ALBERT

Description: The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors.

Description: The Internet of Things (IoT) will feature pervasive sensing and control capabilities via the massive deployment of machine-type communication devices in order to greatly improve daily life. However, machine-type communications can be illegally used (e.g., by criminals or terrorists) which is difficult to monitor, and thus presents new security challenges. The information exchanged in machine-type communications is usually transmitted in short packets. Thus, this paper investigates a legitimate surveillance system via proactive eavesdropping at finite blocklength regime. Under the finite blocklength regime, we analyze the channel coding rate of the eavesdropping link and the suspicious link. We find that the legitimate monitor can still eavesdrop the information sent by the suspicious transmitter as the blocklength decreases, even when the eavesdropping is failed under the Shannon capacity regime. Moreover, we define a metric called the effective eavesdropping rate and study the monotonicity. From the analysis of monotonicity, the existence of a maximum effective eavesdropping rate for a moderate or even high signal-to-noise (SNR) is verified. Finally, numerical results are provided and discussed. In the simulation, we also find that the maximum effective eavesdropping rate slowly increases with the blocklength.

Description: GNSS receiver data crowdsourcing is of interest for multiple applications, e.g., weather monitoring. The bottleneck in this technology is the quality of the GNSS receivers. Therefore, we lay out in an introductory manner the steps to estimate the performance of an arbitrary GNSS receiver via the measurement errors related to its instrumentation. Specifically, we do not need to know the position of the receiver antenna, which allows also for the assessment of smartphone GNSS receivers having integrated antennas. Moreover, the method is independent of atmospheric errors so that no ionospheric or tropospheric correction services provided by base stations are needed. Error models for performance evaluation can be calculated from receiver RINEX (receiver independent exchange format)data using only ephemeris corrections. For the results, we present the quality of different receiver grades through parametrized error models that are likely to be helpful in stochastic modeling, e.g., for Kalman filters, and in assessing GNSS receiver qualities for crowdsourcing applications. Currently, the typical positioning precision for the latest smartphone receivers is around the decimeter level, while for a professional-grade receiver, it is within a few millimeters.

Description: The power transmission lines are the link between power plants and the points of consumption, through substations. Most importantly, the assessment of damaged aerial power lines and rusted conductors is of extreme importance for public safety; hence, power lines and associated components must be periodically inspected to ensure a continuous supply and to identify any fault and defect. To achieve these objectives, recently, Unmanned Aerial Vehicles (UAVs) have been widely used; in fact, they provide a safe way to bring sensors close to the power transmission lines and their associated components without halting the equipment during the inspection, and reducing operational cost and risk. In this work, a drone, equipped with multi-modal sensors, captures images in the visible and infrared domain and transmits them to the ground station. We used state-of-the-art computer vision methods to highlight expected faults (i.e., hot spots) or damaged components of the electrical infrastructure (i.e., damaged insulators). Infrared imaging, which is invariant to large scale and illumination changes in the real operating environment, supported the identification of faults in power transmission lines; while a neural network is adapted and trained to detect and classify insulators from an optical video stream. We demonstrate our approach on data captured by a drone in Parma, Italy.

Description: The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.

Description: One of the most significant challenges in Internet of Things (IoT) environments is the protection of privacy. Failing to guarantee the privacy of sensitive data collected and shared over IoT infrastructures is a critical barrier that delays the wide penetration of IoT technologies in several user-centric application domains. Location information is the most common dynamic information monitored and lies among the most sensitive ones from a privacy perspective. This article introduces a novel mechanism that aims to protect the privacy of location information across Data Centric Sensor Networks (DCSNs) that monitor the location of mobile objects in IoT systems. The respective data dissemination protocols proposed enhance the security of DCSNs rendering them less vulnerable to intruders interested in obtaining the location information monitored. In this respect, a dynamic clustering algorithm is that clusters the DCSN nodes not only based on the network topology, but also considering the current location of the objects monitored. The proposed techniques do not focus on the prevention of attacks, but on enhancing the privacy of sensitive location information once IoT nodes have been compromised. They have been extensively assessed via series of experiments conducted over the IoT infrastructure of FIT IoT-LAB and the respective evaluation results indicate that the dynamic clustering algorithm proposed significantly outperforms existing solutions focusing on enhancing the privacy of location information in IoT.

Description: There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.

Description: The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

Description: Wireless sensor networks are widely used in many fields. Nodes in the network are typically powered by batteries. Because the energy consumption of wireless communication is related to the transmission distance, the energy consumption of nodes in different locations is different, resulting in uneven energy distribution of nodes. In some special applications, all nodes are required to work at the same time, and the uneven energy distribution makes the effective working time of the system subject to the node with the largest energy consumption. The commonly used clustering protocol can play a role in balancing energy consumption, but it does not achieve optimal energy consumption. This paper proposes to use the power supply line to connect the nodes to fully balance the energy. The connection scheme with the shortest power line length is also proposed. On the basis of energy balance, the method of transmitting data with the best hop count is proposed, which fully reduces the power consumption of the data transmission. The simulation results show that the proposed method can effectively reduce the energy consumption and prolong the lifetime of wireless sensor networks.

Description: High frequency guided-waves offer a trade-off between the high sensitivity of local bulk ultrasonic thickness measurements and the large area scanning of lower frequency guided-waves, so it has been a growing interest for corrosion inspection with the dispersive SH1 mode. However, according to the dispersive curve, it is hard to generate the pure SH1 mode since the non-dispersive SH0 mode will be excited simultaneously. Thus, this paper investigates a transducer design method to generate a pure SH1 guided-wave, where the dual periodic-permanent-magnet electromagnetic acoustic transducers (PPM EMATs) are placed on exactly opposite positions either side of the plate symmetrically. The suppression effect for SH0 and the enhancement effect for SH1 of the dual PPM EMATs are mainly discussed by theoretical analysis and simulation analysis, and the influence of positioning errors of PPM EMATs placed on opposite sides of the plate on its performances are analyzed. Employing the proposed dual PPM EMATs, some experiments are performed to verify the reliability of finite element simulation. The results indicate that the dual PPM EMATs can suppress the SH0 mode and generate the pure SH1 mode effectively. Moreover, the longitudinal and lateral positioning errors can affect the dual PPM EMATs performances significantly.

Description: In this paper, we report a wireless gas sensor based on surface acoustic waves (SAW). For room temperature detection of oxygen gas, a novel nanostructured ZnxFeyO gas-sensitive film was deposited on the surface of a SAW resonator by an oblique magnetron co-sputtering method. The measurements of X-ray diffraction (XRD) and a scanning electron microscope (SEM) showed that the crystal phase composition and the microstructures of ZnxFeyO films were significantly affected by the content of Fe. The experimental results showed that the sensors had a good response to O2 at room temperature. The max frequency shift of the sensors reached 258 kHz as the O2 partial pressure was 20%. Moreover, X-ray photoelectron spectroscopy (XPS) was performed to analyze the role of Fe in the sensitization process of the ZnxFeyO film. In addition, the internal relationship between the Fe content of the film and the sensitivity of the sensor was presented and discussed. The research indicates that the nanostructured ZnxFeyO film has a good potential for room temperature O2 gas detection applications.

Description: According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Description: Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows–Abadi–Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.

Description: This paper presents a mathematical model of measuring blood flow based on electromagnetic induction for predicting the rate of arterial stenosis. Firstly, an electrode sensor was used to collect the induced potential differences from human skin surface in a uniform magnetic field. Then, the inversion matrix was constructed by the weight function theory and finite element method. Next, the blood flow volume inversion model was constructed by combining the induction potential differences and inversion matrix. Finally, the rate of arterial stenosis was predicted based on mathematical relationship between blood flow and the area of arterial stenosis. To verify the accuracy of the model, a uniform magnetic field distribution of Helmholtz coil and a 3D geometric model of the ulnar artery of the forearm with different rates of stenosis were established in COMSOL, a finite element analysis software. Simulation results showed that the inversion model had high accuracy in the measurement of blood flow and the prediction of rate of stenosis, and is of great significance for the early diagnosis of arterial stenosis and other vessel diseases.

Description: Magnetic Induction Tomography (MIT) is a non-invasive imaging technique that has been widely applied for imaging materials with high electrical conductivity contrasts. Steel production is among an increasing number of applications that require a contactless method for monitoring the casting process due to the high temperature of hot steel. In this paper, an MIT technique is proposed for detecting defects and deformations in the external surfaces of metal, which has the potential to be used to monitor the external surface of hot steel during the continuous casting process. The Total Variation (TV) reconstruction algorithm was developed to image the conductivity distributions. Nonetheless, the reconstructed image of the deformed square metal obtained using the TV algorithm directly does not yield resonable images of the surface deformation. However, differential images obtained by subtracting the image of a perfect square metal with no deformations from the image obtained for a deformed square metal does provide accurate and repeatable deformation information. It is possible to obtain a more precise image of surface deformation by thresholding the differential image. This TV-based threshold-differencing method has been analysed and verified from both simulation and experimental tests. The simulation results reported that 0.92 % of the image region can be detected, and the experimental results indicated a 0.57 % detectability. Use of the proposed method was demonstareted in a MIT device which was used in continuous casting set up. The paper shows results from computer simulation, lab based cold tests, and real life data from continoeus cating demonstating the effectiveness of the proposed method.

