location:Home > 2020 Vol.3 Jun. No.3 > Research on blind processing optimization algorithm for high frequency signal of electrocardiographic capacitance sensor

2020 Vol.3 Jun. No.3

  • Title: Research on blind processing optimization algorithm for high frequency signal of electrocardiographic capacitance sensor
  • Name: Xiao-fei Li
  • Company: Guangdong University of Petrochemical Technology
  • Abstract:

    At present, the high frequency signal processing algorithm of capacitive sensor based on RBF has the problems of poor filtering effect and high level of signal detection and poor quality of signal separation. In this paper, an optimization algorithm for blind processing of high frequency signal of capacitive sensor is proposed. Based on the gradient method, and the calculation way of improved variance gradient estimation, the gradient of square single- error sample is taken as the estimation of mean square error to filter the capacitive sensor signal, and adjust the filtering step by adjusting the threshold, which can enhance the filtering effect of the sensor signal;The detection threshold is calculated by determining the false alarm probability. The decision condition is used to detect the target signal and get the high accuracy sensor signal. The initialization separation matrix is set according to the number of observation signals, and the correlation matrix of the source signal can be calculated, so as to achieve the efficient separation of high frequency signals. The experiment shows that the algorithm can effectively solve the problems existing in the current signal processing algorithm, and it is reliable.

  • Keyword: capacitive sensor; high frequency signal; blind processing;
  • DOI: 10.12250/jpciams2020030100
  • Citation form: Xiao-fei Li.Research on blind processing optimization algorithm for high frequency signal of electrocardiographic capacitance sensor[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 1-10.
Reference:

[1]A.K.Azad, L.Wang, N.Guo, et al, Signal processing using artificial neural network for BOTDA sensor system, Optics Express, 6(2016),6769-6770.
[2]F.Hu, Intelligent Sensor Networks - The Integration of Sensor Networks, Signal Processing and Machine Learning, Measurement Techniques, 6(2016),535-537.
[3]J.Edwards, Signal Processing Powers a Sensor Revolution [Special Reports], IEEE Signal Processing Magazine, 2(2016),13-16.
[4]A.Guzik, Fabrication of Fiber-Optic Distributed Acoustic Sensor and Its Signal Processing, American Journal of Hypertension, 5(2015),483-91.
[5]A.Hassani, A.Bertrand and M.Moonen, GEVD-Based Low-Rank Approximation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Sensor Networks, IEEE Transactions on Signal Processing, 10(2016),2557-2572.
[6]J.Li, H.Pang, F.Guo, et al, Localization of multiple disjoint sources with prior knowledge on source locations in the presence of sensor location errors, Digital Signal Processing, C(2015),181-197.
[7]A.Gunes and M.B.Guldogan, Joint underwater target detection and tracking with the Bernoulli filter using an acoustic vector sensor, Digital Signal Processing, C(2016),246-258.
[8]D.Han, K.You, L.Xie, et al, Optimal Parameter Estimation Under Controlled Communication Over Sensor Networks, IEEE Transactions on Signal Processing, 24(2015),6473-6485.
[9]F.Erden, S.Velipasalar, A.Z.Alkar, et al, Sensors in Assisted Living: A survey of signal and image processing methods, IEEE Signal Processing Magazine, 2(2016),36-44.
[10]G.Zheng, and B.Wu, Polarisation smoothing for coherent source direction finding with multiple-input and multiple-output electromagnetic vector sensor array, Iet Signal Processing, 8(2016),873-879.
[11]K.A.Mamun, C.M.Steele and T.Chau, Swallowing accelerometry signal feature variations with sensor displacement, Medical Engineering & Physics, 7(2015),665-673.
[12]R.K.Miranda, J.P.C.L.D.Costa, F.Roemer, et al, Low complexity performance assessment of a sensor array via unscented transformation, Digital Signal Processing, C(2017),190-198.
[13]A.Bertrand and M.Moonen, Distributed Canonical Correlation Analysis in Wireless Sensor Networks With Application to Distributed Blind Source Separation, IEEE Transactions on Signal Processing, 18(2015),4800-4813.
[14]J.Ma, S.Sun, Optimal linear estimators for multi-sensor stochastic uncertain systems with packet losses of both sides, Digital Signal Processing, 1(2015),24-34.
[15]X.R.Chen, Nonlinear Distortion Suppression Algorithm of Complex Optical Sensor Network Communication, Bulletin of Science and Technology, 10(2015),58-60.
[16]S.Kisseleff, I.F.Akyildiz and W.H.Gerstacker, Digital Signal Transmission in Magnetic Induction Based Wireless Underground Sensor Networks, IEEE Transactions on Communications, 6(2015),2300-2311.
[17]H.Y.Xiang, T.T.Li, H.Li, et al, Roller Coaster Acceleration Signal Processing Based on Matlab, Computer Simulation, 1(2016),245-249.
[18]M.Bentoumi, D.Chikouche, A.Mezache, et al, Wavelet DT method for water leak-detection using a vibration sensor: an experimental analysis, Iet Signal Processing, 4(2017),396-405.
[19]S.Q.Sun, J.Y.Liu, C.D.Jiang, et al, Least-square weighted smoothing filter technology applied in magnetic resonance sounding signal processing, Journal of Jilin University(Engineering and Technology Edition), 3(2016),985-995.
[20]S.P.Jia, J.Zeng and L.R.Guo, Designing Implementation of Signal Sorting Semi-physical Simulation Analysis Platform, Journal of China Academy of Electronics and Information Technology, 1(2016),59-65.

Tsuruta Institute of Medical Information Technology
Address:[502,5-47-6], Tsuyama, Tsukuba, Saitama, Japan TEL:008148-28809 fax:008148-28808 Japan,Email:jpciams@hotmail.com,2019-09-16