Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain disorders like epileptic seizure. Feature extraction and classification of EEG signals is the crucial task to detect the stages of ictal and interictal signals for treatment and precaution of epileptic patients. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering the non-abruptness phenomena and inconsistency in different brain locations. In this study, the authors present a new approach for feature extraction and classification by exploiting temporal correlation within EEG signals for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods, they divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input into LS-SVM to classify them. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity and accuracy of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.

Although orthogonal frequency division multiplexing is adopted by many standards in wireless communication systems, it generates high peak fluctuations which are affected by radio frequency circuit non-linearities, reducing the power efficiency and the network quality of service. In recent years, tone-reservation using null subcarriers (TRNS) scheme has been proposed to reduce the level of these fluctuations, measured by the peak-to-average power ratio (PAPR). However, TRNS performances only depend on the available number of null subcarriers in the current standards. In this study, a new scheme to improve TRNS performances by including the samples of the guard interval signal within the parameters of the optimisation algorithm is proposed, allowing higher PAPR reduction gain and requiring no side information. Simulations under the wireless local area network IEEE 802.11a standard show that the new method does not violate the specified spectrum mask and achieves a higher PAPR reduction gain compared to TRNS method. Also, the evaluation of bit error rate and error vector magnitude under additive white Gaussian noise and Rayleigh fading channels shows a comparable level of the quality of communication between the two schemes.

Level-crossing (LC) analog-to-digital (A/D) converters can efficiently sample certain classes of signals. An LC A/D converter is a real-time asynchronous system, which encodes the information of an analog signal into a sequence of non-uniformly spaced time instants. In particular, this class of A/D converters uses an asynchronous data conversion approach, which is a power efficient technique. In this study, the authors propose adaptive and multi-level adaptive LC sampling models as alternatives to conventional LC schemes and apply an iterative algorithm to improve the reconstruction quality of LC A/D converters. This simulation results show that multi-level adaptive LC outperforms conventional A/D converters such as sigma-delta A/D converters in terms of performance and computational complexity.

Kalman filter is normally used to enhance speech quality in a noisy environment, in which the speech signals are usually modelled as autoregressive (AR) process, and represented in the state-space domain. It is a known fact that to identify the changing AR coefficients in every time state requires extensive computation. In this paper, the authors develop a bidirectional Kalman filter and apply it in a speech processing system. The proposed filter uses a system dynamics model that utilises the past and the future measurements to form an estimate of the system's current time state. It provides efficient recursive means to estimate the state of a process that minimises the mean of the squared error. Compared to the conventional Kalman filter, the proposed filter reduces the computation time in two ways: (i) by avoiding the computation of AR parameters in each time state, and (ii) by reducing the dimension of the matrices involved in the difference equations and the measurement equations into constant (1 × 1) matrices. The speech recognition result shows that the developed speech recognition system becomes more robust after the proposed filtering process, and the proposed filter's low computational expense makes it applicable in the practical hidden Markov model-based speech recognition system.

A weighted mean square error (WMSE) approach to optimising digital filters is delineated. It is applied in the current work to optimising the classical Al-Alaoui IIR differentiators to obtain new improved wideband differentiators of varying orders. These can be directly used for analog to digital conversion and in many digital signal processing applications. The weighted MSE approach is motivated by the Al-Alaoui WMSE approach to pattern recognition. In addition, the differentiators are converted to integrators of similar orders that are further optimised. Various examples and comparisons are presented to demonstrate the viability of the proposed approach.

This paper proposes a new variable step-size sign algorithm through the minimisation of mean-square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper bound of the MSD is derived for calculating the step size at each iteration. The proposed algorithm is not only robust to impulsive noises, but also has improved filter performance in aspects of convergence rate and steady-state estimation error owing to the proposed variable step-size strategy. The simulation results verify that the proposed algorithm has better performance than the existing algorithms in a system-identification scenario in the presence of impulsive noises.

In recent years, wavelet packet (WP) transform has been used as an important speech representation tool. WP-based acoustic features have found to be more effective than the short-time Fourier transform (STFT)-based features to capture the information of unvoiced phoneme in continuous speech. However, wavelet features fail to carry the same usefulness to represent the voiced phonemes such as vowels, nasals. This paper proposes new WP sub-band-based features by taking care of harmonic information of voiced speech signal. It has been noted that most of the voiced energy of the speech signal lies in between 250 and 2000 Hz. Thus, the proposed technique emphasises the individual sub-band harmonic energy up to 2 kHz. The speech signal is decomposed into 16 wavelet sub-bands and harmonic energy features are combined with WP cepstral (WPCC) features to enhance the performance of voiced phoneme recogniser. A standard phonetically balanced Hindi database is taken to analyse the performance of the proposed feature set. The noisy phoneme recognition task is also carried out to study the robustness. Significant improvement is obtained with the proposed feature set in voiced phoneme recognition over WPCC and conventional Mel frequency cepstral coefficient.