2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献_第8页

11nW Signal Acquisition Platform for Remote Biosensing 11nW生物遥感信号采集平台

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919128

Albert Gancedo, Omer Can Akgun, W. Serdijn

引用次数: 1

Live Demonstration: A Portable ISFET Platform for PoC Diagnosis Powered by Solar Energy 现场演示:太阳能驱动的PoC诊断便携式ISFET平台

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919227

Miguel Cacho-Soblechero, Š. Karolčík, Dorian Haci, Chiara Cicatiello, Charalampos Maxoutis, P. Georgiou

引用次数: 1

Sensor-Array Optimization Based on Mutual Information for Sanitation-Related Malodor Alerts 基于互信息的卫生相关恶臭报警传感器阵列优化

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919132

Jin Zhou, C. Welling, S. Kawadiya, M. Deshusses, S. Grego, K. Chakrabarty

引用次数: 4

Real Time Electrocardiogram Annotation with a Long Short Term Memory Neural Network 基于长短期记忆神经网络的实时心电图标注

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918723

Federico Corradi, J. Buil, H. D. Cannière, W. Groenendaal, P. Vandervoort

{"title":"Real Time Electrocardiogram Annotation with a Long Short Term Memory Neural Network","authors":"Federico Corradi, J. Buil, H. D. Cannière, W. Groenendaal, P. Vandervoort","doi":"10.1109/BIOCAS.2019.8918723","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918723","url":null,"abstract":"Continuous monitoring of electrocardiogram from wearable devices can enable early detection of heart diseases. Ubiquitous monitoring on wearable electronics requires a novel class of algorithms that are low-power and have low-memory requirements. This work proposes a wearable compatible, and automatic solution for annotating Electrocardiogram (ECG) recordings while maintaining high accuracy of detection when users are carrying daily activities such as sitting, walking, and resting. We validate our solution with two Physionet datasets: the MITDB [1] (Boston’s Beth Israel Hospital and MIT Arrhythmia Database), and the EDB [2] (European ST-T Database). In addition, we validate our method on a newly recorded dataset in collaboration with the ’Ziekenhuis Oost-Limburg’ Hospital1 that has been collected using a prototype wearable device [3]. Our solution exploits a recurrent neural network that achieves an average F1 score of 94.8% over all three datasets. Our solution achieves better generalization performance than the gold standard method Pan Tompkins which achieves an average F1 score of 93%. In addition, our method can be extended to full ECG annotation. We used the QTDB dataset [4] and we report an accuracy of 91.6% while annotating all 5 waves (P-Q-R-S-T) of the ECG complex.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"96 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":"122576209","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

SAW Based Passively Bioimpedance Sensing for Zero-Power Wearable Applications of Biosensors 基于SAW的无源生物阻抗传感零功耗可穿戴生物传感器

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919051

Xicai Yue, J. Kiely, C. McLeod, P. Wraith

{"title":"SAW Based Passively Bioimpedance Sensing for Zero-Power Wearable Applications of Biosensors","authors":"Xicai Yue, J. Kiely, C. McLeod, P. Wraith","doi":"10.1109/BIOCAS.2019.8919051","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919051","url":null,"abstract":"A novel passive bio-sensing method, which electrically connects a conventional biosensor to a surface acoustic wave (SAW) device, is proposed for passively sensing bio-impedance for zero-power wearable applications. The biosensor attached to the SAW as the load of the tag (the SAW acting as the loadable reflector), makes the measuring of the biosensor fully passive without power supplied to sensor side, being suitable for power restricted applications. Simulations showed that the paramagnetic particle enhanced biosensor has a better measurement accuracy than that of an ordinary biosensor. The feasibility of this new biosensor has been demonstrated by the preliminary experiment of using a 50ns pulse train containing RF signal to excite the new device, which is implemented by a 3.5 mm diameter coil-shaped electrode of a biosensor connected to a 433MHz single port SAW resonator, and then to collect and analyze the reflected signal. This passive solution of getting bio-impedance information using a SAW connected conventional biosensor, enables massive production of the impedance loaded SAW biosensors.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"33 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":"122586314","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: Tactile Sensory Feedback System based on UWB Optical Link for Prosthetics 现场演示:基于超宽带光链路的触觉感官反馈系统用于假肢

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919060

M. Saleh, Guido Di Patrizio Stanchieri, Mirco Sciulli, A. Marcellis, Yahya Abbass, A. Ibrahim, M. Valle, M. Faccio, E. Palange

引用次数: 0

Memory-Centric Neuromorphic Computing With Nanodevices 以纳米器件为中心的记忆神经形态计算

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919010

D. Querlioz, J. Grollier, T. Hirtzlin, Jacques-Olivier Klein, E. Nowak, E. Vianello, M. Bocquet, J. Portal, M. Romera, P. Talatchian

