunable diode laser absorption spectroscopy (TDLAS) technique. Initial experiments have demonstrated its feasibility and technical advantages in comparison to a chilled mirror hygrometer (CMH). The TDLAS sensor we developed has a dew/frost point temperature range from −93 °C to + 14.5 °C, with a measurement uncertainly of less than 2%, and a response time of about 0.8 s, which is much faster than that of the chilled mirror hygrometer (ranging from several minutes to several hours). A TDLAS-based dew/frost point sensor has many advantages, such as rapid and continuous measurements, low frost point temperature sensing, high accuracy, and non-intrusiveness. Such a sensor would be useful for dew/frost point temperature determinations in various applications. In a cryogenic wind tunnel, real-time dew/frost point temperature measurements are helpful in preventing the formation of condensed liquid and ice, which can affect the model geometry and lead to unreliable test data.

Description: Sensors, Vol. 18, Pages 2736: Optimal Particle Filter Weight for Bayesian Direct Position Estimation in a GNSS Receiver Sensors doi: 10.3390/s18082736 Authors: Jürgen Dampf Kathrin Frankl Thomas Pany Direct Position Estimation (DPE) is a rather new Global Navigation Satellite System (GNSS) technique to estimate the user position, velocity and time (PVT) directly from correlation values of the received GNSS signal with receiver internal replica signals. If combined with Bayesian nonlinear filters—like particle filters—the method allows for coping with multi-modal probability distributions and avoids the linearization step to convert correlation values into pseudoranges. The measurement update equation (particle weight update) is derived from a standard GNSS signal model, but we show that it cannot be used directly in a receiver implementation. The numerical evaluation of the formulas needs to be carried out in a logarithmic scale including various normalizations. Furthermore, the residual user range errors (coming from orbit, satellite clock, multipath or ionospheric errors) need to be included from the very beginning in the stochastic signal model. With these modifications, sensible probability functions can be derived from the GNSS multi-correlator values. The occurrence of multipath yields a natural widening of the probability density function. The approach is demonstrated with simulated and real-world

Description: Sensors, Vol. 18, Pages 2737: Integration of Underwater Radioactivity and Acoustic Sensors into an Open Sea Near Real-Time Multi-Parametric Observation System Sensors doi: 10.3390/s18082737 Authors: Sara Pensieri Dionisis Patiris Stylianos Alexakis Marios N. Anagnostou Aristides Prospathopoulos Christos Tsabaris Roberto Bozzano This work deals with the installation of two smart in-situ sensors (for underwater radioactivity and underwater sound monitoring) on the Western 1-Mediterranean Moored Multisensor Array (W1-M3A) ocean observing system that is equipped with all appropriate modules for continuous, long-term and real-time operation. All necessary tasks for their integration are described such as, the upgrade of the sensors for interoperable and power-efficient operation, the conversion of data in homogeneous and standard format, the automated pre-process of the raw data, the real-time integration of data and metadata (related to data processing and calibration procedure) into the controller of the observing system, the test and debugging of the developed algorithms in the laboratory, and the obtained quality-controlled data. The integration allowed the transmission of the acquired data in near-real time along with a complete set of typical ocean and atmospheric parameters. Preliminary analysis of the data is presented, providing qualitative information during rainfall periods, and combine gamma-ray detection rates with passive acoustic data. The analysis exhibits a satisfactory identification of rainfall events by both sensors according to the estimates obtained by the rain gauge operating on the observatory and the remote observations collected by meteorological radars.

Description: Sensors, Vol. 18, Pages 2735: Green Compressive Sampling Reconstruction in IoT Networks Sensors doi: 10.3390/s18082735 Authors: Stefania Colonnese Mauro Biagi Tiziana Cattai Roberto Cusani Fabrizio De Vico Fallani Gaetano Scarano In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks.

Description: Sensors, Vol. 18, Pages 2741: Device Management and Data Transport in IoT Networks Based on Visible Light Communication Sensors doi: 10.3390/s18082741 Authors: Cheol-Min Kim Seok-Joo Koh LED-based Visible Light Communication (VLC) has been proposed as the IEEE 802.15.7 standard and is regarded as a new wireless access medium in the Internet-of-Things (IoT) environment. With this trend, many works have already been made to improve the performance of VLC. However, the effectively integration of VLC services into IoT networks has not yet been sufficiently studied. In this paper, we propose a scheme for device management and data transport in IoT networks using VLC. Specifically, we discuss how to manage VLC transmitters and receivers, and to support VLC data transmission in IoT networks. The proposed scheme considers uni-directional VLC transmissions from transmitter to receivers for delivery of location-based VLC data. The backward transmission from VLC receivers will be made by using platform server and aggregation agents in the network. For validation and performance analysis, we implemented the proposed scheme with VLC-capable LED lights and open sources of oneM2M. From the experimental results for virtual museum services, we see that the VLC data packets can be exchanged within 590 ms, and the handover between VLC transmitters can be completed within 210 ms in the testbed network.