Description: Gas mixture quantification is essential for the recording and reproducing odors, because an odor consists of multiple chemical compounds. Gas mixture quantification using field asymmetric ion mobility spectrometry (FAIMS) was studied. Acetone, ethanol, and diethyl ether were selected as components of a ternary gas mixture sample as representatives of the ketone, alcohol, and ether chemical classes, respectively. One hundred and twenty-five points with different concentrations were measured. The results were evaluated by error hypersurface, variance, and the coefficient of variation. The error hypersurface showed that it is possible to reach the target composition by following the error-hypersurface gradient. Successful convergence was achieved with the gradient descent method in a simulation based on the measurement data. This result verified the feasibility of the quantification of a gas mixture using FAIMS.

Description: In this study, a novel strain gauge arrangement and error reduction techniques were proposed to minimize crosstalk reading and simultaneously increase sensitivity on a decoupled six-axis force–moment (F/M) sensor. The calibration process that comprises the least squares method and error reduction techniques was implemented to obtain a robust decoupling matrix. A decoupling matrix is very crucial for minimizing error and crosstalk. A novel strain gauge arrangement that comprised double parallel strain gauges in the decoupled six-axis force–moment sensor was implemented to obtain high sensitivity. The experimental results revealed that the maximum calibration error, F/M sensor measurement error, and crosstalk readings were reduced to 3.91%, 1.78%, and 4.78%, respectively.

Description: Conventional eddy current testing (ECT) using a pickup coil probe is widely employed for the detection of structural cracks. However, the inspection of conventional ECT for steel structures is difficult because of the magnetic noise caused by the nonuniform permeability of steel. To combat this challenge, we have developed a small magnetic sensor probe with a dual-channel tunneling magnetoresistance sensor that is capable of reducing magnetic noise. Applying this probe to a complicated component of steel structures—such as the welds joining a U-shaped rib and deck plate together—requires the reduction of signal fluctuation caused by the distance (liftoff) variations between the sensor probe and the subject. In this study, the fundamental crack signal and the liftoff signal were investigated with the dual-channel sensor. The results showed that the liftoff signals could be reduced and differentiated from the crack signals by the differential parameters of the dual-channel sensor. In addition, we proposed an extraction technique for the crack signal using the Lissajous curve of the differential parameters. The extraction technique could be applied to the inspections not only for flat plates but also for welded angles to detect cracks without the influence of the liftoff signal.

Description: The objective of this paper is to study the ability of polymer optical fiber (POF) to be inserted in a knitted fabric and to measure both pressure and friction when walking. Firstly, POF, marketed and in development, have been compared in terms of the required mechanical properties for the insertion of the fiber directly into a knitted fabric on an industrial scale, i.e. elongation, bending rigidity, and minimum bending radius before plastic deformation. Secondly, the chosen optical fiber was inserted inside several types of knitted fabric and was shown to be sensitive to friction and compression. The knitted structure with the highest sensitivity has been chosen for sock prototype manufacturing. Finally, a feasibility study with an instrumented sock showed that it is possible to detect the different phases of walking in terms of compression and friction.

Description: Dielectric grating-based sensors are usually based on the guided mode resonance (GMR) obtained using a thin planar waveguide layer (PWL) adjacent to a thin subwavelength grating layer. In this work, we present a detailed investigation of thick subwavelength dielectric grating structures that exhibit reflection resonances above a certain thickness without the need for the waveguide layer, showing great potential for applications in biosensing and tunable filtering. Analytic and numerical results are thoroughly discussed, as well as an experimental demonstration of the structure as a chemical sensor in the SWIR (short wave infrared) spectral range (1200–1800 nm). In comparison to the GMR structure with PWL, the thick grating structure has several unique properties: (i) It gives higher sensitivity when the spaces are filled, with the analyte peaking at certain space values due to an increase in the interaction volume between the analyte and the evanescent optical field between the grating lines; (ii) the TM (transverse magnetic) resonance, in certain cases, provides a better figure of merit; (iii) the sensitivity increases as the grating height increases; (iv) the prediction of the resonance locations based on the effective medium approximation does not give satisfactory results when the grating height is larger than a certain value, and the invalidity becomes more severe as the period increases; (v) a sudden increase in the Q-factor of the resonance occurs at a specific height value accompanied by the high local field enhancement (~103) characteristic of a nano-antenna type pattern. Rigorous numerical simulations of the field distribution are presented to explain the different observed phenomena.

Description: In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

Description: Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. The results show that the subject’s influence on the EEG signals is substantially higher than that of the emotion and hence it is necessary to account for the subject’s influence on the EEG signals. To do this, we propose a data transformation that seamlessly integrates individual traits into an inter-subject approach, improving classification results.

Description: Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera’s field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.

Description: Two types of urea biosensors were integrated with a wireless measurement system and microfluidic measurement system. The two biosensors used were (i) a magnetic beads (MBs)-urease/graphene oxide (GO)/titanium dioxide (TiO2)-based biosensor and (ii) an MBs-urease/GO/ nickel oxide (NiO)-based biosensor, respectively. The wireless measurement system work exhibited the feasibility for the remote detection of urea, but it will require refinement and modification to improve stability and precision. The microchannel fluidic system showed the measurement reliability. The sensing properties of urea biosensors at different flow rates were investigated. From the measurement results, the decay of average sensitivity may be attributed to the induced vortex-induced vibrations (VIV) at the high flow rate. In the aspect of wireless monitoring, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO was 4.780 mV/(mg/dl) and with the linearity of 0.938. In the aspect of measurement under dynamic conditions, the average sensitivity of the urea biosensor based on MBs-urease/GO/NiO were 5.582 mV/(mg/dl) and with the linearity of 0.959. Both measurements performed NiO was better than TiO2 according to the comparisons.

Description: Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC.

Description: Complex environments pose great challenges for autonomous mobile robot navigation. In this study, we address the problem of autonomous navigation in 3D environments with staircases and slopes. An integrated system for safe mobile robot navigation in 3D complex environments is presented and both the perception and navigation capabilities are incorporated into the modular and reusable framework. Firstly, to distinguish the slope from the staircase in the environment, the robot builds a 3D OctoMap of the environment with a novel Simultaneously Localization and Mapping (SLAM) framework using the information of wheel odometry, a 2D laser scanner, and an RGB-D camera. Then, we introduce the traversable map, which is generated by the multi-layer 2D maps extracted from the 3D OctoMap. This traversable map serves as the input for autonomous navigation when the robot faces slopes and staircases. Moreover, to enable robust robot navigation in 3D environments, a novel camera re-localization method based on regression forest towards stable 3D localization is incorporated into this framework. In addition, we utilize a variable step size Rapidly-exploring Random Tree (RRT) method which can adjust the exploring step size automatically without tuning this parameter manually according to the environment, so that the navigation efficiency is improved. The experiments are conducted in different kinds of environments and the output results demonstrate that the proposed system enables the robot to navigate efficiently and robustly in complex 3D environments.

Description: In this work, we isolate individual wurtzite InAs nanowires and fabricate electrical contacts at both ends, exploiting the single nanostructures as building blocks to realize two different architectures of conductometric sensors: (a) the nanowire is drop-casted onto—supported by—a SiO2/Si substrate, and (b) the nanowire is suspended at approximately 250 nm from the substrate. We test the source-drain current upon changes in the concentration of humidity, ethanol, and NO2, using synthetic air as a gas carrier, moving a step forward towards mimicking operational environmental conditions. The supported architecture shows higher response in the mid humidity range (50% relative humidity), with shorter response and recovery times and lower detection limit with respect to the suspended nanowire. These experimental pieces of evidence indicate a minor role of the InAs/SiO2 contact area; hence, there is no need for suspended nanostructures to improve the sensing performance. Moreover, the sensing capability of single InAs nanowires for detection of NO2 and ethanol in the ambient atmosphere is reported and discussed.

Description: Displacement is crucial for structural health monitoring, although it is very challenging to measure under field conditions. Most existing displacement measurement methods are costly, labor-intensive, and insufficiently accurate for measuring small dynamic displacements. Computer vision (CV)-based methods incorporate optical devices with advanced image processing algorithms to accurately, cost-effectively, and remotely measure structural displacement with easy installation. However, non-target-based CV methods are still limited by insufficient feature points, incorrect feature point detection, occlusion, and drift induced by tracking error accumulation. This paper presents a reference frame-based Deepflow algorithm integrated with masking and signal filtering for non-target-based displacement measurements. The proposed method allows the user to select points of interest for images with a low gradient for displacement tracking and directly calculate displacement without drift accumulated by measurement error. The proposed method is experimentally validated on a cantilevered beam under ambient and occluded test conditions. The accuracy of the proposed method is compared with that of a reference laser displacement sensor for validation. The significant advantage of the proposed method is its flexibility in extracting structural displacement in any region on structures that do not have distinct natural features.

