The proceedings contain 88 papers. The topics discussed include: an online load identification algorithm for non-intrusive load monitoring in homes;a high sensitivity nanorelay based C-P sensor for biomedical implants...
ISBN:
(纸本)9781467355001
The proceedings contain 88 papers. The topics discussed include: an online load identification algorithm for non-intrusive load monitoring in homes;a high sensitivity nanorelay based C-P sensor for biomedical implants;a wake-up switch using a piezoelectric differential pressure sensor;characterization of a new flexible pressure sensor for body sensornetworks;intra-cavity absorption sensor based on erbium-doped fiber laser;birefringence imaging for optical sensing of tissue damage;an active source validation scheme based on path identifier;traffic aware fuzzy-tuned delay range for wireless body area networks medium access control protocol (MAC);wireless accelerometer sensor data filtering using recursive least squares adaptive filter;efficient and secure data aggregation for smart metering networks;energy harvesting from heavy haul railcar vibrations;and a distributed sensing capability for in situ time-domain separation of lamb waves.
The proceedings contain 105 papers. The topics discussed include: development and validation of online surrogate parameters for water quality monitoring at a conventional water treatment plant using a UV absorbance sp...
ISBN:
(纸本)9781457706738
The proceedings contain 105 papers. The topics discussed include: development and validation of online surrogate parameters for water quality monitoring at a conventional water treatment plant using a UV absorbance spectrolyzer;a novel mutual authentication scheme with minimum disclosure for RFID systems;SAR distribution in microwave breast screening: results with TWTLTLA wideband antenna;a convex hull-based approximation of forest fire shape with distributed wireless sensornetworks;design and performance of a directional media access control protocol for optical wireless sensor;node deployment strategy for WSN-based node-sequence localization;towards plug-and-play functionality in low-cost sensor network;modeling inhibitory interactions shaping neural responses of target neurons to multiple features;and a random finite set conjugate prior and application to multi-target tracking.
The following topics are dealt: sensornetworks; sensor fusion; intelligent sensors; informationprocessing in sensornetworks; sensor network security; machine learning and applications; autonomous configuration; con...
The following topics are dealt: sensornetworks; sensor fusion; intelligent sensors; informationprocessing in sensornetworks; sensor network security; machine learning and applications; autonomous configuration; control in dynamic wireless networks; middleware; computational intelligence for sensornetworks; bio-signal processing and networked sensors in healthcare; environmental sensornetworks; and optimization in sensornetworks.
Recently, multisensor fault diagnosis (MSFD) has become a research hotspot in the field of large machine maintenance. However, current research rarely explores in depth the spatial-temporal structural features unique ...
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Recently, multisensor fault diagnosis (MSFD) has become a research hotspot in the field of large machine maintenance. However, current research rarely explores in depth the spatial-temporal structural features unique to sensor signals. To address this gap, this article proposes the ARM-STDS, a novel MSFD model with adaptive spatial-temporal dual-scale remodeling. sensor signals exhibit sparsity and periodicity in the temporal domain, which poses challenges for efficient processing by time-series models. To tackle this challenge, we have designed an adaptive subseries-level patches segmentation (ASPS) method that enhances the information capacity of short-time slices in the signals. This method effectively extracts local semantic information while reducing computational complexity quadratically. In addition, we introduce a directed graph structural modeling algorithm to better capture the dependencies between sensors. Utilizing directed graphs proves more effective in representing the coupling relationships between sensors compared to traditional undirected graphs. In comparison to other state-of-the-art MSFD models, ARM-STDS demonstrates the most effective diagnostic performance on four publicly available datasets. In the constant system, the average accuracy increased by 3%-5%. Notably, in the nonconstant system, ARM-STDS exhibited an accuracy improvement of over 5%.
Endowing tactile sensors with the ability to perceive contact status and contact force can enhance the precise perception and modeling of contact by robotic end effectors, thereby achieving more dexterous manipulation...
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Endowing tactile sensors with the ability to perceive contact status and contact force can enhance the precise perception and modeling of contact by robotic end effectors, thereby achieving more dexterous manipulation tasks. However, existing tactile sensors still fall short in rapidly responding to multimodal tactile information. In this work, we designed a novel tactile sensor called GelEvent, which is based on an event camera. It uses an event camera to collect contact information and is capable of determining contact status, estimating the area of the contact region, and the mechanical information of the contact surface online at an average speed of 180 Hz, with a latency of less than 5.5 ms. To address the issue that event cameras cannot capture the motion information of feature points in a stable state, we designed and fabricated a brand-new flexible contact structure. Using an event-based threshold judgment method, it achieves estimation of contact status and area, as well as continuous and stable mechanical output. Experiments have demonstrated that this sensor is capable of providing data on contact area, status of contact, and forces and torques ( Fx , Fy , Fz ,Tz ) between manipulated object and sensor at an average rate of 180 Hz during interactions. The estimation error of contact area is within +/- 9 mm2, the average accuracy of contact status judgment is 92%, the force estimation accuracy is approximately 0.80 N, and the torque estimation accuracy is about 8.3 ***. This sensor has great potential in promoting the dexterity and speed of robotic operations.
In this article, we investigate an intelligent reflecting surface (IRS)-assisted sensor system, where the average age of information (AoI) is derived to characterize the information freshness of short packets. Althoug...
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In this article, we investigate an intelligent reflecting surface (IRS)-assisted sensor system, where the average age of information (AoI) is derived to characterize the information freshness of short packets. Although the IRS can enhance the beamforming gain which improves the AoI performance, it also introduces higher channel estimation overhead which impairs the AoI performance. The quantitative impact of IRS on AoI performance remains unclear. To this end, our work aims to characterize the relationship of the average AoI in terms of the number of IRS elements and optimize the latter to minimize the former. However, the average AoI expression is difficult to obtain due to the complex composite channel distribution. To tackle this difficulty, we use the moment matching (MM) technique to approximate the composite channel by Gamma distribution. The obtained average AoI expression is still complicated and it is intractable to directly obtain the optimal IRS elements' number. To overcome this challenge, we derive the approximated expressions of the average AoI for small and large IRS elements, and then obtain the intersection between the two approximated expressions as a suboptimal solution. Based on them, we prove that the average AoI first decreases and then increases as the number of IRS elements increases, which indicates there is an optimal number of IRS reflecting elements for attaining the optimal AoI performance. Simulation results verify our theoretical results and demonstrate that the proposed scheme for obtaining the number of IRS elements can achieve almost the same performance as the exhaustive search method and outperforms benchmarks.
The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to ...
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The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to maximize efficiency when sharing positioning, navigation, and timing (PNT) information captured by distributed networks of low size, weight, power, and cost (SWAP-C) RF sensors when operating in congested or contested electromagnetic environments (EMEs). By implementing effective PNT information-sharing strategies, these networks can more easily position the sensors or characterize targets of interest. In this work, we propose a novel ML-inspired compression design framework that improves efficiency when sharing PNT information in a network of sensors receiving radar waveforms. In addition, through novel learning procedures, the network can adapt to unforeseen EMEs such that network efficiency can be maintained in the presence of unforeseen RF waveforms and sensor surroundings. We show that our intelligent, model-driven, ML-inspired data reduction strategies can outperform alternative strategies that do not best-utilize the information content of waveforms in the EME. In addition, we demonstrate the ability of our strategies to adapt to changing mission goals by balancing different types of PNT information and learning from developing EMEs.
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