Description: Sensors, Vol. 18, Pages 2734: An Occlusion-Aware Framework for Real-Time 3D Pose Tracking Sensors doi: 10.3390/s18082734 Authors: Mingliang Fu Yuquan Leng Haitao Luo Weijia Zhou Random forest-based methods for 3D temporal tracking over an image sequence have gained increasing prominence in recent years. They do not require object’s texture and only use the raw depth images and previous pose as input, which makes them especially suitable for textureless objects. These methods learn a built-in occlusion handling from predetermined occlusion patterns, which are not always able to model the real case. Besides, the input of random forest is mixed with more and more outliers as the occlusion deepens. In this paper, we propose an occlusion-aware framework capable of real-time and robust 3D pose tracking from RGB-D images. To this end, the proposed framework is anchored in the random forest-based learning strategy, referred to as RFtracker. We aim to enhance its performance from two aspects: integrated local refinement of random forest on one side, and online rendering based occlusion handling on the other. In order to eliminate the inconsistency between learning and prediction of RFtracker, a local refinement step is embedded to guide random forest towards the optimal regression. Furthermore, we present an online rendering-based occlusion handling to improve the robustness against dynamic occlusion. Meanwhile, a lightweight convolutional neural network-based motion-compensated (CMC) module is designed to cope with fast motion and inevitable physical delay caused by imaging frequency and data transmission. Finally, experiments show that our proposed framework can cope better with heavily-occluded scenes than RFtracker and preserve the real-time performance.

Description: Sensors, Vol. 18, Pages 2701: Broadband Dual-Polarized Base Station Antenna for Fifth-Generation (5G) Applications Sensors doi: 10.3390/s18082701 Authors: Hua Tang Xianzheng Zong Zaiping Nie A broadband dual-polarized base station antenna with special designed feeding structures is investigated in this paper. The proposed antenna contains two pairs of crossed dipoles, two specially designed feeding connectors, two pieces of dielectric pads, a supporter (also a balun), and a reflector. To verify the designed antenna, a prototype is fabricated and measured. The antenna attains a bandwidth of around 46.5% operating over 3.14–5.04 GHz under reflection coefficient lower than −15 dB, and the port-to-port isolation is higher than 32.5 dB. It also achieves very stable radiation patterns with half power beam widths of 71.8° ± 2.5° in both the horizontal and vertical planes and gains of around 8 dBi over its operating band. Besides, the mechanism of the obtained good performances is clearly explained from the angle of current. All of the features ensure that the proposed antenna is suitable for the fifth-generation (5G) mobile communications.

Description: Sensors, Vol. 18, Pages 2766: Preload Monitoring in Bolted Connection Using Piezoelectric-Based Smart Interface Sensors doi: 10.3390/s18092766 Authors: Thanh-Canh Huynh Ngoc-Loi Dang Jeong-Tae Kim In this study, a preload monitoring method using impedance signatures obtained from a piezoelectric-based smart interface is presented for bolted girder connections. Firstly, the background theory of the piezoelectric-based smart interface and its implementation into the health monitoring of bolted connections are outlined. A simplified electro-mechanical (EM) impedance model of a smart interface-embedded bolted connection system is formulated to interpret a mechanistic understanding of the EM impedance signatures under the effect of bolt preload. Secondly, finite element modeling of a bolted connection is carried out to show the numerical feasibility of the presented method, and to predetermine the sensitive frequency band of the impedance signatures. Finally, impedance measurements are conducted on a lab-scaled bolted girder connection, to verify the predetermined sensitive frequency range and to assess the bolt preload changes in the test structure.

Description: Sensors, Vol. 18, Pages 2768: Measurement Method for Nonlinearity in Heterodyne Laser Interferometers Based on Double-Channel Quadrature Demodulation Sensors doi: 10.3390/s18092768 Authors: Haijin Fu Ruidong Ji Pengcheng Hu Yue Wang Guolong Wu Jiubin Tan The phase quadrature measurement method is capable of measuring nonlinearity in heterodyne laser interferometers with picometer accuracy whereas it cannot be applied in the new kind of heterodyne interferometers with bidirectional Doppler frequency shift especially in the condition of non-uniform motion of the target. To solve this problem, a novel measurement method of nonlinearity is proposed in this paper. By employing double-channel quadrature demodulation and substituting the external reference signal with internal ones, this method is free from the type of heterodyne laser interferometer and the motion state of the target. For phase demodulation, the phase differential algorithm is utilized to improve the computing efficiency. Experimental verification is carried out and the results indicate that the proposed measurement method achieves accuracy better than 2 pm.

Description: Sensors, Vol. 18, Pages 2763: Application of FRP Bolts in Monitoring the Internal Force of the Rocks Surrounding a Mine-Shield Tunnel Sensors doi: 10.3390/s18092763 Authors: Zhen Liu Cuiying Zhou Yiqi Lu Xu Yang Yanhao Liang Lihai Zhang Monitoring the internal force of the rocks surrounding a mine-shield tunnel for the initial support of a mine-shield tunnel, in complex geological and hydrological environments, requires bolts with specific features such as high tensile strength, low shear strength, good insulation and resistance to corrosion. As such, internal force monitoring has become an important issue in safety monitoring for such tunneling projects. In this paper, the adaptability of a mine-shield tunnel project in a corrosive environment is investigated. A fiberglass reinforced plastic (FRP) bolt with high tensile strength, low shear strength, resistance to fatigue, non-conductivity and resistance to corrosion is used as a probe in tandem with an anchor-head dynamometer to monitor the internal force of the rocks surrounding a mine-shield tunnel for initial support. Additionally, solar energy collection technology is introduced to create a remote monitoring system. Using a 2.5 km long railway tunnel located in the northeast of the Pearl River Delta of China as a case study, the present study shows that, compared with a conventional steel bolt, the FRP bolt has advantages, such as avoidance of the risks associated with the shield machine, insulation and resistance to corrosion. As a probe, the response of the FRP bolt to events such as a blasting vibration and a construction disturbance that results in internal changes in the surrounding rock demonstrates a clear pattern that is appropriate for monitoring the internal force of the rocks surrounding a mine-shield tunnel in a corrosive environment. FRP bolt-based monitoring not only provides new technological support for controlling the risk involved in the initial support of a mine-shield tunnel but can also be widely deployed in projects with special requirements for disassembly, conductivity and corrosion.

