ALBERT

Description: Sensors, Vol. 18, Pages 82: Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks Sensors doi: 10.3390/s18010082 Authors: Yongxuan Lai Fan Yang Jinsong Su Qifeng Zhou Tian Wang Lu Zhang Yifan Xu Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.

Description: Sensors, Vol. 18, Pages 78: A Triple-Mode Flexible E-Skin Sensor Interface for Multi-Purpose Wearable Applications Sensors doi: 10.3390/s18010078 Authors: Sung-Woo Kim Youngoh Lee Jonghwa Park Seungmok Kim Heeyoung Chae Hyunhyub Ko Jae Kim This study presents a flexible wireless electronic skin (e-skin) sensor system that includes a multi-functional sensor device, a triple-mode reconfigurable readout integrated circuit (ROIC), and a mobile monitoring interface. The e-skin device’s multi-functionality is achieved by an interlocked micro-dome array structure that uses a polyvinylidene fluoride and reduced graphene oxide (PVDF/RGO) composite material that is inspired by the structure and functions of the human fingertip. For multi-functional implementation, the proposed triple-mode ROIC is reconfigured to support piezoelectric, piezoresistance, and pyroelectric interfaces through single-type e-skin sensor devices. A flexible system prototype was developed and experimentally verified to provide various wireless wearable sensing functions—including pulse wave, voice, chewing/swallowing, breathing, knee movements, and temperature—while their real-time sensed data are displayed on a smartphone.

Description: Sensors, Vol. 18, Pages 77: Real-Time Detection of Sporadic Meteors in the Intensified TV Imaging Systems Sensors doi: 10.3390/s18010077 Authors: Stanislav Vítek Maria Nasyrova The automatic observation of the night sky through wide-angle video systems with the aim of detecting meteor and fireballs is currently among routine astronomical observations. The observation is usually done in multi-station or network mode, so it is possible to estimate the direction and the speed of the body flight. The high velocity of the meteorite flying through the atmosphere determines the important features of the camera systems, namely the high frame rate. Thanks to high frame rates, such imaging systems produce a large amount of data, of which only a small fragment has scientific potential. This paper focuses on methods for the real-time detection of fast moving objects in the video sequences recorded by intensified TV systems with frame rates of about 60 frames per second. The goal of our effort is to remove all unnecessary data during the daytime and make free hard-drive capacity for the next observation. The processing of data from the MAIA (Meteor Automatic Imager and Analyzer) system is demonstrated in the paper.

Description: In order to achieve and maintain a high quality factor (high-Q) for the micro resonant pressure sensor, this paper presents a new wafer level package by adopting cross-layer anodic bonding technique of the glass/silicon/silica (GSS) stackable structure and integrated Ti getter. A double-layer structure similar to a silicon-on-insulator (SOI) wafer is formed after the resonant layer and the pressure-sensitive layer are bonded by silicon direct bonding (SDB). In order to form good bonding quality between the pressure-sensitive layer and the glass cap layer, the cross-layer anodic bonding technique is proposed for vacuum package by sputtering Aluminum (Al) on the combination wafer of the pressure-sensitive layer and the resonant layer to achieve electrical interconnection. The model and the bonding effect of this technique are discussed. In addition, in order to enhance the performance of titanium (Ti) getter, the prepared and activation parameters of Ti getter under different sputtering conditions are optimized and discussed. Based on the optimized results, the Ti getter (thickness of 300 nm to 500 nm) is also deposited on the inside of the glass groove by magnetron sputtering to maintain stable quality factor (Q). The Q test of the built testing system shows that the number of resonators with a Q value of more than 10,000 accounts for more than 73% of the total. With an interval of 1.5 years, the Q value of the samples remains almost constant. It proves the proposed cross-layer anodic bonding and getter technique can realize high-Q resonant structure for long-term stable operation.

Description: This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Description: Achieving a high fill factor is a bottleneck problem for capturing high-quality images. There are hardware and software solutions to overcome this problem. In the solutions, the fill factor is known. However, this is an industrial secrecy by most image sensor manufacturers due to its direct effect on the assessment of the sensor quality. In this paper, we propose a method to estimate the fill factor of a camera sensor from an arbitrary single image. The virtual response function of the imaging process and sensor irradiance are estimated from the generation of virtual images. Then the global intensity values of the virtual images are obtained, which are the result of fusing the virtual images into a single, high dynamic range radiance map. A non-linear function is inferred from the original and global intensity values of the virtual images. The fill factor is estimated by the conditional minimum of the inferred function. The method is verified using images of two datasets. The results show that our method estimates the fill factor correctly with significant stability and accuracy from one single arbitrary image according to the low standard deviation of the estimated fill factors from each of images and for each camera.

Description: The European population is ageing, and there is a need for health solutions that keep older adults independent longer. With increasing access to mobile technology, such as smartphones and smartwatches, the development and use of mobile health applications is rapidly growing. To meet the societal challenge of changing demography, mobile health solutions are warranted that support older adults to stay healthy and active and that can prevent or delay functional decline. This paper reviews the literature on mobile technology, in particular wearable technology, such as smartphones, smartwatches, and wristbands, presenting new ideas on how this technology can be used to encourage an active lifestyle, and discusses the way forward in order further to advance development and practice in the field of mobile technology for active, healthy ageing.

Description: Non-destructive subsurface detection of encapsulated, coated, or seal-packaged foods and pharmaceuticals can help prevent distribution and consumption of counterfeit or hazardous products. This study used a Spatially Offset Raman Spectroscopy (SORS) method to detect and identify urea, ibuprofen, and acetaminophen powders contained within one or more (up to eight) layers of gelatin capsules to demonstrate subsurface chemical detection and identification. A 785-nm point-scan Raman spectroscopy system was used to acquire spatially offset Raman spectra for an offset range of 0 to 10 mm from the surfaces of 24 encapsulated samples, using a step size of 0.1 mm to obtain 101 spectral measurements per sample. As the offset distance was increased, the spectral contribution from the subsurface powder gradually outweighed that of the surface capsule layers, allowing for detection of the encapsulated powders. Containing mixed contributions from the powder and capsule, the SORS spectra for each sample were resolved into pure component spectra using self-modeling mixture analysis (SMA) and the corresponding components were identified using spectral information divergence values. As demonstrated here for detecting chemicals contained inside thick capsule layers, this SORS measurement technique coupled with SMA has the potential to be a reliable non-destructive method for subsurface inspection and authentication of foods, health supplements, and pharmaceutical products that are prepared or packaged with semi-transparent materials.

Description: Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results in the film overoxidized for 45 s at 1.35 V presenting a strong adsorption. The other one-step overoxidation film prepared by direct CV ranging from −0.6 V to 1.35 V was polymerized for comparison. Scanning electron microscope (SEM) analysis and Fourier transform infrared (FTIR) spectroscopy were used for monitoring morphological changes and the evolution of functional groups. Both of them indicate increased abundant oxygen functional groups and roughness, yet the products exhibit dendritic morphology and piles of spherical protrusions, respectively. Moreover, double-step overoxidized film showed better electrochemical performance toward lead ion sensing. These characterizations highlight some novel properties that may be beneficial for specific sensing applications.

Description: The extensive applications of multi-function radars (MFRs) have presented a great challenge to the technologies of radar countermeasures (RCMs) and electronic intelligence (ELINT). The recently proposed cognitive electronic warfare (CEW) provides a good solution, whose crux is to perceive present and future MFR behaviours, including the operating modes, waveform parameters, scheduling schemes, etc. Due to the variety and complexity of MFR waveforms, the existing approaches have the drawbacks of inefficiency and weak practicability in prediction. A novel method for MFR behaviour recognition and prediction is proposed based on predictive state representation (PSR). With the proposed approach, operating modes of MFR are recognized by accumulating the predictive states, instead of using fixed transition probabilities that are unavailable in the battlefield. It helps to reduce the dependence of MFR on prior information. And MFR signals can be quickly predicted by iteratively using the predicted observation, avoiding the very large computation brought by the uncertainty of future observations. Simulations with a hypothetical MFR signal sequence in a typical scenario are presented, showing that the proposed methods perform well and efficiently, which attests to their validity.

Description: The increase in the popularity of social media has shattered the gap between the physical and virtual worlds. The content generated by people or social sensors on social media provides information about users and their living surroundings, which allows us to access a user’s preferences, opinions, and interactions. This provides an opportunity for us to understand human behavior and enhance the services provided for both the real and virtual worlds. In this paper, we will focus on the popularity prediction of social images on Flickr, a popular social photo-sharing site, and promote the research on utilizing social sensory data in the context of assisting people to improve their life on the Web. Social data are different from the data collected from physical sensors; in the fact that they exhibit special characteristics that pose new challenges. In addition to their huge quantity, social data are noisy, unstructured, and heterogeneous. Moreover, they involve human semantics and contextual data that require analysis and interpretation based on human behavior. Accordingly, we address the problem of popularity prediction for an image by exploiting three main factors that are important for making an image popular. In particular, we investigate the impact of the image’s visual content, where the semantic and sentiment information extracted from the image show an impact on its popularity, as well as the textual information associated with the image, which has a fundamental role in boosting the visibility of the image in the keyword search results. Additionally, we explore social context, such as an image owner’s popularity and how it positively influences the image popularity. With a comprehensive study on the effect of the three aspects, we further propose to jointly consider the heterogeneous social sensory data. Experimental results obtained from real-world data demonstrate that the three factors utilized complement each other in obtaining promising results in the prediction of image popularity on social photo-sharing site.

Description: For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future.

Description: In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.

Description: Radar imaging based on electromagnetic vortex can achieve azimuth resolution without relative motion. The present paper investigates this imaging technique with the use of a single receiving antenna through theoretical analysis and experimental results. Compared with the use of multiple receiving antennas, the echoes from a single receiver cannot be used directly for image reconstruction using Fourier method. The reason is revealed by using the point spread function. An additional phase is compensated for each mode before imaging process based on the array parameters and the elevation of the targets. A proof-of-concept imaging system based on a circular phased array is created, and imaging experiments of corner-reflector targets are performed in an anechoic chamber. The azimuthal image is reconstructed by the use of Fourier transform and spectral estimation methods. The azimuth resolution of the two methods is analyzed and compared through experimental data. The experimental results verify the principle of azimuth resolution and the proposed phase compensation method.

Description: With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models.

Description: As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver’s health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver’s health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced.

Description: In the dual-axis rotation inertial navigation system (INS), besides the gyro error, accelerometer error, rolling misalignment angle error, and the gimbal angle error, the shaft swing angle and the axis non-orthogonal angle also affect the attitude accuracy. Through the analysis of the structure, we can see that the shaft swing angle and axis non-orthogonal angle will produce coning errors which cause the fluctuation of the attitude. According to the analysis of the rotation vector, it can be seen that the coning error will generate additional drift velocity along the rotating shaft, which can reduce the navigation precision of the system. In this paper, based on the establishment of the modulation average frame, the vector projection is carried out, and then the attitude conversion matrix and the attitude error matrix mainly including the shaft swing angle and axis non-orthogonal are obtained. Because the attitude angles are given under the static condition, the shaft swing angle and the axis non-orthogonal angle are estimated by the static Kalman filter (KF). This kind of KF method has been widely recognized as the standard optimal estimation tool for estimating the parameters such as coning angles (α1 , α2), initial phase angles (ϕ1,ϕ2), and the non-perpendicular angle (η). In order to carry out the system level verification, a dual axis rotation INS is designed. Through simulation and experiments, the results show that the amplitudes of the attitude angles’ variation are reduced by about 20%–30% when the shaft rotates. The attitude error equation is reasonably simplified and the calibration method is accurate enough. The attitude accuracy is further improved.

