Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Efficient Analysis of Time-Varying Multicomponent Signals with Modified LPTFT | EURASIP Journal on Applied Signal Processing 2005 8 1261-1268 2005 Hindawi Publishing Corporation Efficient Analysis of Time-Varying Multicomponent Signals with Modified LPTFT Yongmei Wei School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Email freecathy@ Guoan Bi School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Email egbi@ Received 19 May 2004 Revised 13 October 2004 Recommended for Publication by Yuan-Pei Lin This paper presents efficient algorithms for the analysis of nonstationary multicomponent signals based on modified local polynomial time-frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters for computing the modified local polynomial time-frequency transform in each segment are estimated from polynomial Fourier transform in the frequency domain. Compared to other reported algorithms the length of overlap between consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component. Numerical simulations with synthesized multicomponent signals show that the proposed ones achieve better performance on instantaneous frequency estimation with greatly reduced computational complexity. Keywords and phrases time-frequency analysis time varying multicomponent modified LPTFT impulse noise. 1. INTRODUCTION Due to their superior performance in dealing with non-stationary signals time-frequency transforms TFTs have found various applications in many areas including communications multimedia mechanics and biology 1 . The most popular and simplest TFT is short-time Fourier transform STFT that has been widely used for many practical applications 1 2 . Nevertheless the STFT suffers from low resolution when the analyzed signal is highly nonstationary. Local .
