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Sensors, Volume 19, Issue 18 (September-2 2019) – 242 articles

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Cover Story (view full-size image) The coastal ocean is a complex biogeochemical “jambalaya” of phytoplankton, organic detritus, and [...] Read more.
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Open AccessArticle
Geometry Optimisation of a Hall-Effect-Based Soft Fingertip for Estimating Orientation of Thin Rectangular Objects
Sensors 2019, 19(18), 4056; https://doi.org/10.3390/s19184056 - 19 Sep 2019
Cited by 1 | Viewed by 974
Abstract
Soft tactile sensors have been applied to robotic grippers for assembly. It is a challenging task to obtain contact information and object orientation using tactile sensors during grasping. Currently, the design of Hall-effect-based tactile sensors to perform such tasks is based on trial [...] Read more.
Soft tactile sensors have been applied to robotic grippers for assembly. It is a challenging task to obtain contact information and object orientation using tactile sensors during grasping. Currently, the design of Hall-effect-based tactile sensors to perform such tasks is based on trial and error. We present a method of investigating the optimal geometrical design of a cylindrical soft sensor to increase its sensitivity. The finite element model of a soft fingertip was constructed in Abaqus with two design variables, i.e., hollow radius and magnet position. Then, the model was imported into Isight, with the maximisation of magnet displacement as the objective function. We found that the optimal design was at the boundary of the parameter design space. Four fingertips were fabricated with one intuitive, one optimal, and two optional sets of parameters. Experiments were performed, and object orientation was estimated by utilising linear approximation and a machine learning approach. Good agreements were achieved between optimisation and experiments. The results revealed that the estimated average error in object orientation was decreased by the optimised fingertip design. Furthermore, the 3-axis forces could successfully be estimated based on sensor outputs. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning
Sensors 2019, 19(18), 4055; https://doi.org/10.3390/s19184055 - 19 Sep 2019
Cited by 6 | Viewed by 1186
Abstract
This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is [...] Read more.
This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance. Full article
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Open AccessArticle
Recurrent Neural Network for Inertial Gait User Recognition in Smartphones
Sensors 2019, 19(18), 4054; https://doi.org/10.3390/s19184054 - 19 Sep 2019
Cited by 2 | Viewed by 818
Abstract
In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then [...] Read more.
In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially. Full article
(This article belongs to the Special Issue Sensors for Gait Biometrics)
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Open AccessArticle
Detection of Hydraulic Phenomena in Francis Turbines with Different Sensors
Sensors 2019, 19(18), 4053; https://doi.org/10.3390/s19184053 - 19 Sep 2019
Viewed by 669
Abstract
Nowadays, hydropower is demanded to provide flexibility and fast response into the electrical grid in order to compensate the non-constant electricity generation of other renewable sources. Hydraulic turbines are therefore demanded to work under off-design conditions more frequently, where different complex hydraulic phenomena [...] Read more.
Nowadays, hydropower is demanded to provide flexibility and fast response into the electrical grid in order to compensate the non-constant electricity generation of other renewable sources. Hydraulic turbines are therefore demanded to work under off-design conditions more frequently, where different complex hydraulic phenomena appear, affecting the machine stability as well as reducing the useful life of its components. Hence, it is desirable to detect in real-time these hydraulic phenomena to assess the operation of the machine. In this paper, a large medium-head Francis turbine was selected for this purpose. This prototype is instrumented with several sensors such as accelerometers, proximity probes, strain gauges, pressure sensors and a microphone. Results presented in this paper permit knowing which hydraulic phenomenon is detected with every sensor and which signal analysis technique is necessary to use. With this information, monitoring systems can be optimized with the most convenient sensors, locations and signal analysis techniques. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Polymer Fibers Covered by Soft Multilayered Films for Sensing Applications in Composite Materials
Sensors 2019, 19(18), 4052; https://doi.org/10.3390/s19184052 - 19 Sep 2019
Cited by 1 | Viewed by 806
Abstract
This paper presents the possibility of applying a soft polymer coating by means of a layer-by-layer (LbL) technique to highly birefringent polymer optical fibers designed for laminating in composite materials. In contrast to optical fibers made of pure silica glass, polymer optical fibers [...] Read more.