Description: Sensors, Vol. 18, Pages 2764: Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks Sensors doi: 10.3390/s18092764 Authors: Huanhuan Yang Zongpu Jia Guojun Xie As an auxiliary facility, roadside units (RSUs) can well improve the shortcomings incurred by ad hoc networks and promote network performance in a vehicular ad hoc network (VANET). However, deploying a large number of RSUs will lead to high installation and maintenance costs. Therefore, trying to find the best locations is a key issue when deploying RSUs with the set delay and budget. In this paper, we study the delay-bounded and cost-limited RSU deployment (DBCL) problem in urban VANET. We prove it is non-deterministic polynomial-time hard (NP-hard), and a binary differential evolution scheme is proposed to maximize the number of roads covered by deploying RSUs. Opposite-based learning is introduced to initialize the first generation, and a binary differential mutation operator is designed to obtain binary coding. A random variable is added to the traditional crossover operator to increase population diversity. Also, a greedy-based individual reparation and promotion algorithm is adopted to repair infeasible solutions violating given constraints, and to gain optimal feasible solutions with the compromise of given limits. Moreover, after selection, a solution promotion algorithm is executed to promote the best solution found in generation. Simulation is performed on analog trajectories sets, and results show that our proposed algorithm has a higher road coverage ratio and lower packet loss compared with other schemes.

Description: Sensors, Vol. 18, Pages 2765: Cable-Driven Parallel Robot with Reconfigurable End Effector Controlled with a Compliant Actuator Sensors doi: 10.3390/s18092765 Authors: Alejandro Rodriguez-Barroso Roque Saltaren Gerardo A. Portilla Juan S. Cely Marco Carpio Redundancy in cable-driven parallel robots provides additional degrees of freedom that can be used to achieve different objectives. In this robot, this degree of freedom is used to act on a reconfigurable end effector with one degree of freedom. A compliant actuator actuated by one motor exerts force on both bodies of the platform. Due to the high tension that appears in this cable in comparison with the rest of the cables, an elastic model was developed for solving the kinestostatic and wrench analysis. A linear sensor was used in one branch of this cable mechanism to provide the needed intermediate values. The position of one link of the platform was fixed in order to focus this analysis on the relationship between the cables and the platform’s internal movement. Position values of the reconfigurable end effector were calculated and measured as well as the tension at different regions of the compliant actuator. The theoretical values were compared with dynamic simulations and real prototype results.

Description: Sensors, Vol. 18, Pages 2767: Using Inertial Measurement Units and Electromyography to Quantify Movement during Action Research Arm Test Execution Sensors doi: 10.3390/s18092767 Authors: Eva Repnik Urška Puh Nika Goljar Marko Munih Matjaž Mihelj In patients after stroke, ability of the upper limb is commonly assessed with standardised clinical tests that provide a complete upper limb assessment. This paper presents quantification of upper limb movement during the execution of Action research arm test (ARAT) using a wearable system of inertial measurement units (IMU) for kinematic quantification and electromyography (EMG) sensors for muscle activity analysis. The test was executed with each arm by a group of healthy subjects and a group of patients after stroke allocated into subgroups based on their clinical scores. Tasks were segmented into movement and manipulation phases. Each movement phase was quantified with a set of five parameters: movement time, movement smoothness, hand trajectory similarity, trunk stability, and muscle activity for grasping. Parameters vary between subject groups, between tasks, and between task phases. Statistically significant differences were observed between patient groups that obtained different clinical scores, between healthy subjects and patients, and between the unaffected and the affected arm unless the affected arm shows normal performance. Movement quantification enables differentiation between different subject groups within movement phases as well as for the complete task. Spearman’s rank correlation coefficient shows strong correlations between patient’s ARAT scores and movement time as well as movement smoothness. Weak to moderate correlations were observed for parameters that describe hand trajectory similarity and trunk stability. Muscle activity correlates well with grasping activity and the level of grasping force in all groups.

Description: Sensors, Vol. 18, Pages 2760: ICT to Promote Well-Being within Families Sensors doi: 10.3390/s18092760 Authors: Jure Trilar Andrej Kos Simona Jazbinšek Lea Jensterle Emilija Stojmenova Duh Within the Active Living and Well-Being Project (RRP3), funded by the Republic of Slovenia and the European Regional Development Fund Investing in Your Future program, we aim to develop different approaches and prototype solutions to provide ICT solutions for the family in order to connect its members; communicate; promote quality family time, active life, a health-friendly lifestyle and well-being; and integrate various sensor and user-based data sources into a smart city ecosystem platform. A mixed methodology, combined qualitative and quantitative approaches, was selected to conduct the study. An online survey with a structured questionnaire as well as semi-structured interviews were performed. Through the analysis of the results, we tried to establish a family-centered design approach that would be inclusive as much as possible, creating benefits for all generations in order to develop an interactive prototype solution that would allow us to further test and verify different use-case scenarios.

