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Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals. [ABSTRACT FROM AUTHOR]

Epochs present in the voiced speech are defined as time instants of significant excitation of the vocal tract system during the production of speech. Nonstationary nature of excitation source and vocal tract system makes accurate identification of epochs a difficult task. Most of the existing methods for epoch detection require prior knowledge of voiced regions and a rough estimation of pitch frequency. In this paper, we propose a novel method that relies on time-order representation (TOR) based on short-time Fourier–Bessel (FB) series expansion which can be employed on entire speech signal to detect epochs without any prior information. The proposed method automatically detects voiced regions in the speech signal by computing the marginal energy density with respect to time in the low frequency range (LFR) from the energy distribution in the time-frequency plane. An estimate of pitch frequency for each detected voiced region is then obtained by computing the marginal energy density with respect to frequency in the LFR from the energy distribution in the time-frequency plane. Epochs are located for each detected voiced region as peaks in the derivative of the low pass filtered (LPF) signal corresponding to falling edges of peak negative cycles in the LPF signal synthesized from TOR coefficients corresponding to LFR. Experimental results obtained by the proposed method on speech signals taken from the CMU-Arctic database are found to be promising. The proposed method detects epochs with high accuracy and reliability.

In this paper, we have proposed parametric representation of speech signals employing a novel multi-component amplitude and frequency modulated (AFM) sinusoidal signal model. The Fourier-Bessel (FB) series expansion is used to separate the multi-component speech signal into a set of mono-component signals. It has been shown that the first component or low-frequency component can be modeled with one set of parameters for the complete signal length. For other components of the speech which is a non-stationary signal, segmentation is required in order to apply the AFM signal model. We have proposed modeling of the second and third (and higher) components based on the AFM model with time-varying parameters. Thus, the signal is to be modeled in segments by selecting suitable length where the AFM signal model is admissible. The Itakura-Saito distance and root mean square log-spectral measure have been applied to determine distortion between the actual and modeled speech signals. Simulation results demonstrate the suitability of the AFM signal model for speech signal representation. [ABSTRACT FROM AUTHOR]

Based on the methods of variable separation and matching eigenfunction expansions for velocity potential, analytical solutions are developed for a water wave radiation problem by a submerged vertical cylinder in finite water depth. They are validated by comparison with results from the higher order boundary element method and convergent examinations on the number of expansion models. Numerical analysis is carried out to investigate the influence submerged depth, cylinder length, and water depth on added mass and radiation damping. When the submerged depth is large, the added mass approaches a stable value, and radiation damping tends to zero. At high frequency range, the heave and pitch of the added mass of the submerged cylinder is about twice that of the floating cylinder. The influence of cylinder length for hydrodynamic coefficients is quite complex and shows the various properties at different frequency ranges. Added mass can be increased with the decrease of water depth, whereas the effect of water depth on radiation damping is quite small. [ABSTRACT FROM AUTHOR]

In the last years, Blind Deconvolution methods demonstrated their effectiveness for the diagnostics of rotating machines through the extraction of impulsive signatures directly from noisy observations. Recently, in this scenario the explicit combination between Blind Deconvolution and cyclostationary theory strongly improved the fault detection ability of this diagnostic tool. This work presents a novel criterion based on the Fourier–Bessel series expansion instead of the common Fourier transform. This idea comes from the comparison between the mathematical nature of the Fourier–Bessel and the Fourier series, based on modulated and constant amplitude sinusoidal functions, respectively. The two criteria are compared through the analysis of both simulated and real vibration signals of faulty bearings. The results highlight the ability of the proposed criterion to detect the fault-related source with a lower number of characteristic cyclic frequency harmonics, strongly reducing the computational time required by the algorithm. • Cyclostationary based Blind Deconvolution enables faults detection under low SNR. • Fourier Bessel series expansion permits the reduction of required computational time. • ICS 2FB identifies cyclostationary patterns under strong variable speed conditions. • ICS 2FB allows the assessment of the damaging level during the entire operating life. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a second-order linear time-varying autoregressive (TVAR) process for parametric representation of the electroencephalogram (EEG) signals. The coefficients of the Fourier–Bessel (FB) series expansion have been used to constitute a feature vector for segmentation of the EEG signal. Our approach is novel in the sense that by selecting an appropriate data length, we find a simple model for parametric representation of the EEG signals. The complete method for estimation of model parameters is presented in this work. [Copyright &y& Elsevier]