This paper presents the possibility of applying a soft polymer coating by means of a layer-by-layer (LbL) technique to highly birefringent polymer optical fibers designed for laminating in composite materials. In contrast to optical fibers made of pure silica glass, polymer optical fibers are manufactured without a soft polymer coating. In typical sensor applications, the absence of a buffer coating is an advantage. However, highly birefringent polymer optical fibers laminated in a composite material are much more sensitive to temperature changes than polymer optical fibers in a free space as a result of the thermal expansion of the composite material. To prevent this, we have covered highly birefringent polymer optical fibers with a soft polymer coating of different thickness and measured the temperature sensitivity of each solution. The results obtained show that the undesired temperature sensitivity of the laminated optical fiber decreases as the thickness of the coating layer increases. Full article
(This article belongs to the Special Issue Fiber-Based Sensing Technology: Recent Progresses and New Challenges)
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Open AccessArticle
Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy
Sensors 2019, 19(18), 4051; https://doi.org/10.3390/s19184051 - 19 Sep 2019
Cited by 1 | Viewed by 675
Abstract
Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify [...] Read more.
Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%). Full article
(This article belongs to the Section Biosensors)
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Open AccessFeature PaperArticle
Enhancement of Multimodal Microwave-Ultrasound Breast Imaging Using a Deep-Learning Technique
Sensors 2019, 19(18), 4050; https://doi.org/10.3390/s19184050 - 19 Sep 2019
Cited by 2 | Viewed by 1076
Abstract
We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) [...] Read more.
We present a deep learning method used in conjunction with dual-modal microwave-ultrasound imaging to produce tomographic reconstructions of the complex-valued permittivity of numerical breast phantoms. We also assess tumor segmentation performance using the reconstructed permittivity as a feature. The contrast source inversion (CSI) technique is used to create the complex-permittivity images of the breast with ultrasound-derived tissue regions utilized as prior information. However, imaging artifacts make the detection of tumors difficult. To overcome this issue we train a convolutional neural network (CNN) that takes in, as input, the dual-modal CSI reconstruction and attempts to produce the true image of the complex tissue permittivity. The neural network consists of successive convolutional and downsampling layers, followed by successive deconvolutional and upsampling layers based on the U-Net architecture. To train the neural network, the input-output pairs consist of CSI’s dual-modal reconstructions, along with the true numerical phantom images from which the microwave scattered field was synthetically generated. The reconstructed permittivity images produced by the CNN show that the network is not only able to remove the artifacts that are typical of CSI reconstructions, but can also improve the detectability of tumors. The performance of the CNN is assessed using a four-fold cross-validation on our dataset that shows improvement over CSI both in terms of reconstruction error and tumor segmentation performance. Full article
(This article belongs to the Special Issue Microwave and RF Biosensors)
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Open AccessArticle
Privacy Aware Incentivization for Participatory Sensing
Sensors 2019, 19(18), 4049; https://doi.org/10.3390/s19184049 - 19 Sep 2019
Viewed by 734
Abstract
Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to [...] Read more.
Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments. Full article
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Open AccessReview
Nanosystems, Edge Computing, and the Next Generation Computing Systems
Sensors 2019, 19(18), 4048; https://doi.org/10.3390/s19184048 - 19 Sep 2019
Cited by 2 | Viewed by 1362
Abstract
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition [...] Read more.
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features
Sensors 2019, 19(18), 4047; https://doi.org/10.3390/s19184047 - 19 Sep 2019
Cited by 2 | Viewed by 683
Abstract
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white [...] Read more.
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods. Full article
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
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Open AccessArticle
Geometric Aberration Theory of Offner Imaging Spectrometers
Sensors 2019, 19(18), 4046; https://doi.org/10.3390/s19184046 - 19 Sep 2019
Cited by 1 | Viewed by 691
Abstract
A third-order aberration theory has been developed for the Offner imaging spectrometer comprising an extended source; two concave mirrors; a convex diffraction grating; and an image plane. Analytic formulas of the spot diagram are derived for tracing rays through the system based on [...] Read more.
