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IEEE Journal on Flexible Electronics 2023年第6期2卷 481-485页

作者： Klein-Hitpass, Achim Kidmose, Preben Agarwala, Shweta Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark

Direct-writing techniques of printed electronics (PEs) have great potential within flexible electronics and allow to fabricate circuits on double curved surfaces. In sensor applications, it is often necessary to protect the electrical connections between the sensor and the electrical instrumentation against electromagnetic interference (EMI). This is commonly achieved by shielding the interconnects. In this work, we focused on fabrication of a shielded cable structure for low-frequency signal integrity in high-precision sensor applications. Semiprinted samples, with only some of the layers printed were measured to have an average signal layer resistivity of 3.93m and an insulation resistivity of 5.25 Gm. Fully printed (FP) samples had an average signal layer resistivity of 6.72m and an insulation resistivity of 14.7 Gm. The printed samples demonstrated shielding effectiveness (SE) of -69 dB down to -86 dB. Based on signal-shield capacitance ranging from 85 to 467 pF, the -3 dB cut-off frequency of the signal transfer function ranged from 0.5 to 2.6 kHz. In summary, we demonstrate for the first time a printed coaxial-cable-like structure for lowfrequencies between 1 Hz and 10 kHz. © 2022 IEEE.

关键词： 3D printing

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Body Area Network: Connecting and powering things together around the human body

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IEEE Solid-State Circuits Magazine 2023年第2期15卷 49-58页

作者： Yoo, Jerald National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

A body area network (BAN) provides connectivity and power among on-body/in-body sensors and is an attractive means for realizing the 'human intranet' for health-care, medical, or multimedia applications around the human body [1], [2]. As shown in Figure 1, compared to a WLAN or a wireless personal area network (WPAN), it targets a much smaller range (∼2 m) with greater energy efficiency. © 2009-2012 IEEE.

关键词： Intranets

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Single-channel, ultraportable, real-time imaging system based on deep learning: a proof-of-concept

Single-channel, ultraportable, real-time imaging system base...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Meacci, Valentino Bosco, Edoardo Ramalli, Alessandro Boni, Enrico Tortoli, Piero Mazierli, Daniele Spairani, Edoardo Matrone, Giulia University of Florence Department of Information Engineering Florence Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350346459

There is a growing demand for user-friendly technologies that may empower individuals to independently monitor their physiological parameters. Ultrasound systems are highly promising for healthcare self-monitoring. To address these challenges, in this work, we present the proof of concept (PoC) of a real-time ultraportable system with a single-channel transmission and reception strategy and a deep learning-based image reconstruction method. The PoC uses the ULA-OP 256 scanner, employing a monostatic synthetic aperture focusing technique, along with a convolutional neural network (CNN) trained to generate B-mode images. Testing was carried out using a CNN running on a mid-range graphics processing unit (GPU), both on phantoms and in vivo scenarios, comparing the image quality achieved by the CNN with that of a delay-and-sum (DAS) beamformer. The results highlight that the CNN outperformed the DAS, showing a better image quality. Furthermore, the CNN achieved a real-time frame rate of 37.0 frames per second, proving that ultraportable ultrasound systems are highly promising for the future of self-monitoring instruments. © 2023 IEEE.

关键词： Graphics processing unit

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Latency and Cost Minimization for Task Offloading with Energy Harvesting in UAV-MEC Network

Latency and Cost Minimization for Task Offloading with Energ...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Basharat, Mehak Naeem, Muhammad Anpalagan, Alagan Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoON Canada Comsats University Islamabad Department of Electrical Engineering Wah Campus Pakistan

ISBN: (纸本)9798350310955

Unmanned aerial vehicles (UAVs) are recently considered for various Internet of things (IoT) applications. Furthermore, mobile edge computing offers low-latency data computation and improves network performance by bringing computing resources closer to IoT devices. UAVs equipped with MEC capabilities are expected to play a critical role in providing reliable and efficient communication in future wireless networks. The low-power IoT devices can harvest energy for sustainable operations in different applications such as public safety. Resource management and efficient task offloading are the major challenges to take full advantage of UAV-MEC networks. In this paper, we formulate an optimization problem to minimize latency and cost as well as maximize the number of IoT devices connected with UAVs. We consider constraints on computation, caching, energy harvested, latency, and the maximum number of connections a given UAV allows. A heuristic with learning placement (HLP) algorithm is proposed based on an unsupervised learning algorithm and an iterative rounding algorithm to solve the problem with less complexity. Simulation results show the effectiveness of the proposed HLP algorithm in UAV-MEC networks compared with the branch and bound algorithm (optimal results). © 2023 IEEE.

关键词： Energy harvesting

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Enhanced Vehicle Detection by Optimized Image Compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

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Diagnosis of Mechanical and electrical Faults in Electric Machines Using a Lightweight Frequency-Scaled Convolutional Neural Network

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IEEE Transactions on Energy Conversion 2025年第2期40卷 1589-1599页

作者： Mohammad-Alikhani, Arta Nahid-Mobarakeh, Babak Hsieh, Min-Fu McMaster University Electrical and Computer Engineering HamiltonL8S 4L8 Canada National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Optimizing computational efficiency while maintaining accuracy in electrical machine fault detection is a critical challenge. To address this, the Frequency-Scaled Convolutional Neural Network is proposed as a lightweight yet highly accurate model for detecting electrical machine faults. A key feature of this model is its initial layer, which is inspired by the effects of faults on frequency harmonics in rotating systems. This layer includes a trainable frequency-scaled convolutional layer, designed to optimally separate frequency features over time, hence, reducing the need for a more complex model to achieve high accuracy. Additionally, to further decrease model complexity, a partially connected 2D linear layer is developed in the final layer. The model's performance is evaluated through three case studies. First, the Case Western Reserve University bearing dataset, a well-established benchmark, is used. Despite having only 2,020 trainable parameters and 190,000 floating point operations per second, compared to other models in the literature with millions of parameters, the proposed model achieves 100% accuracy, significantly reducing computational burden while maintaining precision. The model is also applied to the inter-turn short circuit fault dataset for permanent magnet synchronous motors and a public dataset with various fault types, where it again achieves 100% accuracy. © 1986-2012 IEEE.

