Pub Date : 2016-06-01DOI: 10.1109/TBCAS.2015.2480035
Libin George, G. Gargiulo, T. Lehmann, T. J. Hamilton
This paper presents the design of a reconfigurable buck-boost switched-capacitor DC-DC converter suitable for use in a wide range of biomedical implants. The proposed converter has an extremely small footprint and uses a novel control method that allows coarse and fine control of the output voltage. The converter uses adaptive gain control, discrete frequency scaling and pulse-skipping schemes to regulate the power delivered to a range of output voltages and loads. Adaptive gain control is used to implement variable switching gain ratios from a reconfigurable power stage and thereby make coarse steps in output voltage. A discrete frequency scaling controller makes discrete changes in switching frequency to vary the power delivered to the load and perform fine tuning when the output voltage is within 10% of the target output voltage. The control architecture is predominately digital and it has been implemented as part of a fully-integrated switched-capacitor converter design using a standard bulk CMOS 0.18 μm process. Measured results show that the converter has an output voltage range of 1.0 to 2.2 V, can deliver up to 7.5 mW of load power and efficiency up to 75% using an active area of only 0.04 mm2, which is significantly smaller than that of other designs. This low-area, low-complexity reconfigurable power converter can support low-power circuits in biomedical implant applications.
{"title":"A 0.04 mm$^2$ Buck-Boost DC-DC Converter for Biomedical Implants Using Adaptive Gain and Discrete Frequency Scaling Control","authors":"Libin George, G. Gargiulo, T. Lehmann, T. J. Hamilton","doi":"10.1109/TBCAS.2015.2480035","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2480035","url":null,"abstract":"This paper presents the design of a reconfigurable buck-boost switched-capacitor DC-DC converter suitable for use in a wide range of biomedical implants. The proposed converter has an extremely small footprint and uses a novel control method that allows coarse and fine control of the output voltage. The converter uses adaptive gain control, discrete frequency scaling and pulse-skipping schemes to regulate the power delivered to a range of output voltages and loads. Adaptive gain control is used to implement variable switching gain ratios from a reconfigurable power stage and thereby make coarse steps in output voltage. A discrete frequency scaling controller makes discrete changes in switching frequency to vary the power delivered to the load and perform fine tuning when the output voltage is within 10% of the target output voltage. The control architecture is predominately digital and it has been implemented as part of a fully-integrated switched-capacitor converter design using a standard bulk CMOS 0.18 μm process. Measured results show that the converter has an output voltage range of 1.0 to 2.2 V, can deliver up to 7.5 mW of load power and efficiency up to 75% using an active area of only 0.04 mm2, which is significantly smaller than that of other designs. This low-area, low-complexity reconfigurable power converter can support low-power circuits in biomedical implant applications.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2480035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964649","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}
Pub Date : 2016-06-01DOI: 10.1109/TBCAS.2015.2468052
G. Guo, Waichiu Ng, Jie Yuan, Suwen Li, M. Chan
Microelectrode array (MEA) can be used in the study of neurodegenerative diseases by monitoring the chemical neurotransmitter release and the electrical potential simultaneously at the cellular level. Currently, the MEA technology is migrating to more electrodes and higher electrode density, which raises power and area constraints on the design of acquisition IC. In this paper, we report the design of a 200-channel dual-mode acquisition IC with highly efficient usage of power and area. Under the constraints of target noise and fast settling, the current channel design saves power by including a novel current buffer biased in discrete time (DT) before the TIA (transimpedance amplifier). The 200 channels are sampled at 20 kS/s and quantized by column-wise SAR ADCs. The prototype IC was fabricated in a 0.18 μm CMOS process. Silicon measurements show the current channel has 21.6 pArms noise with cyclic voltammetry (CV) and 0.48 pArms noise with constant amperometry (CA) while consuming 12.1 μW. The voltage channel has 4.07 μVrms noise in the bandwidth of 100 kHz and 0.2% nonlinearity while consuming 9.1 μW. Each channel occupies 0.03 mm2 area, which is among the smallest.