A third-order aberration theory has been developed for the Offner imaging spectrometer comprising an extended source; two concave mirrors; a convex diffraction grating; and an image plane. Analytic formulas of the spot diagram are derived for tracing rays through the system based on Fermat’s principle. The proposed theory can be used to discuss in detail individual aberrations of the system such as coma, spherical aberration and astigmatism, and distortion together with the focal conditions. It has been critically evaluated as well in a comparison with exact ray tracing constructed using the commercial software ZEMAX. In regard to the analytic formulas, the results show a high degree of practicality. Full article
(This article belongs to the Special Issue Optical Spectroscopy, Sensing, and Imaging from UV to THz Range)
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Open AccessArticle
Cyber Situation Comprehension for IoT Systems based on APT Alerts and Logs Correlation
Sensors 2019, 19(18), 4045; https://doi.org/10.3390/s19184045 - 19 Sep 2019
Viewed by 777
Abstract
With the emergence of the Advanced Persistent Threat (APT) attacks, many Internet of Things (IoT) systems have faced large numbers of potential threats with the characteristics of concealment, permeability, and pertinence. However, existing methods and technologies cannot provide comprehensive and prompt recognition of [...] Read more.
With the emergence of the Advanced Persistent Threat (APT) attacks, many Internet of Things (IoT) systems have faced large numbers of potential threats with the characteristics of concealment, permeability, and pertinence. However, existing methods and technologies cannot provide comprehensive and prompt recognition of latent APT attack activities in the IoT systems. To address this problem, we propose an APT Alerts and Logs Correlation Method, named APTALCM and a framework of deploying APTALCM on the IoT system, where an edge computing architecture was used to achieve cyber situation comprehension without too much data transmission cost. Specifically, we firstly present a cyber situation ontology for modeling the concepts and properties to formalize APT attack activities in the IoT systems. Then, we introduce a cyber situation instance similarity measurement method based on the SimRank mechanism for APT alerts and logs Correlation. Combining with instance similarity, we further propose an APT alert instances correlation method to reconstruct APT attack scenarios and an APT log instances correlation method to detect log instance communities. Through the coalescence of these methods, APTALCM can accomplish the cyber situation comprehension effectively by recognizing the APT attack intentions in the IoT systems. The exhaustive experimental results demonstrate that the two kernel modules, i.e., Alert Instance Correlation Module (AICM) and Log Instance Correlation Module (LICM) in our APTALCM, can achieve both high true-positive rate and low false-positive rate. Full article
(This article belongs to the Special Issue Threat Identification and Defence for Internet-of-Things)
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Open AccessArticle
UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter
Sensors 2019, 19(18), 4044; https://doi.org/10.3390/s19184044 - 19 Sep 2019
Viewed by 664
Abstract
Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make [...] Read more.
Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessFeature PaperArticle
Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application
Sensors 2019, 19(18), 4043; https://doi.org/10.3390/s19184043 - 19 Sep 2019
Cited by 3 | Viewed by 737
Abstract
Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper [...] Read more.
Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra Rrs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient bb(620) directly from the magnitude of Rrs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water’s colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the Rrs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents. Full article
(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)
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Open AccessArticle
A Deep Learning Framework for Signal Detection and Modulation Classification
Sensors 2019, 19(18), 4042; https://doi.org/10.3390/s19184042 - 19 Sep 2019
Cited by 4 | Viewed by 1217
Abstract
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. [...] Read more.
Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance. Full article
(This article belongs to the Special Issue Sensor Signal and Information Processing III)
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Open AccessArticle
Thin Magnetically Permeable Targets for Inductive Sensing: Application to Limb Prosthetics
Sensors 2019, 19(18), 4041; https://doi.org/10.3390/s19184041 - 19 Sep 2019
Cited by 1 | Viewed by 666
Abstract
The purpose of this research was to create a thin ferrous polymer composite to be used as a target for inductive sensing in limb prosthetics. Inductive sensors are used to monitor limb-to-socket distance in prosthetic sockets, which reflects socket fit. A styrene–ethylene–ethylene/propylene–styrene (SEEPS) [...] Read more.