Description: Defocus of the reconstructed image of synthetic aperture radar (SAR) occurs in the presence of the phase error. In this work, a phase error correction method is proposed for compressed sensing (CS) radar imaging based on approximated observation. The proposed method has better image focusing ability with much less memory cost, compared to the conventional approaches, due to the inherent low memory requirement of the approximated observation operator. The one-dimensional (1D) phase error correction for approximated observation-based CS-SAR imaging is first carried out and it can be conveniently applied to the cases of random-frequency waveform and linear frequency modulated (LFM) waveform without any a priori knowledge. The approximated observation operators are obtained by calculating the inverse of Omega-K and chirp scaling algorithms for random-frequency and LFM waveforms, respectively. Furthermore, the 1D phase error model is modified by incorporating a priori knowledge and then a weighted 1D phase error model is proposed, which is capable of correcting two-dimensional (2D) phase error in some cases, where the estimation can be simplified to a 1D problem. Simulation and experimental results validate the effectiveness of the proposed method in the presence of 1D phase error or weighted 1D phase error.

Description: Wellness is one of the main factors crucial in the avoidance of illness or disease. Experience has shown that healthy lifestyle programs are an important strategy to prevent the major shared risk factors for many diseases including cardiovascular diseases, strokes, diabetes, obesity, and hypertension. Within the ambit of the Smart Health 2.0 project, a Wellness App has been developed which has the aim of providing people with something similar to a personal trainer. This Wellness App is able to gather information about the subject, to classify her/him by evaluating some of her/his specific characteristics (physical parameters and lifestyle) and to make personal recommendations to enhance her/his well-being. The application can also give feedback on the effectiveness of the specified characteristics by monitoring their evolution over time, and can provide a positive incentive to stimulate the subject to achieve her/his wellness goals. In this paper, we present a pilot study conducted in Calabria, a region of Italy, aimed at an evaluation of the validity, usability, and navigability of the app, and of people’s level of satisfaction with it. The preliminary results show an average score of 77.16 for usability and of 76.87 for navigability, with an improvement of the Wellness Index with a significance average of 95% and of the Mediterranean Adequacy Index with a significance average of as high as 99%.

Description: Grassland fire is one of the most important disturbance factors of the natural ecosystem. Climate factors influence the occurrence and development of grassland fire. An analysis of the climate conditions of fire occurrence can form the basis for a study of the temporal and spatial variability of grassland fire. The purpose of this paper is to study the effects of monthly time scale climate factors on the occurrence of grassland fire in HulunBuir, located in the northeast of the Inner Mongolia Autonomous Region in China. Based on the logistic regression method, we used the moderate-resolution imaging spectroradiometer (MODIS) active fire data products named thermal anomalies/fire daily L3 Global 1km (MOD14A1 (Terra) and MYD14A1 (Aqua)) and associated climate data for HulunBuir from 2000 to 2010, and established the model of grassland fire climate index. The results showed that monthly maximum temperature, monthly sunshine hours and monthly average wind speed were all positively correlated with the fire climate index; monthly precipitation, monthly average temperature, monthly average relative humidity, monthly minimum relative humidity and the number of days with monthly precipitation greater than or equal to 5 mm were all negatively correlated with the fire climate index. We used the active fire data from 2011 to 2014 to validate the fire climate index during this time period, and the validation result was good (Pearson’s correlation coefficient was 0.578), which showed that the fire climate index model was suitable for analyzing the occurrence of grassland fire in HulunBuir. Analyses were conducted on the temporal and spatial distribution of the fire climate index from January to December in the years 2011–2014; it could be seen that from March to May and from September to October, the fire climate index was higher, and that the fire climate index of the other months is relatively low. The zones with higher fire climate index are mainly distributed in Xin Barag Youqi, Xin Barag Zuoqi, Zalantun Shi, Oroqen Zizhiqi, and Molidawa Zizhiqi; the zones with medium fire climate index are mainly distributed in Chen Barag Qi, Ewenkizu Zizhiqi, Manzhouli Shi, and Arun Qi; and the zones with lower fire climate index are mainly distributed in Genhe Shi, Ergun city, Yakeshi Shi, and Hailar Shi. The results of this study will contribute to the quantitative assessment and management of early warning and forecasting for mid-to long-term grassland fire risk in HulunBuir.

Description: This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.

Description: A useful method for eliminating the detrimental effect of laser frequency instability on Brillouin signals by employing the self-heterodyne detection of Rayleigh and Brillouin scattering is presented. From the analysis of Brillouin scattering spectra from fibers with different lengths measured by heterodyne detection, the maximum usable pulse width immune to laser frequency instability is obtained to be about 4 µs in a self-heterodyne detection Brillouin optical time domain reflectometer (BOTDR) system using a broad-band laser with low frequency stability. Applying the self-heterodyne detection of Rayleigh and Brillouin scattering in BOTDR system, we successfully demonstrate that the detrimental effect of laser frequency instability on Brillouin signals can be eliminated effectively. Employing the broad-band laser modulated by a 130-ns wide pulse driven electro-optic modulator, the observed maximum errors in temperatures measured by the local heterodyne and self-heterodyne detection BOTDR systems are 7.9 °C and 1.2 °C, respectively.

Description: Hydrogen sulfide (H2S) has attracted attention in biochemical research because it plays an important role in biosystems and has emerged as the third endogenous gaseous signaling compound along with nitric oxide (NO) and carbon monoxide (CO). Since H2S is a kind of gaseous molecule, conventional approaches for H2S detection are mostly based on the detection of sulfide (S2−) for indirectly reflecting H2S levels. Hence, there is a need for an accurate and reliable assay capable of determining sulfide in physiological systems. We report here a colorimetric, economic, and green method for sulfide anion detection using in situ formation of silver nanoparticles (AgNPs) using dopamine as a reducing and protecting agent. The changes in the AgNPs absorption response depend linearly on the concentration of Na2S in the range from 2 to 15 μM, with a detection limit of 0.03 μM. Meanwhile, the morphological changes in AgNPs in the presence of S2− and thiol compounds were characterized by transmission electron microscopy (TEM). The as-synthetized AgNPs demonstrate high selectivity, free from interference, especially by other thiol compounds such as cysteine and glutathione. Furthermore, the colorimetric sensor developed was applied to the analysis of sulfide in fetal bovine serum and spiked serum samples with good recovery.

Description: At present, users can utilize an authenticated key agreement protocol in a Wireless Sensor Network (WSN) to securely obtain desired information, and numerous studies have investigated authentication techniques to construct efficient, robust WSNs. Chang et al. recently presented an authenticated key agreement mechanism for WSNs and claimed that their authentication mechanism can both prevent various types of attacks, as well as preserve security properties. However, we have discovered that Chang et al’s method possesses some security weaknesses. First, their mechanism cannot guarantee protection against a password guessing attack, user impersonation attack or session key compromise. Second, the mechanism results in a high load on the gateway node because the gateway node should always maintain the verifier tables. Third, there is no session key verification process in the authentication phase. To this end, we describe how the previously-stated weaknesses occur and propose a security-enhanced version for WSNs. We present a detailed analysis of the security and performance of our authenticated key agreement mechanism, which not only enhances security compared to that of related schemes, but also takes efficiency into consideration.

Description: The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per year.

Description: Breast cancer is one of the most frequently diagnosed cancers in females worldwide and lacks specific biomarkers for early detection. In a previous study, we obtained a selective RNA-cleaving Fluorogenic DNAzyme (RFD) probe against MDA-MB-231 cells, typical breast cancer cells, through the systematic evolution of ligands by exponential process (SELEX). To improve the performance of this probe for actual application, we carried out a series of optimization experiments on the pH value of a reaction buffer, the type and concentration of cofactor ions, and sequence minimization. The length of the active domain of the probe reduced to 25 nt from 40 nt after optimization, which was synthesized more easily and economically. The detection limit of the optimized assay system was 2000 MDA-MB-231 cells in 30 min, which is more sensitive than the previous one (almost 5000 cells). The DNAzyme probe was also capable of distinguishing MDA-MB-231 cell specifically from 3 normal cells and 10 other tumor cells. This probe with high sensitivity, selectivity, and economic efficiency enhances the feasibility for further clinical application in breast cancer diagnosis. Herein, we developed an optimization system to produce a general strategy to establish an easy-to-use DNAzyme-based assay for other targets.

Description: The Web of Things (WoT) plays an important role in the representation of the objects connected to the Internet of Things in a more transparent and effective way. Thus, it enables seamless and ubiquitous web communication between users and the smart things. Considering the importance of WoT, we propose a WoT-based emerging sensor network (WoT-ESN), which collects data from sensors, routes sensor data to the web, and integrate smart things into the web employing a representational state transfer (REST) architecture. A smart home scenario is introduced to evaluate the proposed WoT-ESN architecture. The smart home scenario is tested through computer simulation of the energy consumption of various household appliances, device discovery, and response time performance. The simulation results show that the proposed scheme significantly optimizes the energy consumption of the household appliances and the response time of the appliances.

Description: Currently, research on structural health monitoring systems is focused on direct integration of the system into a component or structure. The latter results in a so-called smart structure. One example of a smart structure is a component with integrated strain sensing for continuous load monitoring. Additive manufacturing, or 3D printing, now also enables such integration of functions inside components. As a proof-of-concept, the Fused Deposition Modeling (FDM) technique was used to integrate a strain sensing element inside polymer (ABS) tensile test samples. The strain sensing element consisted of a closed capillary filled with a fluid and connected to an externally mounted pressure sensor. The volumetric deformation of the integrated capillary resulted in pressure changes in the fluid. The obtained pressure measurements during tensile testing are reported in this paper and compared to state-of-the-art extensometer measurements. The sensitivity of the 3D printed pressure-based strain sensor is primarily a function of the compressibility of the capillary fluid. Air- and watertightness are of critical importance for the proper functioning of the 3D printed pressure-based strain sensor. Therefore, the best after-treatment procedure was selected on basis of a comparative analysis. The obtained pressure measurements are linear with respect to the extensometer readings, and the uncertainty on the strain measurement of a capillary filled with water (incompressible fluid) is ±3.1 µstrain, which is approximately three times less sensitive than conventional strain gauges (±1 µstrain), but 32 times more sensitive than the same sensor based on air (compressible fluid) (±101 µstrain).

Description: Dielectrophoresis (DEP) uses non-uniform electric fields to cause motion in particles due to the particles’ intrinsic properties. As such, DEP is a well-suited label-free means for cell sorting. Of the various methods of implementing DEP, contactless dielectrophoresis (cDEP) is advantageous as it avoids common problems associated with DEP, such as electrode fouling and electrolysis. Unfortunately, cDEP devices can be difficult to fabricate, replicate, and reuse. In addition, the operating parameters are limited by the dielectric breakdown of polydimethylsiloxane (PDMS). This study presents an alternative way to fabricate a cDEP device allowing for higher operating voltages, improved replication, and the opportunity for analysis using Raman spectroscopy. In this device, channels were formed in fused silica rather than PDMS. The device successfully trapped 3.3 μm polystyrene spheres for analysis by Raman spectroscopy. The successful implementation indicates the potential to use cDEP to isolate and identify biological samples on a single device.

Description: Graphite oxide has been investigated as a possible room-temperature chemiresistive sensor of ammonia in a gas phase. Graphite oxide was synthesized from high purity graphite using the modified Hummers method. The graphite oxide sample was investigated using scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, thermogravimetry and differential scanning calorimetry. Sensing properties were tested in a wide range of ammonia concentrations in air (10–1000 ppm) and under different relative humidity levels (3%–65%). It was concluded that the graphite oxide–based sensor possessed a good response to NH3 in dry synthetic air (ΔR/R0 ranged from 2.5% to 7.4% for concentrations of 100–500 ppm and 3% relative humidity) with negligible cross-sensitivity towards H2 and CH4. It was determined that the sensor recovery rate was improved with ammonia concentration growth. Increasing the ambient relative humidity led to an increase of the sensor response. The highest response of 22.2% for 100 ppm of ammonia was achieved at a 65% relative humidity level.