Description: Sensors, Vol. 18, Pages 2758: A Broad-Spectrum Sweet Taste Sensor Based on Ni(OH)2/Ni Electrode Sensors doi: 10.3390/s18092758 Authors: Yuezhong Mao Shiyi Tian Shuanglin Gong Yumei Qin Jianzhong Han Shaoping Deng A broad-spectrum sweet taste sensor based on Ni(OH)2/Ni electrode was fabricated by the cyclic voltammetry technique. This sensor can be directly used to detect natural sweet substances in 0.1 M NaOH solution by chronoamperometry method. The current value measured by the sensor shows a linear relationship with the concentration of glucose, sucrose, fructose, maltose, lactose, xylitol, sorbitol, and erythritol (R2 = 0.998, 0.983, 0.999, 0.989, 0.985, 0.990, 0.991, and 0.985, respectively). Moreover, the characteristic value of this sensor is well correlated with the concentration and relative sweetness of eight sweet substances. The good correlation between the characteristic value of six fruit samples measured by the sensor and human sensory sweetness measured by sensory evaluation (correlation coefficient = 0.95) indicates that it can reflect the sweetness of fruits containing several sweet substances. In addition, the sensor also exhibits good long-term stability over 40 days (signal ratio fluctuation ranges from 91.5% to 116.2%). Thus, this broad-spectrum sensor is promising for sweet taste sensory application.

Description: Sensors, Vol. 18, Pages 2799: Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model Sensors doi: 10.3390/s18092799 Authors: Sebastien Jean Mambou Petra Maresova Ondrej Krejcar Ali Selamat Kamil Kuca Women’s breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Additionally, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic techniques. Our review of the literature first explored infrared digital imaging, which assumes that a basic thermal comparison between a healthy breast and a breast with cancer always shows an increase in thermal activity in the precancerous tissues and the areas surrounding developing breast cancer. Furthermore, through our research, we realized that a Computer-Aided Diagnostic (CAD) undertaken through infrared image processing could not be achieved without a model such as the well-known hemispheric model. The novel contribution of this paper is the production of a comparative study of several breast cancer detection techniques using powerful computer vision techniques and deep learning models.

Description: Sensors, Vol. 18, Pages 2833: Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data Sensors doi: 10.3390/s18092833 Authors: Saurabh Singh Thakur Shabbir Syed Abdul Hsiao-Yean (Shannon) Chiu Ram Babu Roy Po-Yu Huang Shwetambara Malwade Aldilas Achmad Nursetyo Yu-Chuan (Jack) Li Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

Description: Sensors, Vol. 18, Pages 2828: Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach Sensors doi: 10.3390/s18092828 Authors: Dylan Kobsar Reed Ferber Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.

Description: Sensors, Vol. 18, Pages 2829: Commissioning of the Controlled and Automatized Testing Facility for Human Behavior and Control (CASITA) Sensors doi: 10.3390/s18092829 Authors: Ignacio Rodríguez-Rodríguez Aurora González Vidal Alfonso P. Ramallo González Miguel Ángel Zamora Human behavior is one of the most challenging aspects in the understanding of building physics. The need to evaluate it requires controlled environments and facilities in which researchers can test their methods. In this paper, we present the commissioning of the Controlled and Automatized Testing Facility for Human Behavior (CASITA). This is a controlled space emulation of an office or flat, with more than 20 environmental sensors, 5 electrical meters, and 10 actuators. Our contribution shown in this paper is the development of an infrastructure-Artificial Intelligence (AI) model pair that is perfectly integrated for the study of a variety of human energy use aspects. This facility will help to perform studies about human behavior in a controlled space. To verify this, we have tested this emulation for 60 days, in which equipment was turned on and off, the settings of the conditioning system were modified remotely, and lighting operation was similar to that in real behaviors. This period of commissioning generated 74.4 GB of raw data including high-frequency measurements. This work has shown that CASITA performs beyond expectations and that sensors and actuators could enable research on a variety of disciplines related to building physics and human behavior. Also, we have tested the PROPHET software, which was previously used in other disciplines and found that it could be an excellent complement to CASITA for experiments that require the prediction of several pertinent variables in a given study. Our contribution has also been to proof that this package is an ideal “soft” addition to the infrastructure. A case study forecasting energy consumption has been performed, concluding that the facility and the software PROPHET have a great potential for research and an outstanding accuracy.

Description: Sensors, Vol. 18, Pages 2830: Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing Sensors doi: 10.3390/s18092830 Authors: Long Mai Nhu-Ngoc Dao Minho Park The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

Description: Sensors, Vol. 18, Pages 2832: A Walking-in-Place Method for Virtual Reality Using Position and Orientation Tracking Sensors doi: 10.3390/s18092832 Authors: Juyoung Lee Sang Chul Ahn Jae-In Hwang People are interested in traveling in an infinite virtual environment, but no standard navigation method exists yet in Virtual Reality (VR). The Walking-In-Place (WIP) technique is a navigation method that simulates movement to enable immersive travel with less simulator sickness in VR. However, attaching the sensor to the body is troublesome. A previously introduced method that performed WIP using an Inertial Measurement Unit (IMU) helped address this problem. That method does not require placement of additional sensors on the body. That study proved, through evaluation, the acceptable performance of WIP. However, this method has limitations, including a high step-recognition rate when the user does various body motions within the tracking area. Previous works also did not evaluate WIP step recognition accuracy. In this paper, we propose a novel WIP method using position and orientation tracking, which are provided in the most PC-based VR HMDs. Our method also does not require additional sensors on the body and is more stable than the IMU-based method for non-WIP motions. We evaluated our method with nine subjects and found that the WIP step accuracy was 99.32% regardless of head tilt, and the error rate was 0% for squat motion, which is a motion prone to error. We distinguish jog-in-place as “intentional motion” and others as “unintentional motion”. This shows that our method correctly recognizes only jog-in-place. We also apply the saw-tooth function virtual velocity to our method in a mathematical way. Natural navigation is possible when the virtual velocity approach is applied to the WIP method. Our method is useful for various applications which requires jogging.

