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IEEE Journal on Flexible Electronics 2023年第2期2卷 77-83页

作者： Singh, Lakhvir Bhattacharjee, Mitradip Iiser Bhopal Department of Electrical Engineering and Computer Science Bhopal462066 India

The increase in global average life expectancy over the past century has been largely attributed to medical science developments. In this study, we demonstrate a polymer-based capacitive pressure sensor for measuring heart rate and pulse pressure (PP) signal. The fabricated sensor has been characterized to evaluate its performance and to verify its repeatability and precision. It is observed that the sensor works in the range of 0-8-kPa pressure range and is suitable for measuring the PP. This sensor has a high sensitivity of 7.11 kPa-1 in the low-pressure range of 0-8 kPa. Furthermore, the sensor demonstrated a considerable amount of repeatability with an error of less than 6% in consecutive measurements. Moreover, the sensor demonstrated the use of a wearable, flexible piezo-capacitive pressure sensor for PP monitoring. The wrist pulse was used as the stimulus for the experimental characterization of the sensor, and health markers related to the measured response were also discussed. © 2022 IEEE.

关键词： Pressure sensors

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Design and Characterization of High Quality-Factor Inductors for Wireless Systems Compatible With Flexible Large-Area Electronics

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 234-241页

作者： Ma, Yue Wagner, Sigurd Verma, Naveen Sturm, James C. Princeton University School of Engineering and Applied Science Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Resonant operation, exploiting high quality-factor planar inductors, has recently enabled gigahertz (GHz) applications for large-area electronics (LAE), providing a new technology platform for large-scale and flexible wireless systems. This work first presents the design, analysis, and characterization methodology of flex-compatible large-area planar inductors. Specifically, three distinct radio frequency (RF) inductor characterization methods are experimentally demonstrated and compared, with the most accurate method among them (i.e., S-parameters in a two-port configuration) demonstrating a record-high quality factor of up to 65in the 2.4-GHz frequency band. Enabled by accurate characterization, key inductor design considerations regarding the resistive loss due to inductor's metal traces are then discussed. Finally, a case study of the recently demonstrated LAE resonant switch shows the potential of these high-performance inductors towards large-area and conformal wireless systems for integrated Internet of Things (IoT) and 5G/6G applications. © 2022 IEEE.

关键词： Substrates

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Temperature Detection System Using Oxide TFTs on a Flexible Substrate

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2023年第6期2卷 464-471页

作者： Shrivastava, Suyash Bahubalindruni, Pydi Ganga Choudhary, Vaishali Iiser Bhopal Department of Electrical Engineering and Computer Science Bhopal462066 India

This article presents a compact temperature detection system using amorphous-InGaZnO thin-film transistor (TFT) technology. The proposed system is fabricated on a 30-μ m-thick polymide substrate. This system consists of a temperature readout circuit together with a novel high-performance clocked comparator. Individual blocks and the complete system have been characterized from measurements at a low-power supply voltage of 3 V and a load of 10 M||12 pF. The measured sensitivity of the temperature readout circuit is 16 mV/C. On the other hand, the comparator is capable of discriminating a minimum differential input voltage (Vin min) of 30 mV and showing the input referred noise of 5 mV at an input signal frequency ( fin) of 10 kHz. The circuit is showing a reliable operation up to an fin of 22.5 kHz with a slight degradation in the output voltage swing. This circuit shows a propagation delay of 3 μ s and a power consumption of 48.15 μ W from measurements. The complete system is able to discriminate a temperature variation down to 2 C. This system would find potential application in biomedical wearable devices for temperature detection. © 2022 IEEE.