Description: In a context sensing system in which a sensor-equipped mobile phone runs an unreliable context-aware application, the application can infer the user’s contexts, based on which it provides personalized services. However, the application may sell the user’s contexts to some malicious adversaries to earn extra profits, which will hinder its widespread use. In the real world, the actions of the user, the application and the adversary in the context sensing system affect each other, so that their payoffs are constrained mutually. To figure out under which conditions they behave well (the user releases, the application does not leak and the adversary does not retrieve the context), we take advantage of game theory to analyze the context sensing system. We use the extensive form game and the repeated game, respectively, to analyze two typical scenarios, single interaction and multiple interaction among three players, from which Nash equilibriums and cooperation conditions are obtained. Our results show that the reputation mechanism for the context-sensing system in the former scenario is crucial to privacy preservation, so is the extent to which the participants are concerned about future payoffs in the latter one.

Description: In this paper, we report the results of slight changes in the thermopower of long W, Mo, Zn, Cu, brass, and Ti wires, that resulted from changes in the wire’s diameter or cross-sectional area. The samples used in the tests had a round shape with a diameter that ranged from tens of micron to 2 mm, which was much larger than the corresponding mean free paths of these materials. Nevertheless, a small change in thermopower, at the order of 1–10 nV/K, was repeatedly observed when the wire diameter was changed, or when the cross-sectional area of the wire was altered by mechanical methods, such as grinding or splitting. The results are consistent with previous observations showing that the thermopower in metallic thin film stripes changes with their width, from 100 μm to as little as 70 nm, implying a universal, geometric-boundary-related size effect of thermopower in metal materials, that occurs at the nanometer scale and continuously decreases all the way to the millimeter scale. This effect could be applied in the manufacturing of high-temperature sensors with simple structures.

Description: The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic.

Description: Ultrawideband (UWB) antennas, as core devices in high-speed wireless communication, are widely applied to mobile handsets, wireless sensor networks, and Internet of Things (IoT). A compact printed monopole antenna for UWB applications with triple band-notched characteristics is proposed in this paper. The antenna has a very compact size of 10 x 16 mm2 and is composed of a square slotted radiation patch and a narrow rectangular ground plane on the back of the substrate. First, by etching a pair of inverted T-shaped slots at the bottom of the radiation patch, one notched band at 5-6 GHz for rejecting the Wireless Local Area Network (WLAN) is generated. Then, by cutting a comb-shaped slot on the top of the radiation patch, a second notched band for rejecting 3.5 GHz Worldwide Interoperability for Microwave Access (WiMAX) is obtained. Further, by cutting a pair of rectangular slots and a C-shaped slot as well as adding a pair of small square parasitic patches at the center of the radiating patch, two separate notched bands for rejecting 5.2 GHz lower WLAN and 5.8 GHz upper WLAN are realized, respectively. Additionally, by integrating the slotted radiation patch with the narrow rectangular ground plane, an enhanced impedance bandwidth can be achieved, especially at the higher band. The antenna consists of linear symmetrical sections only and is easy for fabrication and fine-tuning. The measured results show that the designed antenna provides a wide impedance bandwidth of 150% from 2.12 to 14.80 GHz for VSWR 〈 2, except for three notched bands of 3.36–4.16, 4.92–5.36, and 5.68–6.0 GHz. Additionally, the antenna exhibits nearly omnidirectional radiation characteristics, low gain at the stopbands, and flat group delay over the whole UWB except at the stopbands. Simulated and experimental results show that the proposed antenna can provide good frequency-domain and time-domain performances at desired UWB frequencies and be an attractive candidate for portable IoT applications.

Description: Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people’s activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of “smart clothing” system.

Description: This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry (ϕ-OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Description: This paper presents the first attempt at combining Cloud with Graphic Processing Units (GPUs) in a complementary manner within the framework of a real-time high performance computation architecture for the application of detecting and tracking multiple moving targets based on Wide Area Motion Imagery (WAMI). More specifically, the GPU and Cloud Moving Target Tracking (GC-MTT) system applied a front-end web based server to perform the interaction with Hadoop and highly parallelized computation functions based on the Compute Unified Device Architecture (CUDA©). The introduced multiple moving target detection and tracking method can be extended to other applications such as pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis. The cloud and GPUs based computing provides an efficient real-time target recognition and tracking approach as compared to methods when the work flow is applied using only central processing units (CPUs). The simultaneous tracking and recognition results demonstrate that a GC-MTT based approach provides drastically improved tracking with low frame rates over realistic conditions.

Description: To solve the problem on inaccuracy when estimating the point spread function (PSF) of the ideal original image in traditional projection onto convex set (POCS) super-resolution (SR) reconstruction, this paper presents an improved POCS SR algorithm based on PSF estimation of low-resolution (LR) remote sensing images. The proposed algorithm can improve the spatial resolution of the image and benefit agricultural crop visual interpolation. The PSF of the highresolution (HR) image is unknown in reality. Therefore, analysis of the relationship between the PSF of the HR image and the PSF of the LR image is important to estimate the PSF of the HR image by using multiple LR images. In this study, the linear relationship between the PSFs of the HR and LR images can be proven. In addition, the novel slant knife-edge method is employed, which can improve the accuracy of the PSF estimation of LR images. Finally, the proposed method is applied to reconstruct airborne digital sensor 40 (ADS40) three-line array images and the overlapped areas of two adjacent GF-2 images by embedding the estimated PSF of the HR image to the original POCS SR algorithm. Experimental results show that the proposed method yields higher quality of reconstructed images than that produced by the blind SR method and the bicubic interpolation method.

Description: Radio-frequency identification (RFID) is a tracking technology that enables immediate automatic object identification and rapid data sharing for a wide variety of modern applications using radio waves for data transmission from a tag to a reader. RFID is already well established in technical areas, and many companies have developed corresponding standards and measurement techniques. In the construction industry, effective monitoring of materials and equipment is an important task, and RFID helps to improve monitoring and controlling capabilities, in addition to enabling automation for construction projects. However, on construction sites, there are many tagged objects and multiple RFID tags that may interfere with each other’s communications. This reduces the reliability and efficiency of the RFID system. In this paper, we propose an anti-collision algorithm for communication between multiple tags and a reader. In order to suppress interference signals from multiple neighboring tags, the proposed algorithm employs the time-division (TD) technique, where tags in the interrogation zone are assigned a specific time slot so that at every instance in time, a reader communicates with tags using the specific time slot. We present representative computer simulation examples to illustrate the performance of the proposed anti-collision technique for multiple RFID tags.

Description: Sensors, Vol. 17, Pages 1850: Efficient Pedestrian Detection at Nighttime Using a Thermal Camera Sensors doi: 10.3390/s17081850 Authors: Jeonghyun Baek Sungjun Hong Jisu Kim Euntai Kim Most of the commercial nighttime pedestrian detection (PD) methods reported previously utilized the histogram of oriented gradient (HOG) or the local binary pattern (LBP) as the feature and the support vector machine (SVM) as the classifier using thermal camera images. In this paper, we propose a new feature called the thermal-position-intensity-histogram of oriented gradient (TPIHOG or T π HOG) and developed a new combination of the T π HOG and the additive kernel SVM (AKSVM) for efficient nighttime pedestrian detection. The proposed T π HOG includes detailed information on gradient location; therefore, it has more distinctive power than the HOG. The AKSVM performs better than the linear SVM in terms of detection performance, while it is much faster than other kernel SVMs. The combined T π HOG-AKSVM showed effective nighttime PD performance with fast computational time. The proposed method was experimentally tested with the KAIST pedestrian dataset and showed better performance compared with other conventional methods.

Description: Sensors, Vol. 17, Pages 1848: A Compressed Sensing Based Method for Reducing the Sampling Time of A High Resolution Pressure Sensor Array System Sensors doi: 10.3390/s17081848 Authors: Chenglu Sun Wei Li Wei Chen For extracting the pressure distribution image and respiratory waveform unobtrusively and comfortably, we proposed a smart mat which utilized a flexible pressure sensor array, printed electrodes and novel soft seven-layer structure to monitor those physiological information. However, in order to obtain high-resolution pressure distribution and more accurate respiratory waveform, it needs more time to acquire the pressure signal of all the pressure sensors embedded in the smart mat. In order to reduce the sampling time while keeping the same resolution and accuracy, a novel method based on compressed sensing (CS) theory was proposed. By utilizing the CS based method, 40% of the sampling time can be decreased by means of acquiring nearly one-third of original sampling points. Then several experiments were carried out to validate the performance of the CS based method. While less than one-third of original sampling points were measured, the correlation degree coefficient between reconstructed respiratory waveform and original waveform can achieve 0.9078, and the accuracy of the respiratory rate (RR) extracted from the reconstructed respiratory waveform can reach 95.54%. The experimental results demonstrated that the novel method can fit the high resolution smart mat system and be a viable option for reducing the sampling time of the pressure sensor array.

Description: Sensors, Vol. 17, Pages 1847: A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces Sensors doi: 10.3390/s17081847 Authors: Yanjiao Li Sen Zhang Yixin Yin Wendong Xiao Jie Zhang Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

Description: Sensors, Vol. 17, Pages 1849: Design and Implementation of 2.45 GHz Passive SAW Temperature Sensors with BPSK Coded RFID Configuration Sensors doi: 10.3390/s17081849 Authors: Chen Fu Yabing Ke Min Li Jingting Luo Honglang Li Guangxing Liang Ping Fan A surface acoustic wave based passive temperature sensor capable of multiple access is investigated. Binary Phase Shift Keying (BPSK) codes of eight chips were implemented using a reflective delay line scheme on a Y-Z LiNbO3 piezoelectric substrate. An accurate simulation based on the combined finite- and boundary element method (FEM/BEM) was performed in order to determine the optimum design parameters. The scaling factor ‘s’ and time delay factor ‘τ’ were extracted using signal processing techniques based on the wavelet transform of the correlation function, and then evaluated at various ambient temperatures. The scaling factor ‘s’ gave a more stable and reliable response to temperature than the time delay factor ‘τ’. Preliminary results show that the sensor response is fast and consistent subject to ambient temperature and it exhibits good linearity of 0.9992 with temperature varying from 0 to 130 °C.

Description: Sensors, Vol. 17, Pages 1843: Construction Condition and Damage Monitoring of Post-Tensioned PSC Girders Using Embedded Sensors Sensors doi: 10.3390/s17081843 Authors: Kyung-Joon Shin Seong-Cheol Lee Yun Kim Jae-Min Kim Seunghee Park Hwanwoo Lee The potential for monitoring the construction of post-tensioned concrete beams and detecting damage to the beams under loading conditions was investigated through an experimental program. First, embedded sensors were investigated that could measure pre-stress from the fabrication process to a failure condition. Four types of sensors were installed on a steel frame, and the applicability and the accuracy of these sensors were tested while pre-stress was applied to a tendon in the steel frame. As a result, a tri-sensor loading plate and a Fiber Bragg Grating (FBG) sensor were selected as possible candidates. With those sensors, two pre-stressed concrete flexural beams were fabricated and tested. The pre-stress of the tendons was monitored during the construction and loading processes. Through the test, it was proven that the variation in thepre-stress had been successfully monitored throughout the construction process. The losses of pre-stress that occurred during a jacking and storage process, even those which occurred inside the concrete, were measured successfully. The results of the loading test showed that tendon stress and strain within the pure span significantly increased, while the stress in areas near the anchors was almost constant. These results prove that FBG sensors installed in a middle section can be used to monitor the strain within, and the damage to pre-stressed concrete beams.

Description: Sensors, Vol. 17, Pages 1842: A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning Sensors doi: 10.3390/s17081842 Authors: Can Wang Kang Li Guoyuan Liang Haoyao Chen Sheng Huang Xinyu Wu The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.

