The goal of this Chapter is to review the applications of the Thomson Multitaper analysis (Percival and Walden; 1993b), (Thomson; 1982) for problems encountered in communications (Thomson; 1998; Stoica and Sundin; 1999). In particular we will focus on issues related to channel modelling, estimation and prediction. Sum of Sinusoids (SoS) or Sum of Cisoids (SoC) simulators (Patzold; 2002; SCM Editors; 2006) are popular ways of building channel simulators both in SISO and MIMO case. However, this approach is not a very good option when features of communications systems such as prediction and estimation are to be simulated. Indeed, representation of signals as a sum of coherent components with large prediction horizon (Papoulis; 1991) leads to overly optimistic results. In this Chapter we develop an approach which allows one to avoid this difficulty. The proposed simulator combines a representation of the scattering environment advocated in (SCM Editors; 2006; Almers et al.; 2006; Molisch et al.; 2006; Asplund et al.; 2006; Molish; 2004) and the approach for a single cluster environment used in (Fechtel; 1993; Alcocer et al.; 2005; Kontorovich et al.; 2008) with some important modifications (Yip and Ng; 1997; Xiao et al.; 2005). The problem of estimation and interpolation of a moderately fast fading Rayleigh/Rice channel is important in modern communications. TheWiener filter provides the optimum solution when the channel characteristics are known (van Trees; 2001). However, in real-life applications basis expansions such as Fourier bases and discrete prolate spheroidal sequences (DPSS) have been adopted for such problems (Zemen and Mecklenbrauker; 2005; Alcocer-Ochoa et al.; 2006). If the bases and the channel under investigation occupy the same band, accurate

Auscultation is a useful procedure for diagnostics of pulmonary or cardiovascular disorders. The effectiveness of auscultation depends on the skills and experience of the clinician. Further issues may arise due to the fact that heart sounds, for example, have dominant frequencies near the human threshold of hearing, hence can often go undetected (1). Computer-aided sound analysis, on the other hand, allows for rapid, accurate, and reproducible quantification of pathologic conditions, hence has been the focus of more recent research (e.g., (1–5)). During computer-aided auscultation, however, lung sounds are often corrupted by intrusive quasiperiodic heart sounds, which alter the temporal and spectral characteristics of the recording. Separation of heart and lung sound components is a difficult task as both signals have overlapping frequency spectra, in particular at frequencies below 100 Hz (6). For lung sound analysis, signal processing strategies based on conventional time, frequency, or time-frequency signal representations have been proposed for heart sound cancelation. Representative strategies include entropy calculation (7) and recurrence time statistics (8) for heart sound detection-and-removal followed by lung sound prediction, adaptive filtering (e.g., (9; 10)), time-frequency spectrogram filtering (11), and time-frequency wavelet filtering (e.g., (12–14)). Subjective assessment, however, has suggested that due to the temporal and spectral overlap between heart and lung sounds, heart sound removal may result in noisy or possibly “non-recognizable" lung sounds (15). Alternately, for heart sound analysis, blind source extraction based on periodicity detection has recently been proposed for heart sound extraction from breath sound recordings (16); subjective listening tests, however, suggest that the extracted heart sounds are noisy and often unintelligible (17). In order to benefit fully from computer-aided auscultation, both heart and lung sounds should be extracted or blindly separated from breath sound recordings. In order to achieve such a difficult task, a few methods have been reported in the literature, namely, wavelet filtering (18), independent component analysis (19; 20), and more recently, modulation domain filtering (21). The motivation with wavelet filtering lies in the fact that heart sounds contain large components over several wavelet scales, while coefficients associated with lung sounds quickly decrease with increasing scale. Heart and lung sounds are iteratively separated based on an adaptive hard thresholding paradigm. As such, wavelet coefficients at each scale with amplitudes above the threshold are assumed to correspond to heart sounds and the remaining coefficients are associated with lung sounds. Independent component analysis, in turn, makes use

Background A common but debated technique in the management of swallowing difficulties is the chin tuck swallow, where the neck is flexed forward prior to swallowing. Natural variations in chin tuck angles across individuals may contribute to the differential effectiveness of the technique. Methodology To facilitate the study of chin tuck angle variations, we present a template tracking algorithm that automatically extracts neck angles from sagittal videos of individuals performing chin tuck swallows. Three yellow markers geometrically arranged on a pair of dark visors were used as tracking cues. Results The algorithm was applied to data collected from 178 healthy participants during neutral and chin tuck position swallows. Our analyses revealed no major influences of body mass index and age on neck flexion angles during swallowing, while gender influenced the average neck angle only during wet swallows in the neutral position. Chin tuck angles seem to be independent of anthropometry and gender in healthy adults, but deserve further study in pathological populations. Conclusion The proposed neck flexion angle extraction algorithm may be useful in future studies where strict participant compliance to swallowing task protocol can be assured.

The reassignment method is a widespread approach for obtaining high resolution time-frequency representations. Nevertheless, its performance is not always optimal and can deteriorate for low signal-to-noise ratio (SNR) values. In order to overcome these obstacles, a novel method for obtaining high resolution time-frequency representations is proposed in this paper. The new method implements recently proposed nonparametric snakes in order to obtain accurate locations of the signal ridges in the time-frequency domain. The results of numerical analysis show that the proposed method is capable of achieving significantly higher concentration of signals in the time-frequency domain in comparison to the spectrogram and the traditional reassignment method. Furthermore, the new scheme also maintains good performance for low SNR values, while the performance of the other two considered methods significantly diminishes. It is clear from the results that the proposed method might be of significance in applications where accurate estimation of the signal components is required for low SNR values.

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.