2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)最新文献_第10页

Artifact-Recovery in Neuromodulators using Tunable High-Pass Corners 使用可调高通角的神经调节剂的伪影恢复

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842066

Stefan Reich, D. Fritschi, Mark A. Sporer, M. Ortmanns

引用次数: 2

Efficient Scheduling, Mapping and Memory Bandwidth Allocation for Safety-Critical Systems 安全关键系统的高效调度、映射和内存带宽分配

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842219

Alexy Torres Aurora Dugo, Jean-Baptiste Lefoul, Aymen Ben-Salem, Serge Harnois, F. Magalhães, G. Nicolescu

引用次数: 1

A Wideband Low-Power Current-Reuse RF-to-BB Receiver Using a Clock Strategy Technique 基于时钟策略技术的宽带低功耗电流复用射频- bb接收机

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842165

A. Abbasi, Amirhossein Moshrefi, F. Nabki

{"title":"A Wideband Low-Power Current-Reuse RF-to-BB Receiver Using a Clock Strategy Technique","authors":"A. Abbasi, Amirhossein Moshrefi, F. Nabki","doi":"10.1109/NEWCAS52662.2022.9842165","DOIUrl":"https://doi.org/10.1109/NEWCAS52662.2022.9842165","url":null,"abstract":"A wideband and low-power RF-to-baseband (BB) current-reuse receiver (CRR) front-end that employs a clock strategy is proposed to support software-defined radios (SDRs). It includes a capacitively cross-coupled common-gate low noise transconductance amplifier (LNTA) to amplify and convert the RF voltage to a current, a passive mixer to down-convert the RF current at 4 × the local-oscillator (LO) frequency to the intermediate frequency (IF) current using a clock strategy, an active-inductor (AI) technique to improve the noise-figure (NF) performance, and a transimpedance amplifier (TIA) to convert the IF current to a voltage at the output. To achieve low power consumption the receiver features: current-reuse between the LNTA and the BB circuits; current-mode harmonic recombination at the output of the passive mixer; and a clock strategy to reduce the dynamic power consumption of the clock generation for the dividers and the LO buffers. The proposed receiver is implemented in 22-nm CMOS technology and occupies an active area of 0.13mm2. In the nominal condition, at an IF of 10MHz and an RF of 2.4GHz, it achieves a voltage gain of 36dB, a double-sideband (DSB) noise figure (NF) of 5.2dB, S11 of less than −10dB and an IIP3 of −18.5dBm while consuming 2.44mA from a 1.2V supply voltage.","PeriodicalId":198335,"journal":{"name":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133049612","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

Reliable Power Efficient Systems through Run-time Reconfiguration 通过运行时重新配置的可靠节能系统

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9841986

Nahla El-Araby, A. Jantsch

引用次数: 0

A MEMS Electrothermal Actuator Designed for a DC Switch Aimed at Power Switching Applications and High Voltage Resilience 一种用于直流开关的MEMS电热致动器，用于电源开关应用和高电压弹性

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842102

Abdurrashid Hassan Shuaibu, F. Nabki, Y. Blaquière

引用次数: 2

Quick Compact Model Development Through Slow Transient Simulation: An Alternative Approach to Table Models for Emerging Nanodevices 通过慢速瞬态模拟快速紧凑模型开发:新兴纳米器件表模型的替代方法

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842125

Maximilian Reuter, Dakyung Lee, David Riehl, K. Hofmann

引用次数: 0

A 10-b 330nW Third-Order Predictive SAR ADC Dedicated to Neural Recording Brain Implants 一种专用于神经记录脑植入物的10- b330nw三阶预测SAR ADC