Description: Sensors, Vol. 18, Pages 2826: Automatic Emotion Perception Using Eye Movement Information for E-Healthcare Systems Sensors doi: 10.3390/s18092826 Authors: Yang Wang Zhao Lv Yongjun Zheng Facing the adolescents and detecting their emotional state is vital for promoting rehabilitation therapy within an E-Healthcare system. Focusing on a novel approach for a sensor-based E-Healthcare system, we propose an eye movement information-based emotion perception algorithm by collecting and analyzing electrooculography (EOG) signals and eye movement video synchronously. Specifically, we extract the time-frequency eye movement features by firstly applying the short-time Fourier transform (STFT) to raw multi-channel EOG signals. Subsequently, in order to integrate time domain eye movement features (i.e., saccade duration, fixation duration, and pupil diameter), we investigate two feature fusion strategies: feature level fusion (FLF) and decision level fusion (DLF). Recognition experiments have been also performed according to three emotional states: positive, neutral, and negative. The average accuracies are 88.64% (the FLF method) and 88.35% (the DLF with maximal rule method), respectively. Experimental results reveal that eye movement information can effectively reflect the emotional state of the adolescences, which provides a promising tool to improve the performance of the E-Healthcare system.

Description: Sensors, Vol. 18, Pages 2816: Accurate Device-Free Tracking Using Inexpensive RFIDs Sensors doi: 10.3390/s18092816 Authors: Liyao Li Chongzheng Guo Yang Liu Lichao Zhang Xiaofei Qi Yuhui Ren Baoying Liu Feng Chen Without requiring targets to carry any device, device-free-based tracking is playing an important role in many emerging applications such as smart homes, fitness tracking, intruder detection, etc. While promising, current device-free tracking systems based on inexpensive commercial devices perform well in the training environment, but poorly in other environments because of different multipath reflections. This paper introduces RDTrack, a system that leverages changes in Doppler shifts, which are not sensitive to multipath, to accurately track the target. Moreover, RDTrack identifies particular patterns for fine-grained motions such as turning, walking straightly, etc., which can achieve accurate tracking. For the purpose of achieving a fine-grained device-free tracking system, this paper builds a trajectory estimating model using HMM (Hidden Markov Model) to improve the matching accuracy and reduce the time complexity. We address several challenges including estimating the tag influenced time period, identifying moving path and reducing false positives due to multipath. We implement RDTrack with inexpensive commercial off-the-shelf RFID (Radio Frequency IDentification) hardware and extensively evaluate RDTrack in a lobby, staircase and library. Our results show that RDTrack is effective in tracking the moving target, with a low tracking error of 32 cm. This accuracy is robust for different environments, highlighting RDTrack’s ability to enable future essential device-free moving-based interaction with RFID devices.

Description: Sensors, Vol. 18, Pages 2815: Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition Sensors doi: 10.3390/s18092815 Authors: Michele Calì Rita Ambu This paper proposes a replicable methodology to enhance the accuracy of the photogrammetric reconstruction of large-scale objects based on the optimization of the procedures for Unmanned Aerial Vehicle (UAV) camera image acquisition. The relationships between the acquisition grid shapes, the acquisition grid geometric parameters (pitches, image rates, camera framing, flight heights), and the 3D photogrammetric surface reconstruction accuracy were studied. Ground Sampling Distance (GSD), the necessary number of photos to assure the desired overlapping, and the surface reconstruction accuracy were related to grid shapes, image rate, and camera framing at different flight heights. The established relationships allow to choose the best combination of grid shapes and acquisition grid geometric parameters to obtain the desired accuracy for the required GSD. This outcome was assessed by means of a case study related to the ancient arched brick Bridge of the Saracens in Adrano (Sicily, Italy). The reconstruction of the three-dimensional surfaces of this structure, obtained by the efficient Structure-From-Motion (SfM) algorithms of the commercial software Pix4Mapper, supported the study by validating it with experimental data. A comparison between the surface reconstruction with different acquisition grids at different flight heights and the measurements obtained with a 3D terrestrial laser and total station-theodolites allowed to evaluate the accuracy in terms of Euclidean distances.

