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2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)最新文献

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ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing ANT-UNet:用于病理图像处理的精确和耐噪分割
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919150
Yufei Chen, Tingtao Li, Qinming Zhang, Wei Mao, Nan Guan, Mei Tian, Hao Yu, Cheng Zhuo
{"title":"ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing","authors":"Yufei Chen, Tingtao Li, Qinming Zhang, Wei Mao, Nan Guan, Mei Tian, Hao Yu, Cheng Zhuo","doi":"10.1109/BIOCAS.2019.8919150","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919150","url":null,"abstract":"Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by the inconsistent annotation quality. In this paper, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues, which consists of a pre-processing module for data augmentation, a new neural network architecture, ANT-UNet, and a FCCRF inference module. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 4-23% accuracy improvement than other commonly used segmentation methods. Moreover, the proposed architecture is hardware-friendly and can be incorporated with a GPU acceleration flow to reach 24-128× speed-up.","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":"122023850","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: 4
A Soft-Decision Demodulator for WBAN Systems Using Stochastic Computing 基于随机计算的WBAN系统软判决解调器
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918990
Kaining Han, Junchao Wang, Qiang Fang
{"title":"A Soft-Decision Demodulator for WBAN Systems Using Stochastic Computing","authors":"Kaining Han, Junchao Wang, Qiang Fang","doi":"10.1109/BIOCAS.2019.8918990","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918990","url":null,"abstract":"Wireless body area network (WBAN) is an efficient fundamental technology for medical and healthcare applications. Soft decision receiver is attractive for the reason that the performance gains result in significant transmitting energy reduction. However, the soft decision demodulation (SDD) for π/4-DQPSK modulation specified in IEEE 802.15.6 standard suffers from high complexity and power consumption. Stochastic computing is a promising low hardware cost and low power consumption implementation candidate. In this paper, a novel stochastic computing based soft decision demodulation method is proposed to achieve low complexity and low power demodulation. According to the evaluation results, the proposed design utilizes advantages on hardware cost and power consumption with respect to the existing SDD schemes. In addition, the proposed design supports prior probability input, demonstrating a potential for turbo-like iterative receiver for WBAN systems to further improve the performance.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"22 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":"130210314","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: Wearable Body Area Network System Based on Low Power Body Channel Communication 现场演示:基于低功耗身体通道通信的可穿戴身体区域网络系统
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919024
Jingna Mao, Wuqi Wang, Guangxin Ding, Zhiwei Zhang
{"title":"Live Demonstration: Wearable Body Area Network System Based on Low Power Body Channel Communication","authors":"Jingna Mao, Wuqi Wang, Guangxin Ding, Zhiwei Zhang","doi":"10.1109/BIOCAS.2019.8919024","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919024","url":null,"abstract":"In wireless body area network (WBAN), capacitive coupling body channel communication (CC-BCC) has the potential to attain better energy efficiency over conventional wireless communication schemes. The CC-BCC scheme utilizes the human body as the forward signal transmission medium, reducing the path loss in wireless body-centric communications. This demonstration is a wearable body area network system based on low power body channel communication. It includes three wearable devices. Each wearable device mainly contains a temperature sensor, a micro-inertial measurement unit μ-IMU, and a capacitive coupled body channel communication (CC-BCC) module. In this demonstration, visitors can intuitively understand the scheme and application of body channel communication.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"76 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":"130225062","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
Pancreatic beta cell based optical biosensor and system for continuous glucose monitoring 基于胰腺β细胞的光学生物传感器和连续血糖监测系统
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919186
Bakul Vinchhi, Ninad Agashe, C. Boss, Aurélie Hermant, N. Bouché, U. Marchi, C. Dehollain