The purpose of this research was to create a thin ferrous polymer composite to be used as a target for inductive sensing in limb prosthetics. Inductive sensors are used to monitor limb-to-socket distance in prosthetic sockets, which reflects socket fit. A styrene–ethylene–ethylene/propylene–styrene (SEEPS) polymer was mixed with iron powder at three concentrations (75, 77, 85 wt%), and thin disk-shaped samples were fabricated (0.50, 0,75, 1.00 mm thickness). For 85 wt% samples of 0.50 mm thickness, which proved the best combination of high signal strength and low target volume, inductive sensor sensitivity ranged from 3.2E5 counts/mm at 0.00–1.00 mm distances to 7.2E4 counts/mm at 4.00–5.00 mm distances. The application of compressive stress (up to 425 kPa) introduced an absolute measurement error of less than 3.3 μm. Tensile elasticity was 282 kPa, which is comparable to that of commercial elastomeric liners. Durability testing in the shoe of an able-bodied participant demonstrated a change in calibration coefficient of less than 3.8% over two weeks of wear. The ferrous polymer composite may facilitate the development of automatically adjusting sockets that use limb-to-socket distance measurement for feedback control. Full article
(This article belongs to the Special Issue Sensors and Wearable Assistive Devices)
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Open AccessArticle
Constant-Modulus-Waveform Design for Multiple-Target Detection in Colocated MIMO Radar
Sensors 2019, 19(18), 4040; https://doi.org/10.3390/s19184040 - 19 Sep 2019
Cited by 1 | Viewed by 527
Abstract
For improving the performance of multiple-target detection in a colocated multiple-input multiple-output (MIMO) radar system, a constant-modulus-waveform design method is presented in this paper. The proposed method consists of two steps: simultaneous multiple-transmit-beam design and constant-modulus-waveform design. In the first step, each transmit [...] Read more.
For improving the performance of multiple-target detection in a colocated multiple-input multiple-output (MIMO) radar system, a constant-modulus-waveform design method is presented in this paper. The proposed method consists of two steps: simultaneous multiple-transmit-beam design and constant-modulus-waveform design. In the first step, each transmit beam is controlled by an ideal orthogonal waveform and a weight vector. We optimized the weight vectors to maximize the detection probabilities of all targets or minimize the transmit power for the purpose of low intercept probability in the case of predefined worst detection probabilities. Various targets’ radar cross-section (RCS) fluctuation models were also considered in two optimization problems. Then, the optimal weight vectors multiplied by ideal orthogonal waveforms were a set of transmitted waveforms. However, those transmitted waveforms were not constant-modulus waveforms. In the second step, the transmitted waveforms obtained in the first step were mapped to constant-modulus waveforms by cyclic algorithm. Numerical examples are provided to show that the proposed constant-waveform design method could effectively achieve the desired transmit-beam pattern, and that the transmit-beam pattern could be adaptively adjusted according to prior information. Full article
(This article belongs to the Section Sensor Networks)
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Open AccessArticle
Commercial Screen-Printed Electrodes Based on Carbon Nanomaterials for a Fast and Cost-Effective Voltammetric Determination of Paracetamol, Ibuprofen and Caffeine in Water Samples
Sensors 2019, 19(18), 4039; https://doi.org/10.3390/s19184039 - 19 Sep 2019
Cited by 8 | Viewed by 784
Abstract
Carbon screen-printed electrode (SPCE), multi-walled carbon nanotubes modified screen-printed electrode (SPCNTE), carbon nanofibers modified screen-printed electrode (SPCNFE), and graphene modified screen-printed electrode (SPGPHE) were in a pioneer way tested as sensors for the simultaneous determination of the two most consumed pain-killers, paracetamol (PA) [...] Read more.