Description: Sensors, Vol. 18, Pages 2814: An Efficient ISAR Imaging of Targets with Complex Motions Based on a Quasi-Time-Frequency Analysis Bilinear Coherent Algorithm Sensors doi: 10.3390/s18092814 Authors: Cao Zeng Mengyi Qin Dong Li Hongqing Liu Yi Chai The inverse synthetic aperture radar (ISAR) imaging for targets with complex motions has always been a challenging task due to the time-varying Doppler parameter, especially at the low signal-to-noise ratio (SNR) condition. In this paper, an efficient ISAR imaging algorithm for maneuvering targets based on a noise-resistance bilinear coherent integration is developed without the parameter estimation. First, the received signals of the ISAR in a range bin are modelled as a multicomponent quadratic frequency-modulated (QFM) signal after the translational motion compensation. Second, a novel quasi-time-frequency representation noise-resistance bilinear Radon-cubic phase function (CPF)-Fourier transform (RCFT) is proposed, which is based on the coherent integration of the energy of auto-terms along the slope line trajectory. In doing so, the RCFT also effectively suppresses the cross-terms and spurious peaks interference at no expense of the time-frequency resolution loss. Third, the cross-range positions of target’s scatters in ISAR image are obtained via a simple maximization projection from the RCFT result to the Doppler centroid axis, and the final high-resolution ISAR image is thus produced by regrouping all the range-Doppler frequency centroids. Compared with the existing time-frequency analysis-based and parameter estimation-based ISAR imaging algorithms, the proposed method presents the following features: (1) Better cross-term interference suppression at no time-frequency resolution loss; (2) computationally efficient without estimating the parameters of each scatters; (3) higher signal processing gain because of 2-D coherent integration realization and its bilinear function feature. The simulation results are provided to demonstrate the performance of the proposed method.

Description: Sensors, Vol. 18, Pages 2856: On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface Sensors doi: 10.3390/s18092856 Authors: Soo-In Choi Chang-Hee Han Ga-Young Choi Jaeyoung Shin Kwang Soup Song Chang-Hwan Im Han-Jeong Hwang Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

Description: Sensors, Vol. 18, Pages 2853: Experimental Study of LoRa Transmission over Seawater Sensors doi: 10.3390/s18092853 Authors: Nikola Jovalekic Vujo Drndarevic Ermanno Pietrosemoli Iain Zennaro Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868 MHz and 434 MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers’ Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers’ inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, S F ∈ [ 7 , 10 , 12 ] , within a bandwidth of B W = 125 kHz and Coding Rate C R = 4 / 6 . The experiments showed that LoRa links are fully feasible over seawater at distances at least 22 km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28 km obstructed LOS oversea path in ISM 434 MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to - 50 dBm, whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than - 100 dBm. These findings enabled accurate interpretation of the results obtained in field experiments.

Description: Sensors, Vol. 18, Pages 2847: Orbit Determination of Korean GEO Satellite Using Single SLR Sensor Sensors doi: 10.3390/s18092847 Authors: Hyungjik Oh Eunseo Park Hyung-Chul Lim Chandeok Park Geostationary Earth Orbit (GEO)-Korea Multi-Purpose Satellite (KOMPSAT)-2B (GK-2B) is a Korean geostationary Earth orbit (GEO) satellite that is scheduled to be launched in 2020 for meteorological and ocean monitoring. While the primary orbit determination (OD) for GK-2B is by ground-based radar observations and the expected orbit precision is less than 1 km, a satellite laser ranging (SLR) technique has been selected as a subsidiary OD method to verify/complement/enhance primary OD results. In general, the available time and equipment for observing GEO satellites with SLR are limited. Furthermore, because the optical sensors mounted on GK-2B may be defected by laser, only a domestic single SLR station would obtain the tracking data. This research presents the mitigation of these drawbacks to improve orbit precision. Observation data generation and the associated OD of GK-2B are performed by considering numerical SLR data analysis on Compass-G1, a Chinese GEO navigation satellite, and Chinese SLR station at Changchun. With the OD performed for two scenarios with the varying number of observations, the 3D position error is 24.01 m when 13 observations per day are obtained, while the error becomes 43.46 m when 9 observations per day are obtained. To verify these results, the OD of Compass-G1 using actual SLR data from Changchun station is performed to yield 31.89 m for 3D error, which is favorable compared with the external precise ephemeris by GeoForschungsZentrum (GFZ) analysis center. Therefore, the OD based on single SLR station is applicable to estimating the orbit within less than 100 m.

Description: Sensors, Vol. 18, Pages 2851: Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression Sensors doi: 10.3390/s18092851 Authors: Jizhou Wang Changhua Lu Weiwei Jiang Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.

Description: Sensors, Vol. 18, Pages 2850: Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization Sensors doi: 10.3390/s18092850 Authors: Giorgio Biagetti Paolo Crippa Laura Falaschetti Claudio Turchetti The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.

Description: Sensors, Vol. 18, Pages 2846: A Separated Calibration Method for Inertial Measurement Units Mounted on Three-Axis Turntables Sensors doi: 10.3390/s18092846 Authors: Chun-mei Dong Shun-qing Ren Xi-jun Chen Zhen-huan Wang Inertial Measurement Unit (IMU) calibration accuracy is easily affected by turntable errors, so the primary aim of this study is to reduce the dependence on the turntable’s precision during the calibration process. Firstly, the indicated-output of the IMU considering turntable errors is constructed and with the introduction of turntable errors, the functional relationship between turntable errors and the indicated-output was derived. Then, based on a D-suboptimal design, a calibration method for simultaneously identifying the IMU error model parameters and the turntable errors was proposed. Simulation results showed that some turntable errors could thus be effectively calibrated and automatically compensated. Finally, the theoretical validity was verified through experiments. Compared with the traditional method, the method proposed in this paper can significantly reduce the influence of the turntable errors on the IMU calibration accuracy.

