Next Issue
Volume 20, April-1
Previous Issue
Volume 20, March-1
sensors-logo

Journal Browser

Journal Browser

Table of Contents

Sensors, Volume 20, Issue 6 (March-2 2020) – 263 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Cover Story (view full-size image) The Complete Geophysical Survey of the Valley of the Kings (Luxor, Egypt) is a collaborative [...] Read more.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
A Closed-form Expression to Estimate the Uncertainty of THD Starting from the LPIT Accuracy Class
Sensors 2020, 20(6), 1804; https://doi.org/10.3390/s20061804 - 24 Mar 2020
Viewed by 343
Abstract
Power quality is a wide-ranging and current topic that involves a huge effort from the scientific community. Power quality issues have to be avoided or solved in order to preserve the integrity of the network and its assets. To this purpose, several power [...] Read more.
Power quality is a wide-ranging and current topic that involves a huge effort from the scientific community. Power quality issues have to be avoided or solved in order to preserve the integrity of the network and its assets. To this purpose, several power quality indexes and measurement techniques have been developed and used by experts. This paper aims at solving the issue of having an uncertainty associated to the total harmonic distortion (THD) measurements. The idea is to obtain a close-form expression, which only requires the knowledge of the instrument transformer accuracy class, to estimate the mean value and the variance of THD. After the development of such an expression, it has been tested and stressed to confirm its effectiveness and applicability in a variety of conditions, and for harmonics up to 25th (of 50 Hz), defined by the standards. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Open AccessReview
Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review
Sensors 2020, 20(6), 1803; https://doi.org/10.3390/s20061803 - 24 Mar 2020
Viewed by 378
Abstract
Artificial noses are broad-spectrum multisensors dedicated to the detection of volatile organic compounds (VOCs). Despite great recent progress, they still suffer from a lack of sensitivity and selectivity. We will review, in a systemic way, the biomimetic strategies for improving these performance criteria, [...] Read more.
Artificial noses are broad-spectrum multisensors dedicated to the detection of volatile organic compounds (VOCs). Despite great recent progress, they still suffer from a lack of sensitivity and selectivity. We will review, in a systemic way, the biomimetic strategies for improving these performance criteria, including the design of sensing materials, their immobilization on the sensing surface, the sampling of VOCs, the choice of a transduction method, and the data processing. This reflection could help address new applications in domains where high-performance artificial noses are required such as public security and safety, environment, industry, or healthcare. Full article
Show Figures

Figure 1

Open AccessArticle
A Heterogeneous Network Modeling Method Based on Public Goods Game Theory to Explore Cooperative Behavior in VANETs
Sensors 2020, 20(6), 1802; https://doi.org/10.3390/s20061802 - 24 Mar 2020
Viewed by 342
Abstract
Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only [...] Read more.
Cooperative vehicular networking has been widely studied in recent years. Existing evolution game theoretic approaches to study cooperative behavior in Vehicular Ad hoc Network (VANET) are mainly based on the assumption that VANET is constructed as a homogeneous network. This modeling method only extracts part attributes of vehicles and does not distinguish the differences between strategy and attribute. In this paper, we focus on the heterogeneous network model based on the public goods game theory for VANET. Then we propose a Dynamic Altruism Public Goods Game (DAPGG) model consisting of rational nodes, altruistic nodes, and zealots to more realistically characterize the real VANET. Rational nodes only care about their own benefits, altruistic nodes comprehensively consider the payoffs in the neighborhood, while zealots insist on behaving cooperatively. Finally, we explore the impacts of these attributes on the evolution of cooperation under different network conditions. The simulation results show that only adding altruistic nodes can effectively improve the proportion of cooperators, but it may cause conflicts between individual benefits and neighborhood benefits. Altruistic nodes together with zealots can better improve the proportion of cooperators, even if the network conditions are not suitable for the spread of cooperative behavior. Full article
(This article belongs to the Special Issue Sensors for Cooperative Vehicular Communications and Applications)
Show Figures

Figure 1

Open AccessArticle
Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture
Sensors 2020, 20(6), 1801; https://doi.org/10.3390/s20061801 - 24 Mar 2020
Viewed by 351
Abstract
The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, [...] Read more.
The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM. Full article
(This article belongs to the Special Issue Soft Sensors for Motion Capture and Analysis)
Show Figures