Carbon screen-printed electrode (SPCE), multi-walled carbon nanotubes modified screen-printed electrode (SPCNTE), carbon nanofibers modified screen-printed electrode (SPCNFE), and graphene modified screen-printed electrode (SPGPHE) were in a pioneer way tested as sensors for the simultaneous determination of the two most consumed pain-killers, paracetamol (PA) and ibuprofen (IB), and the stimulant caffeine (CF) in water by differential pulse voltammetry (DPV). Their analytical performances were compared, and the resulting sensitivities (2.50, 0.074, and 0.24 μA V mg−1 L for PA, IB, and CF, respectively), detection limits (0.03, 0.6, and 0.05 mg L−1 for PA, IB, and CF, respectively) and quantification limits (0.09, 2.2, and 0.2 mg L−1 for PA, IB, and CF, respectively) suggested that the SPCNFE was the most suitable carbon-based electrode for the voltammetric determination of the selected analytes in water at trace levels. The methodology was validated using both spiked tap water and hospital wastewater samples. The results were compared to those achieved by liquid chromatography–tandem mass spectrometry (LC-MS/MS), the technique of choice for the determination of the target analytes. Full article
(This article belongs to the Special Issue Screen-Printed Electrodes for Sensing)
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Open AccessArticle
Multi-Layer IoT Security Framework for Ambient Intelligence Environments
Sensors 2019, 19(18), 4038; https://doi.org/10.3390/s19184038 - 19 Sep 2019
Cited by 1 | Viewed by 939
Abstract
Ambient intelligence is a new paradigm in the Internet of Things (IoT) world that brings smartness to living environments to make them more sensitive; adaptive; and personalized to human needs. A critical area where ambient intelligence can be used is health and social [...] Read more.
Ambient intelligence is a new paradigm in the Internet of Things (IoT) world that brings smartness to living environments to make them more sensitive; adaptive; and personalized to human needs. A critical area where ambient intelligence can be used is health and social care; where it can improve and sustain the quality of life without increasing financial costs. The adoption of this new paradigm for health and social care largely depends on the technology deployed (sensors and wireless networks), the software used for decision-making and the security, privacy and reliability of the information. IoT sensors and wearables collect sensitive data and must respond in a near real-time manner to input changes. An IoT security framework is meant to offer the versatility and modularization needed to sustain such applications. Our framework was designed to easily integrate with different health and social care applications, separating security tasks from functional ones and being designed with independent modules for each layer (Cloud, gateway and IoT device), that offer functionalities relative to that layer. Full article
(This article belongs to the Special Issue From Sensors to Ambient Intelligence for Health and Social Care)
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Open AccessArticle
Reducing the Cost of Implementing Filters in LoRa Devices
Sensors 2019, 19(18), 4037; https://doi.org/10.3390/s19184037 - 19 Sep 2019
Viewed by 543
Abstract
This paper presents two methods to optimize LoRa (Low-Power Long-Range) devices so that implementing multiplier-less pulse shaping filters is more economical. Basic chirp waveforms can be generated more efficiently using the method of chirp segmentation so that only a quarter of the samples [...] Read more.
This paper presents two methods to optimize LoRa (Low-Power Long-Range) devices so that implementing multiplier-less pulse shaping filters is more economical. Basic chirp waveforms can be generated more efficiently using the method of chirp segmentation so that only a quarter of the samples needs to be stored in the ROM. Quantization can also be applied to the basic chirp samples in order to reduce the number of unique input values to the filter, which in turn reduces the size of the lookup table for multiplier-less filter implementation. Various tests were performed on a simulated LoRa system in order to evaluate the impact of the quantization error on the system performance. By examining the occupied bandwidth, fast Fourier transform used for symbol demodulation, and bit-error rates, it is shown that even performing a high level of quantization does not cause significant performance degradation. Therefore, the memory requirements of LoRa devices can be significantly reduced by using the methods of chirp segmentation and quantization so as to improve the feasibility of implementing multiplier-less filters in LoRa devices. Full article
(This article belongs to the Section Internet of Things)
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Open AccessArticle
AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks
Sensors 2019, 19(18), 4036; https://doi.org/10.3390/s19184036 - 19 Sep 2019
Cited by 1 | Viewed by 732
Abstract
Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. [...] Read more.
Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network partition(s), exhibiting adverse effects on network lifetime. Extending network lifetime while preserving energy and security thus becomes an interesting challenge. In this article, we address the aforesaid shortcomings with the proposed adaptive en-route filtering (AEF) scheme. In energy-aware routing, the fitness function, which is used to select forwarding nodes, considers residual energy and other factors as opposed to distance only. In pre-deterministic key distribution, keys are distributed based on the consideration of having paths with a different number of verification nodes. This, consequently, permits us to have multiple paths with different security levels that can be exploited to counter different attack intensities. Taken together, the integration of the special fitness function with the new key distribution approach enables the AEF to adapt the underlying dynamic network conditions. The simulation experiments under different settings show significant improvements in network lifetime. Full article
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Open AccessArticle
A Machine Learning Approach to Bridge-Damage Detection Using Responses Measured on a Passing Vehicle
Sensors 2019, 19(18), 4035; https://doi.org/10.3390/s19184035 - 19 Sep 2019
Cited by 6 | Viewed by 899
Abstract
This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) [...] Read more.
This paper proposes a new two-stage machine learning approach for bridge damage detection using the responses measured on a passing vehicle. In the first stage, an artificial neural network (ANN) is trained using the vehicle responses measured from multiple passes (training data set) over a healthy bridge. The vehicle acceleration or Discrete Fourier Transform (DFT) spectrum of the acceleration is used. The vehicle response is predicted from its speed for multiple passes (monitoring data set) over the bridge. Root-mean-square error is used to calculate the prediction error, which indicates the differences between the predicted and measured responses for each passage. In the second stage of the proposed method, a damage indicator is defined using a Gaussian process that detects the changes in the distribution of the prediction errors. It is suggested that if the bridge condition is healthy, the distribution of the prediction errors will remain low. A recognizable change in the distribution might indicate a damage in the bridge. The performance of the proposed approach was evaluated using numerical case studies of vehicle–bridge interaction. It was demonstrated that the approach could successfully detect the damage in the presence of road roughness profile and measurement noise, even for low damage levels. Full article
(This article belongs to the Special Issue Bridge Damage Detection with Sensing Technology)
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Open AccessEditorial
Ubiquitous Computing and Ambient Intelligence—UCAmI
Sensors 2019, 19(18), 4034; https://doi.org/10.3390/s19184034 - 19 Sep 2019
Cited by 1 | Viewed by 771
Abstract
The Ubiquitous Computing (UC) idea envisioned by Weiser in 1991 [...] Full article
Open AccessArticle
A Multiple Target Positioning and Tracking System Behind Brick-Concrete Walls Using Multiple Monostatic IR-UWB Radars
Sensors 2019, 19(18), 4033; https://doi.org/10.3390/s19184033 - 18 Sep 2019
Cited by 1 | Viewed by 829
Abstract
Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and [...] Read more.
Recognizing and tracking the targets located behind walls through impulse radio ultra-wideband (IR-UWB) radar provides a significant advantage, as the characteristics of the IR-UWB radar signal enable it to penetrate obstacles. In this study, we design a through-wall radar system to estimate and track multiple targets behind a wall. The radar signal received through the wall experiences distortion, such as attenuation and delay, and the characteristics of the wall are estimated to compensate the distance error. In addition, unlike general cases, it is difficult to maintain a high detection rate and low false alarm rate in this through-wall radar application due to the attenuation and distortion caused by the wall. In particular, the generally used delay-and-sum algorithm is significantly affected by the motion of targets and distortion caused by the wall, rendering it difficult to obtain a good performance. Thus, we propose a novel method, which calculates the likelihood that a target exists in a certain location through a detection process. Unlike the delay-and-sum algorithm, this method does not use the radar signal directly. Simulations and experiments are conducted in different cases to show the validity of our through-wall radar system. The results obtained by using the proposed algorithm as well as delay-and-sum and trilateration are compared in terms of the detection rate, false alarm rate, and positioning error. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning
Sensors 2019, 19(18), 4032; https://doi.org/10.3390/s19184032 - 18 Sep 2019
Cited by 2 | Viewed by 811
Abstract
Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete [...] Read more.