Description: Sensors, Vol. 18, Pages 2844: Femtosecond-Pulsed Laser Written and Etched Fiber Bragg Gratings for Fiber-Optical Biosensing Sensors doi: 10.3390/s18092844 Authors: Sven Schulze Michel Wehrhold Carsten Hille We present the development of a label-free, highly sensitive fiber-optical biosensor for online detection and quantification of biomolecules. Here, the advantages of etched fiber Bragg gratings (eFBG) were used, since they induce a narrowband Bragg wavelength peak in the reflection operation mode. The gratings were fabricated point-by-point via a nonlinear absorption process of a highly focused femtosecond-pulsed laser, without the need of prior coating removal or specific fiber doping. The sensitivity of the Bragg wavelength peak to the surrounding refractive index (SRI), as needed for biochemical sensing, was realized by fiber cladding removal using hydrofluoric acid etching. For evaluation of biosensing capabilities, eFBG fibers were biofunctionalized with a single-stranded DNA aptamer specific for binding the C-reactive protein (CRP). Thus, the CRP-sensitive eFBG fiber-optical biosensor showed a very low limit of detection of 0.82 pg/L, with a dynamic range of CRP detection from approximately 0.8 pg/L to 1.2 µg/L. The biosensor showed a high specificity to CRP even in the presence of interfering substances. These results suggest that the proposed biosensor is capable for quantification of CRP from trace amounts of clinical samples. In addition, the adaption of this eFBG fiber-optical biosensor for detection of other relevant analytes can be easily realized.

Description: Sensors, Vol. 18, Pages 2839: Localization and Discrimination of the Perturbation Signals in Fiber Distributed Acoustic Sensing Systems Using Spatial Average Kurtosis Sensors doi: 10.3390/s18092839 Authors: Fei Jiang Honglang Li Zhenhai Zhang Yixin Zhang Xuping Zhang Location error and false alarm are noticeable problems in fiber distributed acoustic sensing systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). A novel method based on signal kurtosis is proposed to locate and discriminate perturbations in Φ-OTDR systems. The spatial kurtosis (SK) along the fiber is firstly obtained by calculating the kurtosis of acoustic signals at each position of the fiber in a short time period. After the moving average on the spatial dimension, the spatial average kurtosis (SAK) is then obtained, whose peak can accurately locate the center of the vibration segment. By comparing the SAK value with a certain threshold, we may to some degree discriminate the instantaneous destructive perturbations from the system noise and certain ambient environmental interferences. The experimental results show that, comparing with the average of the previous localization methods, the SAK method improves the pencil-break and digging locating signal-to-noise ratio (SNR) by 16.6 dB and 17.3 dB, respectively; and decreases the location standard deviation by 7.3 m and 9.1 m, respectively. For the instantaneous destructive perturbation (pencil-break and digging) detection, the false alarm rate can be as low as 1.02%, while the detection probability is maintained as high as 95.57%. In addition, the time consumption of the SAK method is adequate for a real-time Φ-OTDR system.

Description: Sensors, Vol. 18, Pages 2840: Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks Sensors doi: 10.3390/s18092840 Authors: Lawrence Mwenda Muriira Zhiwei Zhao Geyong Min Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.

Description: Sensors, Vol. 18, Pages 2838: Paper-Based Sensors: Emerging Themes and Applications Sensors doi: 10.3390/s18092838 Authors: Amrita Tribhuwan Singh Darlin Lantigua Akhil Meka Shainlee Taing Manjot Pandher Gulden Camci-Unal Paper is a versatile, flexible, porous, and eco-friendly substrate that is utilized in the fabrication of low-cost devices and biosensors for rapid detection of analytes of interest. Paper-based sensors provide affordable platforms for simple, accurate, and rapid detection of diseases, in addition to monitoring food quality, environmental and sun exposure, and detection of pathogens. Paper-based devices provide an inexpensive technology for fabrication of simple and portable diagnostic systems that can be immensely useful in resource-limited settings, such as in developing countries or austere environments, where fully-equipped facilities and highly trained medical staff are absent. In this work, we present the different types of paper that are currently utilized in fabrication of paper-based sensors, and common fabrication techniques ranging from wax printing to origami- and kirigami-based approaches. In addition, we present different detection techniques that are employed in paper-based sensors such as colorimetric, electrochemical, and fluorescence detection, chemiluminescence, and electrochemiluminescence, as well as their applications including disease diagnostics, cell cultures, monitoring sun exposure, and analysis of environmental reagents including pollutants. Furthermore, main advantages and disadvantages of different types of paper and future trends for paper-based sensors are discussed.

Description: Sensors, Vol. 18, Pages 2837: Lightweight Visual Odometry for Autonomous Mobile Robots Sensors doi: 10.3390/s18092837 Authors: Mohamed Aladem Samir A. Rawashdeh Vision-based motion estimation is an effective means for mobile robot localization and is often used in conjunction with other sensors for navigation and path planning. This paper presents a low-overhead real-time ego-motion estimation (visual odometry) system based on either a stereo or RGB-D sensor. The algorithm’s accuracy outperforms typical frame-to-frame approaches by maintaining a limited local map, while requiring significantly less memory and computational power in contrast to using global maps common in full visual SLAM methods. The algorithm is evaluated on common publicly available datasets that span different use-cases and performance is compared to other comparable open-source systems in terms of accuracy, frame rate and memory requirements. This paper accompanies the release of the source code as a modular software package for the robotics community compatible with the Robot Operating System (ROS).