Figure 1

Open AccessArticle
Electrochemical Sensor Based on a Carbon Veil Modified by Phytosynthesized Gold Nanoparticles for Determination of Ascorbic Acid
Sensors 2020, 20(6), 1800; https://doi.org/10.3390/s20061800 - 24 Mar 2020
Viewed by 357
Abstract
An original voltammetric sensor (Au-gr/CVE) based on a carbon veil (CV) and phytosynthesized gold nanoparticles (Au-gr) was developed for ascorbic acid (AA) determination. Extract from strawberry leaves was used as source of antioxidants (reducers) for Au-gr phytosynthesis. The sensor was characterized by scanning [...] Read more.
An original voltammetric sensor (Au-gr/CVE) based on a carbon veil (CV) and phytosynthesized gold nanoparticles (Au-gr) was developed for ascorbic acid (AA) determination. Extract from strawberry leaves was used as source of antioxidants (reducers) for Au-gr phytosynthesis. The sensor was characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy and electrochemical methods. Optimal parameters of AA determination were chosen. The sensor exhibits a linear response to AA in a wide concentration range (1 μM–5.75 mM) and a limit of detection of 0.05 μM. The developed sensor demonstrated a high intra-day repeatability of 1 μM AA response (RSD = 1.4%) and its stability during six weeks, selectivity of AA determination toward glucose, sucrose, fructose, citric, tartaric and malic acids. The proposed sensor based on Au-gr provides a higher sensitivity and a lower limit of AA detection in comparison with the sensor based on gold nanoparticles synthesized by the Turkevich method. The sensor was successfully applied for the determination of AA content in fruit juices without samples preparation. The recovery of 99%–111% and RSD no more than 6.8% confirm the good reproducibility of the juice analysis results. A good agreement with the potentiometric titration data was obtained. A correlation (r = 0.9867) between the results of AA determination obtained on the developed sensor and integral antioxidant activity of fruit juices was observed. Full article
Show Figures

Figure 1

Open AccessArticle
Analysis and Compensation of Bias Drift for a Micromachined Spinning-Rotor Gyroscope with Electrostatic Suspension
Sensors 2020, 20(6), 1799; https://doi.org/10.3390/s20061799 - 24 Mar 2020
Viewed by 305
Abstract
Bias stability is one of primary characteristics of precise gyroscopes for inertial navigation. Analysis of various sources of the bias drift in a micromachined electrostatically suspended gyroscope (MESG) indicates that the bias stability is dominated by the temperature-induced drift. The analytical results of [...] Read more.
Bias stability is one of primary characteristics of precise gyroscopes for inertial navigation. Analysis of various sources of the bias drift in a micromachined electrostatically suspended gyroscope (MESG) indicates that the bias stability is dominated by the temperature-induced drift. The analytical results of temperature drift resulting from the rotor structure and capacitive position sensing electronics are modeled and analyzed to characterize the drift mechanism of the MESG. The experimental results indicate that the bias drift is mainly composed of two components, i.e., rapidly changing temperature drift and slowly changing time drift. Both the short-term and long-term bias drift of the MESG are tested and discussed to achieve online bias compensation. Finally, a neural network based-bias compensation scheme is presented and verified experimentally with improved bias stability of the MESG. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Open AccessArticle
An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks
Sensors 2020, 20(6), 1798; https://doi.org/10.3390/s20061798 - 24 Mar 2020
Viewed by 277
Abstract
The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks [...] Read more.
The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm. Full article
Show Figures