{"title":"Pancreatic beta cell based optical biosensor and system for continuous glucose monitoring","authors":"Bakul Vinchhi, Ninad Agashe, C. Boss, Aurélie Hermant, N. Bouché, U. Marchi, C. Dehollain","doi":"10.1109/BIOCAS.2019.8919186","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919186","url":null,"abstract":"A bio-electronics system that measures cytosolic [Ca2+] in vitro in the INS-1E pancreatic β-cells using a 430 nm excitation light with the excitation wavelength of the probe being at 435 nm was successfully implemented and tested. We transfected the INS-1E cells with a plasmid that is responsible for the FRET based [Ca2+] protein sensor complex, that emit light at both 485 nm and 535 nm. The F10 cell based biosensor and its working principle are described with protein structure homology modeling. The efficacy of absorption angle independent filters atop the photo-detector is demonstrated with a 1163% improvement in SNR. Oxygenation of cells alongside stimulant circulation is possible with the aid of a magnetic stirrer. The system successfully monitors glucose continuously with 2.5 mM stimulation. Continuously glucose monitoring with periodic 2.5 mM glucose stimulation and binary 11 mM glucose vs. KRBH were successful showing clear repeatable ratio changes.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"119 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":"130483598","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 0.8V Chopper Amplifier with 600mVpp Tolerance to Power-Line Interference for Neural Signal Acquisition 用于神经信号采集的0.8V斩波放大器，可承受600mVpp的电源线干扰
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919167
Deng Luo, Milin Zhang, Zhihua Wang
{"title":"A 0.8V Chopper Amplifier with 600mVpp Tolerance to Power-Line Interference for Neural Signal Acquisition","authors":"Deng Luo, Milin Zhang, Zhihua Wang","doi":"10.1109/BIOCAS.2019.8919167","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919167","url":null,"abstract":"This paper proposes a chopper amplifier working under 0.8V supply voltage implemented in TSMC 0.18um CMOS technology, enabling a 2.02uW per channel, while preserving a good tolerance of power-line interference (PLI) up to 600mVpp, a THD of -65.5dB, and high robustness against the PVT, by implementing a common-mode cancellation loop (CMCL) based on a feedback loop, a new offset cancellation loop (OCL), and a new very-lower transconductance (VLT) OTA. The measured mid-band gain is 43.3dB with a high-pass cut-off of 1.2Hz and a low-pass cut-off of 17kHz. The measured integrated noise is 0.75uVrms and 4.8uVrms in the frequency band of 1 − 200Hz and 0.2 − 17kHz, respectively, leading to a power efficiency factor (PEF) of 8.4 and 4.05.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"2 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":"130508734","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
Cost-Efficient Classification for Neurological Disease Detection 神经系统疾病检测的成本效益分类
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918702
Bingzhao Zhu, Milad Taghavi, Mahsa Shoaran
{"title":"Cost-Efficient Classification for Neurological Disease Detection","authors":"Bingzhao Zhu, Milad Taghavi, Mahsa Shoaran","doi":"10.1109/BIOCAS.2019.8918702","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918702","url":null,"abstract":"Cost-efficient machine learning is essential for on-chip processing of data in resource-limited applications such as brain implants, wearable sensors, and IoT devices. In this paper, we propose a hardware-friendly machine learning model based on gradient boosted decision trees for neurological disease detection. Our model combines fixed point quantization and cost-efficient inference to enable low-power embedded learning. Testing this model on the intracranial EEG data from 14 epilepsy patients, we can reduce the feature extraction cost by 53.1% and quantize the leaf weights with 4 bits, while maintaining the seizure detection performance. In a second experiment on Parkinsonian tremor detection from local field potentials of 12 patients, we achieve a 55.4% cost reduction and 12-bit leaf quantization. The proposed model offers a hardware-friendly solution for on-chip and real-time detection of neurological disorders.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"6 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":"126561494","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: 11
Live Demostration: Sensor fusion using EMG and vision for hand gesture classification in mobile applications 现场演示:传感器融合使用肌电图和视觉手势分类在移动应用程序
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919163
Enea Ceolini, Gemma Taverni, Lyes Khacef, M. Payvand, Elisa Donati
{"title":"Live Demostration: Sensor fusion using EMG and vision for hand gesture classification in mobile applications","authors":"Enea Ceolini, Gemma Taverni, Lyes Khacef, M. Payvand, Elisa Donati","doi":"10.1109/BIOCAS.2019.8919163","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919163","url":null,"abstract":"The demonstration shows a mobile application, called \"RELAX\", for hand gesture classification using multi-sensors fusion. In particular, we integrated the data collected by an electromyography (EMG) sensor with the events produced by an event-based vision sensor, the Dynamic Vision Sensor (DVS). The application runs real-time on any Android smartphone and it is able to recognize five gestures with an accuracy of up to 85%.This demonstration is associated with the track Bio-Inspired and Neuromorphic Circuits and Systems. Associated paper submission identifier: 8114.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"32 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":"125216545","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 Fast-Beamforming Technique for Blind-Adaptive Wireless Power Transfer towards Free-Flying Insects 面向自由飞行昆虫的盲自适应无线输电快速波束形成技术