Unmanned Aerial Vehicles (UAVs) are widely used as mobile information collectors for sensors to prolong the network time in Wireless Sensor Networks (WSNs) due to their flexible deployment, high mobility, and low cost. This paper focuses on the scenario where rotary-wing UAVs complete information collection mission cooperatively. For the first time, we study the problem of minimizing the mission completion time for a multi-UAV system in a monitoring scenario when considering the information collection quality. The mission completion time includes flying time and hovering time. By optimizing the trajectories of all UAVs, we minimize the mission completion time while ensuring that the information of each sensor is collected. This problem can be formulated as a mixed-integer non-convex one which has been proved to be NP-hard. To solve the formulated problem, we first propose a hovering point selection algorithm to select appropriate hovering points where the UAVs can sequentially collect the information from multiple sensors. We model this problem as a BS coverage problem with the information collection quality in consideration. Then, we use a min-max cycle cover algorithm to assign these hovering points and get the trajectory of each UAV. Finally, with the obtained UAVs trajectories, we further consider the UAVs can also collect information when flying and optimize the time allocations. The performance of our algorithm is verified by simulations, which show that the mission completion time is minimum compared with state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Optimization and Communication in UAV Networks)
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Open AccessArticle
DOA Tracking Based on Unscented Transform Multi-Bernoulli Filter in Impulse Noise Environment
Sensors 2019, 19(18), 4031; https://doi.org/10.3390/s19184031 - 18 Sep 2019
Viewed by 744
Abstract
Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli [...] Read more.
Aiming at the problem of multiple-source direction of arrival (DOA) tracking in impulse noise, this paper models the impulse noise by using the symmetric α stable (SαS) distribution, and proposes a DOA tracking algorithm based on the Unscented Transform Multi-target Multi-Bernoulli (UT-MeMBer) filter framework. In order to overcome the problem of particle decay in particle filtering, UT is adopted to select a group of sigma points with different weights to make them close to the posterior probability density of the state. Since the α stable distribution does not have finite covariance, the Fractional Lower Order Moment (FLOM) matrix of the received array data is employed to replace the covariance matrix to formulate a MUSIC spatial spectra in the MeMBer filter. Further exponential weighting is used to enhance the weight of particles at high likelihood area and obtain a better resampling. Compared with the PASTD algorithm and the MeMBer DOA filter algorithm, the simulation results show that the proposed algorithm can more effectively solve the issue that the DOA and number of target are time-varying. In addition, we present the Sequential Monte Carlo (SMC) implementation of the UT-MeMBer algorithm. Full article
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Open AccessArticle
Addressing Challenges in Fabricating Reflection-Based Fiber Optic Interferometers
Sensors 2019, 19(18), 4030; https://doi.org/10.3390/s19184030 - 18 Sep 2019
Cited by 2 | Viewed by 704
Abstract
Fabrication of multimode fiber optic interferometers requires accurate control of certain parameters to obtain reproducible results. This paper evaluates the consequences of practical challenges in fabricating reflection-based, fiber optic interferometers by the use of theory and experiments. A guided-mode propagation approach is used [...] Read more.