Figure 1

Open AccessFeature PaperArticle
Double-Constraint Inpainting Model of a Single-Depth Image
Sensors 2020, 20(6), 1797; https://doi.org/10.3390/s20061797 - 24 Mar 2020
Viewed by 346
Abstract
In real applications, obtained depth images are incomplete; therefore, depth image inpainting is studied here. A novel model that is characterised by both a low-rank structure and nonlocal self-similarity is proposed. As a double constraint, the low-rank structure and nonlocal self-similarity can fully [...] Read more.
In real applications, obtained depth images are incomplete; therefore, depth image inpainting is studied here. A novel model that is characterised by both a low-rank structure and nonlocal self-similarity is proposed. As a double constraint, the low-rank structure and nonlocal self-similarity can fully exploit the features of single-depth images to complete the inpainting task. First, according to the characteristics of pixel values, we divide the image into blocks, and similar block groups and three-dimensional arrangements are then formed. Then, the variable splitting technique is applied to effectively divide the inpainting problem into the sub-problems of the low-rank constraint and nonlocal self-similarity constraint. Finally, different strategies are used to solve different sub-problems, resulting in greater reliability. Experiments show that the proposed algorithm attains state-of-the-art performance. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Figure 1

Open AccessReview
ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges
Sensors 2020, 20(6), 1796; https://doi.org/10.3390/s20061796 - 24 Mar 2020
Viewed by 386
Abstract
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is [...] Read more.
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems. Full article
(This article belongs to the Special Issue ECG Monitoring System)
Show Figures

Figure 1

Open AccessArticle
Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions
Sensors 2020, 20(6), 1795; https://doi.org/10.3390/s20061795 - 24 Mar 2020
Viewed by 282
Abstract
Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental [...] Read more.
Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map. Full article
(This article belongs to the Section Optical Sensors)
Show Figures

Graphical abstract

Open AccessArticle
Integrated Circuit Angular Displacement Sensor with On-chip Pinhole Aperture
Sensors 2020, 20(6), 1794; https://doi.org/10.3390/s20061794 - 24 Mar 2020
Viewed by 280
Abstract
Sensors that remotely track the displacement of a moving object have a wide range of applications from robotic control to motion capture. In this paper, we introduce a simple, small silicon integrated circuit sensor that tracks the angular displacement of an object tagged [...] Read more.
Sensors that remotely track the displacement of a moving object have a wide range of applications from robotic control to motion capture. In this paper, we introduce a simple, small silicon integrated circuit sensor that tracks the angular displacement of an object tagged with a small light source, such as a light-emitting diode (LED). This sensor uses a new angular transduction mechanism, differential diffusion of photoelectrons generated from the light spot cast by the light tag onto a Si anode, that is described by a simple physics model using pinhole optics and carrier diffusion. Because the light spot is formed by a pinhole aperture integrated on the sensor chip, no external focusing optics are needed, reducing system complexity, size, and weight. Prototype sensors based on this model were fabricated and their basic characteristics are presented. These sensors transduce angular displacement of an LED across orthogonal latitudinal and longitudinal arcs into normalized differential photocathode currents with signal linearly proportional to LED angular position across a ± 40° field-of-view. These sensors offer potential performance and ease-of-use benefits compared to existing displacement sensor technologies. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Graphical abstract

Open AccessArticle
Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things
Sensors 2020, 20(6), 1793; https://doi.org/10.3390/s20061793 - 24 Mar 2020
Viewed by 281
Abstract
Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what [...] Read more.
Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost. Full article
(This article belongs to the Special Issue Blockchain Security and Privacy for the Internet of Things)
Show Figures

Figure 1

Open AccessReview
Application of Microfluidic Chip Technology in Food Safety Sensing
Sensors 2020, 20(6), 1792; https://doi.org/10.3390/s20061792 - 24 Mar 2020
Viewed by 316
Abstract
Food safety analysis is an important procedure to control food contamination and supervision. It is urgently needed to construct effective methods for on-site, fast, accurate and popular food safety sensing. Among them, microfluidic chip technology exhibits distinguish advantages in detection, including less sample [...] Read more.
Food safety analysis is an important procedure to control food contamination and supervision. It is urgently needed to construct effective methods for on-site, fast, accurate and popular food safety sensing. Among them, microfluidic chip technology exhibits distinguish advantages in detection, including less sample consumption, fast detection, simple operation, multi-functional integration, small size, multiplex detection and portability. In this review, we introduce the classification, material, processing and application of the microfluidic chip in food safety sensing, in order to provide a good guide for food safety monitoring. Full article
(This article belongs to the Special Issue Smart Spectral Sensors for Aquatic Environments)
Show Figures

