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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Moisello, Elisabetta Malcovati, Piero Bonizzoni, Edoardo University of Pavia Computer and Biomedical Engineering Department of Electrical Italy

ISBN: (纸本)9798350326499

This paper proposes a highly supply voltage-scalable, low-power and compact-area temperature sensor, which satisfies the requirements for Systems-on-Chip (SoCs) and microprocessors hotspots monitoring. The proposed novel sensor structure employs diode-connected NMOS devices operating in sub-thresold, featuring different sizes, as sensing elements, in order to implement a proportional-to-absolute-temperature (PTAT) sensor output voltage. The proposed sensor does not require any additional biasing or start-up circuits and, since it mostly processes the signals generated by the sensing elements in the current domain, enables high supply voltage scalability. Furthermore, a low-cost 1-point temperature calibration algorithm can be employed for trimming the sensor sensitivity. The proposed sensor circuit was designed in a 130-nm CMOS process and its performance was verified through extensive simulations in the Cadence Virtuoso environment. © 2023 IEEE.

关键词： Temperature sensors

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Detection, Identification and Tracking of Anomaly in Medical Images 1

Detection, Identification and Tracking of Anomaly in Medical...

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1st IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems, ADACIS 2023

作者： Bouzid-Daho, Abdellatif Siarry, Patrick Faculty of Electrical and Computer Engineering Biomedical Engineering Department Tizi-Ouzou Algeria France

ISBN: (纸本)9798350344158

In this work, we will develop an algorithm based on unsupervised region segmentation for the detection, identification and traking of anomalies in a set of medical images. We created an algorithmic system based on k-means segmentation to identify cancerous regions and track the progression of infected people. Several experiments were carried out based on images to guarantee the reliability of our software tool. The results are satisfactory and robust for aiding medical diagnosis. © 2023 IEEE.

关键词： Diagnosis

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SynthCheck: A Dashboard for Synthetic Data Quality Assessment

SynthCheck: A Dashboard for Synthetic Data Quality Assessmen...

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17th International Joint Conference on biomedical engineering Systems and Technologies, BIOSTEC 2024

作者： Santangelo, Gabriele Nicora, Giovanna Bellazzi, Riccardo Dagliati, Arianna Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

In recent years, synthetic data generation has become a topic of growing interest, especially in healthcare, where they can support the development of robust Artificial Intelligence (AI) tools. Additionally, synthetic data offer advantages such as easier sharing and consultation compared to original data, which are subject to patient privacy laws that have become increasingly rigorous in recent years. To ensure a safe use of synthetic data, it is necessary to assess their quality. Synthetic data quality evaluation is based on three properties: resemblance, utility, and privacy, that can be measured using different statistical approaches. Automatic evaluation of synthetic data quality can foster their safe usage within medical AI systems. For this reason, we have developed a dashboard application, in which users can perform a comprehensive quality assessment of their synthetic data. This is achieved through a user-friendly interface, providing easy access and intuitive functionalities for generating reports. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

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Assessment of Neuromuscular Function in Patients with Long-Term Symptoms of COVID-19 9th

Assessment of Neuromuscular Function in Patients with Lon...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Cremasco, Emilia F. Zambalde, Ellen P. Germer, Carina M. Elias, Leonardo A. Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783031494062

Part of individuals infected by SARS-CoV-2 experiences long-lasting effects that include a wide range of symptoms, with fatigue being one of the most common complaints. Self-reported fatigue can have different manifestations, and although chronic fatigue has been reported as a long-term effect of other viral infections, the etiology of this complaint is still unclear. In this work, we employed an experimental protocol to assess the neuromuscular function in participants who had COVID-19 (12 women, 5–30 weeks after COVID-19 diagnosis) and age-matched controls (9 women). The COVID participants presented a high degree of variability in their symptoms, and the majority (91%) had mild to moderate manifestations. Participants performed an abduction force of the index finger in three different motor tasks, namely isometric force steadiness, reaction time, and time-to-task failure (fatigue task). The experiment was performed in three stages: pre-fatigue, post-fatigue, and after a resting interval of 5 min. We found significant differences between groups for the maximum voluntary contraction, indicating the COVID group had a lower force generation capacity. Nonetheless, no significant difference was found in measures related to force control;however, a larger and more homogeneous number of participants is needed to confirm these observations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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Eighteen-Element Antenna for Metal-Rimmed Smartphone Sub-6 GHz LTE42 Band Applications

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computers, Materials & Continua 2022年第11期73卷 3181-3198页

作者： Saqer S.Alja’Afreh Mallak H.Alshamaileh Eqab Almajali Jawad Yousaf Amir Altaf Electrical Engineering Department Mutah UniversityAl-karak61710Jordan Electrical Engineering Department University of SharjahSharjahUAE Department of Electrical Computer&Biomedical EngineeringAbu Dhabi UniversityAbu DhabiUAE Department of Electrical and Computer Engineering Sungkyunkwan UniversityKorea