关键词： Substrates

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A Self-Aware Digital Memory Framework Powered by Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3579-3594页

作者： Chakraborty, Prabuddha Bhunia, Swarup University of Maine Department of Electrical & Computer Engineering OronoME04469 United States University of Florida Department of Electrical & Computer Engineering GainesvilleFL32603 United States

Edge computing devices in Internet-of-Things (IoT) systems are being widely used in diverse application domains including industrial automation, surveillance, and smart housing. These applications typically employ a large array of sensors, store a high volume of data, and search within the stored data for specific patterns using machine intelligence. Due to this heavy reliance on data in these applications, optimizing the memory performance in edge devices has become an important research focus. In this work, we note (based on some preliminary quantitative studies) that the memory requirements of such application-specific systems tend to differ drastically from traditional general-purpose computing systems. Inspired by these findings and also through drawing inspiration from the human brain (which excels at being highly adaptive), we design a digital memory framework that can continually adapt to the specific needs of different edge devices. This adaption is made possible through a continual reinforcement-based learning methodology, and it aims at creating a digital memory framework that is always self-aware of the data it hold and queries being made. Through a methodical implementation of the framework, we demonstrate its effectiveness for different use-cases, settings, and hyperparameters in comparison with traditional content-addressable memory. © 2020 IEEE.

关键词： Internet of things

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Fault-tolerant capability analysis of six-phase induction motor with distributed, concentrated, and pseudo-concentrated windings

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Scientia Iranica 2024年第10期31卷 775-789页

作者： Alemi-Rostami, Mehdi Rezazadeh, Ghasem Tahami, Farzad Akbari-Resketi, Hamid Reza Ministry of Science Research and Technology Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran

Six-phase motors are becoming more popular because of their advantages such as lower torque ripple, better power distribution per phase, higher efficiency, and fault-tolerant capability compared to the three-phase ones. This paper presents the fault-tolerant capability analysis of a symmetrical six-phase induction motor equipped with distributed, conventional concentrated, and pseudo-concentrated windings under open-circuit fault scenarios. For further investigation, different load types such as constant-speed, constant-torque, and constant-power are applied to the motor. Two concepts of magnetic and physical phase separations are introduced as factors affecting the motor reliability. Analytically, these factors give an insight into how the pseudo-concentrated winding could be a fault-tolerant alternative. Moreover, five parameters such as the change of output power, power loss, power factor, efficiency, and expected load loss are considered as the fault-tolerant capability parameters to evaluate the windings reliability. The aforementioned parameters are reported using the finite element analysis for different fault scenarios and different load types. Although the baseline motor dimensions are not optimized for applying the pseudo-concentrated winding, the pseudo-concentrated shows a promising performance with high fault-tolerant capability. © 2024 Sharif University of Technology. All rights reserved.

关键词： Reliability analysis

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Joint Frame Drop and Object Detection Task Offloading for Mobile Devices via RL With Lyapunov Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6168-6182页

作者： Sohn, Vaughn Kim, Suhwan Lee, Hyang-Won Korea Advanced Institute of Science and Technology Department of Electrical Engineering Daejeon34141 Korea Republic of Konkuk University Department of Computer Science and Engineering Seoul05029 Korea Republic of

Object detection has become an increasingly important application for mobile devices. However, state-of-the-art object detection relies heavily on deep neural network, which is often burdensome to compute on mobile devices. To this end, we develop a layering framework for joint video frame drop and object detection task offloading. In the lower layer, by invoking Lyapunov optimization, we devise an algorithm for partitioning and offloading the computation tasks of deep neural networks. This algorithm also specifies the flow control for admitting the application traffic into the network. In the upper layer, we use the flow control as a form of guidance in the action space in order to develop a reinforcement learning (RL) algorithm that selectively drops video frames with object detection performance in consideration. By the nature of design, this Lyapunov-guided RL guarantees the network stability. We show through simulations that our Lyapunov-guided RL drops video frames with reasonable object detection performance and reduced latency while keeping the network stable. We also implemented our algorithm on the remote-controlled (RC) car equipped with microprocessor and GPU, and demonstrate the applicability of our algorithm to real-time object detection tasks from the video stream generated as the RC car moves. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Impact of Different Integration Times on Distributions of Rain Rates for Predictions of Rain Attenuation

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Progress in Electromagnetics Research C 2025年 154卷 229-238页

作者： Hassan, Mohammad Rofiqul Rafiqul, Islam Md. Habaebi, Mohamed Hadi Suriza, Ahmad Zabidi Badron, Khairayu Budalal, Asma Ali Mahfuz, Md. Mahmudul Hasan Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia College of Electrical and Electronics Technology Benghazi Libya Department of Electrical and Computer Engineering Concordia University Canada

