2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献_第9页

A Wireless, Multielectrode, User-generic Ear EEG Recording System 一种无线、多电极、用户通用耳脑电图记录系统

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918700

Ryan Kaveh, Justin Doong, Andy Zhou, Carolyn Schwendeman, K. Gopalan, F. Burghardt, A. Arias, M. Maharbiz, R. Muller

引用次数: 6

A Current-Switching Communication Technique for Miniaturized Medical Implants 用于小型医疗植入物的电流开关通信技术

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918683

Weihao Wang, M. Tian, Hong Zhang, Bo Zhao

{"title":"A Current-Switching Communication Technique for Miniaturized Medical Implants","authors":"Weihao Wang, M. Tian, Hong Zhang, Bo Zhao","doi":"10.1109/BIOCAS.2019.8918683","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918683","url":null,"abstract":"Miniaturization of the medical implants is a key requirement for minimally-invasive surgery, while battery and antenna are the main bottlenecks. Wireless power transfer (WPT) is required to minimize or eliminate the battery, but the conventional inductive or capacitive WPT needs to implant a large inductor or capacitor to realize a relatively high efficiency. Based on the WPT, backscattering communication is widely used in medical implants due to its low power and low hardware cost. In conventional backscattering, the inductive or capacitive antenna of the implant should be large to improve the signal-to-noise ratio (SNR). In this work, we propose a new current-switching technique for the communication of medical implants. To avoid the use of antennas, the body tissue is utilized as the power-transfer medium, where a current loop is formed between the implanted and the external electrode pairs. The data of the medical implant controls its input impedance, and then switches the current loop, resulting in a data-modulated current in the external device. In this way, the external device can sense the data, achieving a high communication SNR without implanted antenna. A prototype is implemented to validate the proposed technique, where the implanted electrodes are as small as 200µ×200µm. Operating at 406 MHz of the standard medical band, the measurement shows that the signal-to-blocker ratio (SBR) achieves -35.7 dB, resulting in a high communication SNR of 21.9 dB at a data rate of 10 Mbps.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116965697","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

Live Demonstration: Lensless Highly Sensitive Fluorescence Imaging 现场演示:无透镜高灵敏度荧光成像

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918682

K. Sasagawa, Kenji Sugie, Yasumi Ohta, Mamiko Kawahara, M. Haruta, J. Ohta

引用次数: 0

Neuromorphic Sensory Integration for Combining Sound Source Localization and Collision Avoidance 结合声源定位与避碰的神经形态感觉整合

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919202

Thorben Schoepe, Daniel Gutierrez-Galan, J. P. Dominguez-Morales, A. Jiménez-Fernandez, A. Linares-Barranco, E. Chicca

{"title":"Neuromorphic Sensory Integration for Combining Sound Source Localization and Collision Avoidance","authors":"Thorben Schoepe, Daniel Gutierrez-Galan, J. P. Dominguez-Morales, A. Jiménez-Fernandez, A. Linares-Barranco, E. Chicca","doi":"10.1109/BIOCAS.2019.8919202","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919202","url":null,"abstract":"Animals combine various sensory cues with previously acquired knowledge to safely travel towards a target destination. In close analogy to biological systems, we propose a neuromorphic system which decides, based on auditory and visual input, how to reach a sound source without collisions. The development of this sensory integration system, which identifies the shortest possible path, is a key achievement towards autonomous robotics. The proposed neuromorphic system comprises two event based sensors (the eDVS for vision and the NAS for audition) and the SpiNNaker processor. Open loop experiments were performed to evaluate the system performances. In the presence of acoustic stimulation alone, the heading direction points to the direction of the sound source with a Pearson correlation coefficient of 0.89. When visual input is introduced into the network the heading direction always points at the direction of null optical flow closest to the sound source. Hence, the sensory integration network is able to find the shortest path to the sound source while avoiding obstacles. This work shows that a simple, task dependent mapping of sensory information can lead to highly complex and robust decisions.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122659313","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: 12

SenseBack – Implant considerations for an implantable neural stimulation and recording device SenseBack -植入式神经刺激和记录装置的植入考虑

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919046

I. Williams, Adrien Rapeaux, Jack Pearson, K. Nazarpour, E. Brunton, Song Luan, Yan Liu, T. Constandinou

引用次数: 6

An Imaging Platform for Real-Time In Vitro Microscopic Imaging for Lab-on-CMOS Systems 实验室cmos系统实时体外显微成像的成像平台

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919023

Bathiya Senevirathna, Sheung Lu, E. Smela, P. Abshire

引用次数: 5

Portable Bluetooth Microsystem for Electrochemically Modulated Nitric Oxide-Releasing Catheters 用于电化学调制一氧化氮释放导管的便携式蓝牙微系统

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919232

O. Novák, Kamila Konopińska, M. Meyerhoff, Richard B. Brown

引用次数: 0

A Slim Needle Neural Probe with 160 Active Recording Sites and Selectable ADCs 具有160个活动记录位点和可选adc的细针神经探针

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919180

Boyu Shui, Daniel De Dorigo, A. S. Herbawi, P. Ruther, O. Paul, Y. Manoli, M. Kuhl

引用次数: 2

Hardware Design and Experimental Demonstrations for Digital Acoustofluidic Biochips 数字声流生物芯片的硬件设计与实验演示

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919148

Zhanwei Zhong, Haodong Zhu, Peiran Zhang, J. Morizio, T. Huang, K. Chakrabarty

引用次数: 2

Logarithmic Neural Network Data Converters using Memristors for Biomedical Applications 生物医学应用用忆阻器的对数神经网络数据转换器

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919068

Loai Danial, Kanishka Sharma, Shivanshu Dwivedi, Shahar Kvatinsky

{"title":"Logarithmic Neural Network Data Converters using Memristors for Biomedical Applications","authors":"Loai Danial, Kanishka Sharma, Shivanshu Dwivedi, Shahar Kvatinsky","doi":"10.1109/BIOCAS.2019.8919068","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919068","url":null,"abstract":"Data converters are ubiquitous in electrical data driven systems, where they are heterogeneously distributed across the analog-digital interface. Unfortunately, conventional data converters trade off speed, power, and accuracy. Logarithmic analog-to-digital/digital-to-analog converters (ADC/DACs) are employed in biomedical applications where signals with high dynamic range are recorded. For the same input dynamic range of a linear ADC/DAC, a logarithmic one can efficiently quantize the sampled data by reducing the number of resolution bits, sampling rate, and power consumption, albeit with reduced accuracy for high amplitudes. Previously, we employed novel neural network architectures to design smart data converters that could be trained in real-time for general purpose applications, breaking through the speed-power-accuracy tradeoff, and using machine learning techniques and memristors for synaptic realization. In this paper, we report the results of SPICE simulations performed to train our converters to perform logarithmic quantization. The proposed architecture achieved a 77.19 pJ/conv FOM, 2.55 ENOB, 0.26 LSB INL, and 0.62 LSB DNL. These promising features will pave the way towards adaptive human-machine interfaces with continuous varying conditions for precision medicine applications.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122596803","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: 5