Classification of signals by using normal orthogonal transformation

被引:0
|
作者
Nizhebetska, Y.
机构
关键词
classification of signals; discrete orthogonal transformation; coefficient of transforms; normal filtering; authentification by the dynamically entered signature;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Possibility of pattern recognition is considered on the procedure of creation of discrete ortogonal transformation offered by an author, in that the first transform coincides with a etalon signal. At the coincidence of the investigated signal with a test the spectrum of such transformation contains one unzero transform only, while appearance of other transforms in a spectrum testifies to their differences. Application of normal transformation for the estimation of similarity of signals by coefficients of transforms allows to enter numeral measure of estimation of such similarity. Procedure of recognition is widespread on the cases of two-dimensional and complex signals. Results over of the use of normal transformation are brought for the tasks of authentification of person by the dinamically entered signature and classification of the state of person by the pulse wave.
引用
收藏
页码:58 / 70
页数:13
相关论文
共 50 条
  • [31] Testing Time Series Classification of UCR Archive Signals Using Feature to Image Transformation (FIT) Algorithm
    Salman, Odai S.
    Salman, Ammar S.
    Salman, Adan S.
    PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2021, VOL 2, 2022, 359 : 121 - 137
  • [32] Cardiac Disease Classification Using Total Variation Denoising and Morlet Continuous Wavelet Transformation of ECG Signals
    Al Abdi, Rabah M.
    Jarrah, Mohamad
    2018 IEEE 14TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING & ITS APPLICATIONS (CSPA 2018), 2018, : 57 - 60
  • [33] Pattern recognition of 1D and 2D signals using normalization and normal transformation
    Rybin A.I.
    Melnyk A.D.
    Nizhebetskaya Y.K.
    Sushko I.A.
    Litvintsev S.N.
    Radioelectronics and Communications Systems, 2016, 59 (1) : 28 - 38
  • [34] Classification of Normal and Abnormal ECG Signals Based on their PQRST Intervals
    Naseer, Noman
    Nazeer, Hammad
    2017 INTERNATIONAL CONFERENCE ON MECHANICAL, SYSTEM AND CONTROL ENGINEERING (ICMSC), 2017, : 388 - 391
  • [35] Automated Classification of Normal and Premature Ventricular Contractions in Electrocardiogram Signals
    Jenny, Nam Zheng Ning
    Faust, Oliver
    Yu, Wenwei
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2014, 4 (06) : 886 - 892
  • [36] Iterative algorithm for discrete orthogonal transformation of signals in base of two-dimensional functions
    Doudkin, AA
    Machnev, AG
    Selikhanovich, AM
    IDAACS'2001: PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATION, 2001, : 73 - 76
  • [37] ORTHOGONAL ULTRASONIC SIGNALS
    HENRY, EE
    IEEE TRANSACTIONS ON SONICS AND ULTRASONICS, 1964, SU11 (02): : 108 - &
  • [38] Classification of Normal and Stress Groups Among Females Based on Electroencephalography Signals Using Artificial Neural Network
    Thafa'i, Nor Atiqah
    Ghani, Salmi Abdul
    Zaini, Norliza
    ADVANCED SCIENCE LETTERS, 2017, 23 (06) : 5277 - 5281
  • [39] Classification of the EEG Signals for the Cursor Movement with the Signal-to-Image Transformation
    Yilmaz, Bahar Hatipoglu
    Yilmaz, Cagatay Murat
    Kose, Cemal
    2019 MEDICAL TECHNOLOGIES CONGRESS (TIPTEKNO), 2019, : 483 - 486
  • [40] Recovery of Sparse Signals Using Multiple Orthogonal Least Squares
    Wang, Jian
    Li, Ping
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (08) : 2049 - 2062