Description: Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%.

Description: Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0.75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

Description: Traditional compensation methods based on temperature-related parameters are not effective for complex total reflection prism laser gyro (TRPLG) bias variation. Because the high frequency oscillator voltage (UHFO) fundamentally affects the TRPLG bias, and the UHFO has a stronger correlation with the TRPLG bias when compared with the temperature, an introduction of UHFO into the TRPLG bias compensation can be evaluated. In consideration of the limitations of least squares (LS) regression and multivariate stepwise regression, we proposed a compensation method for TRPLG bias based on iterative re-weighted least squares support vector machine (IR-LSSVM) and compared with LS regression, stepwise regression, and LSSVM algorithm in large temperature cycling experiments. When temperature, slope of temperature variation, and UHFO were selected as inputs, the IR-LSSVM based on myriad weight function improved the TRPLG bias stability by 61.19% to reach the maximum and eliminated TRPLG bias drift. In addition, the UHFO proved to be the most important parameter in the process of TRPLG bias compensation; accordingly, it can alleviate the shortcomings of traditional compensation based on temperature-related parameters and can greatly improve the TRPLG bias stability.

Description: A convolutional neural network (CNN) algorithm was developed to retrieve the land surface temperature (LST) from Advanced Microwave Scanning Radiometer 2 (AMSR2) data in China. Reference data were selected using the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to overcome the problem related to the need for synchronous ground observation data. The AMSR2 brightness temperature (TB) data and MODIS surface temperature data were randomly divided into training and test datasets, and a CNN was constructed to simulate passive microwave radiation transmission to invert the surface temperature. The twelve V/H channel combinations (7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) resulted in the most stable and accurate CNN retrieval model. Vertical polarizations performed better than horizontal polarizations; however, because CNNs rely heavily on large amounts of data, the combination of vertical and horizontal polarizations performed better than a single polarization. The retrievals in different regions indicated that the CNN accuracy was highest over large bare land areas. A comparison of the retrieval results with ground measurement data from meteorological stations yielded R2 = 0.987, RMSE = 2.69 K, and an average relative error of 2.57 K, which indicated that the accuracy of the CNN LST retrieval algorithm was high and the retrieval results can be applied to long-term LST sequence analysis in China.

Description: This paper investigates mobile relaying in wireless powered communication networks (WPCN), where an unmanned aerial vehicle (UAV) is employed to help information delivery from multiple sources to destination with communication channels severely blocked. The sources are low-power without energy supply. To support information transmission, the UAV acts as a hybrid access point (AP) to provide wireless power transfer (WPT) and information reception for sources. We set the issue of system throughput maximization as the optimization problem. On the one hand, the system is subject to the information causality constraint due to the dependent processes of information reception and transmission for the UAV. On the other hand, the sources are constrained by a so-called neutrality constraints due to the dependent processes of energy harvesting and energy consumption. In addition, we take account of the access delay issue of all ground nodes. Specifically, two paradigms of delay-tolerant case and delay-sensitive case are presented. However, the formulated problem including optimizations for time slot scheduling, power allocation and UAV trajectory is non-convex and thus is difficult to obtain its optimal solution. To tackle this problem, we apply the successive convex approximation (SCA) technique and propose an iterative algorithm by which a suboptimal solution can be achieved. Simulation results validate our proposed design, and show that the obtained suboptimal solution is high-quality, as compared to benchmark scheme.

Description: A piezoelectric fiber patch (PFP) is a transducer type that is suitable for guided-wave-based structural health monitoring (SHM) due to its light, thin, and flexible characteristics. In our previous work, a PFP-based transducer design for selective excitation of the zero-order shear horizontal wave mode (SH0) was introduced (shear horizontal PFP (SHPFP)). In this work, two modified SH0 wave PFP transducer designs are proposed: the rounded corner design and the dual design. The degree of improvement is determined by a numerical simulation and the dual design—the design with the most promise—is experimentally realized. Laser Vibrometry measured the generated wave field, confirming the results from the simulation. The new designs can generate an almost pure SH0 wave. The dual design has a very strong directivity that is useful for several guided-wave-based SHM applications. The conclusions on the design’s properties as a transmitter are also valid for its properties as a sensor due to the reciprocity of piezoelectric transducers.

Description: Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Description: Radio frequency identification is one of the key techniques for Internet of Things, which has been widely adopted in many applications for identification. However, there exist various security and privacy issues in radio frequency identification (RFID) systems. Particularly, one of the most serious threats is to clone tags for the goal of counterfeiting goods, which causes great loss and danger to customers. To solve these issues, lots of authentication protocols are proposed based on physical unclonable functions that can ensure an anti-counterfeiting feature. However, most of the existing schemes require secret parameters to be stored in tags, which are vulnerable to physical attacks that can further lead to the breach of forward secrecy. Furthermore, as far as we know, none of the existing schemes are able to solve the security and privacy problems with good scalability. Since many existing schemes rely on exhaustive searches of the backend server to validate a tag and they are not scalable for applications with a large scale database. Hence, in this paper, we propose a lightweight RFID mutual authentication protocol with physically unclonable functions (PUFs). The performance analysis shows that our proposed scheme can ensure security and privacy efficiently in a scalable way.

Description: Sensors provide the foundation of many smart applications and cyber–physical systems by measuring and processing information upon which applications can make intelligent decisions or inform their users. Inertial measurement unit (IMU) sensors—and accelerometers and gyroscopes in particular—are readily available on contemporary smartphones and wearable devices. They have been widely adopted in the area of activity recognition, with fall detection and step counting applications being prominent examples in this field. However, these sensors may also incidentally reveal sensitive information in a way that is not easily envisioned upfront by developers. Far worse, the leakage of sensitive information to third parties, such as recommender systems or targeted advertising applications, may cause privacy concerns for unsuspecting end-users. In this paper, we explore the elicitation of age and gender information from gait traces obtained from IMU sensors, and systematically compare different feature engineering and machine learning algorithms, including both traditional and deep learning methods. We describe in detail the prediction methods that our team used in the OU-ISIR Wearable Sensor-based Gait Challenge: Age and Gender (GAG 2019) at the 12th IAPR International Conference on Biometrics. In these two competitions, our team obtained the best solutions amongst all international participants, and this for both the age and gender predictions. Our research shows that it is feasible to predict age and gender with a reasonable accuracy on gait traces of just a few seconds. Furthermore, it illustrates the need to put in place adequate measures in order to mitigate unintended information leakage by abusing sensors as an unanticipated side channel for sensitive information or private traits.

Description: In rehabilitation procedures related to the lower limbs, gait monitoring is an important source of information for the therapist. However, many of the approaches proposed in the literature require the use of uncomfortable and invasive devices. In this work, an instrumented tip is developed and detailed, which can be connected to any crutch. The instrumented tip provides objective data of the crutch motion, which, combined with patient movement data, might be used to monitor the daily activities or assess the recovery status of the patient. For that purpose, the tip integrates a two-axis inclinometer, a tri-axial gyroscope, and a force sensor to measure the force exerted on the crutch. In addition, a novel algorithm to estimate the pitch angle of the crutch is developed. The proposed approach is tested experimentally, obtaining acceptable accuracies and demonstrating the validity of the proposed lightweight, portable solution for gait monitoring.

Description: The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

Description: In the last years, several attempts to combine the Internet of Things (IoT) and social networking have been made. In the meantime, things involved in IoT are becoming increasingly sophisticated and intelligent, showing a behavior that tends to look like the one of users in social networks. Therefore, it is not out of place to talk about profiles of things and about information and topics exchanged among them. In such a context, constructing topic-driven virtual communities starting from the real ones operating in a Multi-IoT scenario is an extremely challenging issue. This paper aims at providing some contributions in this setting. First of all, it presents the concept of profile of a thing. Then, it introduces the concept of topic-guided virtual IoT. Finally, it illustrates two approaches (one supervised and one unsupervised) to constructing topic-guided virtual IoTs in a Multi-IoT scenario.

Description: As an important development direction of star sensor technology, the All-Time star sensor technology can expand the application of star sensors to flight platforms inside the atmosphere. Due to intense atmospheric background radiation during the daytime, the commonly used star sensors operating in the visible wavelength range are significantly limited in their ability to detect stars, and hence the All-Time star sensor technology which is based on the shortwave infrared (SWIR) imaging system has become an effective research direction. All-Time star sensor detection capability is significantly affected by observation conditions and, therefore, an optimized selection of optical parameters, which mainly includes the field of view (FOV) and the detection wavelength band, can effectively improve the detection performance of All-Time star sensors under harsh observation conditions. This paper uses the model simulation method to analyze and optimize the optical parameters under various observation conditions in a high-altitude environment. A main parameter among those discussed is the analysis of detection band optimization based on the SWIR band. Due to the huge cost constraints of high-altitude experiments, we conducted experiments near the ground to verify the effectiveness of the detection band selection and the correctness of the SWIR star sensor detection model, which thereby proved that the optimization of the optical parameters for high altitudes was effective and could be used as a reference.