All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction method of rain attenuation is established and used globally based on local rain intensity measurement. Required rain intensity used for attenuation prediction is generally measured at a point with 1-min integration time or converted from higher integration time to 1-min. Recent measurements of rain intensity with a 10-second integration time indicate that intensity is not uniform over a 1-minute duration. Consequently, the statistics of rain intensity distribution and attenuation predictions are influenced by measurements with integration times shorter than 1 minute. It has been established that an integration time of 0.01% provides the optimal fit for actual rain rate data. This paper presents the rain intensity distributions from data measured with 2-min, 1-min, 30-sec, 20-sec, and 10-sec integration times, and it has impact on rain rate distributions as well as rain attenuation predictions. © 2025, Electromagnetics Academy. All rights reserved.

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ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

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Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

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Robotic FDM for free-form fabrication: evaluating adaptive non-planar slicing with different contour methods

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Rapid Prototyping Journal 2025年第11期31卷 160-178页

作者： Furlan, Valentina Insero, Federico Giberti, Hermes Department of Electrical Computer and Biomedical Engineering Università di Pavia Pavia Italy

Purpose: This study aims to investigate and compare three nonplanar (NP) slicing algorithms. The algorithms aim to control the layer thickness variation (LTV), which is a common issue in supportless fabrication of free-form parts. The comparison underlines the differences between theoretical and real scenarios, resulting in guidelines for toolpath generation of complex objects. Design/methodology/approach: The algorithm comparison uses a representative complex geometry, i.e. the quarter of torus. It presents an increasing overhang and constant curvature. It is represented using a parametric definition in the first two algorithms and by triangular meshes as the real scenario. The algorithms work on the contouring stage, whereas a B-spline approach is used to build the inner side of layers. Constant layer thickness (LT) is imposed, and an adaptive approach is adopted to avoid over-extrusion. Three algorithms are validated with a robotized fused deposition modeling system. LTs are measured on cross-sectioned samples and compared with the theoretical cases. Findings: The results show that two algorithms can provide an LTV of about 0% on the contour. Nevertheless, the theoretical results on the inner side are divergent from the previous evidence, moving on to higher LTV (approximately 90%). The need for an adaptive approach is demonstrated, resulting in an LTV reduction (approximately 30%). Printed parts present the same trends of theoretical results confirming the algorithms’ capabilities. Originality/value: The work shows, for the first time, a comparison between NP slicing techniques. The LTV problem in hollow and filled components is analyzed through theoretical and experimental evidence. The results are promising for supportless fabrication of free-form parts. © 2025, Valentina Furlan, Federico Insero and Hermes Giberti.

关键词： Fabrication

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Detecting Inaudible Voice Commands via Acoustic Attenuation by Multi-channel Microphones

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IEEE Transactions on Dependable and Secure Computing 2024年 1-17页

作者： Ji, Xiaoyu Zhang, Guoming Li, Xinfeng Qu, Gang Cheng, Xiuzhen Xu, Wenyuan Electrical Engineering of Zhejiang University Hangzhou China School of Computer Science and Technology of Shandong University Qingdao China Department of Electrical and Computer Engineering at the University of Maryland College Park MD USA

DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). Eliminating DolphinAttacks is challenging if ever possible since it requires to modify the microphone hardware. In this paper, we design EarArray, a lightweight method that can not only detect such attacks but also identify the direction of attackers without requiring any extra hardware or hardware modification. Essentially, inaudible voice commands are modulated on ultrasounds that inherently attenuate faster than the one of audible sounds. By inspecting the command sound signals via the built-in multiple microphones on smart devices, EarArray is able to estimate the attenuation rate and thus detect the attacks. We propose a model of the propagation of audible sounds and ultrasounds from the sound source to a voice assistant, e.g., a smart speaker, and illustrate the underlying principle and its feasibility. We implemented EarArray using two specially-designed microphone arrays and our experiments show that EarArray can detect inaudible voice commands with an accuracy of above 99% and recognize the direction of the attackers with an accuracy of 97.89% and can also detect the laser-based attack with an accuracy of 100%. IEEE

关键词： Microphones

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