Figure 1

Open AccessArticle
RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments
Sensors 2020, 20(6), 1791; https://doi.org/10.3390/s20061791 - 24 Mar 2020
Viewed by 272
Abstract
This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide [...] Read more.
This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people. Full article
(This article belongs to the Special Issue Recent Advances in GNSS-based High Precision Positioning Technology)
Show Figures

Figure 1

Open AccessArticle
Machine Learning-Enriched Lamb Wave Approaches for Automated Damage Detection
Sensors 2020, 20(6), 1790; https://doi.org/10.3390/s20061790 - 24 Mar 2020
Viewed by 243
Abstract
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb [...] Read more.
Lamb wave approaches have been accepted as efficiently non-destructive evaluations in structural health monitoring for identifying damage in different states. Despite significant efforts in signal process of Lamb waves, physics-based prediction is still a big challenge due to complexity nature of the Lamb wave when it propagates, scatters and disperses. Machine learning in recent years has created transformative opportunities for accelerating knowledge discovery and accurately disseminating information where conventional Lamb wave approaches cannot work. Therefore, the learning framework was proposed with a workflow from dataset generation, to sensitive feature extraction, to prediction model for lamb-wave-based damage detection. A total of 17 damage states in terms of different damage type, sizes and orientations were designed to train the feature extraction and sensitive feature selection. A machine learning method, support vector machine (SVM), was employed for the learning model. A grid searching (GS) technique was adopted to optimize the parameters of the SVM model. The results show that the machine learning-enriched Lamb wave-based damage detection method is an efficient and accuracy wave to identify the damage severity and orientation. Results demonstrated that different features generated from different domains had certain levels of sensitivity to damage, while the feature selection method revealed that time-frequency features and wavelet coefficients exhibited the highest damage-sensitivity. These features were also much more robust to noise. With increase of noise, the accuracy of the classification dramatically dropped. Full article
(This article belongs to the Special Issue Innovative Sensors for Civil Infrastructure Condition Assessment)
Show Figures

Figure 1

Open AccessArticle
A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data
Sensors 2020, 20(6), 1789; https://doi.org/10.3390/s20061789 - 24 Mar 2020
Viewed by 248
Abstract
Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, [...] Read more.
Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM). Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Show Figures

Figure 1

Open AccessArticle
A Low Temperature Drifting Acoustic Wave Pressure Sensor with an Integrated Vacuum Cavity for Absolute Pressure Sensing
Sensors 2020, 20(6), 1788; https://doi.org/10.3390/s20061788 - 24 Mar 2020
Viewed by 269
Abstract
In this paper we demonstrate a novel acoustic wave pressure sensor, based on an aluminum nitride (AlN) piezoelectric thin film. It contains an integrated vacuum cavity, which is micro-fabricated using a cavity silicon-on-insulator (SOI) wafer. This sensor can directly measure the absolute pressure [...] Read more.
In this paper we demonstrate a novel acoustic wave pressure sensor, based on an aluminum nitride (AlN) piezoelectric thin film. It contains an integrated vacuum cavity, which is micro-fabricated using a cavity silicon-on-insulator (SOI) wafer. This sensor can directly measure the absolute pressure without the help of an external package, and the vacuum cavity gives the sensor a very accurate reference pressure. Meanwhile, the presented pressure sensor is superior to previously reported acoustic wave pressure sensors in terms of the temperature drift. With the carefully designed dual temperature compensation structure, a very low temperature coefficient of frequency (TCF) is achieved. Experimental results show the sensor can measure the absolute pressure in the range of 0 to 0.4 MPa, while the temperature range is from 20 °C to 220 °C with a TCF of −14.4 ppm/°C. Such a TCF is only about half of that of previously reported works. Full article
(This article belongs to the Special Issue Piezoelectric Transducers)
Show Figures

