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Quality of Service Factor based Unfailing Route Formation in Wireless Sensor Network

Quality of Service Factor based Unfailing Route Formation in...

2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, ICIITCEE 2023

作者： Yuvarani, S. Gayathri, A. Velmurugan, K.Jayasakthi Meenakshi, V. Sadhana, S. Srinivasan, C. Department of Computer Applications Tamil Nadu Perambalur India Tamil Nadu Chennai India Jeppiaar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India Vee Eee Technologies Solution Pvt Ltd Research & Development Division Tamil Nadu Chennai India

ISBN: (纸本)9781665462631

Nowadays, several analytical methods exist to make unfailing route formation. However, it is still challenging work because the wireless medium is unreliable. Sensor nodes are utilized for widespread distribution. Thus, security issues greatly affect wireless sensor networks (WSN). The Quality of Service Factor-based Unfailing Route (QFUR) Formation in WSN is introduced to solve these issues. In this approach, Quality of Service (QoS) factors like sensor node delay, packet drop, and residual energy is verified to determine whether the sensor node is normal. The value of the QoS factor is less than the threshold for that node to be a normal sensor node in the WSN. This approach is serious to notice and separate the compromised nodes from evading misinformation through the falsified data inserted by the opponent over compromised nodes. The Reinforcement Learning (RL) algorithm computes the reward value based on node energy utilization, hop count, dropped rate, and delay. The simulation outcomes demonstrate a lesser delay and lesser packet loss in the network. Furthermore, it improves the opponent node's detection and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Quality of service

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An AI Architecture for Power MOS Rds-on-Driven Lifetime Estimation

An AI Architecture for Power MOS Rds-on-Driven Lifetime Esti...

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2023 Smart Systems Integration Conference and Exhibition, SSI 2023

作者： Pino, Carmelo Rundo, Francesco Spampinato, Concetto Battiato, Sebastiano STMicroelectronics Adg Power and Discretes R&d Division Catania Italy University of Catania PerCeiVe Lab Group Department of Electrical Electronics and Computer Engineering Catania Italy University of Catania IP-LAB Group Department of Mathematics and Computer Science Catania Italy

ISBN: (纸本)9798350325065

The components lifetime estimation is one of the main challenge in all engineering process specially in power electronics. The problem involves several domains of interest and is particularly felt, because, the estimation of the residual life with an acceptable degree of confidence, allows to carry out preventive measures in order to avoid the consequences of the malfunction and failure of the device at the end of its life. Considering a Power MOS, the distinctive element for understanding the end of life of this component can be obtained by observing the Rdson curve during its life. The Rdson curve works in an operating range during the correct life cycle, this curve exceeds a certain threshold when the device begins to have the first signs of degradation. The proposed method allows to evaluate the variation of the Rdson by making predictions for understand when the device may stop working well. The method is based on a deep learning model trained with a dataset containing the cycle of life of several Power MOS. The pipeline is based on Artificial Intelligence techniques with regularization of the Jacobian of the gradient, designed to compensate for the inevitable measurement and conversion noises present in the system based on ST MCU. The experiments concern predictions of the Rdson at 10, 15 and 20 steps and allow to obtain a good degree of accuracy for predicting the RDS on at least 20 samples before. This allows to guarantee a preventive intervention when the prediction exceed the threshold considered for the correct operating range. The proposed pipeline allows to robustly characterize the threshold voltage of the device by means of a simple acquisition of voltages and currents at poweron, being able to monitor the evolution of this voltage over time and each time the device is used. The obtained results and the cross-validation performances are very promising and allow us to state that the proposed method is really robust. © 2023 IEEE.

关键词： Threshold voltage

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An sEMG-Controlled Prosthetic Hand Featuring a Tiny CNN-Transformer Model and Force Feedback

An sEMG-Controlled Prosthetic Hand Featuring a Tiny CNN-Tran...

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IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Savanna Blade Zongyan Yao Yuhan Hou Yinfei Li Sihan Zhou Yining Wang Xilin Liu Division of Engineering Science University of Toronto Toronto ON Canada Department of Electrical and Computer Engineering (ECE) University of Toronto Toronto ON Canada

This paper introduces the design of a wireless prosthetic hand with surface electromyography (sEMG)-based control and force feedback. The system features a novel CNN-Transformer model for classifying 21 gestures from sEMG signals. With a tiny size of only 169 kB, the proposed CNN-Transformer model achieves a high average accuracy of 81.39% when evaluated on a public dataset across 10 subjects. The system integrates wireless electronics into a 3D-printed open-source robotic hand. A custom-designed flexible sEMG armband is used for signal acquisition and stimulation delivery. A mobile device is used for real-time model inference. By employing hardware machine learning accelerators on the mobile device, a short inference time of 0.375 ms was achieved, which is 3.2x faster compared to using the CPU alone, rendering a smooth user experience with low latency. Force sensors were mounted on the robotic hand’s fingertips, and the outputs were modulated and delivered as electrical stimulation through the surface electrodes to provide force feedback to the user. The developed deep learning model and system design approaches have broad applicability across a wide range of prosthetic applications.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Multi-UAV Placement for Integrated Access and Backhauling Using LLM-Driven Optimization

