Signal processing methods have been developed over the last 60 years to detect and analyse complicated non-stationary signals, such as speech and seismic activity. The traditional method for analysing such signals is through a spectrogram based on the short-time Fourier transform (STFT). However, the STFT is not ideal since it reflects only the stationary properties contained in any short time-...

Abstract-This paper presents the various methods for the spectral analysis of signals for the stationary as well as non-stationary signals. Due to non-stationary characteristics of the signals, it has been always a challenge to achieve time frequency distribution of such signals. Between the various techniques of signal analysis, this paper uses Fourier transform, Short time Fourier transform, ...

Parameter estimation of various multi-component stationary and non-stationary signals in multiplicative and additive noise is considered in this paper. It is demonstrated that the parameters of complex sinusoidal signal, complex frequency modulated (FM) sinusoidal signal and complex linear chirp signal in presence of additive and multiplicative noise can be estimated using a new definition of t...

Formulas are proposed for estimating and tracking the (time-dependent) frequency and amplitude of a sinusoidal signal. These formulas are recursive in nature, and use only the instantaneous values of the signal, in a low cost and low complexity manner; in particular, there is no need to take square roots or to carry out divisions. Special attention is paid to the convergence behavior of the alg...

In the fluorescence detection system, noise will affect the sensitivity of the system. The method using Stationary Wavelet Transform to de-noise fluorescence signals based on adaptive wavelet entropy is studied. The de-noising effects of dbN families are compared, and then the db7 wavelet is chosen as the optimal wavelet. The noised fluorescence signal is decomposed at 5 levels via Discrete Wav...

In this paper novel compression techniques are developed for portable heart-monitoring equipment that could also form the basis for more intelligent diagnostic systems thanks to the way the compression algorithms depend on signal classification. There are two main categories of compression which are employed for electrocardiogram signals: lossless and lossy. Design of an optimal Wiener filter i...

This paper communicates a novel method of Wiener filtering signals from scientific instruments to reduce their noise content. The main prerequisite for the applicability of this technique is that the pure (i.e. noiseless) signal is reproducible, a condition that is satisfied by a wide range of experimental measurements. The benefits of this Wiener filter design approach are its simplicity, gene...

This paper presents a Modified Error Data Normalized Step Size (MEDNSS) algorithm in which time fluctuating step size relies on standardization of both error and data vector. An Adaptive Noise Canceller (ANC) is utilized to enhance the system performance in the presence of signal leakage components or signal crosstalk. This ANC comprises of three microphones and two adaptive filters that conseq...

The middle temporal (MT) visual area is widely accepted to play important roles in motion processing. It is unclear, however, whether MT contributes to visual perception during the viewing of static scenes, when there is little retinal image motion during the interval between saccades. Some previous studies suggest that MT neurons give little or no response to stationary stimuli that are flashe...

this paper presents a new formulation for warehouse inventory management in a stochastic situation. the primary source of this formulation is derived from fp model, which has been proposed by fletcher and ponnambalam for reservoir management. the new proposed mathematical model is based on the first and the second moments of storage as a stochastic variable. using this model, the expected value...