Description: Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.

Description: Local administrations demand real-time and continuous pollution monitoring in sewer networks. Spectroscopy is a non-destructive technique that can be used to continuously monitor quality in sewers. Covering a wide range of wavelengths can be useful for improving pollution characterization in wastewater. Cost-effective and in-sewer spectrophotometers would contribute to accomplishing discharge requirements. Nevertheless, most available spectrometers are based on incandescent lamps, which makes it unfeasible to place them in a sewerage network for real-time monitoring. This research work shows an innovative calibration procedure that allows (Light-Emitting Diode) LED technology to be used as a replacement for traditional incandescent lamps in the development of spectrophotometry equipment. This involves firstly obtaining transmittance values similar to those provided by incandescent lamps, without using any optical components. Secondly, this calibration process enables an increase in the range of wavelengths available (working range) through a better use of the LED’s spectral width, resulting in a significant reduction in the number of LEDs required. Thirdly, this method allows important reductions in costs, dimensions and consumptions to be achieved, making its implementation in a wide variety of environments possible.

Description: Cloud providers such as Amazon Web Services (AWS) stand out as useful platforms to teach distributed computing concepts as well as the development of Cloud-native scalable application architectures on real-world infrastructures. Instructors can benefit from high-level tools to track the progress of students during their learning paths on the Cloud, and this information can be disclosed via educational dashboards for students to understand their progress through the practical activities. To this aim, this paper introduces CloudTrail-Tracker, an open-source platform to obtain enhanced usage analytics from a shared AWS account. The tool provides the instructor with a visual dashboard that depicts the aggregated usage of resources by all the students during a certain time frame and the specific use of AWS for a specific student. To facilitate self-regulation of students, the dashboard also depicts the percentage of progress for each lab session and the pending actions by the student. The dashboard has been integrated in four Cloud subjects that use different learning methodologies (from face-to-face to online learning) and the students positively highlight the usefulness of the tool for Cloud instruction in AWS. This automated procurement of evidences of student activity on the Cloud results in close to real-time learning analytics useful both for semi-automated assessment and student self-awareness of their own training progress.

Description: The design and operation of a custom-built LIDAR-compatible, four-channel Raman spectrometer integrated to a 532 nm pulsed laser is presented. The multichannel design allowed for simultaneous collection of Raman photons at two spectral regions identified as highly sensitive to changes in water temperature. For each of these spectral bands, the signals having polarization parallel to (∥) and perpendicular to (⟂), the excitation polarization were collected. Four independent temperature markers were calculated from the Raman signals: two-colour(∥), two-colour(⟂), depolarization(A) and depolarization(B). A total of sixteen datasets were analysed for one ultrapure (Milli-Q) and three samples of natural water. Temperature accuracies of ±0.4 °C–±0.8 °C were achieved using the two-colour(∥) marker. When multiple linear regression models were constructed (linear combination) utilizing all simultaneously acquired temperature markers, improved accuracies of ±0.3 °C–±0.7 °C were achieved.

Description: The low energy adaptive clustering hierarchy (LEACH) is the classical hierarchical topology control algorithm and still widely used today in wireless sensor networks. There are some problems in LEACH such us an unreasonable selection and uneven distribution of cluster heads, not considering the residual energy of nodes, the cluster head is overloaded and there is a high energy consumption of data transmission. In this paper, a topology control algorithm with double cluster heads and multi-hop based on affinity propagation clustering (APDC-M) was proposed. In APDC-M, firstly, a cluster head election algorithm with double choices based on the affinity propagation clustering algorithm (AP) and reference node strategy (APDC) was proposed. APDC is responsible to elect the fusion cluster head for node clustering. Secondly, a forwarding cluster head is elected within each cluster. The forwarding cluster head is responsible for the data transmission in order to reduce the energy consumption of a single cluster head. Lastly, the forwarding cluster heads complete the data transmission from a cluster to the base station by the shortest path inter-cluster multi-hop mode. The comparison simulation results show that APDC-M can make the cluster head election more reasonable and the cluster head distribution more uniform, and effectively reduce the energy consumption of the cluster head nodes when transmitting data, thus prolonging the network life.

Description: This study investigates the potential of the limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular (CV) parameters. In conjunction with the reference CV parameters (including diastolic, pulse, and systolic pressures, stroke volume, cardiac output, and total peripheral resistance), an upper-limb BCG based on an accelerometer embedded in a wearable armband and a lower-limb BCG based on a strain gauge embedded in a weighing scale were instrumented simultaneously with a finger photoplethysmogram (PPG). To standardize the analysis, the more convenient yet unconventional armband BCG was transformed into the more conventional weighing scale BCG (called the synthetic weighing scale BCG) using a signal processing procedure. The characteristic features were extracted from these BCG and PPG waveforms in the form of wave-to-wave time intervals, wave amplitudes, and wave-to-wave amplitudes. Then, the relationship between the characteristic features associated with (i) the weighing scale BCG-PPG pair and (ii) the synthetic weighing scale BCG-PPG pair versus the CV parameters, was analyzed using the multivariate linear regression analysis. The results indicated that each of the CV parameters of interest may be accurately estimated by a combination of as few as two characteristic features in the upper-limb or lower-limb BCG, and also that the characteristic features recruited for the CV parameters were to a large extent relevant according to the physiological mechanism underlying the BCG.

Description: Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

Description: Based on the situation that the traditional SINS (strapdown inertial navigation system)/CNS (celestial navigation system) integrated navigation system fails to realize all-day and all-weather navigation, this paper proposes a SINS/Landmark integrated navigation method based on landmark attitude determination to solve this problem. This integrated navigation system takes SINS as the basic scheme and uses landmark navigation to correct the error of SINS. The way of the attitude determination is to use the landmark information photographed by the landmark camera to complete feature matching. The principle of the landmark navigation and the process of attitude determination are discussed, and the feasibility of landmark attitude determination is analyzed, including the orthogonality of the attitude transform matrix, as well as the influences of the factors such as quantity and geometric position of landmarks. On this basis, the paper constructs the equations of the SINS/Landmark integrated navigation system, testifies the effectiveness of landmark attitude determination on the integrated navigation by Kalman filter, and improves the navigation precision of the system.

Description: Ground-based arc-scanning synthetic aperture radar (ArcSAR) is the novel ground-based synthetic aperture radar (GBSAR). It scans 360-degree surrounding scenes by the antenna attached to rotating boom. Therefore, compared with linear scanning GBSAR, ArcSAR has larger field of view. Although the feasibility of ArcSAR has been verified in recent years, its imaging algorithm still presents difficulties. The imaging accuracy of ArcSAR is affected by terrain fluctuation. For rotating scanning ArcSAR, even if targets in scenes have the same range and Doppler with antenna, if the heights of targets are different, their range migration will be different. Traditional ArcSAR imaging algorithms achieve imaging on reference plane. The height difference between reference plane and target in scenes will cause the decrease of imaging quality or even image defocusing because the range migration cannot be compensated correctly. For obtaining high-precision ArcSAR image, we propose interferometric DEM (digital elevation model)-assisted high precision imaging method for ArcSAR. The interferometric ArcSAR is utilized to acquire DEM. With the assist of DEM, target in scenes can be imaged on its actual height. In this paper, we analyze the error caused by ArcSAR imaging on reference plane. The method of extracting DEM on ground range for assisted ArcSAR imaging is also given. Besides, DEM accuracy and deformation monitoring accuracy of proposed method are analyzed. The effectiveness of the proposed method was verified by experiments.

Description: The validation of significant wave height (SWH) data measured by the Sentinel-3A/3B SAR Altimeter (SRAL) is essential for the application of the data in ocean wave monitoring, forecasting and wave climate studies. Sentinel-3A/3B SWH data are validated by comparisons with U. S. National Data Buoy Center (NDBC) buoys, using a spatial scale of 25 km and a temporal scale of 30 min, and with Jason-3 data at their crossovers, using a time difference of less than 30 min. The comparisons with NDBC buoy data show that the root-mean-square error (RMSE) of Sentinel-3A SWH is 0.30 m, and that of Sentinel-3B is no more than 0.31 m. The pseudo-Low-Resolution Mode (PLRM) SWH is slightly better than that of the Synthetic Aperture Radar (SAR) mode. The statistical analysis of Sentinel-3A/3B SWH in the bin of 0.5 m wave height shows that the accuracy of Sentinel-3A/3B SWH data decreases with increasing wave height. The analysis of the monthly biases and RMSEs of Sentinel-3A SWH shows that Sentinel-3A SWH are stable and have a slight upward trend with time. The comparisons with Jason-3 data show that SWH of Sentinel-3A and Jason-3 are consistent in the global ocean. Finally, the piecewise calibration functions are given for the calibration of Sentinel-3A/3B SWH. The results of the study show that Sentinel-3A/3B SWH data have high accuracy and remain stable.