关键词： Bearings (machine parts)

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A Singly Curved Conformal Phased Array with Integrated Particles-Based CRLH Phase Shifters

Electronics

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Electronics 2023年第2期27卷 43-49页

作者： Ayaz, Muhammad Ullah, Irfan Department of Electrical & Computer Engineering COMSATS University Islamabad Abbottabad Campus Pakistan

In this paper, a four unit cell micron-sized magnetic particles embedded composite right/left-handed (CRLH) transmission line metamaterial phase shifter is proposed. The variable phases are achieved by activating the micron-sized magnetic particles along the length of stub transmission lines of the CRLH transmission line structure. This causes to effectively change the inductance of stub transmission lines, which changes the phase of the RF signal. These structures can be activated in a symmetric or asymmetrical manner to obtain wide range of phase shifts. A prototype of cascaded four unit cell CRLH transmission line phase shifter with embedded silver-coated ferrites (magnetic particles) is designed, fabricated and tested for phase shifters applications. The measured phase response of the proposed phase shifter is in close agreement with the simulated phase response. Next, for the performance analysis of proposed phase shifter, a 1 × 4 conformal curved-shaped phased array operating at 5.8 GHz is fed with the four unit cell cascaded phase shifter in CST full-wave simulator. The conformal phased array with proposed integrated phase shifters was simulated for broadside pattern correction and main beam scanning at 20 degree on curved-shaped surface with different bend angles. The radiation pattern results obtained with the proposed integrated phase shifters are in close agreement with those obtained with direct excitation of the array. © 2023, University of Banja Luka. All rights reserved.

关键词： Phase shifters

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Monte Carlo modelling of Raman signal enhancement using power-efficient Apple processors 15

Monte Carlo modelling of Raman signal enhancement using powe...

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biomedical Light Scattering XV 2025

作者： Vladyko, Ilya Yakovlev, Vladislav Doronin, Alexander School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Biomedical Engineering Department of Electrical and Computer Engineering Department of Physics and Astronomy Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (纸本)9781510683884

Modern optical technologies encompass classical light phenomena and non-linear effects, crucial for biomedical imaging and therapies. Despite substantial interest and many experimental studies, non-linear optical effects in inhomogeneous scattering media require extensive computer modeling to understand light-material interactions. This report focuses on Monte Carlo simulations of Raman scattering in turbid media, such as biological tissues, essential for clinical applications. We developed a novel, open-source, power-efficient Monte Carlo algorithm optimized for Apple s M-series chips. By incorporating single-photon tracking, the algorithm accurately models time-resolved light-Tissue interactions. Validated against experiments, it matches traditional methods accuracy while offering superior efficiency, advancing Raman functional imaging. © 2025 SPIE.

关键词： Tissue

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Transformer based Answer-Aware Bengali Question Generation

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 314-326页

作者： Ruma, Jannatul Ferdous Mayeesha, Tasmiah Tahsin Rahman, Rashedur M. Department of Electrical & Computer Engineering North South University Dhaka1229 Bangladesh

Question generation (QG), the task of generating questions from text or other forms of data, a significant and challenging subject, has recently attracted more attention in natural language processing (NLP) due to its vast range of business, healthcare, and education applications through creating quizzes, Frequently Asked Questions (FAQs) and documentation. Most QG research has been conducted in languages with abundant resources, such as English. However, due to the dearth of training data in low-resource languages, such as Bengali, thorough research on Bengali question generation has yet to be conducted. In this article, we propose a system for producing varied and pertinent Bengali questions from context passages in natural language in an answer-aware input format using a series of fine-tuned text-to-text transformer (T5) based models. During our studies with various transformer-based encoder-decoder models and various decoding processes, along with delivering 98% grammatically accurate questions, our fine-tuned BanglaT5 model had the highest 35.77 F-score in RougeL and 38.57 BLEU-1 score with beam search. Our automated and human evaluation results show that our answer-aware QG models can create realistic, human-like questions relevant to the context passage and answer. We also release our code, generated questions, dataset, and models to enable broader question generation research for the Bengali-speaking community. © 2023 The Authors

关键词： Decoding

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Distributed Estimation of the Geometric Center of Multi-target by a Group of Agents With Bearing Measurements

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Journal of Institute of Control, Robotics and Systems 2023年第11期29卷 880-886页

作者： Hyeon, Soojeong ASRI Department of Electrical and Computer Engineering Seoul National University Korea Republic of

This paper presents a distributed estimation scheme for determining the geometric center of multiple targets by a group of agents in 2-dimensional space. These agents have the knowledge of bearing measurements to a part of the targets and communicate with each other. Specifically, we utilize a continuous-time dynamics with strong coupling among agents to process local information. This approach allows us to assign the equilibrium point of the dynamics describing collective behavior of the agents to the geometric center of the targets. The benefits of this approach include: (i) no initialization process is required and (ii) global information, such as the numbers of targets and agents, is not required. These properties make the proposed estimation method suitable for plug-and-play operation. © ICROS 2023.

关键词： Multi agent systems

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