Description: Sensors, Vol. 17, Pages 1855: Validating MODIS and Sentinel-2 NDVI Products at a Temperate Deciduous Forest Site Using Two Independent Ground-Based Sensors Sensors doi: 10.3390/s17081855 Authors: Maximilian Lange Benjamin Dechant Corinna Rebmann Michael Vohland Matthias Cuntz Daniel Doktor Quantifying the accuracy of remote sensing products is a timely endeavor given the rapid increase in Earth observation missions. A validation site for Sentinel-2 products was hence established in central Germany. Automatic multispectral and hyperspectral sensor systems were installed in parallel with an existing eddy covariance flux tower, providing spectral information of the vegetation present at high temporal resolution. Normalized Difference Vegetation Index (NDVI) values from ground-based hyperspectral and multispectral sensors were compared with NDVI products derived from Sentinel-2A and Moderate-resolution Imaging Spectroradiometer (MODIS). The influence of different spatial and temporal resolutions was assessed. High correlations and similar phenological patterns between in situ and satellite-based NDVI time series demonstrated the reliability of satellite-based phenological metrics. Sentinel-2-derived metrics showed better agreement with in situ measurements than MODIS-derived metrics. Dynamic filtering with the best index slope extraction algorithm was nevertheless beneficial for Sentinel-2 NDVI time series despite the availability of quality information from the atmospheric correction procedure.

Description: Sensors, Vol. 17, Pages 1853: The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information Sensors doi: 10.3390/s17081853 Authors: Arijit Sinharay Raj Rakshit Anwesha Khasnobish Tapas Chakravarty Deb Ghosh Arpan Pal Pulmonary ailments are conventionally diagnosed by spirometry. The complex forceful breathing maneuver as well as the extreme cost of spirometry renders it unsuitable in many situations. This work is aimed to facilitate an emerging direction of tidal breathing-based pulmonary evaluation by designing a novel, equitable, precise and portable device for acquisition and analysis of directional tidal breathing patterns, in real time. The proposed system primarily uses an in-house designed blow pipe, 40-kHz air-coupled ultrasound transreceivers, and a radio frequency (RF) phase-gain integrated circuit (IC). Moreover, in order to achieve high sensitivity in a cost-effective design philosophy, we have exploited the phase measurement technique, instead of selecting the contemporary time-of-flight (TOF) measurement; since application of the TOF principle in tidal breathing assessments requires sub-micro to nanosecond time resolution. This approach, which depends on accurate phase measurement, contributed to enhanced sensitivity using a simple electronics design. The developed system has been calibrated using a standard 3-L calibration syringe. The parameters of this system are validated against a standard spirometer, with maximum percentage error below 16%. Further, the extracted respiratory parameters related to tidal breathing have been found to be comparable with relevant prior works. The error in detecting respiration rate only is 3.9% compared to manual evaluation. These encouraging insights reveal the definite potential of our tidal breathing pattern (TBP) prototype for measuring tidal breathing parameters in order to extend the reach of affordable healthcare in rural regions and developing areas.

Description: Sensors, Vol. 17, Pages 1854: Fabrication and Characterization of Flexible and Miniaturized Humidity Sensors Using Screen-Printed TiO2 Nanoparticles as Sensitive Layer Sensors doi: 10.3390/s17081854 Authors: Georges Dubourg Apostolos Segkos Jaroslav Katona Marko Radović Slavica Savić Georgios Niarchos Christos Tsamis Vesna Crnojević-Bengin This paper describes the fabrication and the characterization of an original example of a miniaturized resistive-type humidity sensor, printed on flexible substrate in a large-scale manner. The fabrication process involves laser ablation for the design of interdigitated electrodes on PET (Poly-Ethylene Terephthalate) substrate and a screen-printing process for the deposition of the sensitive material, which is based on TiO2 nanoparticles. The laser ablation process was carefully optimized to obtain micro-scale and well-resolved electrodes on PET substrate. A functional paste based on cellulose was prepared in order to allow the precise screen-printing of the TiO2 nanoparticles as sensing material on the top of the electrodes. The current against voltage (I–V) characteristic of the sensor showed good linearity and potential for low-power operation. The results of a humidity-sensing investigation and mechanical testing showed that the fabricated miniaturized sensors have excellent mechanical stability, sensing characteristics, good repeatability, and relatively fast response/recovery times operating at room temperature.

Description: Sensors, Vol. 17, Pages 1837: Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN Sensors doi: 10.3390/s17081837 Authors: Hao Guo Danni Wu Jubai An Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Description: Sensors, Vol. 17, Pages 1839: Monocular Stereo Measurement Using High-Speed Catadioptric Tracking Sensors doi: 10.3390/s17081839 Authors: Shaopeng Hu Yuji Matsumoto Takeshi Takaki Idaku Ishi This paper presents a novel concept of real-time catadioptric stereo tracking using a single ultrafast mirror-drive pan-tilt active vision system that can simultaneously switch between hundreds of different views in a second. By accelerating video-shooting, computation, and actuation at the millisecond-granularity level for time-division multithreaded processing in ultrafast gaze control, the active vision system can function virtually as two or more tracking cameras with different views. It enables a single active vision system to act as virtual left and right pan-tilt cameras that can simultaneously shoot a pair of stereo images for the same object to be observed at arbitrary viewpoints by switching the direction of the mirrors of the active vision system frame by frame. We developed a monocular galvano-mirror-based stereo tracking system that can switch between 500 different views in a second, and it functions as a catadioptric active stereo with left and right pan-tilt tracking cameras that can virtually capture 8-bit color 512 × 512 images each operating at 250 fps to mechanically track a fast-moving object with a sufficient parallax for accurate 3D measurement. Several tracking experiments for moving objects in 3D space are described to demonstrate the performance of our monocular stereo tracking system.

Description: Sensors, Vol. 17, Pages 1834: A Comprehensive System for Monitoring Urban Accessibility in Smart Cities Sensors doi: 10.3390/s17081834 Authors: Higinio Mora Virgilio Gilart-Iglesias Raquel Pérez-del Hoyo María Andújar-Montoya The present work discusses the possibilities offered by the evolution of Information and Communication Technologies with the aim of designing a system to dynamically obtain knowledge of accessibility issues in urban environments. This system is facilitated by technology to analyse the urban user experience and movement accessibility, which enabling accurate identification of urban barriers and monitoring its effectiveness over time. Therefore, the main purpose of the system is to meet the real needs and requirements of people with movement disabilities. The information obtained can be provided as a support service for decision-making to be used by city government, institutions, researchers, professionals and other individuals of society in general to improve the liveability and quality of the lives of citizens. The proposed system is a means of social awareness that makes the most vulnerable groups of citizens visible by involving them as active participants. To perform and implement the system, the latest communication and positioning technologies for smart sensing have been used, as well as the cloud computing paradigm. Finally, to validate the proposal, a case study has been presented using the university environment as a pre-deployment step in urban environments.

Description: Sensors, Vol. 17, Pages 1840: Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis Sensors doi: 10.3390/s17081840 Authors: Udaya Wijenayake Soon-Yong Park Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors.

Description: Sensors, Vol. 17, Pages 1835: Amalgam Electrode-Based Electrochemical Detector for On-Site Direct Determination of Cadmium(II) and Lead(II) from Soils Sensors doi: 10.3390/s17081835 Authors: Lukas Nejdl Jindrich Kynicky Martin Brtnicky Marketa Vaculovicova Vojtech Adam Toxic metal contamination of the environment is a global issue. In this paper, we present a low-cost and rapid production of amalgam electrodes used for determination of Cd(II) and Pb(II) in environmental samples (soils and wastewaters) by on-site analysis using difference pulse voltammetry. Changes in the electrochemical signals were recorded with a miniaturized potentiostat (width: 80 mm, depth: 54 mm, height: 23 mm) and a portable computer. The limit of detection (LOD) was calculated for the geometric surface of the working electrode 15 mm2 that can be varied as required for analysis. The LODs were 80 ng·mL−1 for Cd(II) and 50 ng·mL−1 for Pb(II), relative standard deviation, RSD ≤ 8% (n = 3). The area of interest (Dolni Rozinka, Czech Republic) was selected because there is a deposit of uranium ore and extreme anthropogenic activity. Environmental samples were taken directly on-site and immediately analysed. Duration of a single analysis was approximately two minutes. The average concentrations of Cd(II) and Pb(II) in this area were below the global average. The obtained values were verified (correlated) by standard electrochemical methods based on hanging drop electrodes and were in good agreement. The advantages of this method are its cost and time effectivity (approximately two minutes per one sample) with direct analysis of turbid samples (soil leach) in a 2 M HNO3 environment. This type of sample cannot be analyzed using the classical analytical methods without pretreatment.

Description: Sensors, Vol. 17, Pages 1833: Terrestrial Laser Scanner Two-Face Measurements for Analyzing the Elevation-Dependent Deformation of the Onsala Space Observatory 20-m Radio Telescope’s Main Reflector in a Bundle Adjustment Sensors doi: 10.3390/s17081833 Authors: Christoph Holst David Schunck Axel Nothnagel Rüdiger Haas Lars Wennerbäck Henrik Olofsson Roger Hammargren Heiner Kuhlmann For accurate astronomic and geodetic observations based on radio telescopes, the elevation-dependent deformation of the radio telescopes’ main reflectors should be known. Terrestrial laser scanning has been used for determining the corresponding changes of focal lengths and areal reflector deformations at several occasions before. New in this publication is the situation in which we minimize systematic measurement errors by an improved measurement and data-processing concept: Sampling the main reflector in both faces of the laser scanner and calibrating the laser scanner in situ in a bundle adjustment. This concept is applied to the Onsala Space Observatory 20-m radio telescope: The focal length of the main reflector decreases by 9.6 mm from 85 ∘ to 5 ∘ elevation angle. Further local deformations of the main reflector are not detected.

Description: Sensors, Vol. 17, Pages 1832: Determination of Zinc, Cadmium, Lead, Copper and Silver Using a Carbon Paste Electrode and a Screen Printed Electrode Modified with Chromium(III) Oxide Sensors doi: 10.3390/s17081832 Authors: Zuzana Koudelkova Tomas Syrovy Pavlina Ambrozova Zdenek Moravec Lubomir Kubac David Hynek Lukas Richtera Vojtech Adam In this study, the preparation and electrochemical application of a chromium(III) oxide modified carbon paste electrode (Cr-CPE) and a screen printed electrode (SPE), made from the same material and optimized for the simple, cheap and sensitive simultaneous determination of zinc, cadmium, lead, copper and the detection of silver ions, is described. The limits of detection and quantification were 25 and 80 µg·L−1 for Zn(II), 3 and 10 µg·L−1 for Cd(II), 3 and 10 µg·L−1 for Pb(II), 3 and 10 µg·L−1 for Cu(II), and 3 and 10 µg·L−1 for Ag(I), respectively. Furthermore, this promising modification was transferred to the screen-printed electrode. The limits of detection for the simultaneous determination of zinc, cadmium, copper and lead on the screen printed electrodes were found to be 350 µg·L−1 for Zn(II), 25 µg·L−1 for Cd(II), 3 µg·L−1 for Pb(II) and 3 µg·L−1 for Cu(II). Practical usability for the simultaneous detection of these heavy metal ions by the Cr-CPE was also demonstrated in the analyses of wastewaters.

Description: Sensors, Vol. 17, Pages 1836: Auxiliary Sensor-Based Borehole Transient Electromagnetic System for the Nondestructive Inspection of Multipipe Strings Sensors doi: 10.3390/s17081836 Authors: Bo Dang Ling Yang Na Du Changzan Liu Ruirong Dang Bin Wang Yan Xie Transient electromagnetic (TEM) techniques are widely used in the field of geophysical prospecting. In borehole detection, the nondestructive inspection (NDI) of a metal pipe can be performed efficiently using the properties of eddy currents. However, with increasing concern for safety in oil and gas production, more than one string of pipe is used to protect wellbores, which complicates data interpretation. In this paper, an auxiliary sensor-based borehole TEM system for the NDI of multipipe strings is presented. On the basis of the characteristics of the borehole TEM model, we investigate the principle behind the NDI of multipipe strings using multiple time slices of induced electromotive force (EMF) in a single sensor. The results show that the detection performance of NDI is strongly influenced by eddy-current diffusion in the longitudinal direction. To solve this problem, we used time slices of the induced EMF in both the main and auxiliary sensors. The performance of the proposed system was verified by applying it to an oil well with a production casing and liner. Moreover, field experiments were conducted, and the results demonstrate the effectiveness of the proposed method.