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8919183
Yunshan Zhang, Zhiguo Shi, Junfeng Wu, M. Tian, Hong Zhang, Bo Zhao
{"title":"A Fast-Beamforming Technique for Blind-Adaptive Wireless Power Transfer towards Free-Flying Insects","authors":"Yunshan Zhang, Zhiguo Shi, Junfeng Wu, M. Tian, Hong Zhang, Bo Zhao","doi":"10.1109/BIOCAS.2019.8919183","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8919183","url":null,"abstract":"A free-flying insect is usually adopted to study the bio-signals for bionics. As an insect is not able to carry a large battery, far-field wireless power transfer (WPT) is required to maintain the uninterrupted operating. Conventional beamforming WPT technique can easily focus the power from a transmitting (Tx) array to a stationary receiving (Rx) target, while it takes a long time to reform the power beams towards a fast-flying insect. In this paper, we propose a fast-beamforming WPT technique to shorten the time duration of the blind-adaptive WPT. Instead of the constant perturbation factor in conventional beamforming WPT, our WPT system dynamically adjusts the perturbation factor during the powering-up process, which can achieve the optimized weight vector of the Tx power in a much shorter time. The proposed technique is compared with the conventional far-field blind-adaptive beamforming WPT, and the results indicate the reduction on the powering-up time towards a free-flying insect.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"45 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":"131474291","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
[BioCAS 2019 Front Matter] 【BioCAS 2019前沿内容】
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/biocas.2019.8919161
{"title":"[BioCAS 2019 Front Matter]","authors":"","doi":"10.1109/biocas.2019.8919161","DOIUrl":"https://doi.org/10.1109/biocas.2019.8919161","url":null,"abstract":"","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"146 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":"132352731","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
An impedance probing system for real-time intraoperative brain tumour tissue discrimination 术中实时识别脑肿瘤组织的阻抗探测系统
2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) Pub Date : 2019-10-01 DOI: 10.1109/BIOCAS.2019.8918743
Steven S. Wong, J. Ekanayake, Yan Liu, T. Constandinou
{"title":"An impedance probing system for real-time intraoperative brain tumour tissue discrimination","authors":"Steven S. Wong, J. Ekanayake, Yan Liu, T. Constandinou","doi":"10.1109/BIOCAS.2019.8918743","DOIUrl":"https://doi.org/10.1109/BIOCAS.2019.8918743","url":null,"abstract":"The ability to acquire realtime diagnostics of brain tissue intraoperatively represents a key goal in the field of brain tumour neurosurgery. This can greatly enhance the precision, extent and effectiveness of key surgical procedures such as those performed for brain tumour resection and biopsy. To achieve this requires a miniature, handheld tool which can perform intraoperative in situ, in-vivo characterisation of different types of tissues e.g. normal brain tissue versus tumour tissue. Here we explored the feasibility and requirements of implementing a portable impedance characterisation system for brain tumour detection. We proposed and implemented a novel system based on PCB-based instrumentation using a square four-electrode microendoscopic probe. The system uses a digital-to-analogue converter to generate a multi-tone sinusoid waveform, and a floating bi-directional voltage-to-current converter to output the differential stimulation current to one pair of electrodes. The other pair of electrodes are connected to the sensing circuit based on an instrumentation amplifier. The recorded data is pre-processed by the micro-controller and then analysed on a host computer. To evaluate the system, tetrapolar impedances have been recorded from a number of different electrode configurations to sense pre-defined resistance values. The overall system consumed 143mA current, achieved 0.1% linearity and 15µV noise level, with a maximum signal bandwidth of 100kHz. Initial experimental results on tissue were carried out on a piece of rib-eye steak. Electrical impedance maps (EIM) and contour plots were then reconstructed to represent the impedance value in different tissue region.","PeriodicalId":222264,"journal":{"name":"2019 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"6 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":"134292420","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
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