Description: Magnetic resonance (MR) images are often corrupted by Rician noise which degrades the accuracy of image-based diagnosis tasks. The nonlocal means (NLM) method is a representative filter in denoising MR images due to its competitive denoising performance. However, the existing NLM methods usually exploit the gray-level information or hand-crafted features to evaluate the similarity between image patches, which is disadvantageous for preserving the image details while smoothing out noise. In this paper, an improved nonlocal means method is proposed for removing Rician noise in MR images by using the refined similarity measures. The proposed method firstly extracts the intrinsic features from the pre-denoised image using a shallow convolutional neural network named Laplacian eigenmaps network (LEPNet). Then, the extracted features are used for computing the similarity in the NLM method to produce the denoised image. Finally, the method noise of the denoised image is utilized to further improve the denoising performance. Specifically, the LEPNet model is composed of two cascaded convolutional layers and a nonlinear output layer, in which the Laplacian eigenmaps are employed to learn the filter bank in the convolutional layers and the Leaky Rectified Linear Unit activation function is used in the final output layer to output the nonlinear features. Due to the advantage of LEPNet in recovering the geometric structure of the manifold in the low-dimension space, the features extracted by this network can facilitate characterizing the self-similarity better than the existing NLM methods. Experiments have been performed on the BrainWeb phantom and the real images. Experimental results demonstrate that among several compared denoising methods, the proposed method can provide more effective noise removal and better details preservation in terms of human vision and such objective indexes as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Description: An experimental wideband chaotic ground penetrating radar is proposed to locate underground pipes. A chaotic signal with a bandwidth of 1.56 GHz is utilized as the probe signal. The localization of the pipes is achieved by correlating the chaotic echo signal with its delayed duplicate and back-projection algorithm. Experimental results demonstrate that plastic pipe, metallic pipe, and multiple pipes can be located with a range resolution of 10 cm. Limited by the height of the sand, the detectable range is estimated to be 0.7 m for both the plastic pipes and the metallic pipes when the transmitting power is −12 dBm. The proposed system has the potential to detect buried pipes, and it is suitable for geological and civil engineering applications.

Description: Electrode reversal errors in standard 12-lead electrocardiograms (ECG) can produce significant ECG changes and, in turn, misleading diagnoses. Their detection is important but mostly limited to the design of criteria using ECG databases with simulated reversals, without Wilson’s central terminal (WCT) potential change. This is, to the best of our knowledge, the first study that presents an algebraic transformation for simulation of all possible ECG cable reversals, including those with displaced WCT, where most of the leads appear with distorted morphology. The simulation model of ECG electrode swaps and the resultant WCT potential change is derived in the standard 12-lead ECG setup. The transformation formulas are theoretically compared to known limb lead reversals and experimentally proven for unknown limb–chest electrode swaps using a 12-lead ECG database from 25 healthy volunteers (recordings without electrode swaps and with 5 unicolor pairs swaps, including red (right arm—C1), yellow (left arm—C2), green (left leg (LL) —C3), black (right leg (RL)—C5), all unicolor pairs). Two applications of the transformation are shown to be feasible: ‘Forward’ (simulation of reordered leads from correct leads) and ‘Inverse’ (reconstruction of correct leads from an ECG recorded with known electrode reversals). Deficiencies are found only when the ground RL electrode is swapped as this case requires guessing the unknown RL electrode potential. We suggest assuming that potential to be equal to that of the LL electrode. The ‘Forward’ transformation is important for comprehensive training platforms of humans and machines to reliably recognize simulated electrode swaps using the available resources of correctly recorded ECG databases. The ‘Inverse’ transformation can save time and costs for repeated ECG recordings by reconstructing the correct lead set if a lead swap is detected after the end of the recording. In cases when the electrode reversal is unknown but a prior correct ECG recording of the same patient is available, the ‘Inverse’ transformation is tested to detect the exact swapping of the electrodes with an accuracy of (96% to 100%).

Description: This paper presents a payload designed to perform semi-autonomous contact inspection tasks without any type of positioning system external to the UAV, such as a global navigation satellite system (GNSS) or motion capture system, making possible inspection in challenging GNSS- denied sites. This payload includes two LiDAR sensors which measure the distance between the UAV and the target structure and their inner orientation angle. The system uses this information to control the approaching of the UAV to the structure and the contact between both, actuating over the pitch and yaw signals. This control is performed using a hybrid automaton with different states that represent all the possible UAV status during the inspection tasks. It uses different control strategies in each state. An ultrasonic gauge has been used as the inspection sensor of the payload to measure the thickness of a metallic sheet. The sensor requires a stable contact in order to collect reliable measurements. Several tests have been performed on the system, reaching accurate results which show it is able to maintain a stable contact with the target structure.

Description: The past decades have witnessed the rapid development in soft, stretchable, and biocompatible devices for applications in biomedical monitoring, personal healthcare, and human–machine interfaces. In particular, the design of soft devices in optics has attracted tremendous interests attributed to their distinct advantages such as inherent electrical safety, high stability in long-term operation, potential to be miniaturized, and free of electromagnetic interferences. As the alternatives to conventional rigid optical waveguides, considerable efforts have been made to develop light-guiding devices by using various transparent and elastic polymers, which offer desired physiomechanical properties and enable wearable/implantable applications in optical sensing, diagnostics, and therapy. Here, we review recent progress in soft and stretchable optical waveguides and sensors, including advanced structural design, fabrication strategies, and functionalities. Furthermore, the potential applications of those optical devices for various wearable and biomedical applications are discussed. It is expected that the newly emerged soft and stretchable optical technologies will provide a safe and reliable alternative to next-generation, smart wearables and healthcare devices.

Description: Aiming at solving the degradation problem of Luojia 1-01 night-light remote sensing images, the main reason for the “glow” phenomenon was analyzed. The APSF (Atmospheric Point Spread Function) template of night-light image was obtained from atmospheric source scattering. The template was used as the initial value in the regularization restoration model in this paper. Experiments were carried out using single point and regional images. The results demonstrate that the estimated APSF and restoration results of the method are better than those from other methods, and the image quality is improved after restoration.

Description: Nowadays, analyzing circulating tumor DNA (ctDNA), a very small part of circulating free DNA (cfDNA) carried by blood, is considered to be an interesting alternative to conventional single-site tumor tissue biopsies, both to assess tumor burden and provide a more comprehensive snapshot of the time-related and spatial heterogeneity of cancer genetic/epigenetic scenery. The determination of ctDNA and/or mapping its characteristic features, including tumor-specific mutations, chromosomal aberrations, microsatellite alterations, and epigenetic changes, are minimally invasive, powerful and credible biomarkers for early diagnosis, follow-up, prediction of therapy response/resistance, relapse monitoring, and tracking the rise of new mutant subclones, leading to improved cancer outcomes This review provides an outline of advances published in the last five years in electrochemical biosensing of ctDNA and surrogate markers. It emphasizes those strategies that have been successfully applied to real clinical samples. It highlights the unique opportunities they offer to shift the focus of cancer patient management methods from actual decision making, based on clinic-pathological features, to biomarker-driven treatment strategies, based on genotypes and customized targeted therapies. Also highlighted are the unmet hurdles and future key points to guide these devices in the development of liquid biopsy cornerstone tools in routine clinical practice for the diagnosis, prognosis, and therapy response monitoring in cancer patients.

Description: Wear is a common cause for aseptic loosening in artificial joints. The purpose of this study was to develop an automated diagnostical method for identification of the number and size distribution of wear debris. For this purpose, metal debris samples were extracted from a hip simulator and then analyzed by the electrospray method combined with a differential mobility analyzer, allowing particle detection ranging from several nanometers up to 1 µm. Wear particles were identified with a characteristic peak at 15 nm. The electrospray setup was successfully used and validated for the first time to characterize wear debris from simulated total joint replacement. The advantages of this diagnostic method are its time- and financial efficiency and its suitability for testing of different materials.

Description: Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p 〈 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

Description: For systems of measurement, geometric errors such as manufacturing and assembly errors could have a significant impact on the accuracy of the angle encoders of the system. In this study, an error model of angular measurement with geometric errors of a torsional characteristic measurement system was developed based on multibody system theory, the aim of which was to reveal the impact of geometric errors on angular measurement and to compensate the measurement error. According to the principle of spatial error transfer, the decomposition of geometric errors is illustrated and the error matrix of geometric errors is constructed by the Denavit–Hartenberg (DH) method. Subsequently, an error compensation function can be obtained and the impact of geometric error on angular measurement is discussed. Finally, we demonstrate by the experimental results of an ultra-autocollimator that the proposed error compensation method reduced the angular measurement error from 3.7% to 0.7%, which shows that the proposed error model can effectively predict the angular measurement error. In addition, it can be seen from the measurement results of the RV reducer that the error of the torsional characteristic measurement system decreased significantly.