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9841982

Mohsen Namavar, R. Lotfi, A. M. Sodagar

{"title":"A 10-b 330nW Third-Order Predictive SAR ADC Dedicated to Neural Recording Brain Implants","authors":"Mohsen Namavar, R. Lotfi, A. M. Sodagar","doi":"10.1109/NEWCAS52662.2022.9841982","DOIUrl":"https://doi.org/10.1109/NEWCAS52662.2022.9841982","url":null,"abstract":"This paper reports on a predictive analog-todigital converter (ADC). The proposed ADC employs a linear predictive filter to prepare a prediction for the current sample based on the values of the previous digital codes. This leads to significant reduction in the mean bit cycle of the converter. It is shown in this work that this idea is significantly more effective for the digitization of biological signals (e.g., intra-cortical neural signals). Compared with other similar techniques available in the literature, the proposed predictive ADC is significantly more successful for small signal-to-noise ratios. The proposed algorithm results in 48% and 37% reduction in the converter’s mean bit cycle compared with the conventional and LSB-first structures, respectively. Designed and post-layout simulated in a 90-nm standard CMOS technology and operated at 200 kS/s with a supply voltage of 0.4 V, the 10-bit predictive ADC consumes 330 nW. The circuit occupies a core area of 0.025 mm2, achieves an ENOB of 9.42 bits, a figure-of-merit of 2.4 fJ/conv.-step, and an SFDR of 65.8 dB. The DNL and INL of the circuit are within 0.45 LSB and 0.56 LSB, respectively.","PeriodicalId":198335,"journal":{"name":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124316104","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

Low complex Hardware Architecture Design Methodology for Cubic Spline Interpolation Technique for Assistive Technologies 辅助技术三次样条插值技术的低复杂硬件体系结构设计方法

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842110

Ganesh Cheduluri, Swati Bhardwaj, G. Naik, Vidhumouli Hansigida, Appa Rao Nali, A. Acharyya

引用次数: 1

Towards Current-Mode Analog Implementation of Deep Neural Network Functions 深度神经网络函数的电流模式模拟实现

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842017

Shihao Wang, K. M. Al-Tamimi, Issam Hammad, K. El-Sankary

引用次数: 6

A Multilayer Perceptron (MLP) Regressor Network for Monitoring the Depth of Anesthesia 用于麻醉深度监测的多层感知器(MLP)回归网络

2022 20th IEEE Interregional NEWCAS Conference (NEWCAS) Pub Date : 2022-06-19 DOI: 10.1109/NEWCAS52662.2022.9842242

Muhammad Ibrahim Dutt, Wala Saadeh

{"title":"A Multilayer Perceptron (MLP) Regressor Network for Monitoring the Depth of Anesthesia","authors":"Muhammad Ibrahim Dutt, Wala Saadeh","doi":"10.1109/NEWCAS52662.2022.9842242","DOIUrl":"https://doi.org/10.1109/NEWCAS52662.2022.9842242","url":null,"abstract":"Monitoring the depth of anesthesia (DoA) during surgical procedures is very critical for patients’ health. Any inaccurate dosage of the anesthetic agents can result in postoperative health issues or complications such as comma or intraoperative awareness. The accurate estimation of DoA using the electroencephalogram (EEG) signals is still a problem in many DoA monitors. This paper proposes a novel approach for accurate DoA estimation based on Stationary Wavelet Transform (SWT) spectral features while using Multilayer Perceptron (MLP) Regressor Network for the prediction of DoA index. The regressor utilizes an optimized temporal, fractal, and spectral feature set to identify the patient’s conscious level irrespective of age and the type of anesthetic agent. The proposed algorithm is validated on 95 patients (age 5 months-67 years), (weight: 6 - 90 Kg). The anesthetic agents used in this study include Propofol, Sevoflurane, Isoflurane, Fentanyl, Ketamine, and Caudal. The proposed DoA regressor outperforms the state-of-the-art DoA prediction algorithms by predicting highly accurate DoA indexes with an overall Mean Absolute Error (MAE) of 0.014 and Mean Squared Error (MSE) of 0.02 while utilizing minimized feature set and a deep learning-based MLP regression network. The minimized feature set allows efficient implementation on-chip.","PeriodicalId":198335,"journal":{"name":"2022 20th IEEE Interregional NEWCAS Conference (NEWCAS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121263759","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: 2