The proceedings contain 212 papers. The topics discussed include: WiMi: target material identification with commodity Wi-Fi Devices;quorum selection for byzantine fault tolerance;road gradient estimation using smartph...
ISBN:
(纸本)9781728125190
The proceedings contain 212 papers. The topics discussed include: WiMi: target material identification with commodity Wi-Fi Devices;quorum selection for byzantine fault tolerance;road gradient estimation using smartphones: towards accurate estimation on fuel consumption and air pollution emission on roads;automating system configuration of distributed machine learning;DMRA: a decentralized resource allocation scheme for multi-SP mobile edge computing;the cask effect of multi-source content delivery: measurement and mitigation;and maintaining social connections through direct link placement in wireless networks.
The proceedings contain 74 papers. The topics discussed include: leveraging cross-technology broadcast communication to build gateway-free smart homes;semi-supervised contrastive learning for human activity recognitio...
ISBN:
(纸本)9781665439299
The proceedings contain 74 papers. The topics discussed include: leveraging cross-technology broadcast communication to build gateway-free smart homes;semi-supervised contrastive learning for human activity recognition;Batterfly: battery-free daily living activity recognition system through distributed execution over energy harvesting analog pir sensors;towards context aware adaptive deployment in ML applications using state machines;resource-constrained target classification on distant aerial targets;an IoT-based system for autonomous, continuous, real-time patient monitoring and its application to pressure injury management;and segregating keys from noncense: timely exfil of ephemeral keys from embedded systems.
This research article provides a computational study of light interaction with two dimensional photonic crystal (PhC) structure. This PhC structure is used to measure ethanol percentage in liquid water by identifying ...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
This research article provides a computational study of light interaction with two dimensional photonic crystal (PhC) structure. This PhC structure is used to measure ethanol percentage in liquid water by identifying and computing mid gap wavelength shift. Analysis of the Maxwell's equations that govern the photon behaviour in the periodic structure is discussed in detail for PhC structures. The photonic band gap (PBG) which prohibits the photon momentum in certain directions in the crystal is computed using plane wave expansion (PWE) method by deriving solutions to the eigenvalue equation. A two dimensional (2D) PhC structure is designed by taking 18 × 17 number of holes on a dielectric slab. This holes on the dielectric configuration is used to sense the ethanol level in liquid water. The PBG width for the design in transverse electric mode is [ωa/2πc] ranging from 0.3841 to 0.4428. The band gap size Δλ 480 nm and mid gap wavelength λ c = 2391.2 nm is achieved from the structure. The sensor achieves a sensitivity of 10645.24 nm/RIU and a transmission ratio more than 94%. This structure is a suitable candidate for ethanol sensing applications as it offers a wide bandgap for light propagation.
To improve the operational efficiency and reliability of high-speed trains (HSTs), the Automatic Train Control System has received considerable attention. Train-to-Train (T2T) communication is one of the critical tech...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
To improve the operational efficiency and reliability of high-speed trains (HSTs), the Automatic Train Control System has received considerable attention. Train-to-Train (T2T) communication is one of the critical technologies for autonomous train operation. However, the development of $T 2 T$ communication technology still faces many challenges, particularly regarding communication distance limitations and reliability issues. To address these challenges, this paper introduces an innovative T2T collaborative communication model for HSTs, focusing on highly reliable information interaction and sharing in distributed collaboration environments. The model employs dynamic and static nodes to enable direct T2T information exchange. Using formal verification and state space analysis to ensure the system's reliability and performance. A Build-in-Test (BIT) mechanism is implemented for fault detection and isolation, enhancing the reliability of the system. By analyzing the performance indicators of the system in different scenarios, the reliability of the system was verified.
The proceedings contain 25 papers. The topics discussed include: distributed mining of popular paths in road networks;white space prediction for low-power wireless networks: a data-driven approach;projection-based con...
ISBN:
(纸本)9781538654705
The proceedings contain 25 papers. The topics discussed include: distributed mining of popular paths in road networks;white space prediction for low-power wireless networks: a data-driven approach;projection-based constrained fusion performance with link loss and measurement bias;recursive truth estimation of time-varying sensing data from online open sources;on designing provably correct DODAG formation criteria for the IPv6 routing protocol for low-power and lossy networks (RPL);duty-cycle-aware real-time scheduling of wireless links in low power WANs;knowledge transfer between embedded controllers;resilient distributed diffusion for multi-task estimation;leveraging knowledge for path exposure;frequency scaling in time synchronization for wireless sensor networks;and on cost-sensitive task allocation in social sensing: an online learning approach.
The proceedings contain 67 papers. The topics discussed include: end-to-end gesture recognition framework for the identification of allergic rhinitis symptoms;semi-supervised multi-source domain adaptation in wearable...