Description: Tomographic SAR (TomoSAR) is a remote sensing technique that extends the conventional two-dimensional (2-D) synthetic aperture radar (SAR) imaging principle to three-dimensional (3-D) imaging. It produces 3-D point clouds with unavoidable noise that seriously deteriorates the quality of 3-D imaging and the reconstruction of buildings over urban areas. However, existing methods for TomoSAR point cloud processing notably rely on data segmentation, which influences the processing efficiency and denoising performance to a large extent. Inspired by regression analysis, in this paper, we propose an automatic method using neural networks to regularize the 3-D building structures from TomoSAR point clouds. By changing the point heights, the surface points of a building are refined. The method has commendable performance on smoothening the building surface, and keeps a precise preservation of the building structure. Due to the regression mechanism, the method works in a high automation level, which avoids data segmentation and complex parameter adjustment. The experimental results demonstrate the effectiveness of our method to denoise and regularize TomoSAR point clouds for urban buildings.

Description: Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals.

Description: Due to multi-factor coupling behavior, the performance evaluation of an accelerometer subject to high-temperature and high-impact loads poses a significant challenge during its design phase. In this paper, the simulation-based method is applied to optimize the design of the accelerometer. The proposed method can reduce the uncertainties and improve the fidelity of the simulation in the sense that (i) the preloading conditions of fasteners are taken into consideration and modeled in static analysis; (ii) all types of loadings, including bolt preloads, thermal loads, and impact loads, are defined in virtual dynamic prototype of the accelerometer. It is our finding that from static and dynamic analysis, an accelerometer is exposed to the risk of malfunction and even a complete failure if the temperature rises to a certain limit; it has been proved that the thermal properties of sensing components are the most critical factors for an accelerometer to achieve its desired performance. Accordingly, we use a simulation-based method to optimize the thermal expansion coefficient of the sensing element and get the expected design objectives.

Description: With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.

Description: Electromagnetic testing is the most widely used technique for the inspection of steel wire ropes. As one of the electromagnetic detecting approaches, the magnetic flux leakage (MFL) method has the best effect for the detection of broken wires. However, existing sensors based on MFL method still have some problems. (1) The size of the permanent magnet exciter is usually designed according to experience or rough calculation, and there is not enough depth analysis for its excitation performance; (2) Since the detectable angular range for a single Hall component is limited, Hall sensor arrays are often employed in the design of MFL sensors, which will increase the complexity of the subsequent signal processing due to the extensive use of Hall components; (3) Although the new magneto-resistance sensor has higher sensitivity, it is difficult to be applied in practice because of the requirement of the micron-level lift-off. To solve these problems, a sensor for the detection of broken wires of steel wire ropes based on the principle of magnetic concentration is developed. A circumferential multi-circuit permanent magnet exciter (CMPME) is employed to magnetize the wire rope to saturation. The traditional Hall sensor array is replaced by a magnetic concentrator to collect MFL. The structural parameters of the CMPME are optimized and the performance of the magnetic concentrator is analyzed by the finite element method. Finally, the effectiveness of the designed sensor is verified by wire breaking experiment. 1–5 external broken wires, handcrafted on the wire rope with a diameter of 24 mm, can be clearly identified, which shows great potential for the inspection of steel wire ropes.

Description: Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system’s viability as an affordable method for evaluation of ataxic disorders.

Description: Distributed acoustic sensing based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been widely used in many fields. Phase demodulation of the Φ-OTDR signal is essential for undistorted acoustic measurement. Digital coherent detection is a universal method to implement phase demodulation, but it may cause severe computational burden. In this paper, analog I/Q demodulation is introduced into the Φ-OTDR based DAS system to solve this problem, which can directly obtain the I and Q components of the beat signal without any digital processing, meaning that the computational cost can be sharply reduced. Besides, the sampling frequency of the data acquisition card can theoretically be lower than the beat frequency as the spectrum aliasing would not affect the demodulation results, thus further reducing the data volume of the system. Experimental results show that the proposed DAS system can demodulate the phase signal with good linearity and wide frequency response range. It can also adequately recover the sound signal sensed by the optical fiber, indicating that it can be a promising solution for computational-cost-sensitive distributed acoustic sensing applications.

Description: This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance. The performance of the proposed method was evaluated using BCI Competition III Dataset IVa (18 channels) and BCI Competition IV Dataset I (59 channels).

Description: Using unmanned robotic systems in military operations such as reconnaissance or surveillance, as well as in many civil applications, is common practice. In this article, the problem of monitoring the specified area of interest by a fleet of unmanned aerial systems is examined. The monitoring is planned via the Cooperative Aerial Model, which deploys a number of waypoints in the area; these waypoints are visited successively by unmanned systems. The original model proposed in the past assumed that the area to be explored is perfectly flat. A new formulation of this model is introduced in this article so that the model can be used in a complex environment with uneven terrain and/or with many obstacles, which may occlude some parts of the area of interest. The optimization algorithm based on the simulated annealing principles is proposed for positioning of waypoints to cover as large an area as possible. A set of scenarios has been designed to verify and evaluate the proposed approach. The key experiments are aimed at finding the minimum number of waypoints needed to explore at least the minimum requested portion of the area. Furthermore, the results are compared to the algorithm based on the lawnmower pattern.

Description: This paper presents a passive displacement sensor based on a normal mode helical antenna. The sensor consists of an external helical antenna and an inserting dielectric rod. First, the perturbation theory is adopted to demonstrate that both the electric intensity and magnetic intensity have a noticeable gradient change within the in-and-out entrance of the helical antenna, which will cause the sensor to experience a resonant frequency shift. This phenomenon was further verified by numerical simulation using the Ansoft high frequency structure simulator (HFSS), and results show the linear correlation between the retrieved resonant frequency and the displacement. Two sets of proposed sensors were fabricated. The experiments validated that the resonant frequency shifts are linearly proportional to the applied displacement, and the sensing range can be adjusted to accommodate the user’s needs.

Description: Vision-based fall-detection methods have been previously studied but many have limitations in terms of practicality. Due to differences in rooms, users do not set the camera or sensors at the same height. However, few studies have taken this into consideration. Moreover, some fall-detection methods are lacking in terms of practicality because only standing, sitting and falling are taken into account. Hence, this study constructs a data set consisting of various daily activities and fall events and studies the effect of camera/sensor height on fall-detection accuracy. Each activity in the data set is carried out by eight participants in eight directions and taken with the depth camera at five different heights. Many related studies heavily depended on human segmentation by using Kinect SDK but this is not reliable enough. To address this issue, this study proposes Enhanced Tracking and Denoising Alex-Net (ETDA-Net) to improve tracking and denoising performance and classify fall and non-fall events. Experimental results indicate that fall-detection accuracy is affected by camera height, against which ETDA-Net is robust, outperforming traditional deep learning based fall-detection methods.

Description: Recently, environmental pollution resulting from industrial waste has been emerging in an endless stream. The industrial waste contains chemical materials, heavy metal ions, and other toxic materials. Once the industrial waste is discharged without standards, it might lead to water or environmental pollution. Hence, it has become more important to provide evidence-based water quality monitoring. The use of a multifunctional miniaturized water quality monitoring system (WQMS), that contains continuous monitoring, water quality monitoring, and wireless communication applications, simultaneously, is infrequent. Thus, electrodes integrated with polydimethylsiloxane flow channels were presented in this study to be a compound sensor, and the sensor can be adopted concurrently to measure temperature, pH, electrical conductivity, and copper ion concentration, whose sensitivities are determined as 0.0193 °C/mV, −0.0642 pH/mV, 1.1008 mS/V·cm (from 0 mS/cm to 2 mS/cm) and 1.1975 mS/V·cm (from 2 mS/cm to 5.07 mS/cm), and 0.0111 ppm/mV, respectively. A LoRa shield connected into the system could provide support as a node of long range wide area network (LoRaWAN) for wireless communication application. As mentioned above, the sensors, LoRa, and circuit have been integrated in this study to a continuous monitoring system, WQMS. The advantages of the multifunctional miniaturized WQMS are low cost, small size, easy maintenance, continuous sampling and long-term monitoring for many days. Every tested period is 180 min, and the measured rate is 5 times per 20 min. The feedback signals of the miniaturized WQMS and measured values of the instrument were obtained to compare the difference. In the measured results at three different place-to-place locations the errors of electrical conductivity are 0.051 mS/cm, 0.106 mS/cm, and 0.092 mS/cm, respectively. The errors of pH are 0.68, 0.87, and 0.56, respectively. The errors of temperature are 0.311 °C, 0.252 °C, and 0.304 °C, respectively. The errors of copper ion concentration are 0.051 ppm, 0.058 ppm, 0.050 ppm, respectively.

