Pub Date : 2015-11-03DOI: 10.1109/TBCAS.2015.2481940
Qinwei Li, X. Xiao, Liang Wang, Hang Song, H. Kono, Peifang Liu, Hong Lu, T. Kikkawa
A direct extraction method of tumor response based on ensemble empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.
{"title":"Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB","authors":"Qinwei Li, X. Xiao, Liang Wang, Hang Song, H. Kono, Peifang Liu, Hong Lu, T. Kikkawa","doi":"10.1109/TBCAS.2015.2481940","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2481940","url":null,"abstract":"A direct extraction method of tumor response based on ensemble empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"710-724"},"PeriodicalIF":5.1,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2481940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965024","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 : 2015-10-30DOI: 10.1109/TBCAS.2015.2492460
Lukasz Farian, J. A. Leñero-Bardallo, P. Häfliger
This article investigates the potential of a bio-inspired vision sensor with pixels that detect transients between three primary colors. The in-pixel color processing is inspired by the retinal color opponency that are found in mammalian retinas. Color transitions in a pixel are represented by voltage spikes, which are akin to a neuron's action potential. These spikes are conveyed off-chip by the Address Event Representation (AER) protocol. To achieve sensitivity to three different color spectra within the visual spectrum, each pixel has three stacked photodiodes at different depths in the silicon substrate. The sensor has been fabricated in the standard TSMC 90 nm CMOS technology. A post-processing method to decode events into color transitions has been proposed and implemented as a custom interface to display real-time color changes in the visual scene. Experimental results are provided. Color transitions can be detected at high speed (up to 2.7 kHz). The sensor has a dynamic range of 58 dB and a power consumption of 22.5 mW. This type of sensor can be of use in industrial, robotics, automotive and other applications where essential information is contained in transient emissions shifts within the visual spectrum.
{"title":"A Bio-Inspired AER Temporal Tri-Color Differentiator Pixel Array","authors":"Lukasz Farian, J. A. Leñero-Bardallo, P. Häfliger","doi":"10.1109/TBCAS.2015.2492460","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2492460","url":null,"abstract":"This article investigates the potential of a bio-inspired vision sensor with pixels that detect transients between three primary colors. The in-pixel color processing is inspired by the retinal color opponency that are found in mammalian retinas. Color transitions in a pixel are represented by voltage spikes, which are akin to a neuron's action potential. These spikes are conveyed off-chip by the Address Event Representation (AER) protocol. To achieve sensitivity to three different color spectra within the visual spectrum, each pixel has three stacked photodiodes at different depths in the silicon substrate. The sensor has been fabricated in the standard TSMC 90 nm CMOS technology. A post-processing method to decode events into color transitions has been proposed and implemented as a custom interface to display real-time color changes in the visual scene. Experimental results are provided. Color transitions can be detected at high speed (up to 2.7 kHz). The sensor has a dynamic range of 58 dB and a power consumption of 22.5 mW. This type of sensor can be of use in industrial, robotics, automotive and other applications where essential information is contained in transient emissions shifts within the visual spectrum.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 1","pages":"686-698"},"PeriodicalIF":5.1,"publicationDate":"2015-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2492460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964964","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 : 2015-10-01DOI: 10.1109/TBCAS.2015.2498758
P. Georgiou, W. Fang, S. Sonkusale
The papers in this special issue were presented at the 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS 2014) on Breakthrough for Distributed Diagnostic and Therapy, that was held October 22–24, 2014, at EPFL, Lausanne, Switzerland.
{"title":"Guest Editorial - Special Issue on Selected Papers From IEEE BioCAS 2014","authors":"P. Georgiou, W. Fang, S. Sonkusale","doi":"10.1109/TBCAS.2015.2498758","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2498758","url":null,"abstract":"The papers in this special issue were presented at the 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS 2014) on Breakthrough for Distributed Diagnostic and Therapy, that was held October 22–24, 2014, at EPFL, Lausanne, Switzerland.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 5 1","pages":"605-6"},"PeriodicalIF":5.1,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2498758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964978","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 : 2015-09-22DOI: 10.1109/TBCAS.2015.2483618
Yi Chen, Enyi Yao, A. Basu
Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.
