Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines (original) (raw)

Wavelet Time Shift Properties Integration with Support Vector Machines

Modeling Decisions for Artificial Intelligence, 2004

This paper presents a short evaluation about the integration of information derived from wavelet non-linear-time-invariant (non- LTI) projection properties using Support Vector Machines (SVM). These properties may give additional information for a classifier trying to detect known patterns hidden by noise. In the experiments we present a simple electromagnetic pulsed signal recognition scheme, where some improve is achieved with respect

Time Frequency Analysis of Continuous Signal using Wavelet Transform

2014

In many communication systems, it is necessary to use the concepts of instantaneous amplitude and phase signals. A signal, stationary or non-stationary, needs to be processed and analyzed before it can be of any use. Signal in its raw form contains noise which is generally the high frequency component of the data. This noise needs to be eliminated for better representation of the signal. In this paper, we first demonstrate the implementation of Wavelet Transform and how it helps in efficient de-noising. Thresholding is done in the wavelet domain to de-noise the signal. A study of timefrequency analysis of the de-noised signal has been performed to achieve relevant conclusions. A case study of seismic signal is given at the end to support the theoretical analysis.

Signal detection using time-frequency analysis

1992

A review of detection methods has been provided and it has been seen that time-frequency based detectors form an important subclass of these methods. Of the time-frequency based detectors, the WVD and XWVD based ones have been seen to be particularly useful, due to their energy preserving properties and their ability to provide good. localisation in time and frequency. Time-varying higher order spectra have also been introduced as a possible means for detection.