2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)最新文献_第2页

Analysis of Word Line Shaping Techniques for In-Memory Computing in SRAMs sram内存计算中的字线整形技术分析

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665469

K.L.N. Prasad, Aditya Biswas, Joycee Mekie

引用次数: 1

A Comparative Study of Switchable Capacitor Structures for LC Oscillators in a 28-nm Technology 28纳米工艺中LC振荡器可切换电容器结构的比较研究

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665608

Lantao Wang, Jonas Meier, R. Wunderlich, S. Heinen

引用次数: 1

A Squarewave-Based Multi-Frequency Impedance Analyzer Based on the Heterodyne Architecture 基于外差结构的方波多频阻抗分析仪

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665624

Alireza Mesri, M. Sampietro, G. Ferrari

引用次数: 0

Computationally Light Algorithms for Tactile Sensing Signals Elaboration and Classification 触觉感知信号精化与分类的计算光算法

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665554

Youssef Amin, C. Gianoglio, M. Valle

{"title":"Computationally Light Algorithms for Tactile Sensing Signals Elaboration and Classification","authors":"Youssef Amin, C. Gianoglio, M. Valle","doi":"10.1109/icecs53924.2021.9665554","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665554","url":null,"abstract":"Tactile sensing systems require embedded processing to extract structured information in many application domains as prosthetics and robotics. In this regard, this paper proposes computationally light strategies to pre-process the sensor signals and extract features, feeding single layer feed-forward neural networks (SLFNNs) that proved good generalization performance keeping low the computational cost. We validate our proposal by integrating a tactile sensing system on a Baxter robot to collect and classify data from three objects of different stiffness. We compare different features extraction techniques and five SLFNNs to show the trade-off between generalization accuracy and computational cost of the whole processing unit. The results show that the processing unit that extracts the mean and standard deviation features from signals and adopts a fully connected neural network (FCNN) with 50 neurons and ReLu activation function achieves a high accuracy (94.4%) in the 3-class classification problem with a low computational cost, leading to the deployment on a resource-constrained device.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128174698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Low Power, Low Noise and Reconfigurable Readout Circuit for Physiological Biomarkers 一种用于生理生物标志物的低功耗、低噪声和可重构读出电路

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665476

Angelito A. Silverio, Danny Wen-Yaw Chung, Leandro Silvério

{"title":"A Low Power, Low Noise and Reconfigurable Readout Circuit for Physiological Biomarkers","authors":"Angelito A. Silverio, Danny Wen-Yaw Chung, Leandro Silvério","doi":"10.1109/icecs53924.2021.9665476","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665476","url":null,"abstract":"This work presents the design and performance verification of an ultra-low power and low noise sensor front-end circuit for biopotential signal acquisition or biosensor interfacing. The circuit consists of an input switching matrix for selecting the input sensing mode, a high gain and high common-mode rejection ratio instrumentation amplifier incorporating a fully differential input/output stage built upon a second-generation current conveyor pair and a differential bandpass filter section for post amplification and band limiting. A current mode bandgap reference utilizing subthreshold MOS devices and tunable MOS pseudoresistors provides the biasing voltages that exhibit high power supply ripple rejection. The readout circuit core has a noise efficiency factor of 2.52 while exhibiting a high differential gain and common-mode rejection ratio of 91 dB and 154 dB, respectively. The circuit dissipates around $2 mu mathrm{W}$ of power under a single supply rail of 1V. The circuit has been designed using TSMC $0.18 mumathrm{m}$ technology whose model file parameters are obtained from MOSIS. The circuit finds application in either wearable or implantable devices.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

1-D Convolutional Neural Networks for Touch Modalities Classification 一维卷积神经网络在触觉模态分类中的应用

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665576

C. Gianoglio, E. Ragusa, R. Zunino, M. Valle

{"title":"1-D Convolutional Neural Networks for Touch Modalities Classification","authors":"C. Gianoglio, E. Ragusa, R. Zunino, M. Valle","doi":"10.1109/icecs53924.2021.9665576","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665576","url":null,"abstract":"Artificial tactile systems can facilitate the life of people suffering from a loss of the sense of touch. These systems use sensors and digital, battery-operated embedded units for data processing. Therefore, low-power, resource-constrained devices should host those embedded devices. The paper presents a framework based on 1-D convolutional neural networks (CNNs), which tackles the problem of classifying touch modalities, while limiting the number of architecture parameters. The paper also considers the computational cost of the pre-processing stage that handles tactile-sensor data before classification. The related pre-processing unit affects resources occupancy, computational cost, and ultimately classification accuracy. The experimental session involved a state-of-the-art real-world dataset containing three touch modalities. The 1-D CNN outperformed existing solutions in terms of accuracy, and showed a satisfactory trade-off between accuracy, computational cost, and resources occupancy. The implementation of the 1-D CNN classifier on an Arduino Nano 33 BLE device yielded real-time performances.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134466577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Simple, Versatile Integration Platform based on a Printed Circuit Board for Lab-on-a-Chip Systems 基于印刷电路板的芯片实验室系统简单通用集成平台

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665484

Simon Dallaire, Paul-Vahé Cicek

引用次数: 0

Deep Learning Autoencoder-based Compression for Current Source Model Waveforms 基于深度学习自编码器的电流源模型波形压缩

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665573

W. Raslan, Y. Ismail

引用次数: 2

Frequency-Interleaved ADC with RF Equivalent Ideal Filter for Broadband Optical Communication Receivers 宽带光通信接收机用射频等效理想滤波器的频率交织ADC

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665589

Ryo Kabeya, Y. Umeda, K. Takano

{"title":"Frequency-Interleaved ADC with RF Equivalent Ideal Filter for Broadband Optical Communication Receivers","authors":"Ryo Kabeya, Y. Umeda, K. Takano","doi":"10.1109/icecs53924.2021.9665589","DOIUrl":"https://doi.org/10.1109/icecs53924.2021.9665589","url":null,"abstract":"To process an ultra-wideband signal with narrower-band analog-to-digital converters (ADCs), frequency-interleaved (FI) receiver technique that divides an ultra-broadband signal to narrower-band signals has been reported to date. Conventional FI ADCs use analog filters such as LC filters to divide an ultrawideband signal to separated narrower band signals. However, aliasing and level deviation of the signals that occurs in crossover frequency region of the filters degrades the signal transmission quality of the receivers that use FI ADCs. To mitigate this degradation digital signal processing technique to compensate the distortion due to the aliasing and level deviation has been reported. However, the compensation of the aliasing and level deviation need large-scale signal processing. In addition, degradation in signal transmission quality due to decrease in signal to noise ratio and distortion due to nonlinearity caused by the level deviation cannot be removed completely. To omit these degradations and improve the signal transmission quality we propose FI ADCs with an RF equivalent ideal filter (EIF). Computer simulation with ideal circuit components shows a potential to have an excellent band-separation capability and highly-accurate signal transmission quality without large-scale signal processing for the distortion compensation.","PeriodicalId":448558,"journal":{"name":"2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114560377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structured Recurrent Neural Network Model Order Reduction for SISO and SIMO LTI Systems SISO和SIMO LTI系统的结构化递归神经网络模型降阶

2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS) Pub Date : 2021-11-28 DOI: 10.1109/icecs53924.2021.9665593

W. Raslan, Y. Ismail

引用次数: 0