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Implementation of a Multipath Fully Differential OTA in 0.18-μm CMOS Process

IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第1期31卷 147-151页

作者： Akbari, Meysam Hussein, Safwan Mawlood Hashim, Yasir Khateb, Fabian Kulej, Tomasz Tang, Kea-Tiong University of Kurdistan Department of Electrical Engineering Sanandaj66177 Iran Tishk International University Faculty of Engineering Department of Computer Engineering Erbil44001 Iraq Brno University of Defence Department of Electrical Engineering Brno66210 Czech Republic Czech Technical University in Prague Faculty of Biomedical Engineering Kladno16636 Czech Republic Czestochowa University of Technology Department of Electrical Engineering Czestochowa42201 Poland National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

This brief implements a highly efficient fully differential transconductance amplifier, based on several input-to-output paths. Some traditional techniques, such as positive feedback, nonlinear tail current sources, and current mirror-based paths, are combined to increase the transconductance, thus leading to larger dc gain and higher gain bandwidth (GBW) product. Two flipped voltage-follower (FVF) cells are employed as variable current sources to provide class-AB operation and adaptive biasing of all other drivers. The proposed structure includes several input-to-output paths that play the role of dynamic current boosters during the slewing phase, thus improving the slew rate (SR) performance. The circuit was fabricated in a TSMC 0.18-μm CMOS process with a silicon area of 54.5×30.1μm. Experimental results show a GBW of 173.3 MHz, a dc gain of 72.7 dB, and an SR of 139.4 V/μs for a capacitive load of 2×5 pF. The proposed circuit consumes 619 μW of power, under a supply voltage of 1.8 V. © 1993-2012 IEEE.

关键词： Operational amplifiers

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Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview 17

Sparsity-Aware Hardware-Software Co-Design of Spiking Neural...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Aliyev, Ilkin Svoboda, Kama Adegbija, Tosiron Fellous, Jean-Marc University of Arizona Department of Electrical & Computer Engineering TucsonAZ United States University of Arizona Departments of Psychology and Biomedical Engineering TucsonAZ United States

ISBN: (纸本)9798331530471

Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. However, realizing their efficiency benefits requires specialized hardware and a co-design approach that effectively leverages sparsity. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. We analyze the impact of static and dynamic sparsity, discuss the implications of different neuron models and encoding schemes, and investigate the need for adaptability in hardware designs. Our work aims to illuminate the path towards embedded neuro-morphic systems that fully exploit the computational advantages of sparse SNNs. © 2024 IEEE.

关键词： Neural network models

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Impact of Environmental Conditions on the Remaining Useful Lifetime of SiC MOSFET

Impact of Environmental Conditions on the Remaining Useful L...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hasan, Md Zakir Amin, Ashik Moniruzzaman, Md Choi, Seungdeog Singh, Prashant Liu, Chun-Hung Mississippi State University Department of Electrical and Computer Engineering Starkville United States University of Tennessee Department of Mechanical Aerospace & Biomedical Engineering Knoxville United States

ISBN: (纸本)9798350376067

Silicon carbide (SiC) power MOSFETs are widely applied to critical infrastructure in modern energy systems. Thus, accurately predicting its remaining useful lifetime (RUL) in real-world applications has become crucial. State-of-the-art explored its RUL through a power cycling test mostly considering constant environmental conditions (e.g., fixed temperature and humidity), which has resulted in considerable RUL prediction errors in real-world applications. This study directly integrates environmental factors into the RUL modeling to address this issue. Specifically, the junction temperature (Tj), on-state voltage (Vds,on), on-state resistance (Rds,on), and case temperature (TC) have been explored in various environmental conditions to understand their tight correlations with the RUL in the real world. Time series statistics models Autoregressive (AR) and Autoregressive Integrated Moving Average (ARIMA) have been used to predict SiC MOSFET RUL to gain new insights systematically. © 2024 IEEE.

关键词： Power MOSFET

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Far-field super-resolution chemical microscopy

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Light(Science & Applications) 2023年第7期12卷 1246-1267页

作者： Mingwei Tang Yubing Han Danchen Jia Qing Yang Ji-Xin Cheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Intelligent Perception Research Institute Zhejiang LabHangzhou 311100China Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02459USA

Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.

关键词： chemical details optics

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Wireless agents for brain recording and stimulation modalities

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Bioelectronic Medicine 2023年第1期9卷 1-12页

作者： Bok, Ilhan Vareberg, Adam Gokhale, Yash Bhatt, Suyash Masterson, Emily Phillips, Jack Zhu, Tianxiang Ren, Xiaoxuan Hai, Aviad Department of Biomedical Engineering University of WI – Madison 1550 Engineering Dr MadisonWIRm 2112 United States Department of Electrical and Computer Engineering University of WI – Madison MadisonWI United States MadisonWI United States

New sensors and modulators that interact wirelessly with medical modalities unlock uncharted avenues for in situ brain recording and stimulation. Ongoing miniaturization, material refinement, and sensitization to specific neurophysiological and neurochemical processes are spurring new capabilities that begin to transcend the constraints of traditional bulky and invasive wired probes. Here we survey current state-of-the-art agents across diverse realms of operation and evaluate possibilities depending on size, delivery, specificity and spatiotemporal resolution. We begin by describing implantable and injectable micro- and nano-scale electronic devices operating at or below the radio frequency (RF) regime with simple near field transmission, and continue with more sophisticated devices, nanoparticles and biochemical molecular conjugates acting as dynamic contrast agents in magnetic resonance imaging (MRI), ultrasound (US) transduction and other functional tomographic modalities. We assess the ability of some of these technologies to deliver stimulation and neuromodulation with emerging probes and materials that provide minimally invasive magnetic, electrical, thermal and optogenetic stimulation. These methodologies are transforming the repertoire of readily available technologies paired with compatible imaging systems and hold promise toward broadening the expanse of neurological and neuroscientific diagnostics and therapeutics. © 2023, The Author(s).