Figure 1

Open AccessArticle
Gas Sensing Properties of Cobalt Titanate with Multiscale Pore Structure: Experiment and Simulation
Sensors 2020, 20(6), 1787; https://doi.org/10.3390/s20061787 - 24 Mar 2020
Viewed by 269
Abstract
A diffusion-reaction coupled model was presented to investigate the effects of multiscale pore structure characteristics on gas sensing properties. A series of CoTiO3 powders with different pore size distributions were fabricated by sol-gel method. Experimental results on cobalt titanate thick films show [...] Read more.
A diffusion-reaction coupled model was presented to investigate the effects of multiscale pore structure characteristics on gas sensing properties. A series of CoTiO3 powders with different pore size distributions were fabricated by sol-gel method. Experimental results on cobalt titanate thick films show that a well-defined multiscale pore structure is particularly desired for the improvement of sensing performance, instead of just increasing the specific surface area. The theoretical responses of sensing elements with different pore size distributions were derived and compared with experimental data on CoTiO3 sensors exposed to ethanol. The calculated sensitivities considering the influence of pore size changes were also found to be in agreement with the experimental results. A dimensionless Thiele modulus Th was introduced for assessing the critical point corresponding to the transformation from surface reaction-controlled sensitivity into diffusion-controlled sensitivity. Full article
(This article belongs to the Special Issue Gas Sensing Materials)
Show Figures

Figure 1

Open AccessArticle
Cognitive Radio-Assisted NOMA Broadcasting for 5G Cellular V2X Communications: Model of Roadside Unit Selection and SWIPT
Sensors 2020, 20(6), 1786; https://doi.org/10.3390/s20061786 - 24 Mar 2020
Viewed by 305
Abstract
The outage performance is a significant problem to implement the Cognitive Radio (CR) paradigm in the Vehicle to Everything (V2X) networks. Recently, more interest has focused on Non-Orthogonal Multiple Access (NOMA) in wireless-powered communication. In the conventional CR-enabled V2X-NOMA network, spectrum sensing and [...] Read more.
The outage performance is a significant problem to implement the Cognitive Radio (CR) paradigm in the Vehicle to Everything (V2X) networks. Recently, more interest has focused on Non-Orthogonal Multiple Access (NOMA) in wireless-powered communication. In the conventional CR-enabled V2X-NOMA network, spectrum sensing and limited battery capacity at the Roadside Unit (RSU) may cause serious outage performance. In this study, RSU selection scheme is adopted. This paper presents an interesting model of a system with Simultaneous Wireless Information and Power Transfer (SWIPT) and a CR-enabled V2X-NOMA network. In the downlink, the RSU harvests wireless energy from Radio Frequency (RF) signals and senses the spectrum state at the same time. A CR-enabled V2X-NOMA system performance is presented by deriving exact expressions of outage probability of distant vehicles. In the overlay CR-enabled V2X-NOMA network, the constraints are transmit power and the number of designed RSU that make significant impacts on system performance. Simulation results show that the CR-enabled V2X-NOMA get benefits from energy harvesting and RSU selection scheme. Full article
(This article belongs to the Section Communications)
Show Figures

Figure 1

Open AccessArticle
Fabrication and Characteristics of a Three-Axis Accelerometer with Double L-Shaped Beams
Sensors 2020, 20(6), 1780; https://doi.org/10.3390/s20061780 - 24 Mar 2020
Viewed by 300
Abstract
A three-axis accelerometer with a double L-shaped beams structure was designed and fabricated in this paper, consisting of a supporting body, four double L-shaped beams and intermediate double beams connected to two mass blocks. When applying acceleration to the accelerometer chip, according to [...] Read more.
A three-axis accelerometer with a double L-shaped beams structure was designed and fabricated in this paper, consisting of a supporting body, four double L-shaped beams and intermediate double beams connected to two mass blocks. When applying acceleration to the accelerometer chip, according to the output voltage changes of three Wheatstone bridges constituted by twelve piezoresistors on the roots of the beams, the corresponding acceleration along three axes can be measured based on the elastic force theory and piezoresistive effect. To improve the characteristics of the three-axis accelerometer, we simulated how the width of the intermediate double beams affected the characteristics. Through optimizing the structure size, six chips with different widths of intermediate double beams were fabricated on silicon-on-insulator (SOI) wafers using micro-electromechanical systems (MEMS) technology and were packaged on printed circuit boards (PCB) by using an electrostatic bonding process and inner lead bonding technology. At room temperature and VDD = 5.0 V, the resulting accelerometer with an optimized size (w = 500 μm) realized sensitivities of 0.302 mV/g, 0.235 mV/g and 0.347 mV/g along three axes, with a low cross-axis sensitivity. This result provides a new strategy to further improve the characteristics of the three-axis accelerometer. Full article
(This article belongs to the Section Physical Sensors)
Show Figures