Description: This paper investigates the lateral pull-in effect of an in-plane overlap-varying transducer. The instability is induced by the translational and rotational displacements. Based on the principle of virtual work, the equilibrium conditions of force and moment in lateral directions are derived. The analytical solutions of the critical voltage, at which the pull-in phenomenon occurs, are developed when considering only the translational stiffness or only the rotational stiffness of the mechanical spring. The critical voltage in a general case is numerically determined by using nonlinear optimization techniques, taking into account the combined effect of translation and rotation. The influences of possible translational offsets and angular deviations to the critical voltage are modeled and numerically analyzed. The investigation is then expanded for the first time to anti-phase operation mode and Bennet’s doubler configuration of the two transducers.

Description: With the growth of the urban population, the rapid development of smart cities has become the focus of urban regional development. Smart medical care is an indispensable part of smart city construction, which promotes the development of the medical industry. However, the security of data and timely service are the current problems faced by intelligent medical systems. Based on the public key encryption with filtered equality test and identity-based cryptography, an identity-based encryption with the filtered equality test (IBE-FET) is proposed for smart healthcare, in which a data receiver can use the private key and the message set to generate a warrant and send it to the cloud server. A cloud server can verify the equality between ciphertexts without decryption and check whether the encrypted message belongs to the same message set. Furthermore, the security analysis shows that the proposed scheme satisfies one-way security against the chosen identity and ciphertext attack in the random oracle model under the computational bilinear Diffie-Hellman assumption. The performance comparison shows that the scheme is feasible and practical in real life.

Description: Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG signals and used an engagement index to calculate engagement levels. This research is expected to add value to research on smart city infrastructures and on assistive technologies to increase productivity in smart offices.

Description: Silica nanoparticles have been shown to exhibit many characteristics that allow for additional oil to be recovered during sand-pack flooding experiments. Additionally various imaging techniques have been employed in the past to visually compare flooding procedures including x-ray computed tomography and magnetic resonance imaging; however, these techniques require the sample to be destroyed or sliced after the flooding experiment finishes. Electrical resistance tomography (ERT) overcomes these limitations by offering a non-destructive visualization method allowing for online images to be taken during the flooding process by the determination of spatial distribution of electrical resistivity, thus making it suitable for sand-packs. During the scope of this research a new sand-pack system and methodology was created which utilized ERT as a monitoring tool. Two concentrations, 0.5 wt% and 1.0 wt%, of SiO2 nanoparticles were compared with runs using only brine to compare the recovery efficiency and explore the ability of ERT to monitor the flooding process. Electrical resistance tomography was found to be an effective tool in monitoring local recovery efficiency revealing 1.0 wt% SiO2 to be more effective than 0.5 wt% and brine only runs during the scope of this research. A new method involving the slope function in excel was used to compare the effects of nanofluids on resistivity trends also revealing information about the rate of recovery against time. SiO2 nanofluid recovery mechanisms such interfacial tension reduction and viscosity enhancement were then considered to explain why the nanofluids resulted in greater oil recovery.

Description: This article proposes an interferometric method for a variable depolarizer characterization with features that distinguish it from the polarimetric system. Information about the behavior of a vertically aligned nematic cell as a variable depolarizer can be extracted from Young interferometer measurements in real time. These results could be significant for understanding the polarization phenomena in depolarizing media such as biological tissue.

Description: As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

Description: This paper proposes a general stability control method that uses the concept of zero-moment-point (ZMP) and a turning algorithm with a light detection and ranging (LiDAR) sensor for a bipedal alpine skiing robot. There is no elaborate simulator for skiing robots since the snow has complicated characteristics, such as compression and melting. However, real experiments are laborious because of the many varied skiing conditions. The proposed skiing simulator could be used, so that a humanoid robot can track its desired turning radius by modeled forces that are similar to real ones in the snow. Subsequently, the robot will be able to pass through gates with LiDAR sensors. By using ZMP control, the robot can avoid falling down while tracking its desired path. The performance of the proposed stabilization method and autonomous turning algorithm are verified by a dynamics simulation software, Webots, and the simulation results are obtained while using the small humanoid robot platform DARwIn-OP.

Description: Quantum dots (QDs) are very attractive nanomaterials for analytical chemistry, due to high photostability, large surface area featuring numerous ways of bioconjugation with biomolecules, usually high quantum yield and long decay times. Their broad absorption spectra and narrow, sharp emission spectra of size-tunable fluorescence make them ideal tools for pattern-based sensing. However, almost always they are applied for specific sensing with zero-dimensional (0D) signal reporting (only peak heights or peak shifts are considered), without taking advantage of greater amount of information hidden in 1D signal (emission spectra), or huge amount of information hidden in 2D fluorescence maps (Excitation-Emission Matrixes, EEMs). Therefore, in this work we propose opposite strategy—non-specific interactions of QDs, which are usually avoided and regarded as their disadvantage, were exploited here for 2D fluorescence fingerprinting. Analyte-specific multivariate fluorescence response of QDs is decoded with the use of Partial Least Squares—Discriminant Analysis. Even though only one type of QDs is studied, the proposed pattern-based method enables to obtain satisfactory accuracy for all studied compounds—various neurotransmitters, amino-acids and oligopeptides. This is a proof of principle of the possibility of the identification of various bioanalytes by such fluorescence fingerprinting with the use of QDs.

Description: Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.

Description: The standard technology used to capture motion for biomechanical analysis in sports has employed marker-based optical systems. While these systems are excellent at providing positional information, they suffer from a limited ability to accurately provide fundamental quantities such as velocity and acceleration (hence forces and torques) during high-speed motion typical of many sports. Conventional optical systems require considerable setup time, can exhibit sensitivity to extraneous light, and generally sample too slowly to accurately capture extreme bursts of athletic activity. In recent years, wireless wearable sensors have begun to penetrate devices used in sports performance assessment, offering potential solutions to these limitations. This article, after determining pressing problems in sports that such sensors could solve and surveying the state-of-the-art in wearable motion capture for sports, presents a wearable dual-range inertial and magnetic sensor platform that we developed to enable an end-to-end investigation of high-level, very wide dynamic-range biomechanical parameters. We tested our system on collegiate and elite baseball pitchers, and have derived and measured metrics to glean insight into performance-relevant motion. As this was, we believe, the first ultra-wide-range wireless multipoint and multimodal inertial and magnetic sensor array to be used on elite baseball pitchers, we trace its development, present some of our results, and discuss limitations in accuracy from factors such as soft-tissue artifacts encountered with extreme motion. In addition, we discuss new metric opportunities brought by our systems that may be relevant for the assessment of micro-trauma in baseball.

Description: Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

Description: There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique.

Description: Paper-based sensors fabricated using the pencil-on-paper method are expected to find wide usage in many fields owing to their low cost and high reproducibility. Here, hydrogen (H2) detection was realized by applying palladium (Pd) nanoparticles (NPs) to electronic circuits printed on paper using a metal mask and a pencil. We confirmed that multilayered graphene was produced by the pencil, and then characterized Pd NPs were added to the pencil marks. To evaluate the gas-sensing ability of the sensor, its sensitivities and reaction rates in the presence and absence of H2 were measured. In addition, sensing tests performed over a wide range of H2 concentrations confirmed that the sensor had a detection limit as low as 1 ppm. Furthermore, the sensor reacted within approximately 50 s at all H2 concentrations tested. The recovery time of the sensor was 32 s at 1 ppm and 78 s at 1000 ppm. Sensing tests were also performed using Pd NPs of different sizes to elucidate the relationship between the sensing rate and catalyst size. The experimental results confirmed the possibility of fabricating paper-based gas sensors with a superior sensing capability and response rate.

Description: Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

Description: The edge platform has evolved to become a part of a distributed computing environment. While typical edges do not have enough processing power to train machine learning models in real time, it is common to generate models in the cloud for use on the edge. The pattern of heterogeneous Internet of Things (IoT) data is dependent on individual circumstances. It is not easy to guarantee prediction performance when a monolithic model is used without considering the spatial characteristics of the space generating those data. In this paper, we propose a collaborative framework using a new method to select the best model for the edge from candidate models of cloud based on sample data correlation. This method lets the edge use the most suitable model without any training tasks on the edge side, and it also minimizes privacy issues. We apply the proposed method to predict future fine particulate matter concentration in an individual space. The results suggest that our method can provide better performance than the previous method.

Description: A novel high-precision subdivision system for high-speed encoders is designed in this work. The system is designed with an arc second of Sin-Cos Encoder (SCE) based on zero phase bandpass filter. The system collects the analog output signals of an encoder with a high-speed data acquisition system (DAS); the noise of a digital signal can be effectively eliminated by zero phase bandpass filter with appropriate prior parameters. Finally, the actual rotation angle of the encoder is calculated by the software subdivision technique in the system. The software subdivision technique includes two methods, which are the Analog Pulse Counter (APC) and the Arc Tangent Subdivision (ATS). The APC method calculates the encoder angle by counting the analog pulses acquired by the arc tangent signal. The ATS method calculates the encoder angle by computing the arc tangent results of each point. The accuracy and stability of the system are first verified with a simulated signal; second, the real signals of an SCE are acquired by a high speed DAS on a test bench of a precision reducer, which is employed in industrial robots. The results of the proposed system are compared. The experimental results show that the system can significantly improve the accuracy of the encoder angle calculation, with controllable costs.