关键词： Magnetic resonance imaging

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A Time-Frequency Analysis for Broken Rotor Bar Detection in Closed Loop Inverter Fed Induction Motor at Imposed Speed 14

A Time-Frequency Analysis for Broken Rotor Bar Detection in ...

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14th IEEE International Symposium on Diagnostics for electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mantione, Lorenzo Fernandez-Cavero, Vanesa Morinigo-Sotelo, Daniel Frosini, Lucia University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy Universidad de Valladolid School of Industrial Engineering Department of Electrical Engineering Valladolid Spain Valladolid Spain

ISBN: (纸本)9798350320770

In this paper, an analysis to detect broken rotor bars while the motor is operating in closed loop control is presented. Due to changes in the supply frequency, which occur to keep the speed constant, a time-frequency technique is required even when the transient is completed. Signals from the phase current of the induction motor are analysed using traditional methods such as the Fast Fourier Transform and the Short Time Fourier Transform. Finally, the same signals are analysed through the Dragon Transform, that ensures high resolution frequency and time and allows to detect trajectories of the harmonics with different load conditions. © 2023 IEEE.

关键词： Induction motors

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A Pulsed Electrochemistry Readout IC with Slew-rate Booting Technique and Phase-domain ΔΣ ADC for Si-Nanowire electrical Double-layer Capacitance Measurement

A Pulsed Electrochemistry Readout IC with Slew-rate Booting ...

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2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024

作者： Chu, Po- Hsun Tsai, Cheng- Tse Chou, Yu-Siang Kumar, Nitish Lin, Shu-Ping Liao, Yu-Te Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taiwan

ISBN: (纸本)9798350361469

This paper presents a pulse-driven method for detecting Cardiac Troponin I using an electrical double-layer (EDL) capacitance sensing readout circuit and an on-chip pattern generator. The pattern generator is equipped with a slew rate-boosting driver circuit capable of handling EDL capacitance ranging up to 100 nF with a slew rate greater than 6.6 V /us while consuming only 13.5 μW. Additionally, the design achieves a dynamic range (DR) of 77.4 dB by implementing an oscillator-based readout circuit and a limit of detection (LoD) of 200 fg/mL in an undiluted PBS solution with a total power consumption of 120 μW. © 2024 IEEE.

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Investigating Deep Learning for Classification of Psychiatric Disorders on Brain MRI Data Using GAN-Based Augmentation

Investigating Deep Learning for Classification of Psychiatri...

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2023 International Conference on electrical and Information Technology, IEIT 2023

作者： Masengi, Gracia Angelina Jeniffer Sardjono, Tri Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Biomedical Engineering Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Department of Electrical Engineering Surabaya Indonesia

ISBN: (纸本)9798350327298

One of the difficulties in diagnosing patients in the field of psychiatry is the lack of objective biomarker measurements. In general, diagnosis is based on traditional methods relying on behavioral symptoms. Currently, diagnostic approaches predominantly rely on traditional methods grounded in behavioral symptoms. The evolution of neuroimaging techniques has prompted extensive investigations into biological indicators of psychiatric disorders. Among these endeavors, the application of deep learning for classification has demonstrated substantial promise. Through generative models like the generative adversarial network (GAN), deep learning is being investigated more in the field of medical image analysis to address the problems of small data samples and class imbalance. In this study, deep learning is implemented for automatic detection of psychiatric diseases from brain magnetic resonance imaging (MRI). Convolutional neural networks (CNN) are used for the diagnosis to categorize brain images into one of the classes: (1) normal, (2) schizophrenia, and (3) bipolar disorder. Image synthesis as data augmentation was performed with GAN. The suggested approach aims to advance the state-of-the-art in psychiatric diagnosis and promote additional investigation into the use of brain imaging in diagnostic plans. The findings show that the amount of data and subject variability significantly affect the efficacy of approaches for diagnosing psychiatric disorders. In this study, the data scarcity problem CNN training was not successfully resolved by GAN-based augmentation. It is determined that in order to develop a successful automatic psychiatric disease diagnostic system, a sufficient data sample must be collected while taking into account variables like subject variability. © 2023 IEEE.

关键词： Generative adversarial networks

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MXene Nanosheets-Decorated Paper as a Green Electronics Material for Biosensing

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ACS Measurement Science Au 2024年第1期4卷 81-91页

作者： Yu, Shan-Chu Huang, Tzu-Yen Lin, Tzu-En Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Kaohsiung Medical University Hospital Kaohsiung Medical University Kaoshiung 807378 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

This research delves into the development and optimization of MXene nanosheet-based paper electrodes, emphasizing their adaptability in green electronics and diverse applications. Xuan paper, a cellulose-based material, was identified as an ideal substrate for its mechanical attributes and capacity to accommodate MXene, further yielding outstanding electrical conductivity. The MXene paper electrode demonstrated consistent performance under various conditions, showing its potential in the field of wearable electronics and medical devices. Notably, its impressive electrothermal capabilities and environmentally conscious decomposition mechanism make it a promising candidate for future green electronic applications. Overall, this study underscores the electrode’s harmonization of performance and environmental sustainability, paving the way for its integration into futuristic electronic solutions. © 2023 The Authors. Published by American Chemical Society.

关键词： electrothermal performance green electronics MXene nanosheets paper electrode sustainable design wearable devices Xuan paper

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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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