Figure 1

Open AccessArticle
Heterogeneous Iris One-to-One Certification with Universal Sensors Based On Quality Fuzzy Inference and Multi-Feature Fusion Lightweight Neural Network
Sensors 2020, 20(6), 1785; https://doi.org/10.3390/s20061785 - 23 Mar 2020
Viewed by 355
Abstract
Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into [...] Read more.
Due to the unsteady morphology of heterogeneous irises generated by a variety of different devices and environments, the traditional processing methods of statistical learning or cognitive learning for a single iris source are not effective. Traditional iris recognition divides the whole process into several statistically guided steps, which cannot solve the problem of correlation between various links. The existing iris data set size and situational classification constraints make it difficult to meet the requirements of learning methods under a single deep learning framework. Therefore, aiming at a one-to-one iris certification scenario, this paper proposes a heterogeneous iris one-to-one certification method with universal sensors based on quality fuzzy inference and a multi-feature entropy fusion lightweight neural network. The method is divided into an evaluation module and a certification module. The evaluation module can be used by different devices to design a quality fuzzy concept inference system and an iris quality knowledge concept construction mechanism, transform human logical cognition concepts into digital concepts, and select appropriate concepts to determine iris quality according to different iris quality requirements and get a recognizable iris. The certification module is a lightweight neural network based on statistical learning ideas and a multi-source feature fusion mechanism. The information entropy of the iris feature label was used to set the iris entropy feature category label and design certification module functions according to the category label to obtain the certification module result. As the requirements for the number and quality of irises changes, the category labels in the certification module function were dynamically adjusted using a feedback learning mechanism. This paper uses iris data collected from three different sensors in the JLU (Jilin University) iris library. The experimental results prove that for the lightweight multi-state irises, the abovementioned problems are ameliorated to a certain extent by this method. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Open AccessArticle
High Sensitive Biosensors Based on the Coupling Between Surface Plasmon Polaritons on Titanium Nitride and a Planar Waveguide Mode
Sensors 2020, 20(6), 1784; https://doi.org/10.3390/s20061784 - 23 Mar 2020
Viewed by 295
Abstract
High sensitivity biosensors based on the coupling of surface plasmon polaritons on titanium nitride (TiN) and a planar waveguide mode were built; they were proved by sensing three different media: air, water and dried egg white; sensors described here could be useful for [...] Read more.
High sensitivity biosensors based on the coupling of surface plasmon polaritons on titanium nitride (TiN) and a planar waveguide mode were built; they were proved by sensing three different media: air, water and dried egg white; sensors described here could be useful for sensing materials with a refractive index between 1.0 and 1.6; in particular, materials of biological interest with a refractive index in the range 1.3–1.6, like those containing biotin and/or streptavidin. They were built by depositing Nb2O5/SiO2/TiN multilayer structures on the flat surface of D-shaped sapphire prisms by using the dc magnetron sputtering technique. Attenuated total reflection (ATR) experiments in the Kretschmann configuration were accomplished for the air/TiN/Prism and S/Nb2O5/SiO2/TiN/Prism structures, S being the sample or sensing medium. ATR spectra for plasmons at the TiN/air interface showed a broad absorption band for angles of incidence between 36 and 85°, with full width at half maximum (FWHM) of approximately 40°. For the S/Nb2O5/SiO2/TiN/Prism structures, ATR spectra showed a sharp reflectivity peak, within the broad plasmonic absorption band, which was associated with Fano resonances. The angular position and FWHM of the Fano resonances strongly depend on the refractive index of the sensing medium. ATR spectra were fitted by using the transfer-matrix method. Additionally, we found that angular sensitivity and figure of merit increase with increasing the refractive index of the sensing medium. Full article
Show Figures