{"title":"A 200-Channel Area-Power-Efficient Chemical and Electrical Dual-Mode Acquisition IC for the Study of Neurodegenerative Diseases","authors":"G. Guo, Waichiu Ng, Jie Yuan, Suwen Li, M. Chan","doi":"10.1109/TBCAS.2015.2468052","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2468052","url":null,"abstract":"Microelectrode array (MEA) can be used in the study of neurodegenerative diseases by monitoring the chemical neurotransmitter release and the electrical potential simultaneously at the cellular level. Currently, the MEA technology is migrating to more electrodes and higher electrode density, which raises power and area constraints on the design of acquisition IC. In this paper, we report the design of a 200-channel dual-mode acquisition IC with highly efficient usage of power and area. Under the constraints of target noise and fast settling, the current channel design saves power by including a novel current buffer biased in discrete time (DT) before the TIA (transimpedance amplifier). The 200 channels are sampled at 20 kS/s and quantized by column-wise SAR ADCs. The prototype IC was fabricated in a 0.18 μm CMOS process. Silicon measurements show the current channel has 21.6 pArms noise with cyclic voltammetry (CV) and 0.48 pArms noise with constant amperometry (CA) while consuming 12.1 μW. The voltage channel has 4.07 μVrms noise in the bandwidth of 100 kHz and 0.2% nonlinearity while consuming 9.1 μW. Each channel occupies 0.03 mm2 area, which is among the smallest.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2468052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964323","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}
Pub Date : 2016-06-01DOI: 10.1109/TBCAS.2015.2477264
Zisheng Zhang, K. Parhi
Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a total of 44 features including 8 absolute spectral powers, 8 relative spectral powers and 28 spectral power ratios are extracted every two seconds using a 4-second window with a 50% overlap. These features are then ranked and selected in a patient-specific manner using a two-step feature selection. Selected features are further processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains 80 seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains 78 seizures from 17 children in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100% and an average false positive rate (FPR) of 0.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68% and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is always left out from the training set. The proposed algorithm also has a low complexity as the spectral powers can be computed using FFT. The area and power consumption of the proposed linear SVM are 2 to 3 orders of magnitude less than a radial basis function kernel SVM (RBF-SVM) classifier. Furthermore, the total energy consumption of a system using linear SVM is reduced by 8% to 23% compared to system using RBF-SVM.
{"title":"Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power","authors":"Zisheng Zhang, K. Parhi","doi":"10.1109/TBCAS.2015.2477264","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2477264","url":null,"abstract":"Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a total of 44 features including 8 absolute spectral powers, 8 relative spectral powers and 28 spectral power ratios are extracted every two seconds using a 4-second window with a 50% overlap. These features are then ranked and selected in a patient-specific manner using a two-step feature selection. Selected features are further processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains 80 seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains 78 seizures from 17 children in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100% and an average false positive rate (FPR) of 0.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68% and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is always left out from the training set. The proposed algorithm also has a low complexity as the spectral powers can be computed using FFT. The area and power consumption of the proposed linear SVM are 2 to 3 orders of magnitude less than a radial basis function kernel SVM (RBF-SVM) classifier. Furthermore, the total energy consumption of a system using linear SVM is reduced by 8% to 23% compared to system using RBF-SVM.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2477264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964682","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}
Pub Date : 2016-06-01DOI: 10.1109/TBCAS.2015.2487300
Sinan Hersek, H. Toreyin, O. Inan
We present a robust vector bioimpedance measurement system for longitudinal knee joint health assessment, capable of acquiring high resolution static (slowly varying over the course of hours to days) and dynamic (rapidly varying on the order of milli-seconds) bioresistance and bioreactance signals. Occupying an area of 78×90 mm2 and consuming 0.25 W when supplied with ±5 V, the front-end achieves a dynamic range of 345 Ω and noise floor of 0.018 mΩrms (resistive) and 0.055 mΩrms (reactive) within a bandwidth of 0.1-20 Hz. A microcontroller allows real-time calibration to minimize errors due to environmental variability (e.g., temperature) that can be experienced outside of lab environments, and enables data storage on a micro secure digital card. The acquired signals are then processed using customized physiology-driven algorithms to extract musculoskeletal (edema) and cardiovascular (local blood volume pulse) features from the knee joint. In a feasibility study, we found statistically significant differences between the injured and contralateral static knee impedance measures for two subjects with recent unilateral knee injury compared to seven controls. Specifically, the impedance was lower for the injured knees, supporting the physiological expectations for increased edema and damaged cell membranes. In a second feasibility study, we demonstrate the sensitivity of the dynamic impedance measures with a cold-pressor test, with a 20 mΩ decrease in the pulsatile resistance associated with increased downstream peripheral vascular resistance. The proposed system will serve as a foundation for future efforts aimed at quantifying joint health status continuously during normal daily life.