Multi-UAV Placement for Integrated Access and Backhauling Us...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Wang, Yuhui Farooq, Junaid Ghazzai, Hakim Setti, Gianluca University of Michigan-Dearborn Department of Electrical and Computer Engineering DearbornMI48126 United States King Abdullah University of Science & Technology Computer Electrical and Mathematical Sciences Division Saudi Arabia

ISBN: (纸本)9798350368369

Unmanned aerial vehicles (UAVs) can enhance wireless access by dynamically positioning themselves closer to users while maintaining a backhaul connection to cellular base stations. In scenarios where users are geographically dispersed, multiple UAVs can be orchestrated to establish multi-hop integrated access and backhaul (IAB) connections. Traditionally, finding optimal UAV placement has required computationally demanding methods, such as combinatorial optimization or reinforcement learning, which are often impractical for real-time applications due to their complexity and training requirements. Additionally, UAV operators may lack the capability to solve complex optimization problems during live operations. This paper presents a novel framework that leverages large language models (LLMs) for optimizing the placement of multiple UAVs through iterative structured prompting. The proposed method achieves near-optimal solutions in significantly fewer iterations compared to traditional methods, making it suitable for real-time deployment without extensive mathematical modeling. Simulation result demonstrate that the proposed method achieves scores over 82% of the theoretical optimal solution while reducing computational time from hours to minutes compared to the baseline deep Q network approach, ensuring robust network connectivity and service quality. The LLM-driven framework simplifies problem-solving for UAV network operators, paving the way for its application in more complex real-world scenarios. © 2025 IEEE.

关键词： Drones

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Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis

Tackling the Qubit Mapping Problem with Permutation-Aware Sy...

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Ji Liu Ed Younis Mathias Weiden Paul Hovland John Kubiatowicz Costin Iancu Mathematics and Computer Science Division Argonne National Laboratory Computational Research Division Lawrence Berkeley National Laboratory Department of Electrical Engineering and Computer Science University of California Berkeley

We propose a scalable, hierarchical qubit mapping and routing algorithm that harnesses the power of circuit synthesis. First, we decompose large circuits into subcircuits small enough to be directly resynthesized. For each block, we pre-synthesize them for all permutations of its input and output qubits. Following this offline step, we employ a permutation-aware, block-based generalization of the popular SABRE mapping algorithm. This mapping step stitches together blocks by choosing an input-output permutation that minimizes intrablock gate count and required inter-block communication (SWAP and bridge gates). Our approach has a twofold advantage: 1) circuit synthesis may eliminate more two-qubit gates than other optimizing compilers; 2) considering all permutations of input and output qubits eliminates communication operations transparently. In contrast, other mapping algorithms can only introduce communication operations. We show that we can produce better-quality circuits than commercial compilers: shorter by up to 68% (18% on average) fewer gates than Qiskit, up to 36% (9% on average) fewer gates than Tket. We outperform BQSkit, a permutation-unaware, synthesis-based compiler, by up to 67% (21% on average) fewer gates. We also exceed experimental optimal mappers such as OLSQ in quality (10.7% shorter circuits) and time to solution. Our scalable, heuristic approach can be seamlessly integrated into any quantum circuit compiler or optimization infrastructure, and it applies well to any qubit technology, such as superconducting and trapped ions.

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Effects of Crystallinity and Defects of Layered Carbon Materials on Potassium Storage:A Review and Prediction

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Electrochemical Energy Reviews 2022年第2期5卷 401-433页

作者： Xiaoxu Liu Tianyi Ji Hai Guo Hui Wang Junqi Li Hui Liu Zexiang Shen Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials School of Material Science and EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore College of Computer Science and Technology Dalian Minzu UniversityDalian 116650LiaoningChina

Layered carbon materials(LCMs)are composed of basic carbon layer units,such as graphite,soft carbon,hard carbon,and *** they have been widely applied in the anode of potassium-ion batteries,the potassium storage mechanisms and performances of various LCMs are isolated and difficult to relate to each *** importantly,there is a lack of a systematic understanding of the correlation between the basic microstructural unit(crystallinity and defects)and the potas-sium storage *** this review,we explored the key structural factors affecting the potassium storage in LCMs,namely,the crystallinity and defects of carbon layers,and the key parameters(L_(a),L_(c),d_(002),I_(D)/I_(G))that characterize the crystallinity and defects of different carbon materials were extracted from various databases and literature sources.A structure–property database of LCMs was thus built,and the effects of these key structural parameters on the potassium storage properties,including the capacity,the rate and the working voltage plateau,were systematically *** on the structure–prop-erty database analysis and the guidance of thermodynamics and kinetics,a relationship between various LCMs and potas-sium storage properties was ***,with the help of machine learning,the key structural parameters of layered carbon anodes were used for the first time to predict the potassium storage performance so that the large amount of research data in the database could more effectively guide the scientific research and engineering application of LCMs in the future.