Figure 1

Open AccessArticle
Photoplethysmographic Time-Domain Heart Rate Measurement Algorithm for Resource-Constrained Wearable Devices and its Implementation
Sensors 2020, 20(6), 1783; https://doi.org/10.3390/s20061783 - 23 Mar 2020
Viewed by 351
Abstract
This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned [...] Read more.
This paper presents an algorithm for the measurement of the human heart rate, using photoplethysmography (PPG), i.e., the detection of the light at the skin surface. The signal from the PPG sensor is processed in time-domain; the peaks in the preprocessed and conditioned PPG waveform are detected by using a peak detection algorithm to find the heart rate in real time. Apart from the PPG sensor, the accelerometer is also used to detect body movement and to indicate the moments in time, for which the PPG waveform can be unreliable. This paper describes in detail the signal conditioning path and the modified algorithm, and it also gives an example of implementation in a resource-constrained wrist-wearable device. The algorithm was evaluated by using the publicly available PPG-DaLia dataset containing samples collected during real-life activities with a PPG sensor and accelerometer and with an ECG signal as ground truth. The quality of the results is comparable to the other algorithms from the literature, while the required hardware resources are lower, which can be significant for wearable applications. Full article
(This article belongs to the Special Issue Low-Cost Sensors and Biological Signals)
Show Figures

Figure 1

Open AccessArticle
Optimization of Experimental Variables Influencing Apoptosome Biosensor in HEK293T Cells
Sensors 2020, 20(6), 1782; https://doi.org/10.3390/s20061782 - 23 Mar 2020
Viewed by 324
Abstract
The apoptotic protease-activating factor 1 (Apaf-1) split luciferase biosensor has been used as a biological tool for the detection of early stage of apoptosis. The effect of doxorubicin in a cell-based assay and the addition of cytochrome c and ATP in a cell-free [...] Read more.
The apoptotic protease-activating factor 1 (Apaf-1) split luciferase biosensor has been used as a biological tool for the detection of early stage of apoptosis. The effect of doxorubicin in a cell-based assay and the addition of cytochrome c and ATP in a cell-free system have been used to test the functionality of the reporter for the detection of apoptosome formation. Here, our data established a drug- and cytochrome c/ATP-independent way of apoptosis induction relying on the expression of the biosensor itself to induce formation of apoptosome. Overexpression of Apaf-1 constructs led to increased split luciferase activity and caspase-3 activity in the absence of any drug treatment. Caspase-3 activity was significantly inhibited when caspase-9DN was co-overexpressed, while the activity of the Apaf1 biosensor was significantly increased. Our results show that the Apaf-1 biosensor does not detect etoposide-induced apoptosis. Full article
(This article belongs to the Special Issue Micro/Nanosensors for Cellular/Tissue Measurement)
Show Figures

Figure 1

Open AccessArticle
A Novel Smooth Variable Structure Smoother for Robust Estimation
Sensors 2020, 20(6), 1781; https://doi.org/10.3390/s20061781 - 23 Mar 2020
Viewed by 324
Abstract
The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) [...] Read more.
The smooth variable structure filter (SVSF) is a new-type filter based on the sliding-mode concepts and has good stability and robustness in overcoming the modeling uncertainties and errors. However, SVSF is insufficient to suppress Gaussian noise. A novel smooth variable structure smoother (SVSS) based on SVSF is presented here, which mainly focuses on this drawback and improves the SVSF estimation accuracy of the system. The estimation of the linear Gaussian system state based on SVSS is divided into two steps: Firstly, the SVSF state estimate and covariance are computed during the forward pass in time. Then, the smoothed state estimate is computed during the backward pass by using the innovation of the measured values and covariance estimate matrix. According to the simulation results with respect to the maneuvering target tracking, SVSS has a better performance compared with another smoother based on SVSF and the Kalman smoother in different tracking scenarios. Therefore, the SVSS proposed in this paper could be widely applied in the field of state estimation in dynamic system. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Show Figures