Description: Sensors, Vol. 17, Pages 1868: Cyber Physical Systems for User Reliability Measurements in a Sharing Economy Environment Sensors doi: 10.3390/s17081868 Authors: Aria Seo Junho Jeong Yeichang Kim As the sharing economic market grows, the number of users is also increasing but many problems arise in terms of reliability between providers and users in the processing of services. The existing methods provide shared economic systems that judge the reliability of the provider from the viewpoint of the user. In this paper, we have developed a system for establishing mutual trust between providers and users in a shared economic environment to solve existing problems. In order to implement a system that can measure and control users’ situation in a shared economic environment, we analyzed the necessary factors in a cyber physical system (CPS). In addition, a user measurement system based on a CPS structure in a sharing economic environment is implemented through analysis of the factors to consider when constructing a CPS.

Description: Sensors, Vol. 17, Pages 1874: Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration Sensors doi: 10.3390/s17081874 Authors: Chi-Yi Tsai Chih-Hung Huang With the increasing popularity of RGB-depth (RGB-D) sensor, research on the use of RGB-D sensors to reconstruct three-dimensional (3D) indoor scenes has gained more and more attention. In this paper, an automatic point cloud registration algorithm is proposed to efficiently handle the task of 3D indoor scene reconstruction using pan-tilt platforms on a fixed position. The proposed algorithm aims to align multiple point clouds using extrinsic parameters of the RGB-D camera obtained from every preset pan-tilt control point. A computationally efficient global registration method is proposed based on transformation matrices formed by the offline calibrated extrinsic parameters. Then, a local registration method, which is an optional operation in the proposed algorithm, is employed to refine the preliminary alignment result. Experimental results validate the quality and computational efficiency of the proposed point cloud alignment algorithm by comparing it with two state-of-the-art methods.

Description: Sensors, Vol. 17, Pages 1900: Permalloy-Based Thin Film Structures: Magnetic Properties and the Giant Magnetoimpedance Effect in the Temperature Range Important for Biomedical Applications Sensors doi: 10.3390/s17081900 Authors: Anna A. Chlenova Alexey A. Moiseev Mikhail S. Derevyanko Aleksandr V. Semirov Vladimir N. Lepalovsky Galina V. Kurlyandskaya Permalloy-based thin film structures are excellent materials for sensor applications. Temperature dependencies of the magnetic properties and giant magneto-impedance (GMI) were studied for Fe19Ni81-based multilayered structures obtained by the ion-plasma sputtering technique. Selected temperature interval of 25 °C to 50 °C corresponds to the temperature range of functionality of many devices, including magnetic biosensors. A (Cu/FeNi)5/Cu/(Cu/FeNi)5 multilayered structure with well-defined traverse magnetic anisotropy showed an increase in the GMI ratio for the total impedance and its real part with temperature increased. The maximum of the GMI of the total impedance ratio ΔZ/Z = 56% was observed at a frequency of 80 MHz, with a sensitivity of 18%/Oe, and the maximum GMI of the real part ΔR/R = 170% at a frequency of 10 MHz, with a sensitivity of 46%/Oe. As the magnetization and direct current electrical resistance vary very little with the temperature, the most probable mechanism of the unexpected increase of the GMI sensitivity is the stress relaxation mechanism associated with magnetoelastic anisotropy.

Description: Sensors, Vol. 17, Pages 1898: Insight into the Mechanism of CO Oxidation on WO3(001) Surfaces for Gas Sensing: A DFT Study Sensors doi: 10.3390/s17081898 Authors: Hua Jin Hegen Zhou Yongfan Zhang The mechanism of CO oxidation on the WO3(001) surface for gas sensing performance has been systematically investigated by means of first principles density functional theory (DFT) calculations. Our results show that the oxidation of CO molecule on the perfect WO3(001) surface induces the formation of surface oxygen vacancies, which results in an increase of the surface conductance. This defective WO3(001) surface can be re-oxidized by the O2 molecules in the atmosphere. During this step, the active O2− species is generated, accompanied with the obvious charge transfer from the surface to O2 molecule, and correspondingly, the surface conductivity is reduced. The O2− species tends to take part in the subsequent reaction with the CO molecule, and after releasing CO2 molecule, the perfect WO3(001) surface is finally reproduced. The activation energy barriers and the reaction energies associated with above surface reactions are determined, and from the kinetics viewpoint, the oxidation of CO molecule on the perfect WO3(001) surface is the rate-limiting step with an activation barrier of about 0.91 eV.

Description: Sensors, Vol. 17, Pages 1893: Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition Sensors doi: 10.3390/s17081893 Authors: Hyo-Rim Choi TaeYong Kim Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user’s location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

Description: Sensors, Vol. 17, Pages 1895: Ag Nanorods-Oxide Hybrid Array Substrates: Synthesis, Characterization, and Applications in Surface-Enhanced Raman Scattering Sensors doi: 10.3390/s17081895 Authors: Lingwei Ma Jianghao Li Sumeng Zou Zhengjun Zhang Over the last few decades, benefitting from the sufficient sensitivity, high specificity, nondestructive, and rapid detection capability of the surface-enhanced Raman scattering (SERS) technique, numerous nanostructures have been elaborately designed and successfully synthesized as high-performance SERS substrates, which have been extensively exploited for the identification of chemical and biological analytes. Among these, Ag nanorods coated with thin metal oxide layers (AgNRs-oxide hybrid array substrates) featuring many outstanding advantages have been proposed as fascinating SERS substrates, and are of particular research interest. The present review provides a systematic overview towards the representative achievements of AgNRs-oxide hybrid array substrates for SERS applications from diverse perspectives, so as to promote the realization of real-world SERS sensors. First, various fabrication approaches of AgNRs-oxide nanostructures are introduced, which are followed by a discussion on the novel merits of AgNRs-oxide arrays, such as superior SERS sensitivity and reproducibility, high thermal stability, long-term activity in air, corrosion resistivity, and intense chemisorption of target molecules. Next, we present recent advances of AgNRs-oxide substrates in terms of practical applications. Intriguingly, the recyclability, qualitative and quantitative analyses, as well as vapor-phase molecule sensing have been achieved on these nanocomposites. We further discuss the major challenges and prospects of AgNRs-oxide substrates for future SERS developments, aiming to expand the versatility of SERS technique.

Description: Sensors, Vol. 17, Pages 1897: Non-Destructive Analysis of the Internal Anatomical Structures of Mosquito Specimens Using Optical Coherence Tomography Sensors doi: 10.3390/s17081897 Authors: Naresh Ravichandran Ruchire Wijesinghe Seung-Yeol Lee Kwang Choi Mansik Jeon Hee-Young Jung Jeehyun Kim The study of mosquitoes and analysis of their behavior are of crucial importance in the on-going efforts to control the alarming increase in mosquito-borne diseases. Furthermore, a non-destructive and real-time imaging technique to study the anatomical features of mosquito specimens can greatly aid the study of mosquitoes. In this study, we demonstrate the three-dimensional imaging capabilities of optical coherence tomography (OCT) for structural analysis of Anopheles sinensis mosquitoes. The anatomical features of An. sinensis head, thorax, and abdominal regions, along with the morphology of internal structures, such as foregut, midgut, and hindgut, were studied using OCT imaging. Two-dimensional and three-dimensional OCT images, used in conjunction with histological images, proved useful for anatomical analysis of mosquito specimens. By presenting this work as an initial study, we demonstrate the applicability of OCT for future mosquito-related entomological research, and also in identifying changes in mosquito anatomical structure.

Description: Sensors, Vol. 17, Pages 1901: Surface-Enhanced Raman Scattering in Molecular Junctions Sensors doi: 10.3390/s17081901 Authors: Madoka Iwane Shintaro Fujii Manabu Kiguchi Surface-enhanced Raman scattering (SERS) is a surface-sensitive vibrational spectroscopy that allows Raman spectroscopy on a single molecular scale. Here, we present a review of SERS from molecular junctions, in which a single molecule or molecules are made to have contact from the top to the bottom of metal surfaces. The molecular junctions are nice platforms for SERS as well as transport measurement. Electronic characterization based on the transport measurements of molecular junctions has been extensively studied for the development of miniaturized electronic devices. Simultaneous SERS and transport measurement of the molecular junctions allow both structural (geometrical) and electronic information on the single molecule scale. The improvement of SERS measurement on molecular junctions open the door toward new nanoscience and nanotechnology in molecular electronics.

Description: Sensors, Vol. 17, Pages 1915: Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements Sensors doi: 10.3390/s17081915 Authors: André Ferreira Duarte Fernandes André Catarino João Monteiro Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system.

Description: Sensors, Vol. 17, Pages 1916: Development and Evaluation of A Novel and Cost-Effective Approach for Low-Cost NO2 Sensor Drift Correction Sensors doi: 10.3390/s17081916 Authors: Li Sun Dane Westerdahl Zhi Ning Emerging low-cost gas sensor technologies have received increasing attention in recent years for air quality measurements due to their small size and convenient deployment. However, in the diverse applications these sensors face many technological challenges, including sensor drift over long-term deployment that cannot be easily addressed using mathematical correction algorithms or machine learning methods. This study aims to develop a novel approach to auto-correct the drift of commonly used electrochemical nitrogen dioxide (NO2) sensor with comprehensive evaluation of its application. The impact of environmental factors on the NO2 electrochemical sensor in low-ppb concentration level measurement was evaluated in laboratory and the temperature and relative humidity correction algorithm was evaluated. An automated zeroing protocol was developed and assessed using a chemical absorbent to remove NO2 as a means to perform zero correction in varying ambient conditions. The sensor system was operated in three different environments in which data were compared to a reference NO2 analyzer. The results showed that the zero-calibration protocol effectively corrected the observed drift of the sensor output. This technique offers the ability to enhance the performance of low-cost sensor based systems and these findings suggest extension of the approach to improve data quality from sensors measuring other gaseous pollutants in urban air.

Description: Sensors, Vol. 17, Pages 1913: Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation Sensors doi: 10.3390/s17081913 Authors: Enrique Martinez-Berti Antonio-José Sánchez-Salmerón Carlos Ricolfe-Viala The goal of this research work is to improve the accuracy of human pose estimation using the Deformation Part Model (DPM) without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to DPM, which was formerly defined based only on red–green–blue (RGB) channels, in order to obtain a four-dimensional DPM (4D-DPM). In addition, computational complexity can be controlled by reducing the number of joints by taking it into account in a reduced 4D-DPM. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematics models. In this context, the main goal of this paper is to analyze the effect on pose estimation timing cost when using dual quaternions to solve the inverse kinematics.

Description: Sensors, Vol. 17, Pages 1957: Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images Sensors doi: 10.3390/s17091957 Authors: Yiming Yan Zhichao Tan Nan Su Chunhui Zhao In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such as exceeding resolution, bad correction and terrain influence. Data collected by multiple sensors, as light detection and ranging (LIDAR), optical sensor etc., are used to improve the extraction. Using digital surface model (DSM) obtained from LIDAR data and optical images, traditional method can improve the extraction effect to a certain extent, but there are some defects in feature extraction. Since stacked sparse autoencoder (SSAE) neural network can learn the essential characteristics of the data in depth, SSAE was employed to extract buildings from the combined DSM data and optical image. A better setting strategy of SSAE network structure is given, and an idea of setting the number and proportion of training samples for better training of SSAE was presented. The optical data and DSM were combined as input of the optimized SSAE, and after training by an optimized samples, the appropriate network structure can extract buildings with great accuracy and has good robustness.