{"title":"A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces","authors":"Yi Chen, Enyi Yao, A. Basu","doi":"10.1109/TBCAS.2015.2483618","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2483618","url":null,"abstract":"Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"10 1","pages":"679-692"},"PeriodicalIF":5.1,"publicationDate":"2015-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2483618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62965145","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 : 2015-08-01DOI: 10.1109/TBCAS.2015.2472315
R. Sarpeshkar
{"title":"Guest Editorial - Special Issue on Synthetic Biology","authors":"R. Sarpeshkar","doi":"10.1109/TBCAS.2015.2472315","DOIUrl":"https://doi.org/10.1109/TBCAS.2015.2472315","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"9 4 1","pages":"449-52"},"PeriodicalIF":5.1,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TBCAS.2015.2472315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62964420","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}
Feeding and swallowing disorders are relatively common in early infancy. In Clinical, it shows negative impacts on growth and neurodevelopmental, therefore it has become a high risk of neurodevelopmental delays in preterm infants. Oral feeding that requires suckling, swallowing, and breathing coordination, and it is the most complex sensorimotor process for the newborn infant. Currently, both preterm infant's oral feeding disorders and severity are dependent on subjective clinical experience. Directly monitoring sucking-swallowing-breathing activities of oral is difficult for preterm infants. In this study, a wireless monitoring system for oral feeding of preterm infants was developed to monitor the events of sucking-swallowing-breathing activities continuously and objectively. Finally, the experimental results show that the proposed system can detect the events of sucking, swallowing, and breathing activities effectively.
{"title":"Wireless monitoring system for oral-feeding evaluation of preterm infants","authors":"Chen-An Wang, Yi-Chien Liao, Pei-Jung Wu, Yu-Lin Wang, Bor-Shing Lin, Bor-Shyh Lin","doi":"10.1109/BioCAS.2014.6981713","DOIUrl":"https://doi.org/10.1109/BioCAS.2014.6981713","url":null,"abstract":"Feeding and swallowing disorders are relatively common in early infancy. In Clinical, it shows negative impacts on growth and neurodevelopmental, therefore it has become a high risk of neurodevelopmental delays in preterm infants. Oral feeding that requires suckling, swallowing, and breathing coordination, and it is the most complex sensorimotor process for the newborn infant. Currently, both preterm infant's oral feeding disorders and severity are dependent on subjective clinical experience. Directly monitoring sucking-swallowing-breathing activities of oral is difficult for preterm infants. In this study, a wireless monitoring system for oral feeding of preterm infants was developed to monitor the events of sucking-swallowing-breathing activities continuously and objectively. Finally, the experimental results show that the proposed system can detect the events of sucking, swallowing, and breathing activities effectively.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"4 1","pages":"264-267"},"PeriodicalIF":5.1,"publicationDate":"2014-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BioCAS.2014.6981713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62153256","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}
{"title":"Proceedings of the 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS 2013) on Advancing Healthcare Technology, October 31-November 2, 2013, Rotterdam, Netherlands.","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"8 5","pages":"605-750"},"PeriodicalIF":5.1,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34304001","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 : 2014-03-06DOI: 10.1109/ISSCC.2014.6757453
Po-Hung Kuo, J. Hsieh, Yi-Chun Huang, Yu-Jie Huang, Rong-Da Tsai, Tao Wang, Hung-Wei Chiu, Shey-Shi Lu
As implantable medical CMOS devices become a reality [1], motion control of such implantable devices has become the next challenge in the advanced integrated micro-system domain. With integrated sensors and a controllable propulsion mechanism, a micro-system will be able to perform tumor scan, drug delivery, neuron stimulation, bio-test, etc, in a revolutionary way and with minimum injury. Such devices are especially suitable for human hollow organs, such as urinary bladder and stomach. Motivated by the art reported in ISSCC 2012 [2], we demonstrate a remotely-controlled locomotive CMOS IC which is realized in TSMC 0.35μm technology. As illustrated in Fig. 18.7.1, a bare CMOS chip flipped on a liquid surface can be moved to the desired position without any wire connections. Instead of Lorentz forces [2], this chip utilizes the gas pressure resulting from electrolytic bubbles as the propulsive force. By appointing voltages to the on-chip electrolysis electrodes, one can decide the electrolysis location and thereby control the bubbles emissions as well as the direction of motion. With power management circuits, wireless receiver and micro-control unit (MCU), the received signal can be exploited as the movement control as well as wireless power. Experiments show a moving speed of 0.3mm/s of this chip. The total size is 21.2mm2 and the power consumption of the integrated circuits and the electrolysis electrodes are 125.4μW and 82μW, respectively.