{"title":"A Robust System for Longitudinal Knee Joint Edema and Blood Flow Assessment Based on Vector Bioimpedance Measurements","authors":"Sinan Hersek, H. Toreyin, O. Inan","doi":"10.1109/TBCAS.2015.2487300","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2487300","url":null,"abstract":"We present a robust vector bioimpedance measurement system for longitudinal knee joint health assessment, capable of acquiring high resolution static (slowly varying over the course of hours to days) and dynamic (rapidly varying on the order of milli-seconds) bioresistance and bioreactance signals. Occupying an area of 78×90 mm2 and consuming 0.25 W when supplied with ±5 V, the front-end achieves a dynamic range of 345 Ω and noise floor of 0.018 mΩrms (resistive) and 0.055 mΩrms (reactive) within a bandwidth of 0.1-20 Hz. A microcontroller allows real-time calibration to minimize errors due to environmental variability (e.g., temperature) that can be experienced outside of lab environments, and enables data storage on a micro secure digital card. The acquired signals are then processed using customized physiology-driven algorithms to extract musculoskeletal (edema) and cardiovascular (local blood volume pulse) features from the knee joint. In a feasibility study, we found statistically significant differences between the injured and contralateral static knee impedance measures for two subjects with recent unilateral knee injury compared to seven controls. Specifically, the impedance was lower for the injured knees, supporting the physiological expectations for increased edema and damaged cell membranes. In a second feasibility study, we demonstrate the sensitivity of the dynamic impedance measures with a cold-pressor test, with a 20 mΩ decrease in the pulsatile resistance associated with increased downstream peripheral vascular resistance. The proposed system will serve as a foundation for future efforts aimed at quantifying joint health status continuously during normal daily life.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2487300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964914","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}
Pub Date : 2016-05-19DOI: 10.1109/TBCAS.2016.2539244
Yih-Chun Cheng, P. Tsai, Minghao Huang
Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.