Fabrication of multimode fiber optic interferometers requires accurate control of certain parameters to obtain reproducible results. This paper evaluates the consequences of practical challenges in fabricating reflection-based, fiber optic interferometers by the use of theory and experiments. A guided-mode propagation approach is used to investigate the effect of the end-face cleave angle and the accuracy of the splice in core-mismatched fiber optic sensors. Cleave angles from high-end fiber cleavers give differences in optical path lengths approaching the wavelength close to the circumference of the fiber, and the core-mismatched splice decides the ensemble of cladding modes excited. This investigation shows that the cleave angle may significantly alter the spectrum, whereas the splice is more robust. It is found that the interferometric visibility can be decreased by up to 70% for cleave angles typically obtained. An offset splice may reduce the visibility, but for offsets experienced experimentally the effect is negligible. An angled splice is found not to affect the visibility but causes a lower overall intensity in the spectrum. The sensitivity to the interferometer length is estimated to 60 nm/mm, which means that a 17 µm difference in length will shift the spectrum 1 nm. Comparisons to experimental results indicate that the spliced region also plays a significant role in the resulting spectrum. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution
Sensors 2019, 19(18), 4029; https://doi.org/10.3390/s19184029 - 18 Sep 2019
Cited by 3 | Viewed by 1661
Abstract
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this [...] Read more.
Metal oxide (MOX) sensors are widely used for chemical sensing due to their low cost, miniaturization, low power consumption and durability. Yet, getting instantaneous measurements of fluctuating gas concentration in turbulent plumes is not possible due to their slow response time. In this paper, we show that the slow response of MOX sensors can be compensated by deconvolution, provided that an invertible, parametrized, sensor model is available. We consider a nonlinear, first-order dynamic model that is mathematically tractable for MOX identification and deconvolution. By transforming the sensor signal in the log-domain, the system becomes linear in the parameters and these can be estimated by the least-squares techniques. Moreover, we use the MOX diversity in a sensor array to avoid training with a supervised signal. The information provided by two (or more) sensors, exposed to the same flow but responding with different dynamics, is exploited to recover the ground truth signal (gas input). This approach is known as blind deconvolution. We demonstrate its efficiency on MOX sensors recorded in turbulent plumes. The reconstructed signal is similar to the one obtained with a fast photo-ionization detector (PID). The technique is thus relevant to track a fast-changing gas concentration with MOX sensors, resulting in a compensated response time comparable to that of a PID. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC
Sensors 2019, 19(18), 4028; https://doi.org/10.3390/s19184028 - 18 Sep 2019
Cited by 1 | Viewed by 792
Abstract
Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial [...] Read more.
Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes’ characteristics. Second, a ridge detector is proposed to extract each lane’s feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Optimal Message Bundling with Delay and Synchronization Constraints in Wireless Sensor Networks
Sensors 2019, 19(18), 4027; https://doi.org/10.3390/s19184027 - 18 Sep 2019
Cited by 1 | Viewed by 1060
Abstract
Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture [...] Read more.
Energy efficiency and end-to-end delay are two of the major requirements for the monitoring and detection applications based on resource-constrained wireless sensor networks (WSNs). As new advanced technologies for accurate monitoring and detection—such as device-free wireless sensing schemes for human activity and gesture recognition—have been developed, time synchronization accuracy becomes an important requirement for those WSN applications too. Message bundling is considered one of the effective methods to reduce the energy consumption for message transmissions in WSNs, but bundling more messages increases the transmission interval of bundled messages and thereby their end-to-end delays; the end-to-end delays need to be maintained within a certain value for time-sensitive applications like factory monitoring and disaster prevention, while the message transmission interval affects time synchronization accuracy when the bundling includes synchronization messages as well. Taking as an example a novel WSN time synchronization scheme recently proposed for energy efficiency, we investigate an optimal approach for message bundling to reduce the number of message transmissions while maintaining the user-defined requirements on end-to-end delay and time synchronization accuracy. Formulating the optimal message bundling problem as integer linear programming, we compute a set of optimal bundling numbers for the sensor nodes to constrain their link-level delays, thereby achieving and maintaining the required end-to-end delay and synchronization accuracy. Extensive experimental results based on a real WSN testbed using TelosB sensor nodes demonstrate that the proposed optimal bundling could reduce the number of message transmissions about 70% while simultaneously maintaining the required end-to-end delay and time synchronization accuracy. Full article
(This article belongs to the Section Sensor Networks)
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