IEEE Transactions on Biomedical Circuits and Systems最新文献_第4页

Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics 生物信号动态自适应压缩感知中的超低功耗动态旋钮

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-19 DOI: 10.1109/TBCAS.2015.2497304

Aosen Wang, Feng Lin, Zhanpeng Jin, Wenyao Xu

{"title":"Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics","authors":"Aosen Wang, Feng Lin, Zhanpeng Jin, Wenyao Xu","doi":"10.1109/TBCAS.2015.2497304","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2497304","url":null,"abstract":"Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm 2 fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"579-592"},"PeriodicalIF":5.1,"publicationDate":"2016-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2497304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Systematic Design of a Quorum Sensing-Based Biosensor for Enhanced Detection of Metal Ion in Escherichia Coli 基于群体感应的大肠杆菌金属离子强化检测生物传感器系统设计

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-18 DOI: 10.1109/TBCAS.2015.2495151

Chih-Yuan Hsu, Bing-Kun Chen, Rei-Hsing Hu, Bor-Sen Chen

{"title":"Systematic Design of a Quorum Sensing-Based Biosensor for Enhanced Detection of Metal Ion in Escherichia Coli","authors":"Chih-Yuan Hsu, Bing-Kun Chen, Rei-Hsing Hu, Bor-Sen Chen","doi":"10.1109/TBCAS.2015.2495151","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2495151","url":null,"abstract":"With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. The non-degradability of heavy metals may lead to accumulation in food chains and the resulting toxicity could cause damage in organisms. Hence, detection techniques have gradually received attention. In this study, a quorum sensing (QS)-based amplifier is introduced to improve the detection performance of metal ion biosensing. The design utilizes diffusible signal molecules, which freely pass through the cell membrane into the environment to communicate with others. Bacteria cooperate via the cell-cell communication process, thereby displaying synchronous behavior, even if only a minority of the cells detect the metal ion. In order to facilitate the design, the ability of the engineered biosensor to detect metal ion is described in a steady state model. The design can be constructed according to user-oriented specifications by selecting adequate components from corresponding libraries, with the help of a genetic algorithm (GA)-based design method. The experimental results validate enhanced efficiency and detection performance of the quorum sensing-based biosensor of metal ions.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"38 1","pages":"593-601"},"PeriodicalIF":5.1,"publicationDate":"2016-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2495151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Low-Power ASIC Signal Processor for a Vestibular Prosthesis 用于前庭假体的低功耗ASIC信号处理器

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-18 DOI: 10.1109/TBCAS.2015.2495341

H. Toreyin, P. Bhatti

引用次数: 5

A CMOS Amperometric System for Multi-Neurotransmitter Detection 一种用于多神经递质检测的CMOS安培系统

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-08 DOI: 10.1109/TBCAS.2015.2490225

G. Massicotte, S. Carrara, G. Micheli, M. Sawan

引用次数: 18

An Integrated Circuit for Chip-Based Analysis of Enzyme Kinetics and Metabolite Quantification 一种基于芯片的酶动力学和代谢物定量分析集成电路

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-06 DOI: 10.1109/TBCAS.2015.2487603

B. C. Cheah, A. Macdonald, Christopher Martin, A. Streklas, Gordon Campbell, M. Al-Rawhani, B. Németh, J. Grant, M. Barrett, D. Cumming

{"title":"An Integrated Circuit for Chip-Based Analysis of Enzyme Kinetics and Metabolite Quantification","authors":"B. C. Cheah, A. Macdonald, Christopher Martin, A. Streklas, Gordon Campbell, M. Al-Rawhani, B. Németh, J. Grant, M. Barrett, D. Cumming","doi":"10.1109/TBCAS.2015.2487603","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2487603","url":null,"abstract":"We have created a novel chip-based diagnostic tools based upon quantification of metabolites using enzymes specific for their chemical conversion. Using this device we show for the first time that a solid-state circuit can be used to measure enzyme kinetics and calculate the Michaelis-Menten constant. Substrate concentration dependency of enzyme reaction rates is central to this aim. Ion-sensitive field effect transistors (ISFET) are excellent transducers for biosensing applications that are reliant upon enzyme assays, especially since they can be fabricated using mainstream microelectronics technology to ensure low unit cost, mass-manufacture, scaling to make many sensors and straightforward miniaturisation for use in point-of-care devices. Here, we describe an integrated ISFET array comprising 216 sensors. The device was fabricated with a complementary metal oxide semiconductor (CMOS) process. Unlike traditional CMOS ISFET sensors that use the Si3N4 passivation of the foundry for ion detection, the device reported here was processed with a layer of Ta2O5 that increased the detection sensitivity to 45 mV/pH unit at the sensor readout. The drift was reduced to 0.8 mV/hour with a linear pH response between pH 2-12. A high-speed instrumentation system capable of acquiring nearly 500 fps was developed to stream out the data. The device was then used to measure glucose concentration through the activity of hexokinase in the range of 0.05 mM-231 mM, encompassing glucose's physiological range in blood. Localised and temporal enzyme kinetics of hexokinase was studied in detail. These results present a roadmap towards a viable personal metabolome machine.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"10 1","pages":"721-730"},"PeriodicalIF":5.1,"publicationDate":"2016-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2487603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