Description: Sensors, Vol. 17, Pages 1946: A Behaviour Monitoring System (BMS) for Ambient Assisted Living Sensors doi: 10.3390/s17091946 Authors: Samih Eisa Adriano Moreira Unusual changes in the regular daily mobility routine of an elderly person at home can be an indicator or early symptom of developing health problems. Sensor technology can be utilised to complement the traditional healthcare systems to gain a more detailed view of the daily mobility of a person at home when performing everyday tasks. We hypothesise that data collected from low-cost sensors such as presence and occupancy sensors can be analysed to provide insights on the daily mobility habits of the elderly living alone at home and to detect routine changes. We validate this hypothesis by designing a system that automatically learns the daily room-to-room transitions and permanence habits in each room at each time of the day and generates alarm notifications when deviations are detected. We present an algorithm to process the sensors’ data streams and compute sensor-driven features that describe the daily mobility routine of the elderly as part of the developed Behaviour Monitoring System (BMS). We are able to achieve low detection delay with confirmation time that is high enough to convey the detection of a set of common abnormal situations. We illustrate and evaluate BMS with synthetic data, generated by a developed data generator that was designed to mimic different user’s mobility profiles at home, and also with a real-life dataset collected from prior research work. Results indicate BMS detects several mobility changes that can be symptoms of common health problems. The proposed system is a useful approach for learning the mobility habits at the home environment, with the potential to detect behaviour changes that occur due to health problems, and therefore, motivating progress toward behaviour monitoring and elder’s care.

Description: Sensors, Vol. 17, Pages 1954: A Simple and Selective Fluorescent Sensor Chip for Indole-3-Butyric Acid in Mung Bean Sprouts Based on Molecularly Imprinted Polymer Coatings Sensors doi: 10.3390/s17091954 Authors: Jiahua Chang Bota Bahethan Turghun Muhammad Burabiye Yakup Mamatimin Abbas In this paper, we report the preparation of molecularly imprinted polymer coatings on quartz chips for selective solid-phase microextraction and fluorescence sensing of the auxin, indole-3-butyric acid. The multiple copolymerization method was used to prepare polymer coatings on silylated quartz chips. The polymer preparation conditions (e.g., the solvent, monomer, and cross-linker) were investigated systemically to enhance the binding performance of the imprinted coatings. Direct solid-phase fluorescence measurements on the chips facilitated monitoring changes in coating performance. The average binding capacity of an imprinted polymer coated chip was approximately 152.9 µg, which was higher than that of a non-imprinted polymer coated chip (60.8 µg); the imprinted coatings showed the highest binding to IBA among the structural analogues, indicating that the coatings possess high selectivity toward the template molecule. The developed method was used for the determination of the auxin in mung bean extraction, and the recovery was found to be in the range of 91.5% to 97.5%, with an RSD (n = 3) of less than 7.4%. Thus, the present study provides a simple method for fabricating a fluorescent sensor chip for selective analysis.

Description: Sensors, Vol. 17, Pages 1948: Fibre Bragg Gratings in Embedded Microstructured Optical Fibres Allow Distinguishing between Symmetric and Anti-Symmetric Lamb Waves in Carbon Fibre Reinforced Composites Sensors doi: 10.3390/s17091948 Authors: Ben De Pauw Sidney Goossens Thomas Geernaert Dimitrios Habas Hugo Thienpont Francis Berghmans Conventional contact sensors used for Lamb wave-based ultrasonic inspection, such as piezo-electric transducers, measure omnidirectional strain and do not allow distinguishing between fundamental symmetric and anti-symmetric modes. In this paper, we show that the use of a single fibre Bragg grating created in a dedicated microstructured optical fibre allows one to directly make the distinction between these fundamental Lamb wave modes. This feature stems from the different sensitivities of the microstructured fibre to axial and transverse strain. We fabricated carbon fibre-reinforced polymer panels equipped with embedded microstructured optical fibre sensors and experimentally demonstrated the strain waves associated with the propagating Lamb waves in both the axial and transverse directions of the optical fibre.

Description: Sensors, Vol. 17, Pages 1943: Research and Development of Electrostatic Accelerometers for Space Science Missions at HUST Sensors doi: 10.3390/s17091943 Authors: Yanzheng Bai Zhuxi Li Ming Hu Li Liu Shaobo Qu Dingyin Tan Haibo Tu Shuchao Wu Hang Yin Hongyin Li Zebing Zhou High-precision electrostatic accelerometers have achieved remarkable success in satellite Earth gravity field recovery missions. Ultralow-noise inertial sensors play important roles in space gravitational wave detection missions such as the Laser Interferometer Space Antenna (LISA) mission, and key technologies have been verified in the LISA Pathfinder mission. Meanwhile, at Huazhong University of Science and Technology (HUST, China), a space accelerometer and inertial sensor based on capacitive sensors and the electrostatic control technique have also been studied and developed independently for more than 16 years. In this paper, we review the operational principle, application, and requirements of the electrostatic accelerometer and inertial sensor in different space missions. The development and progress of a space electrostatic accelerometer at HUST, including ground investigation and space verification are presented.

Description: Sensors, Vol. 17, Pages 1942: A Biosensor-CMOS Platform and Integrated Readout Circuit in 0.18-μm CMOS Technology for Cancer Biomarker Detection Sensors doi: 10.3390/s17091942 Authors: Abdulaziz Alhoshany Shilpa Sivashankar Yousof Mashraei Hesham Omran Khaled N. Salama This paper presents a biosensor-CMOS platform for measuring the capacitive coupling of biorecognition elements. The biosensor is designed, fabricated, and tested for the detection and quantification of a protein that reveals the presence of early-stage cancer. For the first time, the spermidine/spermine N1 acetyltransferase (SSAT) enzyme has been screened and quantified on the surface of a capacitive sensor. The sensor surface is treated to immobilize antibodies, and the baseline capacitance of the biosensor is reduced by connecting an array of capacitors in series for fixed exposure area to the analyte. A large sensing area with small baseline capacitance is implemented to achieve a high sensitivity to SSAT enzyme concentrations. The sensed capacitance value is digitized by using a 12-bit highly digital successive-approximation capacitance-to-digital converter that is implemented in a 0.18 μm CMOS technology. The readout circuit operates in the near-subthreshold regime and provides power and area efficient operation. The capacitance range is 16.137 pF with a 4.5 fF absolute resolution, which adequately covers the concentrations of 10 mg/L, 5 mg/L, 2.5 mg/L, and 1.25 mg/L of the SSAT enzyme. The concentrations were selected as a pilot study, and the platform was shown to demonstrate high sensitivity for SSAT enzymes on the surface of the capacitive sensor. The tested prototype demonstrated 42.5 μS of measurement time and a total power consumption of 2.1 μW.

Description: Sensors, Vol. 17, Pages 1941: A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System Sensors doi: 10.3390/s17091941 Authors: Zhen Zhang Cheng Ma Rong Zhu Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas.

Description: Sensors, Vol. 17, Pages 1932: Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor Sensors doi: 10.3390/s17091932 Authors: Lvwen Huang Siyuan Chen Jianfeng Zhang Bang Cheng Mingqing Liu Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields.

Description: Sensors, Vol. 17, Pages 1938: Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios Sensors doi: 10.3390/s17091938 Authors: Linguo Chai Baigen Cai Wei ShangGuan Jian Wang Huashen Wang To enhance the reality of Connected and Autonomous Vehicles (CAVs) kinematic simulation scenarios and to guarantee the accuracy and reliability of the verification, a four-layer CAVs kinematic simulation framework, which is composed with road network layer, vehicle operating layer, uncertainties modelling layer and demonstrating layer, is proposed in this paper. Properties of the intersections are defined to describe the road network. A target position based vehicle position updating method is designed to simulate such vehicle behaviors as lane changing and turning. Vehicle kinematic models are implemented to maintain the status of the vehicles when they are moving towards the target position. Priorities for individual vehicle control are authorized for different layers. Operation mechanisms of CAVs uncertainties, which are defined as position error and communication delay in this paper, are implemented in the simulation to enhance the reality of the simulation. A simulation platform is developed based on the proposed methodology. A comparison of simulated and theoretical vehicle delay has been analyzed to prove the validity and the creditability of the platform. The scenario of rear-end collision avoidance is conducted to verify the uncertainties operating mechanisms, and a slot-based intersections (SIs) control strategy is realized and verified in the simulation platform to show the supports of the platform to CAVs kinematic simulation and verification.

Description: Sensors, Vol. 17, Pages 1937: EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution Sensors doi: 10.3390/s17091937 Authors: Rami Alazrai Hisham Alwanni Yara Baslan Nasim Alnuman Mohammad Daoud This paper presents an EEG-based brain-computer interface system for classifying eleven motor imagery (MI) tasks within the same hand. The proposed system utilizes the Choi-Williams time-frequency distribution (CWD) to construct a time-frequency representation (TFR) of the EEG signals. The constructed TFR is used to extract five categories of time-frequency features (TFFs). The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals. To evaluate the performance of the proposed approach, EEG data were recorded for eighteen intact subjects and four amputated subjects while imagining to perform each of the eleven hand MI tasks. Two performance evaluation analyses, namely channel- and TFF-based analyses, are conducted to identify the best subset of EEG channels and the TFFs category, respectively, that enable the highest classification accuracy between the MI tasks. In each evaluation analysis, the hierarchical classification model is trained using two training procedures, namely subject-dependent and subject-independent procedures. These two training procedures quantify the capability of the proposed approach to capture both intra- and inter-personal variations in the EEG signals for different MI tasks within the same hand. The results demonstrate the efficacy of the approach for classifying the MI tasks within the same hand. In particular, the classification accuracies obtained for the intact and amputated subjects are as high as 88 . 8 % and 90 . 2 % , respectively, for the subject-dependent training procedure, and 80 . 8 % and 87 . 8 % , respectively, for the subject-independent training procedure. These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.

Description: Sensors, Vol. 17, Pages 1935: Cross-Reactive Plasmonic Aptasensors for Controlled Substance Identification Sensors doi: 10.3390/s17091935 Authors: Joshua Yoho Brian Geier Claude Grigsby Joshua Hagen Jorge Chávez Nancy Kelley-Loughnane In this work, we developed an assay to determine if an arbitrary white powder is a controlled substance, given the plasmonic response of aptamer-gold nanoparticle conjugates (Apt-AuNPs). Toward this end, we designed Apt-AuNPs with specific a response to common controlled substances without cross reactivity to chemicals typically used as fillers in street formulations. Plasmonic sensor variation was shown to produce unique data fingerprints for each chemical analyzed, supporting the application of multivariate statistical techniques to annotate unknown samples by chemical similarity. Importantly, the assay takes less than fifteen minutes to run, and requires only a few micrograms of the material, making the proposed assay easily deployable in field operations.

Description: Sensors, Vol. 17, Pages 1940: Benchmarking Foot Trajectory Estimation Methods for Mobile Gait Analysis Sensors doi: 10.3390/s17091940 Authors: Julius Hannink Malte Ollenschläger Felix Kluge Nils Roth Jochen Klucken Bjoern M. Eskofier Mobile gait analysis systems based on inertial sensing on the shoe are applied in a wide range of applications. Especially for medical applications, they can give new insights into motor impairment in, e.g., neurodegenerative disease and help objectify patient assessment. One key component in these systems is the reconstruction of the foot trajectories from inertial data. In literature, various methods for this task have been proposed. However, performance is evaluated on a variety of datasets due to the lack of large, generally accepted benchmark datasets. This hinders a fair comparison of methods. In this work, we implement three orientation estimation and three double integration schemes for use in a foot trajectory estimation pipeline. All methods are drawn from literature and evaluated against a marker-based motion capture reference. We provide a fair comparison on the same dataset consisting of 735 strides from 16 healthy subjects. As a result, the implemented methods are ranked and we identify the most suitable processing pipeline for foot trajectory estimation in the context of mobile gait analysis.

