Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck...

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Autores principales:	Lin Huang, Xingguang Geng, Hao Xu, Yitao Zhang, Zhiqiang Li, Jun Zhang, Haiying Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sensor array interference signal convolutional neural network (CNN) field-programmable gate array (FPGA) Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/f70934262f4843be86129a0432959249
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spelling	oai:doaj.org-article:f70934262f4843be86129a04329592492021-11-25T17:25:29ZInterference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation10.3390/electronics102228672079-9292https://doaj.org/article/f70934262f4843be86129a04329592492021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2867https://doaj.org/toc/2079-9292The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.Lin HuangXingguang GengHao XuYitao ZhangZhiqiang LiJun ZhangHaiying ZhangMDPI AGarticlesensor arrayinterference signalconvolutional neural network (CNN)field-programmable gate array (FPGA)ElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2867, p 2867 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sensor array interference signal convolutional neural network (CNN) field-programmable gate array (FPGA) Electronics TK7800-8360
spellingShingle	sensor array interference signal convolutional neural network (CNN) field-programmable gate array (FPGA) Electronics TK7800-8360 Lin Huang Xingguang Geng Hao Xu Yitao Zhang Zhiqiang Li Jun Zhang Haiying Zhang Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
description	The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.
format	article
author	Lin Huang Xingguang Geng Hao Xu Yitao Zhang Zhiqiang Li Jun Zhang Haiying Zhang
author_facet	Lin Huang Xingguang Geng Hao Xu Yitao Zhang Zhiqiang Li Jun Zhang Haiying Zhang
author_sort	Lin Huang
title	Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
title_short	Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
title_full	Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
title_fullStr	Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
title_full_unstemmed	Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation
title_sort	interference signal identification of sensor array based on convolutional neural network and fpga implementation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f70934262f4843be86129a0432959249
work_keys_str_mv	AT linhuang interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT xingguanggeng interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT haoxu interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT yitaozhang interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT zhiqiangli interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT junzhang interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation AT haiyingzhang interferencesignalidentificationofsensorarraybasedonconvolutionalneuralnetworkandfpgaimplementation
_version_	1718412384747388928

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

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