Description: Dancing is an activity that positively enhances the mood of people that consists of feeling the music and expressing it in rhythmic movements with the body. Learning how to dance can be challenging because it requires proper coordination and understanding of rhythm and beat. In this paper, we present the first implementation of the Dancing Coach (DC), a generic system designed to support the practice of dancing steps, which in its current state supports the practice of basic salsa dancing steps. However, the DC has been designed to allow the addition of more dance styles. We also present the first user evaluation of the DC, which consists of user tests with 25 participants. Results from the user test show that participants stated they had learned the basic salsa dancing steps, to move to the beat and body coordination in a fun way. Results also point out some direction on how to improve the future versions of the DC.

Description: This study presents a set of experimental and numerical investigations to study the sensitivity of the piezoelectric-based smart interface device to structural damage in a bolted connection. The study aims to identify the proper geometric sizes of smart interfaces for damage detection tasks. First, the fundamentals of the damage monitoring technique via lead zirconate titanate(PZT) interface is briefly described for a bolted connection. Second, a lab-scaled girder connection is selected as the test structure for the experimental investigation. PZT interface prototypes with varying geometric sizes are designed for the test connection. Under the bolt-loosening inflicted in the connection, the impedance responses of the PZT interfaces are analyzed to understand the effect of geometric parameters on the damage sensitivity of the impedance responses. Subsequently, the bolt-loosening detection capabilities of the PZT interfaces are comparatively evaluated for identifying the proper geometric sizes of the devices. Finally, a finite element model of the PZT interface-bolted connection system is established for the numerical investigation. The damage sensitivity of the numerical impedance responses is compared with the experimental results for the verification.

Description: The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

Description: Nanofibers or nanofibrous membranes prepared by electrospinning possess many attractive properties, including excellent mechanical properties, high specific surface area and high porosity, making them attractive for sensor application, especially for the electrochemical sensors. Many nanomaterials are used as additives to improve the conductivity, sensitivity and selectivity of sensors. Based on the different modifiers of electrode materials, electrochemical sensors can be divided into enzyme sensors and non-enzyme sensors. In this review, we summarize the recent progress of the electrochemical sensors fabricated by electrospinning, including hydrogen peroxide (H2O2) sensors, glucose sensors and other sensors. In addition, the sensing mechanisms of various electrochemical sensors are introduced in detail. Finally, future research directions of electrochemical sensors based on electrospinning and the challenges faced by large-scale applications of electrospun electrochemical sensors are presented.

Description: Ammonia (NH3) emission is one of the major environmental issues in livestock farming. Gas measurements are required to study the emission process, to establish emission factors, and to assess the efficiency of emission reduction techniques. However, the current methods for acquiring reference measurements of NH3 are either high in cost or labor intensive. In this study, a cost-effective ammonia monitoring system (AMS) was constructed from a commercially-available gas analyzing module based on tunable diode laser absorption (TDLA) spectroscopy. To cope with the negative measurement biases caused by differing inlet pressures, a set of correction equations was formulated. Field validation of the AMS on NH3 measurement was conducted in a fattening pig barn, where the system was compared to a Fourier-transform infrared (FTIR) spectroscopy analyzer. Under two test conditions in a fattening pig barn, the absolute error of the AMS measurements with respect to the average obtained values between the AMS and the FTIR was respectively 0.66 and 0.08 ppmv, corresponding to 5.9% and 0.5% relative error. Potential sources of the measurement uncertainties in both the AMS and FTIR were discussed. The test results demonstrated that the AMS was capable of performing high-quality measurement with sub-ppm accuracy, making it a promising cost-effective tool for establishing NH3 emission factors and studying NH3 emission processes in pig houses.

Description: In this paper, an improved method of measuring wavefront aberration based on image with machine learning is proposed. This method had better real-time performance and higher estimation accuracy in free space optical communication in cases of strong atmospheric turbulence. We demonstrated that the network we optimized could use the point spread functions (PSFs) at a defocused plane to calculate the corresponding Zernike coefficients accurately. The computation time of the network was about 6–7 ms and the root-mean-square (RMS) wavefront error (WFE) between reconstruction and input was, on average, within 0.1263 waves in the situation of D/r0 = 20 in simulation, where D was the telescope diameter and r0 was the atmospheric coherent length. Adequate simulations and experiments were carried out to indicate the effectiveness and accuracy of the proposed method.

Description: Waveguide-based photonic sensors provide a unique combination of high sensitivity, compact size and label-free, multiplexed operation. Interferometric configurations furthermore enable a simple, fixed-wavelength read-out making them particularly suitable for low-cost diagnostic and monitoring devices. Their limit of detection, i.e., the lowest analyte concentration that can be reliably observed, mainly depends on the sensors response to small refractive index changes, and the noise in the read-out system. While enhancements in the sensors response have been extensively studied, noise optimization has received much less attention. Here we show that order-of-magnitude enhancements in the limit of detection can be achieved through systematic noise reduction, and demonstrate a limit of detection of ∼ 10 − 8 RIU with a silicon nitride sensor operating at telecom wavelengths.

Description: A depth sensor with integrated frame difference detection is proposed. Instead of frame difference detection using light intensity, which is vulnerable to ambient light, the difference in depth between successive frames can be acquired. Because the conventional time-of-flight depth sensor requires two frames of depth-image acquisition with four-phase modulation, it has large power consumption, as well as a large area for external frame memories. Therefore, we propose a simple two-step comparison scheme for generating the depth frame difference in a single frame. With the proposed scheme, only a single frame is needed to obtain the frame difference, with less than half of the power consumption of the conventional depth sensor. Because the frame difference is simply generated by column-parallel circuits, no access of the external frame memory is involved, nor is a digital signal processor. In addition, we used an over-pixel metal–insulator–metal capacitor to store temporary signals for enhancing the area efficiency. A prototype chip was fabricated using a 90 nm backside illumination complementary metal–oxide–semiconductor (CMOS) image sensor process. We measured the depth frame difference in the range of 1–2.5 m. With a 10 MHz modulation frequency, a depth frame difference of 〉10 cm was successfully detected even for objects with different reflectivity. The maximum relative error from the difference of the reflectivity (white and wooden targets) was 〈3%.

Description: Internet of Thing (IoT) is the most emerging technology in which all the objects in the real world can use the Internet to communicate with each other as parts of a single unified system. This eventually leads to the development of many smart applications such as smart cities, smart homes, smart healthcare, smart transportation, etc. Due to the fact that the IoT devices have limited resources, the cybersecurity approaches that relied on complex and long processing cryptography are not a good fit for these constrained devices. Moreover, the current IoT systems experience critical security vulnerabilities that include identifying which devices were affected, what data or services were accessed or compromised, and which users were impacted. The cybersecurity challenge in IoT systems is to find a solution for handling the identity of the user, things/objects and devices in a secure manner. This paper proposes an effective multifactor authentication (CMA) solution based on robust combiners of the hash functions implemented in the IoT devices. The proposed CMA solution mitigates the authentication vulnerabilities of IoT and defends against several types of attacks. Also, it achieves multi-property robustness and preserves the collision-resistance, the pseudo-randomness, the message authentication code, and the one-wayness. It also ensures the integrity, authenticity and availability of sensed data for the legitimate IoT devices. The simulation results show that CMA outperforms the TOTP in term of the authentication failure rate. Moreover, the evaluation of CMA shows an acceptable QoS measurement in terms of computation time overhead, throughput, and packet loss ratio.

Description: For the research and development of sensor systems, the collection and fusion of sensing data is the core. In order to make sensor data acquisition change with the change in environment, a dynamic data acquisition and fusion method based on feedback control is proposed in this paper. According to the sensing data acquisition and fusion model, the optimal acquisition of sensor data is achieved through real-time dynamic judgment of the collected data, decision-making of the next acquisition time interval, and adjustment. This model enables the sensor system to adapt to different environments. An experimental study of the proposed model was carried out on an experimental platform, and the results show that the proposed model can not only reflect the change in sensing data but also improve the transmission efficiency.

Description: Hierarchical Ni-Co double transition metal hydroxide nanosheets have been explored as an effective strategy for the design of nonenzymatic glucose sensors. Ni-Co hydroxide nanosheets constructed hollow cubes were successfully synthesized by using Cu2O cubes as templates and subsequently etched by Na2S2O3 to achieve a hollow cubic structure. The molar ratio between Ni and Co was tuned by varying the precursor ratio of NiCl2 and CoCl2. It was observed by transmission electron microscopy (TEM) that the increasing Ni precursor resulted in particle morphology, and the increasing ratio of the Co precursor resulted in more lamellar morphology. The sample with the composition of Ni0.7Co0.3(OH)2 displayed the best performance for glucose sensing with high selectivity (1541 μA mM–1 cm–2), low detection limit (3.42 µM with S/N = 3), and reasonable selectivity. Similar strategies could be applied for the design of other electrode materials with high efficiency for nonenzymatic glucose determination.