Interference Resilient Sigma Delta-Based Pulse Oximeter 抗干扰Sigma - delta型脉搏血氧仪

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2016-01-04 DOI: 10.1109/TBCAS.2015.2501359

Mohsen Shokouhian, R. Morling, I. Kale

引用次数: 9

A Double-Side CMOS-CNT Biosensor Array With Padless Structure for Simple Bare-Die Measurements in a Medical Environment 用于医疗环境中简单裸模测量的双面无衬垫结构CMOS-CNT生物传感器阵列

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2015-12-29 DOI: 10.1109/TBCAS.2015.2500911

Jin-Hong Ahn, Sang-Hoon Hong, Youngjune Park

{"title":"A Double-Side CMOS-CNT Biosensor Array With Padless Structure for Simple Bare-Die Measurements in a Medical Environment","authors":"Jin-Hong Ahn, Sang-Hoon Hong, Youngjune Park","doi":"10.1109/TBCAS.2015.2500911","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2500911","url":null,"abstract":"This paper presents a double-side CMOS-carbon nanotube (CNT) sensor array for simple bare-die measurements in a medical environment based on a 0.35 μm standard CMOS process. This scheme allows robust measurements due to its inherent back-side rectifying diodes with a high latch-up resistance. In particular, instead of using pads, only two contact metal structures: a wide ring structure around the sensor area on the front side and a plate structure at the backside are used for both power and single I/O line. The back-side rectification is made possible by creating VDD and VSS through the back-side and front-side, respectively. The single I/O line is conditioned such that it doubles as either the power source or the ground, depending on whether the chip is face down or face up. A modified universal asynchronous receiver/transmitter (UART) serial communication scheme with pulse based I/O signal transmission is developed to reduce the power degradation during the signaling intervals. In addition, communication errors and I/O power dissipation for the receiver path are minimized by using level sensitive switch control and double sampling difference amplifier. In order to implement these special functions, a controller chip with a special I/O protocol is designed. Using this controller chip, issuing commands and receiving data can both be performed on a single line and the results are flexibly measured through either the backside or the front side of the chip contacts. As a result, a stable operation of under 150 mW maximum power at 2 MHz data rate can be achieved. The double-side chips with 32 × 32 and 64 × 64 sensor arrays occupy areas of 1.9×2.3 mm2 and 3.7×3.9 mm2, respectively.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"815-824"},"PeriodicalIF":5.1,"publicationDate":"2015-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2500911","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A 6.45 $mu{rm W}$ Self-Powered SoC With Integrated Energy-Harvesting Power Management and ULP Asymmetric Radios for Portable Biomedical Systems 6.45 $mu{rm W}$自供电SoC集成能量收集电源管理和ULP非对称无线电便携式生物医学系统

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2015-12-28 DOI: 10.1109/TBCAS.2015.2498643

Abhishek Roy, Alicia Klinefelter, Farah B. Yahya, Xing Chen, Luis Gonzalez-Guerrero, Christopher J. Lukas, Divya Akella, James Boley, Kyle Craig, M. Faisal, Seunghyun Oh, N. Roberts, Y. Shakhsheer, A. Shrivastava, D. Vasudevan, D. Wentzloff, B. Calhoun