{"title":"18.7 A remotely controlled locomotive IC driven by electrolytic bubbles and wireless powering","authors":"Po-Hung Kuo, J. Hsieh, Yi-Chun Huang, Yu-Jie Huang, Rong-Da Tsai, Tao Wang, Hung-Wei Chiu, Shey-Shi Lu","doi":"10.1109/ISSCC.2014.6757453","DOIUrl":"https://doi.org/10.1109/ISSCC.2014.6757453","url":null,"abstract":"As implantable medical CMOS devices become a reality [1], motion control of such implantable devices has become the next challenge in the advanced integrated micro-system domain. With integrated sensors and a controllable propulsion mechanism, a micro-system will be able to perform tumor scan, drug delivery, neuron stimulation, bio-test, etc, in a revolutionary way and with minimum injury. Such devices are especially suitable for human hollow organs, such as urinary bladder and stomach. Motivated by the art reported in ISSCC 2012 [2], we demonstrate a remotely-controlled locomotive CMOS IC which is realized in TSMC 0.35μm technology. As illustrated in Fig. 18.7.1, a bare CMOS chip flipped on a liquid surface can be moved to the desired position without any wire connections. Instead of Lorentz forces [2], this chip utilizes the gas pressure resulting from electrolytic bubbles as the propulsive force. By appointing voltages to the on-chip electrolysis electrodes, one can decide the electrolysis location and thereby control the bubbles emissions as well as the direction of motion. With power management circuits, wireless receiver and micro-control unit (MCU), the received signal can be exploited as the movement control as well as wireless power. Experiments show a moving speed of 0.3mm/s of this chip. The total size is 21.2mm2 and the power consumption of the integrated circuits and the electrolysis electrodes are 125.4μW and 82μW, respectively.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"1 1","pages":"322-323"},"PeriodicalIF":5.1,"publicationDate":"2014-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ISSCC.2014.6757453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62165926","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 : 2012-05-20DOI: 10.1109/ISCAS.2012.6271420
I. Williams, T. Constandinou
This paper presents an 8 channel energy-efficient neural stimulator for generating charge-balanced asymmetric pulses. Power consumption is reduced by implementing a fully-integrated DC-DC converter that uses a reconfigurable switched capacitor topology to provide 4 output voltages for Dynamic Voltage Scaling (DVS). DC conversion efficiencies of up to 82% are achieved using integrated capacitances of under 1 nF and the DVS approach offers power savings of up to 50% compared to the front end of a typical current controlled neural stimulator. A novel charge balancing method is implemented which has a low level of accuracy on a single pulse and a much higher accuracy over a series of pulses. The method used is robust to process and component variation and does not require any initial or ongoing calibration. Measured results indicate that the charge imbalance is typically between 0.05%-0.15% of charge injected for a series of pulses. Ex-vivo experiments demonstrate the viability in using this circuit for neural activation. The circuit has been implemented in a commercially-available 0.18 μm HV CMOS technology and occupies a core die area of approximately 2.8 mm2 for an 8 channel implementation.
{"title":"An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis","authors":"I. Williams, T. Constandinou","doi":"10.1109/ISCAS.2012.6271420","DOIUrl":"https://doi.org/10.1109/ISCAS.2012.6271420","url":null,"abstract":"This paper presents an 8 channel energy-efficient neural stimulator for generating charge-balanced asymmetric pulses. Power consumption is reduced by implementing a fully-integrated DC-DC converter that uses a reconfigurable switched capacitor topology to provide 4 output voltages for Dynamic Voltage Scaling (DVS). DC conversion efficiencies of up to 82% are achieved using integrated capacitances of under 1 nF and the DVS approach offers power savings of up to 50% compared to the front end of a typical current controlled neural stimulator. A novel charge balancing method is implemented which has a low level of accuracy on a single pulse and a much higher accuracy over a series of pulses. The method used is robust to process and component variation and does not require any initial or ongoing calibration. Measured results indicate that the charge imbalance is typically between 0.05%-0.15% of charge injected for a series of pulses. Ex-vivo experiments demonstrate the viability in using this circuit for neural activation. The circuit has been implemented in a commercially-available 0.18 μm HV CMOS technology and occupies a core die area of approximately 2.8 mm2 for an 8 channel implementation.","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"7 1","pages":"129-139"},"PeriodicalIF":5.1,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ISCAS.2012.6271420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62145716","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}
{"title":"Proceedings of the 2011 IEEE International Symposium on Circuits and Systems (ISCAS 2011), May 15-18, 2011, Rio de Janeiro, Brazil.","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":13151,"journal":{"name":"IEEE Transactions on Biomedical Circuits and Systems","volume":"6 2","pages":"85-187"},"PeriodicalIF":5.1,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32547239","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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