{"title":"Memory-Centric Neuromorphic Computing With Nanodevices","authors":"D. Querlioz, J. Grollier, T. Hirtzlin, Jacques-Olivier Klein, E. Nowak, E. Vianello, M. Bocquet, J. Portal, M. Romera, P. Talatchian","doi":"10.1109/BIOCAS.2019.8919010","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919010","url":null,"abstract":"When performing artificial intelligence, CPUs and GPUs consume considerably more energy for moving data between logic and memory units than for doing arithmetic. Brains, by contrast, achieve superior energy efficiency by fusing logic and memory entirely. Currently, emerging memory nanodevices give us an opportunity to reproduce this concept. In this overview paper, we look at neuroscience inspiration to extract lessons on the design of memory-centric neuromorphic systems. We study the reliance of brains on approximate memory strategies, which can be translated to AI. We give the example of a hardware binarized neural network with resistive memory. Based on measurements on a hybrid CMOS/resistive memory chip, we see that such systems can exploit the properties of emerging memories without error correction, and achieve extremely high energy efficiency. Second, we see that brains use the physics of their memory devices in a way much richer than only storage. This can inspire radical electronic designs, where memory devices become a core part of computing. We have for example fabricated neural networks where magnetic memories are used as nonlinear oscillators to implement neurons, and their electrical couplings implement synapses. Such designs can harness the rich physics of nanodevices, without suffering from their drawbacks.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"17 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":"124053828","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

A reconfigurable cyclic ADC for biomedical applications 用于生物医学应用的可重构循环ADC

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919110

Amandeep Kaur, Deepak Mishra

{"title":"A reconfigurable cyclic ADC for biomedical applications","authors":"Amandeep Kaur, Deepak Mishra","doi":"10.1109/BIOCAS.2019.8919110","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919110","url":null,"abstract":"Bio-signals such as electroencephalogram (EEG) contain low activity regions often called B-noise and high activity regions called active potentials. The high activity regions are more important as compared to their counterpart. In addition, the signals are considerably sparse in the low activity regions. Thus a full n-bit conversion of low activity samples into digital domain increases readout power and reduces data acquisition rate of analog to digital converter (ADC). To alleviate these problems, a reconfigurable cyclic ADC is presented in this paper. Input range and conversion cycles of the proposed ADC are varied according to the samples of the neural signal. The high activity region samples are resolved using conventional n-bits, however, the low activity region is resolved using less number of bits. This saves readout power and also reduces the digital data content. The proposed ADC is designed and fabricated in UMC 180 nm CMOS technology. The ADC operates at a sampling rate of 200 kS/s and consumes 61.8 µW of power. The chip occupies an area of 0.031 mm2. Using reconfiguration, the power saving of 28.6% is achieved compared to the conventional n-bit full conversion.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"38 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":"115953049","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

Multispectral Near-infrared Imaging Technologies for Nonmydriatic Fundus Camera 非散光眼底相机的多光谱近红外成像技术

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918695

H. Takehara, H. Sumi, Wang Ze, T. Kondo, M. Haruta, K. Sasagawa, J. Ohta

引用次数: 5

AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing AdaptHD:大脑启发的超维计算的自适应高效训练

2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918974

M. Imani, Justin Morris, Samuel Bosch, Helen Shu, G. Micheli, T. Simunic

{"title":"AdaptHD: Adaptive Efficient Training for Brain-Inspired Hyperdimensional Computing","authors":"M. Imani, Justin Morris, Samuel Bosch, Helen Shu, G. Micheli, T. Simunic","doi":"10.1109/BIOCAS.2019.8918974","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918974","url":null,"abstract":"Brain-inspired Hyperdimensional (HD) computing is a promising solution for energy-efficient classification. HD emulates cognition tasks by exploiting long-size vectors instead of working with numeric values used in contemporary processors. However, the existing HD computing algorithms have lack of controllability on the training iterations which often results in slow training or divergence. In this work, we propose AdaptHD, an adaptive learning approach based on HD computing to address the HD training issues. AdaptHD introduces the definition of learning rate in HD computing and proposes two approaches for adaptive training: iteration-dependent and data-dependent. In the iteration-dependent approach, AdaptHD uses a large learning rate to speedup the training procedure in the first iterations, and then adaptively reduces the learning rate depending on the slope of the error rate. In the data-dependent approach, AdaptHD changes the learning rate for each data point depending on how far off the data was misclassified. Our evaluations on a wide range of classification applications show that AdaptHD achieves 6.9× speedup and 6.3× energy efficiency improvement during training as compared to the state-of-the-art HD computing algorithm.","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":"130944835","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: 27