Figure 1

Open AccessArticle
Using Rough Sets to Improve Activity Recognition Based on Sensor Data
Sensors 2020, 20(6), 1779; https://doi.org/10.3390/s20061779 - 23 Mar 2020
Viewed by 336
Abstract
Activity recognition plays a central role in many sensor-based applications, such as smart homes for instance. Given a stream of sensor data, the goal is to determine the activities that triggered the sensor data. This article shows how spatial information can be used [...] Read more.
Activity recognition plays a central role in many sensor-based applications, such as smart homes for instance. Given a stream of sensor data, the goal is to determine the activities that triggered the sensor data. This article shows how spatial information can be used to improve the process of recognizing activities in smart homes. The sensors that are used in smart homes are in most cases installed in fixed locations, which means that when a particular sensor is triggered, we know approximately where the activity takes place. However, since different sensors may be involved in different occurrences of the same type of activity, the set of sensors associated with a particular activity is not precisely defined. In this article, we use rough sets rather than standard sets to denote the sensors involved in an activity to model, which enables us to deal with this imprecision. Using publicly available data sets, we will demonstrate that rough sets can adequately capture useful information to assist with the activity recognition process. We will also show that rough sets lend themselves to creating Explainable Artificial Intelligence (XAI). Full article
(This article belongs to the Section Intelligent Sensors)
Show Figures

Figure 1

Open AccessArticle
Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms
Sensors 2020, 20(6), 1778; https://doi.org/10.3390/s20061778 - 23 Mar 2020
Viewed by 318
Abstract
The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST [...] Read more.
The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Show Figures

Figure 1

Open AccessArticle
A Method of Interstory Drift Monitoring Using a Smartphone and a Laser Device
Sensors 2020, 20(6), 1777; https://doi.org/10.3390/s20061777 - 23 Mar 2020
Viewed by 265
Abstract
Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This [...] Read more.
Interstory drift is an important engineering parameter in building design and building structural health monitoring. However, many problems exist in current interstory drift monitoring methods. The traditional method is imprecise—double numerical integration of acceleration data—and other direct monitoring methods need professional equipment. This paper proposes a method to solve these problems by monitoring the interstory drift with a smartphone and a laser device. In this method, a laser device is installed on the ceiling while a smartphone is fixed on a steel projection plate on the floor. Compared with a reference sensor, the method designed in this study shows that a smartphone is competent in monitoring the interstory drift. This method utilizes a smartphone application (APP) named D-Viewer to implement monitoring and data storage just in one place, which is also inexpensive. The results showed that this method has an average percent error of 3.37%, with a standard deviation of 2.67%. With the popularization of the smartphone, this method is promising in acquiring large amounts of data, which will be significant for building assessment after an earthquake. Full article
(This article belongs to the Special Issue Sensors in Structural Health Monitoring and Seismic Protection)
Show Figures

Figure 1

Open AccessArticle
Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data
Sensors 2020, 20(6), 1776; https://doi.org/10.3390/s20061776 - 23 Mar 2020
Viewed by 286
Abstract
Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of [...] Read more.
Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles. Full article
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
Show Figures

Figure 1

Open AccessArticle
Robust Time-Delay Feedback Control of Vehicular CACC Systems with Uncertain Dynamics
Sensors 2020, 20(6), 1775; https://doi.org/10.3390/s20061775 - 23 Mar 2020
Viewed by 270
Abstract
This paper proposes a new, robust time-delay cooperative adaptive cruise control (CACC) approach for vehicle platooning systems with uncertain dynamics and varying communication delay. The uncertain CACC models with perturbed parameters are used to describe the uncertain dynamics of the vehicle platooning system. [...] Read more.
This paper proposes a new, robust time-delay cooperative adaptive cruise control (CACC) approach for vehicle platooning systems with uncertain dynamics and varying communication delay. The uncertain CACC models with perturbed parameters are used to describe the uncertain dynamics of the vehicle platooning system. By combining the constant time headway strategy and predecessor-following communication topology, a set of robust delay feedback controllers is designed for the uncertain vehicle platoon with varying communication delay. Then, the set of CACC controllers is computed by solving some linear matrix inequalities, which further establish the robust (string) stability of the uncertain platooning system with the varying communication delay. The co-simulation experiment of CarSim and Simulink with a group of a seven-car platoons and varying velocity is used to demonstrate the effectiveness of the presented method. Full article
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop