The paper presents two novel applications of Thomson Multitaper Analysis. It is shown how a wideband simulator of a double mobile MIMO channel could be developed based on geometrical channel model. It is also shown how modification of Discrete Prolate Spheroidal Sequences could be used to better estimation of sparse channels. A number of other potential applications is also mentioned.

Pattern recognition is a very powerful tool in automated data analysis and it is widely used in many different applications (Chou & Juang, 2003; Jiang,1994; Blue et al., 1994; Milosavljevic, 1994; Moreels & Smrekar, 2003). However, the application of such a tool can be a difficult task in some cases. For example, in a correlation-type scheme, the basic idea is to correlate the signal being analyzed with a known template or templates (Shiavi, 1999; Scharf, 1991) and make decisions based on the magnitude of the correlation coefficients, which is between 0 and 1. In practice, these extreme values are seldom achieved due to corrupting signals/noise that can affect the accuracy of pattern matching and subsequently lead to errors in classification (Kil & Shin, 1996). The corrupting signals may also bear some resemblance to the template being matched. This is particularly true if the pattern of interest is a non-stationary transient signal. Furthermore, it is well known that traditional time domain correlation-based pattern recognition methods do not fully utilize the frequency characteristics of the template and the signal being analyzed. Hence, such methods perform poorly when applied to transient signals. To overcome these difficulties, a scheme known as selective regional correlation (SRC) has been developed (Sejdic & Jiang, 2007). It has been shown that if a template has bandlimited characteristics, significant improvement in the performance of pattern recognition can be readily made by a relatively simple preprocessing of the signal and the template in the time-frequency domain (Sejdic & Jiang, 2007). The redundant representation of a 1D signal in a 2D time-frequency domain can provide an additional degree of freedom for signal analysis. Such pre-processing effectively separates the intertwined time domain features of the signal, allowing the important characteristics to be exposed in the time-frequency domain, resulting in more effective pattern matching. Hence, correlation between the signal being analyzed and the template needs to be conducted only in selected regions of interest in the time-frequency domain. An overview of the theoretical developments behind the SRC is provided in this chapter along with some recent results. The performance of the scheme is briefly reviewed and compared with that of the general correlation technique through the analysis of a set of O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Instantaneous frequency (IF) estimation through the estimation of peak locations in the time-frequency plane is an important approach for signals contaminated with additive white Gaussian noise. In this paper, the forementioned analysis is carried out for continuous wavelet transform. The analysis of the scalogram as the instantaneous frequency estimator is performed for any FM signal regardless of the mother wavelet. Accurate expressions for the bias and the variance of the estimator are derived, and reveal that the bias and the variance are signal dependent. Results are statistically confirmed through the numerical analysis for several mother wavelets, and among considered wavelets, the Morlet wavelet produces the smallest estimation error. Furthermore, the performance of the IF estimator based on the scalogram and the spectrogram were compared through analysis of mean square error. These results showed that the scalogram with the Morlet wavelet exhibited good performance for the sample linear FM signal and the sample hyperbolic FM signal in comparison to the spectrogram.

Instantaneous frequency (IF) is a fundamental concept that can be found in many disciplines such as communications, speech, and music processing. In this letter, analysis of an IF estimator, based on a time-frequency technique known as S-transform, is performed. The performance analysis is carried out in a white Gaussian noise environment, and expressions for the bias and the variance of the estimator are determined. The results show that the bias and the variance are signal dependent. This has been statistically confirmed through numerical simulations of several signal classes.

Energy concentration of the S-transform in the time-frequency domain has been addressed in this paper by optimizing the width of the window function used. A new scheme is developed and referred to as a window width optimized S-transform. Two optimization schemes have been proposed, one for a constant window width, the other for time-varying window width. The former is intended for signals with constant or slowly varying frequencies, while the latter can deal with signals with fast changing frequency components. The proposed scheme has been evaluated using a set of test signals. The results have indicated that the new scheme can provide much improved energy concentration in the time-frequency domain in comparison with the standard S-transform. It is also shown using the test signals that the proposed scheme can lead to higher energy concentration in comparison with other standard linear techniques, such as short-time Fourier transform and its adaptive forms. Finally, the method has been demonstrated on engine knock signal analysis to show its effectiveness.

In this paper, it is demonstrated that time-frequency techniques may be used for an enhanced diagnosis of the heart disease. Three time-frequency analysis techniques are compared: spectrogram, Wigner distribution and S- method. The results show that the S-method could be used in the heart sounds analysis, and that is also capable of enhancing the diagnostic techniques available to medical personnel.