{"title":"A 6.45 $mu{rm W}$ Self-Powered SoC With Integrated Energy-Harvesting Power Management and ULP Asymmetric Radios for Portable Biomedical Systems","authors":"Abhishek Roy, Alicia Klinefelter, Farah B. Yahya, Xing Chen, Luis Gonzalez-Guerrero, Christopher J. Lukas, Divya Akella, James Boley, Kyle Craig, M. Faisal, Seunghyun Oh, N. Roberts, Y. Shakhsheer, A. Shrivastava, D. Vasudevan, D. Wentzloff, B. Calhoun","doi":"10.1109/TBCAS.2015.2498643","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2498643","url":null,"abstract":"This paper presents a batteryless system-on-chip (SoC) that operates off energy harvested from indoor solar cells and/or thermoelectric generators (TEGs) on the body. Fabricated in a commercial 0.13 μW process, this SoC sensing platform consists of an integrated energy harvesting and power management unit (EH-PMU) with maximum power point tracking, multiple sensing modalities, programmable core and a low power microcontroller with several hardware accelerators to enable energy-efficient digital signal processing, ultra-low-power (ULP) asymmetric radios for wireless transmission, and a 100 nW wake-up radio. The EH-PMU achieves a peak end-to-end efficiency of 75% delivering power to a 100 μA load. In an example motion detection application, the SoC reads data from an accelerometer through SPI, processes it, and sends it over the radio. The SPI and digital processing consume only 2.27 μW, while the integrated radio consumes 4.18 μW when transmitting at 187.5 kbps for a total of 6.45 μW.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"862-874"},"PeriodicalIF":5.1,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2498643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 74

Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC 在ADC内直接实现矩阵乘法实现低能量分类系统

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2015-12-28 DOI: 10.1109/TBCAS.2015.2500101

Zhuo Wang, Jintao Zhang, N. Verma

{"title":"Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC","authors":"Zhuo Wang, Jintao Zhang, N. Verma","doi":"10.1109/TBCAS.2015.2500101","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2500101","url":null,"abstract":"In wearable and implantable medical-sensor applications, low-energy classification systems are of importance for deriving high-quality inferences locally within the device. Given that sensor instrumentation is typically followed by A-D conversion, this paper presents a system implementation wherein the majority of the computations required for classification are implemented within the ADC. To achieve this, first an algorithmic formulation is presented that combines linear feature extraction and classification into a single matrix transformation. Second, a matrix-multiplying ADC (MMADC) is presented that enables multiplication between an analog input sample and a digital multiplier, with negligible additional energy beyond that required for A-D conversion. Two systems mapped to the MMADC are demonstrated: (1) an ECG-based cardiac arrhythmia detector; and (2) an image-pixel-based facial gender detector. The RMS error over all multiplication performed, normalized to the RMS of ideal multiplication results is 0.018. Further, compared to idealized versions of conventional systems, the energy savings obtained are estimated to be 13× and 29×, respectively, while achieving similar level of performance.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"825-837"},"PeriodicalIF":5.1,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2500101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications 面向大数据应用的四并行MIMD架构节能可扩展深度学习/推理处理器

IF 5.1 2区医学

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 2015-12-01 DOI: 10.1109/TBCAS.2015.2504563

Seongwook Park, Junyoung Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, H. Yoo

{"title":"An Energy-Efficient and Scalable Deep Learning/Inference Processor With Tetra-Parallel MIMD Architecture for Big Data Applications","authors":"Seongwook Park, Junyoung Park, Kyeongryeol Bong, Dongjoo Shin, Jinmook Lee, Sungpill Choi, H. Yoo","doi":"10.1109/TBCAS.2015.2504563","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2504563","url":null,"abstract":"Deep Learning algorithm is widely used for various pattern recognition applications such as text recognition, object recognition and action recognition because of its best-in-class recognition accuracy compared to hand-crafted algorithm and shallow learning based algorithms. Long learning time caused by its complex structure, however, limits its usage only in high-cost servers or many-core GPU platforms so far. On the other hand, the demand on customized pattern recognition within personal devices will grow gradually as more deep learning applications will be developed. This paper presents a SoC implementation to enable deep learning applications to run with low cost platforms such as mobile or portable devices. Different from conventional works which have adopted massively-parallel architecture, this work adopts task-flexible architecture and exploits multiple parallelism to cover complex functions of convolutional deep belief network which is one of popular deep learning/inference algorithms. In this paper, we implement the most energy-efficient deep learning and inference processor for wearable system. The implemented 2.5 mm ×4.0 mm deep learning/inference processor is fabricated using 65 nm 8-metal CMOS technology for a battery-powered platform with real-time deep inference and deep learning operation. It consumes 185 mW average power, and 213.1 mW peak power at 200 MHz operating frequency and 1.2 V supply voltage. It achieves 411.3 GOPS peak performance and 1.93 TOPS/W energy efficiency, which is 2.07× higher than the state-of-the-art.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"838-848"},"PeriodicalIF":5.1,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2504563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62966130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 37