{"title":"Matrix-Inversion-Free Compressed Sensing With Variable Orthogonal Multi-Matching Pursuit Based on Prior Information for ECG Signals","authors":"Yih-Chun Cheng, P. Tsai, Minghao Huang","doi":"10.1109/TBCAS.2016.2539244","DOIUrl":"https://doi.org/10.1109/TBCAS.2016.2539244","url":null,"abstract":"Low-complexity compressed sensing (CS) techniques for monitoring electrocardiogram (ECG) signals in wireless body sensor network (WBSN) are presented. The prior probability of ECG sparsity in the wavelet domain is first exploited. Then, variable orthogonal multi-matching pursuit (vOMMP) algorithm that consists of two phases is proposed. In the first phase, orthogonal matching pursuit (OMP) algorithm is adopted to effectively augment the support set with reliable indices and in the second phase, the orthogonal multi-matching pursuit (OMMP) is employed to rescue the missing indices. The reconstruction performance is thus enhanced with the prior information and the vOMMP algorithm. Furthermore, the computation-intensive pseudo-inverse operation is simplified by the matrix-inversion-free (MIF) technique based on QR decomposition. The vOMMP-MIF CS decoder is then implemented in 90 nm CMOS technology. The QR decomposition is accomplished by two systolic arrays working in parallel. The implementation supports three settings for obtaining 40, 44, and 48 coefficients in the sparse vector. From the measurement result, the power consumption is 11.7 mW at 0.9 V and 12 MHz. Compared to prior chip implementations, our design shows good hardware efficiency and is suitable for low-energy applications.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2016.2539244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965892","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}
Pub Date : 2016-04-25DOI: 10.1109/TBCAS.2016.2515541
Ahmed Ibrahim, M. Kiani
Power transmission efficiency (PTE) has been the key parameter for wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions. It has been suggested that for mm-sized implants increasing the power carrier frequency (fp) of the WPT link to hundreds of MHz improves PTE. However, increasing fp significantly reduces the maximum allowable power that can be transmitted under the specific absorption rate (SAR) constraints. This paper presents a new figure-of-merit (FoM) and a design methodology for optimal WPT to mm-sized implants via inductive coupling by striking a balance between PTE and maximum delivered power under SAR constraints (PL,SAR). First, the optimal mm-sized receiver (Rx) coil geometry is identified for a wide range of fp to maximize the Rx coil quality factor (Q). Secondly, the optimal transmitter (Tx) coil geometry and fp are found to maximize the proposed FoM under a low-loss Rx matched-load condition. Finally, proper Tx coil and tissue spacing is identified based on FoM at the optimal fp. We demonstrate that fp in order of tens of MHz still offer higher PL,SAR and FoM, which is key in applications that demand high power such as optogenetics. An inductive link to power a 1 mm 3 implant was designed based on our FoM and verified through full-wave electromagnetic field simulations and measurements using de-embedding method. In our measurements, an Rx coil with 1 mm diameter, located 10 mm inside the tissue, achieved PTE and PL,SAR of 1.4% and 2.2 mW at fp of 20 MHz, respectively.
{"title":"A Figure-of-Merit for Design and Optimization of Inductive Power Transmission Links for Millimeter-Sized Biomedical Implants","authors":"Ahmed Ibrahim, M. Kiani","doi":"10.1109/TBCAS.2016.2515541","DOIUrl":"https://doi.org/10.1109/TBCAS.2016.2515541","url":null,"abstract":"Power transmission efficiency (PTE) has been the key parameter for wireless power transmission (WPT) to biomedical implants with millimeter (mm) dimensions. It has been suggested that for mm-sized implants increasing the power carrier frequency (fp) of the WPT link to hundreds of MHz improves PTE. However, increasing fp significantly reduces the maximum allowable power that can be transmitted under the specific absorption rate (SAR) constraints. This paper presents a new figure-of-merit (FoM) and a design methodology for optimal WPT to mm-sized implants via inductive coupling by striking a balance between PTE and maximum delivered power under SAR constraints (PL,SAR). First, the optimal mm-sized receiver (Rx) coil geometry is identified for a wide range of fp to maximize the Rx coil quality factor (Q). Secondly, the optimal transmitter (Tx) coil geometry and fp are found to maximize the proposed FoM under a low-loss Rx matched-load condition. Finally, proper Tx coil and tissue spacing is identified based on FoM at the optimal fp. We demonstrate that fp in order of tens of MHz still offer higher PL,SAR and FoM, which is key in applications that demand high power such as optogenetics. An inductive link to power a 1 mm 3 implant was designed based on our FoM and verified through full-wave electromagnetic field simulations and measurements using de-embedding method. In our measurements, an Rx coil with 1 mm diameter, located 10 mm inside the tissue, achieved PTE and PL,SAR of 1.4% and 2.2 mW at fp of 20 MHz, respectively.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2016.2515541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965829","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}
Pub Date : 2016-04-18DOI: 10.1109/TBCAS.2016.2579164
S. Friedmann, J. Schemmel, Andreas Grübl, Andreas Hartel, Matthias Hock, K. Meier
We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.