Description: Sensors, Vol. 17, Pages 1934: Virtual Sensor for Kinematic Estimation of Flexible Links in Parallel Robots Sensors doi: 10.3390/s17091934 Authors: Pablo Bengoa Asier Zubizarreta Itziar Cabanes Aitziber Mancisidor Charles Pinto Sara Mata The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.

Description: Sensors, Vol. 17, Pages 1970: Method for Estimating Three-Dimensional Knee Rotations Using Two Inertial Measurement Units: Validation with a Coordinate Measurement Machine Sensors doi: 10.3390/s17091970 Authors: Rachel Vitali Stephen Cain Ryan McGinnis Antonia Zaferiou Lauro Ojeda Steven Davidson Noel Perkins Three-dimensional rotations across the human knee serve as important markers of knee health and performance in multiple contexts including human mobility, worker safety and health, athletic performance, and warfighter performance. While knee rotations can be estimated using optical motion capture, that method is largely limited to the laboratory and small capture volumes. These limitations may be overcome by deploying wearable inertial measurement units (IMUs). The objective of this study is to present a new IMU-based method for estimating 3D knee rotations and to benchmark the accuracy of the results using an instrumented mechanical linkage. The method employs data from shank- and thigh-mounted IMUs and a vector constraint for the medial-lateral axis of the knee during periods when the knee joint functions predominantly as a hinge. The method is carefully validated using data from high precision optical encoders in a mechanism that replicates 3D knee rotations spanning (1) pure flexion/extension, (2) pure internal/external rotation, (3) pure abduction/adduction, and (4) combinations of all three rotations. Regardless of the movement type, the IMU-derived estimates of 3D knee rotations replicate the truth data with high confidence (RMS error < 4 ° and correlation coefficient r ≥ 0.94 ).

Description: Sensors, Vol. 17, Pages 1976: Full Tensor Eigenvector Analysis on Air-Borne Magnetic Gradiometer Data for the Detection of Dipole-Like Magnetic Sources Sensors doi: 10.3390/s17091976 Authors: Boxin Zuo Lizhe Wang Weitao Chen The detection of dipole-like sources, such as unexploded ordnances (UXO) and other metallic objects, based on a magnetic gradiometer system, has been increasingly applied in recent years. In this paper, a novel dipole-like source detection algorithm, based on eigenvector analysis with magnetic gradient tensor data interpretation is presented. Firstly, the theoretical basis of the eigenvector decomposition of magnetic gradient tensor is analyzed. Then, a detection algorithm is proposed by using the properties of the tensor eigenvector decomposition to locate dipole-like magnetic sources. The algorithm can automatically detect magnetic dipole-like sources without estimating the magnetic moment direction. It performs well for locating weak, anomalous dipole-like sources in air-borne magnetic data through quantitative interpretation. The effectiveness of the proposed algorithm has been demonstrated in the designed synthetic experiment. Finally, an air-borne magnetic field data taken at high altitude with exact source position information is used to validate the practicality of the proposed algorithm. All of the experiments prove that the proposed algorithm is suitable for magnetic dipole-like source detecting and air-borne magnetic gradiometer data interpretation.

Description: Sensors, Vol. 17, Pages 1978: A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications Sensors doi: 10.3390/s17091978 Authors: Manuel Suárez-Albela Tiago Fernández-Caramés Paula Fraga-Lamas Luis Castedo Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes that collect data, the cloud, and interconnected IoT gateways that exchange messages with the IoT nodes and with the cloud. This article focuses on securing IoT gateways, which are assumed to be constrained in terms of computational resources, but that are able to offload some processing from the cloud and to reduce the latency in the responses to the IoT nodes. However, it is usually taken for granted that IoT gateways have direct access to the electrical grid, which is not always the case: in mission-critical applications like natural disaster relief or environmental monitoring, it is common to deploy IoT nodes and gateways in large areas where electricity comes from solar or wind energy that charge the batteries that power every device. In this article, how to secure IoT gateway communications while minimizing power consumption is analyzed. The throughput and power consumption of Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) are considered, since they are really popular, but have not been thoroughly analyzed when applied to IoT scenarios. Moreover, the most widespread Transport Layer Security (TLS) cipher suites use RSA as the main public key-exchange algorithm, but the key sizes needed are not practical for most IoT devices and cannot be scaled to high security levels. In contrast, ECC represents a much lighter and scalable alternative. Thus, RSA and ECC are compared for equivalent security levels, and power consumption and data throughput are measured using a testbed of IoT gateways. The measurements obtained indicate that, in the specific fog computing scenario proposed, ECC is clearly a much better alternative than RSA, obtaining energy consumption reductions of up to 50% and a data throughput that doubles RSA in most scenarios. These conclusions are then corroborated by a frame temporal analysis of Ethernet packets. In addition, current data compression algorithms are evaluated, concluding that, when dealing with the small payloads related to IoT applications, they do not pay off in terms of real data throughput and power consumption.

Description: Sensors, Vol. 17, Pages 1977: Geometric Calibration and Accuracy Verification of the GF-3 Satellite Sensors doi: 10.3390/s17091977 Authors: Ruishan Zhao Guo Zhang Mingjun Deng Kai Xu Fengcheng Guo The GF-3 satellite is the first multi-polarization synthetic aperture radar (SAR) imaging satellite in China, which operates in the C band with a resolution of 1 m. Although the SAR satellite system was geometrically calibrated during the in-orbit commissioning phase, there are still some system errors that affect its geometric positioning accuracy. In this study, these errors are classified into three categories: fixed system error, time-varying system error, and random error. Using a multimode hybrid geometric calibration of spaceborne SAR, and considering the atmospheric propagation delay, all system errors can be effectively corrected through high-precision ground control points and global atmospheric reference data. The geometric calibration experiments and accuracy evaluation for the GF-3 satellite are performed using ground control data from several regions. The experimental results show that the residual system errors of the GF-3 SAR satellite have been effectively eliminated, and the geometric positioning accuracy can be better than 3 m.

Description: Sensors, Vol. 17, Pages 1988: Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0 Sensors doi: 10.3390/s17091988 Authors: Ángela Hernández-Solana David Perez-Diaz-de-Cerio Antonio Valdovinos Jose Luis Valenzuela The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios.

Description: Sensors, Vol. 17, Pages 1981: GPS Satellite Orbit Prediction at User End for Real-Time PPP System Sensors doi: 10.3390/s17091981 Authors: Hongzhou Yang Yang Gao This paper proposed the high-precision satellite orbit prediction process at the user end for the real-time precise point positioning (PPP) system. Firstly, the structure of a new real-time PPP system will be briefly introduced in the paper. Then, the generation of satellite initial parameters (IP) at the sever end will be discussed, which includes the satellite position, velocity, and the solar radiation pressure (SRP) parameters for each satellite. After that, the method for orbit prediction at the user end, with dynamic models including the Earth’s gravitational force, lunar gravitational force, solar gravitational force, and the SRP, are presented. For numerical integration, both the single-step Runge–Kutta and multi-step Adams–Bashforth–Moulton integrator methods are implemented. Then, the comparison between the predicted orbit and the international global navigation satellite system (GNSS) service (IGS) final products are carried out. The results show that the prediction accuracy can be maintained for several hours, and the average prediction error of the 31 satellites are 0.031, 0.032, and 0.033 m for the radial, along-track and cross-track directions over 12 h, respectively. Finally, the PPP in both static and kinematic modes are carried out to verify the accuracy of the predicted satellite orbit. The average root mean square error (RMSE) for the static PPP of the 32 globally distributed IGS stations are 0.012, 0.015, and 0.021 m for the north, east, and vertical directions, respectively; while the RMSE of the kinematic PPP with the predicted orbit are 0.031, 0.069, and 0.167 m in the north, east and vertical directions, respectively.

Description: Sensors, Vol. 17, Pages 1989: Development of Embedded EM Sensors for Estimating Tensile Forces of PSC Girder Bridges Sensors doi: 10.3390/s17091989 Authors: Junkyeong Kim Ju-Won Kim Chaggil Lee Seunghee Park The tensile force of pre-stressed concrete (PSC) girders is the most important factor for managing the stability of PSC bridges. The tensile force is induced using pre-stressing (PS) tendons of a PSC girder. Because the PS tendons are located inside of the PSC girder, the tensile force cannot be measured after construction using conventional NDT (non-destructive testing) methods. To monitor the induced tensile force of a PSC girder, an embedded EM (elasto-magnetic) sensor was proposed in this study. The PS tendons are made of carbon steel, a ferromagnetic material. The magnetic properties of the ferromagnetic specimen are changed according to the induced magnetic field, temperature, and induced stress. Thus, the tensile force of PS tendons can be estimated by measuring their magnetic properties. The EM sensor can measure the magnetic properties of ferromagnetic materials in the form of a B (magnetic density)-H (magnetic force) loop. To measure the B-H loop of a PS tendon in a PSC girder, the EM sensor should be embedded into the PSC girder. The proposed embedded EM sensor can be embedded into a PSC girder as a sheath joint by designing screw threads to connect with the sheath. To confirm the proposed embedded EM sensors, the experimental study was performed using a down-scaled PSC girder model. Two specimens were constructed with embedded EM sensors, and three sensors were installed in each specimen. The embedded EM sensor could measure the B-H loop of PS tendons even if it was located inside concrete, and the area of the B-H loop was proportionally decreased according to the increase in tensile force. According to the results, the proposed method can be used to estimate the tensile force of unrevealed PS tendons.

Description: Sensors, Vol. 17, Pages 2004: Peg-in-Hole Assembly Based on Two-phase Scheme and F/T Sensor for Dual-arm Robot Sensors doi: 10.3390/s17092004 Authors: Xianmin Zhang Yanglong Zheng Jun Ota Yanjiang Huang This paper focuses on peg-in-hole assembly based on a two-phase scheme and force/torque sensor (F/T sensor) for a compliant dual-arm robot, the Baxter robot. The coordinated operations of human beings in assembly applications are applied to the behaviors of the robot. A two-phase assembly scheme is proposed to overcome the inaccurate positioning of the compliant dual-arm robot. The position and orientation of assembly pieces are adjusted respectively in an active compliant manner according to the forces and torques derived by a six degrees-of-freedom (6-DOF) F/T sensor. Experiments are conducted to verify the effectiveness and efficiency of the proposed assembly scheme. The performances of the dual-arm robot are consistent with those of human beings in the peg-in-hole assembly process. The peg and hole with 0.5 mm clearance for round pieces and square pieces can be assembled successfully.

Description: Sensors, Vol. 17, Pages 2002: Alumina Concentration Detection Based on the Kernel Extreme Learning Machine Sensors doi: 10.3390/s17092002 Authors: Sen Zhang Tao Zhang Yixin Yin Wendong Xiao The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

Description: Sensors, Vol. 17, Pages 2003: Wearable Devices for Classification of Inadequate Posture at Work Using Neural Networks Sensors doi: 10.3390/s17092003 Authors: Eya Barkallah Johan Freulard Martin Otis Suzy Ngomo Johannes Ayena Christian Desrosiers Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture.