Due to the limited space and large mutual coupling levels,the design of sub-6 GHz massive Multi-Input Multi-Output(m-MIMO)smartphone antenna system attracts antennas’designers and engineers ***,this paper presents 18-element m-MIMO antenna system that covers the long-term evolution 42(LTE42)frequency band(3.4–3.6 GHz)for the fifth generation(5G)applications in metallic frame *** proposed array system is etched on the long sides of a metal rim of the mobile chassis symmetrically,which is electrically connected to the system ground plane with zero ground clearance.A low-profile frame of height 7 mm(λ/12.3)is symmetrically placed below&above the ground system *** two frame parts(above/below the ground level)are utilized separately for the implantation of the antenna elements to achieve good space exploitation and perfect pattern diversity between *** feeds are used to further increase the level of the isolation where each antenna element above the ground level is fed by a 50-ohm microstrip line,and each antenna element below the ground level is fed by a 50-ohm coplanar waveguide(CPW).The proposed antenna structure is a capacitive coupled-fed open-slot antenna with a small footprint area of 12×3.5 mm2(λ/7.2×λ/24.5,whereλrepresents the free space wavelength at 3.5 GHz).A small coupling L-shaped strip provides a capacitive coupling for the proposed 5G antenna *** establish the contribution of the proposed 18-element m-MIMO,the prototype of the proposed system was manufactured and successfully *** measured and simulated results are shown to be in good *** proves that the proposed antenna provides coverage for the LTE42 band(3.4–3.6 GHz)with acceptable isolation and ***,the performance of the proposed m-MIMO were further verified via channel capacity calculation and the envelope correlation coefficient(ECC)*** on that,the proposed m-MIMO is shown to provide a desira

关键词： Metal-rim smartphone antennas massive MIMO 5G band

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Factors Affecting the Performance of FastICA Algorithm for Decomposition of High-Density Surface Electromyogram 9th

Factors Affecting the Performance of FastICA Algorithm for D...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Castilhos, Mateus Augusto Schneider Elias, Leonardo Abdala Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas SP Campinas Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas SP Campinas Brazil

ISBN: (纸本)9783031494031

The motor unit (MU) physiology and the neural control of human movement can be studied via surface electromyogram (sEMG) decomposition techniques, which estimate the discharge times of MUs during a given contraction. The recent decomposition methods are based on the blind source separation (BSS) methods, and some approaches such as independent component analysis (ICA) can be applied to solve the problem. This study aims to investigate the influence of several parameters of the fast independent component analysis (FastICA) algorithm in the decomposition process of high-density sEMG. The extension factor, the number of iterations, and the method of initialization of the separation vector were explored for three evaluation metrics, namely, the execution time of decomposition, the absolute number of unique decomposed MUs, and the ratio of the number of unique MUs to the total number of extracted MUs. The number of iterations increased the execution time of decomposition and the absolute number of unique decomposed MUs. Besides, the method of initialization of the separation vector had little influence on the execution time of decomposition, an optimal value can be achieved for the absolute number of unique decomposed MUs. Moreover, the repeated convergence of the FastICA algorithm to the same source was mainly affected by the number of iterations. The study has reinforced the importance of evaluating the parameter combinations to achieve a better FastICA decomposition performance based on the chosen evaluation metric. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Blind source separation

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Spatial Dynamics Complementarity Learning with Graph Convolutional Network for Wearable Massive-sensor computers

Spatial Dynamics Complementarity Learning with Graph Convolu...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Zou, Jiadao Jackman, Christopher Zhang, Qingxue Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering IN United States Indiana University School of Medicine Clinical Neurology Riley Hospital IN United States Purdue Univeristy School of Engineering and Technology Electrical and Computer Engineering Biomedical Engineering IN United States

ISBN: (纸本)9798350324136

Wearable massive-sensor computers show great promise for comprehensive bio-dynamics capturing towards smart health big data. Nevertheless, mining the critical and complementary patterns from a large amount of data, especially with a high dimensionality, is challenging. In this study, we propose a novel deep learning framework, with both deep graph convolutional learning and multi-view representation, to boost the spatial dynamics complementarity mining. More specifically, we have proposed a multi-stage deep learning architecture, with a graph convolutional network for spatial pattern propagation and fusion, and a convolutional network for further pattern abstraction. Further, we have proposed a multi-view data representation approach with both temporal and spectral feature formation. Evaluated on the wearable massive-sensor data mining application, the proposed framework has greatly boosted the complementarity learning capability and yielded a significantly improved detection performance. This study is expected to greatly advance spatial dynamics complementarity learning with graph convolutional learning. © 2024 IEEE.

关键词： Big Data Wearable Massive-sensor computers Deep Learning Graph Convolutional Network

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Adaptive 3D descattering with a dynamic synthesis network

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Light(Science & Applications) 2022年第5期11卷 766-786页

作者： Waleed Tahir Hao Wang Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Deep learning has been broadly applied to imaging in scattering applications.A common framework is to train a descattering network for image recovery by removing scattering *** achieve the best results on a broad spectrum of scattering conditions,individual“expert”networks need to be trained for each ***,the expert’s performance sharply degrades when the testing condition differs from the *** alternative brute-force approach is to train a“generalist”network using data from diverse scattering *** generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid ***,we propose an adaptive learning framework,termed dynamic synthesis network(DSN),which dynamically adjusts the model weights and adapts to different scattering *** adaptability is achieved by a novel“mixture of experts”architecture that enables dynamically synthesizing a network by blending multiple experts using a gating *** demonstrate the DSN in holographic 3D particle imaging for a variety of scattering *** show in simulation that our DSN provides generalization across a continuum of scattering *** addition,we show that by training the DSN entirely on simulated data,the network can generalize to experiments and achieve robust 3D *** expect the same concept can find many other applications,such as denoising and imaging in scattering ***,our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.

关键词： network scattering synthesis

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