Description: Sensors, Vol. 18, Pages 2843: Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors Sensors doi: 10.3390/s18092843 Authors: Alice Cavaliere Federico Carotenuto Filippo Di Gennaro Beniamino Gioli Giovanni Gualtieri Francesca Martelli Alessandro Matese Piero Toscano Carolina Vagnoli Alessandro Zaldei A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO2, NO2, O3, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full “heating season” (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m3, RMSE of 4.056 and 6.084 µg/m3, and R2 of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

Description: Sensors, Vol. 18, Pages 2845: A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer Sensors doi: 10.3390/s18092845 Authors: Chi-Hsiang Huang Chian Zeng Yi-Chia Wang Hsin-Yi Peng Chia-Sheng Lin Che-Jui Chang Hsiao-Yu Yang Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

Description: Sensors, Vol. 18, Pages 2841: Detection of Leak-Induced Pipeline Vibrations Using Fiber—Optic Distributed Acoustic Sensing Sensors doi: 10.3390/s18092841 Authors: Pavol Stajanca Sebastian Chruscicki Tobias Homann Stefan Seifert Dirk Schmidt Abdelkarim Habib In the presented work, the potential of fiber-optic distributed acoustic sensing (DAS) for detection of small gas pipeline leaks (<1%) is investigated. Helical wrapping of the sensing fiber directly around the pipeline is used to increase the system sensitivity for detection of weak leak-induced vibrations. DAS measurements are supplemented with reference accelerometer data to facilitate analysis and interpretation of recorded vibration signals. The results reveal that a DAS system using direct fiber application approach is capable of detecting pipeline natural vibrations excited by the broadband noise generated by the leaking medium. In the performed experiment, pipeline vibration modes with acceleration magnitudes down to single μg were detected. Simple leak detection approach based on spectral integration of time-averaged DAS signals in frequency domain was proposed. Potential benefits and limitations of the presented monitoring approach were discussed with respect to its practical applicability. We demonstrated that the approached is potentially capable of detection and localization of gas pipeline leaks with leak rates down to 0.1% of the pipeline flow volume and might be of interest for monitoring of short- and medium-length gas pipelines.

Description: Sensors, Vol. 18, Pages 2842: High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor Sensors doi: 10.3390/s18092842 Authors: Wei Liu Bing Liang Zhenyuan Jia Di Feng Xintong Jiang Xiao Li Mengde Zhou High precision position control is essential in the process of parts manufacturing and assembling, where eddy current displacement sensors (ECDSs) are widely used owing to the advantages of non-contact sensing, compact volume, and resistance to harsh conditions. To solve the nonlinear characteristics of the sensors, a high-accuracy calibration method based on linearity adjustment is proposed for ECDSs in this paper, which markedly improves the calibration accuracy and then the measurement accuracy. After matching the displacement value and the output voltage of the sensors, firstly, the sensitivity is adjusted according to the specified output range. Then, the weighted support vector adjustment models with the optimal weight of the zero-scale, mid-scale and full-scale are established respectively to cyclically adjust the linearity of the output characteristic curve. Finally, the final linearity adjustment model is obtained, and both the calibration accuracy and precision are verified by the established calibration system. Experimental results show that the linearity of the output characteristic curve of ECDS adjusted by the calibration method reaches over 99.9%, increasing by 1.9–5.0% more than the one of the original. In addition, the measurement accuracy improves from 11–25 μ m to 1–10 μ m in the range of 6mm, which provides a reliable guarantee for high accuracy displacement measurement.

Description: Sensors, Vol. 18, Pages 2834: Hand Tracking and Gesture Recognition Using Lensless Smart Sensors Sensors doi: 10.3390/s18092834 Authors: Lizy Abraham Andrea Urru Niccolò Normani Mariusz P. Wilk Michael Walsh Brendan O’Flynn The Lensless Smart Sensor (LSS) developed by Rambus, Inc. is a low-power, low-cost visual sensing technology that captures information-rich optical data in a tiny form factor using a novel approach to optical sensing. The spiral gratings of LSS diffractive grating, coupled with sophisticated computational algorithms, allow point tracking down to millimeter-level accuracy. This work is focused on developing novel algorithms for the detection of multiple points and thereby enabling hand tracking and gesture recognition using the LSS. The algorithms are formulated based on geometrical and mathematical constraints around the placement of infrared light-emitting diodes (LEDs) on the hand. The developed techniques dynamically adapt the recognition and orientation of the hand and associated gestures. A detailed accuracy analysis for both hand tracking and gesture classification as a function of LED positions is conducted to validate the performance of the system. Our results indicate that the technology is a promising approach, as the current state-of-the-art focuses on human motion tracking that requires highly complex and expensive systems. A wearable, low-power, low-cost system could make a significant impact in this field, as it does not require complex hardware or additional sensors on the tracked segments.

Description: Sensors, Vol. 18, Pages 2835: Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes Sensors doi: 10.3390/s18092835 Authors: Zhongjie Hou Jinxi Xiang Yonggui Dong Xiaohui Xue Hao Xiong Bin Yang A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

Description: Sensors, Vol. 18, Pages 2836: Star Centroiding Based on Fast Gaussian Fitting for Star Sensors Sensors doi: 10.3390/s18092836 Authors: Xiaowei Wan Gangyi Wang Xinguo Wei Jian Li Guangjun Zhang The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.