ISBN:
(纸本)9781665495127
The proceedings contain 67 papers. The topics discussed include: end-to-end gesture recognition framework for the identification of allergic rhinitis symptoms;semi-supervised multi-source domain adaptation in wearable activity recognition;real-time human pose estimation at the edge for gait analysis at a distance;SELF-CARE: selective fusion with context-aware low-power edge computing for stress detection;publishing asynchronous event times with pufferfish privacy;tradeoff between accuracy and message complexity for approximate data aggregation;low-power distinct sum for wireless sensor networks;a software-defined underwater visible light communication testbed;a differential BCG sensor system for long term health monitoring experiment on the ISS;network economics-based crowdsourcing in UAV-assisted smart cities environments;and cost-aware inference of bovine respiratory disease in calves using precision livestock technology.
The proceedings contain 10 papers. The topics discussed include: QROSS: QUBO relaxation parameter optimization via learning solver surrogates;VPN-nonVPN traffic classification using deep reinforced naive bayes and fuz...
ISBN:
(纸本)9781665449328
The proceedings contain 10 papers. The topics discussed include: QROSS: QUBO relaxation parameter optimization via learning solver surrogates;VPN-nonVPN traffic classification using deep reinforced naive bayes and fuzzy k-means clustering;personalized federated learning by structured and unstructured pruning under data heterogeneity;effect-centric networking;joint offloading and resource allocation of UAV-assisted mobile edge computing with delay constraints;backup placement in WSNs in the network management distributed setting;AI-based robust convex relaxations for supporting diverse QoS in next-generation wireless systems;dynamic DNN decomposition for lossless synergistic inference;and joint offloading and resource allocation of UAV-assisted mobile edge computing with delay constraints.
We proposed a human-robot bidirectional trust-triggered cyber-physical-human (CPH) system framework for human-robot collaborative assembly in flexible manufacturing. For this purpose, we developed a one human-one robo...
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ISBN:
(数字)9798350356755
ISBN:
(纸本)9798350356762
We proposed a human-robot bidirectional trust-triggered cyber-physical-human (CPH) system framework for human-robot collaborative assembly in flexible manufacturing. For this purpose, we developed a one human-one robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operation of different manufacturing components in a flexible manufacturing setup. We configured the human-robot collaborative system in three interconnected components of a CPH system: (i) cyber system (software system), (ii) physical system (the robot, sensors, hardware accessories, and the work environment), and (iii) human system (the human co-worker and the supervisor). We divided the functions of the CPH system into three modules: (i) computing, (ii) communication, and (iii) control. We used a model to compute the human and robot's bidirectional trust in each other in real-time. We implemented the proposed trust-triggered CPH framework in the human-robot collaborative assembly setup and conducted an experiment to modulate the communication methods and strategies among the cyber, physical and human components of the CPH system. The results showed that variations in communication methods significantly impacted the performance and interactions of the CPH system in terms of human-robot interaction (HRI), and task performance (efficiency and quality). The results can transform the design, development, analysis and control of human-robot collaborative systems configuring them in the form of a CPH system and thus enhance the scope, ease and effectiveness of human-robot collaborative systems for various applications such as manufacturing, construction, etc.
The proceedings contain 68 papers. The topics discussed include: AnguLoc: concurrent angle of arrival estimation for indoor localization with UWB radios;social-aware energy balancing in mobile opportunistic networks;a...
ISBN:
(纸本)9781728143514
The proceedings contain 68 papers. The topics discussed include: AnguLoc: concurrent angle of arrival estimation for indoor localization with UWB radios;social-aware energy balancing in mobile opportunistic networks;an IoT based solar park health monitoring system for PID and hotspots effects;RIDER: proactive and reactive approach for urban traffic management in vehicular networks;privacy-preserving solutions in the industrial Internet of things;a gesture recognition approach to classifying allergic rhinitis gestures using wrist-worn devices - a multidisciplinary case study;improving the vehicular mobility analysis using time-varying graphs;and a SLIPT-assisted visible light communication scheme.
Precise fault distance estimation is critical for effective transmission system protection, especially in modern power systems with integrated distributed generators (DGs). As the global demand for energy continues to...
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ISBN:
(数字)9798331530402
ISBN:
(纸本)9798331530419
Precise fault distance estimation is critical for effective transmission system protection, especially in modern power systems with integrated distributed generators (DGs). As the global demand for energy continues to rise, the adoption of renewable energy sources and their integration into the grid has significantly increased. However, the presence of various DGs complicates fault distance estimation due to the diverse power flows and interactions between DGs and existing grid infrastructure. The integration of large-scale DGs introduces additional challenges in fault detection and location, as the dynamic behavior of these generators affects the fault characteristics. This paper focuses on precise fault distance estimation within the ieee-9 bus system, which includes two distinct DGs. The proposed solution leverages an ensemble deep learning approach, combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This hybrid CNN-LSTM model takes advantage of CNN’s ability to extract spatial features and LSTM’s strength in capturing temporal dependencies, making it well-suited for fault distance prediction. The model’s performance is evaluated using mean absolute error (MAE), giving 0.6387 average MAE for the studied system. Comparative analysis with existing deep learning models and validated through OPAL-RT 4510 real-time simulator shows that the proposed model delivers superior performance, demonstrating its effectiveness in handling the complexities introduced by DG integration.
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