{"title":"Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System","authors":"S. Friedmann, J. Schemmel, Andreas Grübl, Andreas Hartel, Matthias Hock, K. Meier","doi":"10.1109/TBCAS.2016.2579164","DOIUrl":"https://doi.org/10.1109/TBCAS.2016.2579164","url":null,"abstract":"We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2016.2579164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62966029","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}
Pub Date : 2016-04-01DOI: 10.1109/TBCAS.2015.2431272
Jeeun Kang, Changhan Yoon, Jae Jin Lee, Sang-Bum Kye, Yongbae Lee, J. Chang, Gi-Duck Kim, Y. Yoo, T. Song
In this paper, we present a novel system-on-chip (SOC) solution for a portable ultrasound imaging system (PUS) for point-of-care applications. The PUS-SOC includes all of the signal processing modules (i.e., the transmit and dynamic receive beamformer modules, mid- and back-end processors, and color Doppler processors) as well as an efficient architecture for hardware-based imaging methods (e.g., dynamic delay calculation, multi-beamforming, and coded excitation and compression). The PUS-SOC was fabricated using a UMC 130-nm NAND process and has 16.8 GFLOPS of computing power with a total equivalent gate count of 12.1 million, which is comparable to a Pentium-4 CPU. The size and power consumption of the PUS-SOC are 27×27 mm2 and 1.2 W, respectively. Based on the PUS-SOC, a prototype hand-held US imaging system was implemented. Phantom experiments demonstrated that the PUS-SOC can provide appropriate image quality for point-of-care applications with a compact PDA size ( 200×120×45 mm3) and 3 hours of battery life.
{"title":"A System-on-Chip Solution for Point-of-Care Ultrasound Imaging Systems: Architecture and ASIC Implementation","authors":"Jeeun Kang, Changhan Yoon, Jae Jin Lee, Sang-Bum Kye, Yongbae Lee, J. Chang, Gi-Duck Kim, Y. Yoo, T. Song","doi":"10.1109/TBCAS.2015.2431272","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2431272","url":null,"abstract":"In this paper, we present a novel system-on-chip (SOC) solution for a portable ultrasound imaging system (PUS) for point-of-care applications. The PUS-SOC includes all of the signal processing modules (i.e., the transmit and dynamic receive beamformer modules, mid- and back-end processors, and color Doppler processors) as well as an efficient architecture for hardware-based imaging methods (e.g., dynamic delay calculation, multi-beamforming, and coded excitation and compression). The PUS-SOC was fabricated using a UMC 130-nm NAND process and has 16.8 GFLOPS of computing power with a total equivalent gate count of 12.1 million, which is comparable to a Pentium-4 CPU. The size and power consumption of the PUS-SOC are 27×27 mm2 and 1.2 W, respectively. Based on the PUS-SOC, a prototype hand-held US imaging system was implemented. Phantom experiments demonstrated that the PUS-SOC can provide appropriate image quality for point-of-care applications with a compact PDA size ( 200×120×45 mm3) and 3 hours of battery life.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2431272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964245","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}
Pub Date : 2016-04-01DOI: 10.1109/TBCAS.2015.2420117
Zhaohong Chen, Yongdong Chen, Qinghua Huang
Ultrasound elastography is an important medical imaging tool for characterization of lesions. In this paper, we present a wireless and near real-time 3D ultrasound strain imaging system. It uses a 3D translating device to control a commercial linear ultrasound transducer to collect pre-compression and post-compression radio-frequency (RF) echo signal frames. The RF frames are wirelessly transferred to a high-performance server via a local area network (LAN). A dynamic programming strain estimation algorithm is implemented with the compute unified device architecture (CUDA) on the graphic processing unit (GPU) in the server to calculate the strain image after receiving a pre-compression RF frame and a post-compression RF frame at the same position. Each strain image is inserted into a strain volume which can be rendered in near real-time. We take full advantage of the translating device to precisely control the probe movement and compression. The GPU-based parallel computing techniques are designed to reduce the computation time. Phantom and in vivo experimental results demonstrate that our system can generate strain volumes with good quality and display an incrementally reconstructed volume image in near real-time.