关键词： Layered carbon materials Crystallinity and defects Potassium storage properties Machine learning

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GPU-Based Low-Precision Detection Approach for Massive MIMO Systems 38th

GPU-Based Low-Precision Detection Approach for Massive MIMO ...

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38th International Conference on High Performance Computing, ISC High Performance 2023

作者： Dabah, Adel Ltaief, Hatem Rezki, Zouheir Alouini, Slim Keyes, David Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Jeddah23955 Saudi Arabia University of California Santa Cruz 1156 High Street Santa CruzCA95064 United States

ISBN: (纸本)9783031320408

Massive Multiple-Input Multiple-Output (M-MIMO) uses hundreds of antennas in mobile communications base stations to increase the amount of transmitted data and the number of connected devices in 5G and beyond. However, M-MIMO systems increase the complexity of recovering the transmitted data (detection phase). To address this challenge, we leverage low-precision arithmetic in recent NVIDIA GPUs to improve the latency/scalability/accuracy of M-MIMO detection. We propose a GPU tree-based detection algorithm that aggregates multiple tree levels and formulates the computation as a matrix multiplication operation followed by a square-norm calculation and sorting (reduction) phase. This process is repeated until reaching the last level of the detection tree. The obtained results show near-optimal data detection with a 10 × speedup compared to a two-socket 28-core IceLake CPU implementation. We further deploy low-precision arithmetic operations. We show that moving from single-precision 32-bit floating-point arithmetic (FP32) to half-precision 16-bit representation (FP16) does not affect the accuracy performance while translating into an additional 1.7 × speedup. In addition, exploiting 8-bit integer representation results in an acceptable error rate degradation that can be compensated by increasing the number of aggregated levels. In addition, we propose a multi-GPU version that computes the matrix-multiplication operation of subsequent iterations in parallel. This latter operation represents more than 80% of the elapsed time for dense constellations. Results with four A100 GPUs show an additional 2.3 × relative speedup compared to our single GPU version. The achieved accuracy/scalability balance may accelerate the deployment of this technology and promote low-precision GPU computations within the wireless communication community. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

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npj Computational Materials 2023年第1期9卷 733-743页

作者： Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Department of Chemistry Stanford UniversityStanfordCAUSA Department of Applied Physics Stanford UniversityStanfordCAUSA Google Research Mountain ViewCAUSA Department of Physics Stanford UniversityStanfordCAUSA Department of Mathematics Stanford UniversityStanfordCAUSA Department of Computer Science Stanford UniversityStanfordCAUSA Department of Materials Science and Engineering Stanford UniversityStanfordCAUSA

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials *** this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical *** reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation *** a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human ***,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability *** development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关键词： inorganic crystalline utilize

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A Shape Memory Alloy Based Chipless RF Temperature Sensor

A Shape Memory Alloy Based Chipless RF Temperature Sensor

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Srabana Maiti Md Mirazur Rahman Md Ashif Islam Oni Sayan Roy Dipankar Mitra Shuvashis Dey Department of Electrical and Computer Engineering North Dakota State University Fargo ND USA Electrical and Computer Engineering Division of Energy Matter and Systems University of Missouri-Kansas City MO USA Department of Computer Science and Computer Engineering University of Wisconsin-La Crosse WI USA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

This article details the design and simulation of a chipless RF temperature sensor featuring a square-shaped tag on a Rogers Ultralam 3850 substrate. The sensor is meticulously designed to resonate at 2.746 GHz, exhibiting a reflection coefficient of −38.54 dB. Its temperature-sensing capabilities span the frequency range of 2.0-3.6 GHz. Employing Nitinol as the sensing material, the sensor responds to temperature-induced deformation, resulting in a discernible shift in the resonant frequency and a concurrent reduction in the reflection coefficient amplitude. This innovative sensor presents a compelling solution for temperature-sensing applications, as evidenced by a calibration curve derived from data, illustrating a consistent linear correlation between temperature and reflection coefficient amplitude. The comprehensive simulation and analysis of the chipless RF sensor were executed using CST Microwave Studio for simulation and CST MPhysics Studio for in-depth analysis.

关键词： Temperature sensors Radio frequency Temperature distribution Deformation Simulation Resonant frequency Reflection coefficient

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