Description: Sensors, Vol. 17, Pages 2005: Multi-Mode GF-3 Satellite Image Geometric Accuracy Verification Using the RPC Model Sensors doi: 10.3390/s17092005 Authors: Taoyang Wang Guo Zhang Lei Yu Ruishan Zhao Mingjun Deng Kai Xu The GaoFen-3 (GF-3) satellite is the first C-band multi-polarization synthetic aperture radar (SAR) imaging satellite with a resolution up to 1 m in China. It is also the only SAR satellite of the High-Resolution Earth Observation System designed for civilian use. There are 12 different imaging models to meet the needs of different industry users. However, to use SAR satellite images for related applications, they must possess high geometric accuracy. In order to verify the geometric accuracy achieved by the different modes of GF-3 images, we analyze the SAR geometric error source and perform geometric correction tests based on the RPC model with and without ground control points (GCPs) for five imaging modes. These include the spotlight (SL), ultra-fine strip (UFS), Fine Strip I (FSI), Full polarized Strip I (QPSI), and standard strip (SS) modes. Experimental results show that the check point residuals are large and consistent without GCPs, but the root mean square error of the independent checkpoints for the case of four corner control points is better than 1.5 pixels, achieving a similar level of geometric positioning accuracy to that of international satellites. We conclude that the GF-3 satellite can be used for high-accuracy geometric processing and related industry applications.

Description: Sensors, Vol. 17, Pages 2006: SNORAP: A Device for the Correction of Impaired Sleep Health by Using Tactile Stimulation for Individuals with Mild and Moderate Sleep Disordered Breathing Sensors doi: 10.3390/s17092006 Authors: Mete Yağanoğlu Murat Kayabekir Cemal Köse Sleep physiology and sleep hygiene play significant roles in maintaining the daily lives of individuals given that sleep is an important physiological need to protect the functions of the human brain. Sleep disordered breathing (SDB) is an important disease that disturbs this need. Snoring and Obstructive Sleep Apnea Syndrome (OSAS) are clinical conditions that affect all body organs and systems that intermittently, repeatedly, with at least 10 s or more breathing stops that decrease throughout the night and disturb sleep integrity. The aim of this study was to produce a new device for the treatment of patients especially with position and rapid eye movement (REM)-dependent mild and moderate OSAS. For this purpose, the main components of the device (the microphone (snore sensor), the heart rate sensor, and the vibration motor, which we named SNORAP) were applied to five volunteer patients (male, mean age: 33.2, body mass index mean: 29.3). After receiving the sound in real time with the microphone, the snoring sound was detected by using the Audio Fingerprint method with a success rate of 98.9%. According to the results obtained, the severity and the number of the snoring of the patients using SNORAP were found to be significantly lower than in the experimental conditions in the apnea hypopnea index (AHI), apnea index, hypopnea index, in supine position’s AHI, and REM position’s AHI before using SNORAP (Paired Sample Test, p < 0.05). REM sleep duration and nocturnal oxygen saturation were significantly higher when compared to the group not using the SNORAP (Paired Sample Test, p < 0.05).

Description: Sensors, Vol. 17, Pages 2010: A Survey on Data Quality for Dependable Monitoring in Wireless Sensor Networks Sensors doi: 10.3390/s17092010 Authors: Gonçalo Jesus António Casimiro Anabela Oliveira Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provide a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data are reliable or, more generically, that they have the necessary quality. In this survey, we look into the problem of ensuring the desired quality of data for dependable monitoring using WSNs. We take a dependability-oriented perspective, reviewing the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, we give particular attention to understanding which faults can affect sensors, how they can affect the quality of the information and how this quality can be improved and quantified.

Description: Sensors, Vol. 17, Pages 2014: An Effective Semantic Event Matching System in the Internet of Things (IoT) Environment Sensors doi: 10.3390/s17092014 Authors: Noura Alhakbani Mohammed Mehedi Hassan Mourad Ykhlef IoT sensors use the publish/subscribe model for communication to benefit from its decoupled nature with respect to space, time, and synchronization. Because of the heterogeneity of communicating parties, semantic decoupling is added as a fourth dimension. The added semantic decoupling complicates the matching process and reduces its efficiency. Our proposed algorithm clusters subscriptions and events according to topic and performs the matching process within these clusters, which increases the throughput by reducing the matching time from the range of 16–18 ms to 2–4 ms. Moreover, the accuracy of matching is improved when subscriptions must be fully approximated, as demonstrated by an over 40% increase in F-score results. This work shows the benefit of clustering, as well as the improvement in the matching accuracy and efficiency achieved using this approach.

Description: Sensors, Vol. 17, Pages 2016: Analysis of Multi-Level Simultaneous Driving Technique for Capacitive Touch Sensors Sensors doi: 10.3390/s17092016 Authors: Jong Park Chang-Ju Lee Jong Kim The signal-to-noise ratio (SNR) and driving levels of capacitive touch sensors determine the applicability of these sensors to thinner displays and sensor-integrated modules. The simultaneous driving technique has been widely applied to capacitive touch sensors to cope with various types of environmental noise. A Hadamard matrix has been used to determine the driving code and multiplex capacitive signals required to increase the SNR and responsivity of touch sensors. Using multi-level Hadamard matrices, a new driving technique for sensing concurrent capacitive elements across multiple rows of a touch panel was developed. The technique provides more effective design choices than the existing bipolar driving method by supporting a variety of orders of matrices and regular capacity. The required TX voltage can be reduced by applying the Kronecker product for higher orders of simultaneous driving. A system model is presented for multiplexing capacitive signals to extract the SNR of the existing Hadamard matrices as well as one of the proposed multi-level sequences. In addition, the corresponding multi-level drivers and receivers were implemented to verify the theoretical expectations and simulation results of the proposed technique.

Description: Sensors, Vol. 17, Pages 2018: Characterizations of Anti-Alpha-Fetoprotein-Conjugated Magnetic Nanoparticles Associated with Alpha-Fetoprotein for Biomedical Applications Sensors doi: 10.3390/s17092018 Authors: Shu-Hsien Liao Han-Sheng Huang Jen-Jie Chieh Yu-Kai Su Yuan-Fu Tong Kai-Wen Huang In this work, we report characterizations of biofunctionalized magnetic nanoparticles (BMNPs) associated with alpha-fetoprotein (AFP) for biomedical applications. The example BMNP in this study is anti-alpha-fetoprotein (anti-AFP) conjugated onto dextran-coated Fe3O4 labeled as Fe3O4-anti-AFP, and the target is AFP. We characterize magnetic properties, such as increments of magnetization ΔMH and effective relaxation time Δτeff in the reaction process. It is found that both ΔMH and Δτeff are enhanced when the concentration of AFP, ФAFP, increases. The enhancements are due to magnetic interactions among BMNPs in magnetic clusters, which contribute extra MH after the association with MH and in turn enhance τeff. The screening of patients carrying hepatocellular carcinoma (HCC) is verified via ΔMH/MH. The proposed method can be applied to detect a wide variety of analytes. The scaling characteristics of ΔMH/MH show the potential to develop a vibrating sample magnetometer system with low field strength for clinic applications.

Description: Sensors, Vol. 17, Pages 2019: Capturing Complex 3D Human Motions with Kernelized Low-Rank Representation from Monocular RGB Camera Sensors doi: 10.3390/s17092019 Authors: Xuan Wang Fei Wang Yanan Chen Recovering 3D structures from the monocular image sequence is an inherently ambiguous problem that has attracted considerable attention from several research communities. To resolve the ambiguities, a variety of additional priors, such as low-rank shape basis, have been proposed. In this paper, we make two contributions. First, we introduce an assumption that 3D structures lie on the union of nonlinear subspaces. Based on this assumption, we propose a Non-Rigid Structure from Motion (NRSfM) method with kernelized low-rank representation. To be specific, we utilize the soft-inextensibility constraint to accurately recover 3D human motions. Second, we extend this NRSfM method to the marker-less 3D human pose estimation problem by combining with Convolutional Neural Network (CNN) based 2D human joint detectors. To evaluate the performance of our methods, we apply our marker-based method on several sequences from Utrecht Multi-Person Motion (UMPM) benchmark and CMU MoCap datasets, and then apply the marker-less method on the Human3.6M datasets. The experiments demonstrate that the kernelized low-rank representation is more suitable for modeling the complex deformation and the method consequently yields more accurate reconstructions. Benefiting from the CNN-based detector, the marker-less approach can be applied to more real-life applications.

Description: Sensors, Vol. 17, Pages 2042: Anti-Sweep Jamming Design and Implementation Using Multi-Channel Harmonic Timing Sequence Detection for Short-Range FMCW Proximity Sensors Sensors doi: 10.3390/s17092042 Authors: Zhijie Kong Ping Li Xiaopeng Yan Xinhong Hao Currently, frequency-modulated continuous-wave (FMCW) proximity sensors are widely used. However, they suffer from a serious sweep jamming problem, which significantly reduces the ranging performance. To improve its anti-jamming capability, this paper analyzed the response mechanism of a proximity sensor with the existence of real target echo signals and sweep jamming, respectively. Then, a multi-channel harmonic timing sequence detection method, using the spectrum components’ distribution difference between the real echo signals and sweep jamming, is proposed. Moreover, a novel fast Fourier transform (FFT)-based implementation was employed to extract multi-channel harmonic information. Compared with the traditional band-pass filter (BPF) implementation, this novel realization scheme only computes FFT once, in each transmission cycle, which significantly reduced hardware resource consumption and improved the real-time performance of the proximity sensors. The proposed method was implemented and proved to be feasible through the numerical simulations and prototype experiments. The results showed that the proximity sensor utilizing the proposed method had better anti-sweep jamming capability and ranging performance.

Description: Sensors, Vol. 17, Pages 2039: Tapered Optical Fiber Functionalized with Palladium Nanoparticles by Drop Casting and Laser Radiation for H2 and Volatile Organic Compounds Sensing Purposes Sensors doi: 10.3390/s17092039 Authors: Nancy González-Sierra Luz Gómez-Pavón Gerardo Pérez-Sánchez Arnulfo Luis-Ramos Plácido Zaca-Morán Jesús Muñoz-Pacheco Francisco Chávez-Ramírez A comparative study on the sensing properties of a tapered optical fiber pristine and functionalized with the palladium nanoparticles to hydrogen and volatile organic compounds (VOCs), is presented. The sensor response and, response/recovery times were extracted from the measurements of the transient response of the device. The tapered optical fiber sensor was fabricated using a single-mode optical fiber by the flame-brushing technique. Functionalization of the optical fiber was performed using an aqueous solution of palladium chloride by drop-casting technique assisted for laser radiation. The detection principle of the sensor is based on the changes in the optical properties of palladium nanoparticles when exposed to reducing gases, which causes a variation in the absorption of evanescent waves. A continuous wave laser diode operating at 1550 nm is used for the sensor characterization. The sensor functionalized with palladium nanoparticles by this technique is viable for the sensing of hydrogen and VOCs, since it shows an enhancement in sensor response and response time compared to the sensor based on the pristine optical microfiber. The results show that the fabricated sensor is competitive with other fiber optic sensors functionalized with palladium nanoparticles to the hydrogen.

Description: Sensors, Vol. 17, Pages 2048: Studying Electrotaxis in Microfluidic Devices Sensors doi: 10.3390/s17092048 Authors: Yung-Shin Sun Collective cell migration is important in various physiological processes such as morphogenesis, cancer metastasis and cell regeneration. Such migration can be induced and guided by different chemical and physical cues. Electrotaxis, referring to the directional migration of adherent cells under stimulus of electric fields, is believed to be highly involved in the wound-healing process. Electrotactic experiments are conventionally conducted in Petri dishes or cover glasses wherein cells are cultured and electric fields are applied. However, these devices suffer from evaporation of the culture medium, non-uniformity of electric fields and low throughput. To overcome these drawbacks, micro-fabricated devices composed of micro-channels and fluidic components have lately been applied to electrotactic studies. Microfluidic devices are capable of providing cells with a precise micro-environment including pH, nutrition, temperature and various stimuli. Therefore, with the advantages of reduced cell/reagent consumption, reduced Joule heating and uniform and precise electric fields, microfluidic chips are perfect platforms for observing cell migration under applied electric fields. In this paper, I review recent developments in designing and fabricating microfluidic devices for studying electrotaxis, aiming to provide critical updates in this rapidly-growing, interdisciplinary field.