{"title":"Development of a Wireless and Near Real-Time 3D Ultrasound Strain Imaging System","authors":"Zhaohong Chen, Yongdong Chen, Qinghua Huang","doi":"10.1109/TBCAS.2015.2420117","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2420117","url":null,"abstract":"Ultrasound elastography is an important medical imaging tool for characterization of lesions. In this paper, we present a wireless and near real-time 3D ultrasound strain imaging system. It uses a 3D translating device to control a commercial linear ultrasound transducer to collect pre-compression and post-compression radio-frequency (RF) echo signal frames. The RF frames are wirelessly transferred to a high-performance server via a local area network (LAN). A dynamic programming strain estimation algorithm is implemented with the compute unified device architecture (CUDA) on the graphic processing unit (GPU) in the server to calculate the strain image after receiving a pre-compression RF frame and a post-compression RF frame at the same position. Each strain image is inserted into a strain volume which can be rendered in near real-time. We take full advantage of the translating device to precisely control the probe movement and compression. The GPU-based parallel computing techniques are designed to reduce the computation time. Phantom and in vivo experimental results demonstrate that our system can generate strain volumes with good quality and display an incrementally reconstructed volume image in near real-time.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2420117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964677","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}
Pub Date : 2016-04-01DOI: 10.1109/TBCAS.2015.2435256
Md. Shamsul Arefin, Jean-Michel Redouté, M. Yuce
This paper presents an interface circuit for capacitive and inductive MEMS biosensors using an oscillator and a charge pump based frequency-to-voltage converter. Frequency modulation using a differential crossed coupled oscillator is adopted to sense capacitive and inductive changes. The frequency-to-voltage converter is designed with a negative feedback system and external controlling parameters to adjust the sensitivity, dynamic range, and nominal point for the measurement. The sensitivity of the frequency-to-voltage converter is from 13.28 to 35.96 mV/MHz depending on external voltage and charging current. The sensitivity ranges of the capacitive and inductive interface circuit are 17.08 to 54.4 mV/pF and 32.11 to 82.88 mV/mH, respectively. A capacitive MEMS based pH sensor is also connected with the interface circuit to measure the high acidic gastric acid throughout the digestive tract. The sensitivity for pH from 1 to 3 is 191.4 mV/pH with 550 μV pp noise. The readout circuit is designed and fabricated using the UMC 0.18 μm CMOS technology. It occupies an area of 0.18 mm 2 and consumes 11.8 mW.
{"title":"A MEMS Interface IC With Low-Power and Wide-Range Frequency-to-Voltage Converter for Biomedical Applications","authors":"Md. Shamsul Arefin, Jean-Michel Redouté, M. Yuce","doi":"10.1109/TBCAS.2015.2435256","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2435256","url":null,"abstract":"This paper presents an interface circuit for capacitive and inductive MEMS biosensors using an oscillator and a charge pump based frequency-to-voltage converter. Frequency modulation using a differential crossed coupled oscillator is adopted to sense capacitive and inductive changes. The frequency-to-voltage converter is designed with a negative feedback system and external controlling parameters to adjust the sensitivity, dynamic range, and nominal point for the measurement. The sensitivity of the frequency-to-voltage converter is from 13.28 to 35.96 mV/MHz depending on external voltage and charging current. The sensitivity ranges of the capacitive and inductive interface circuit are 17.08 to 54.4 mV/pF and 32.11 to 82.88 mV/mH, respectively. A capacitive MEMS based pH sensor is also connected with the interface circuit to measure the high acidic gastric acid throughout the digestive tract. The sensitivity for pH from 1 to 3 is 191.4 mV/pH with 550 μV pp noise. The readout circuit is designed and fabricated using the UMC 0.18 μm CMOS technology. It occupies an area of 0.18 mm 2 and consumes 11.8 mW.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2435256","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964278","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}
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