In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter- and intra-expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.

Target echoes are inevitably contaminated by the direct-path signal, multipath signal and possible interference in passive bistatic radar (PBR). Cancellation performance is deteriorated as the interference signal is irrelevant to the transmitted signal and the target echoes are submerged by in the sidelobe of the spread clutter echoes. In this study, the cancellation problem is addressed, involving clutter and interference. In this method, the spread clutter echoes are cancelled by constructing a tailored clutter subspace. Besides, the interference is also cancelled by constructing a tailored interference subspace. The sample of the interference signal is obtained from the beam channel after clutter cancellation. The performance of the proposed method is first verified by the simulation result. Then its effectiveness is also demonstrated with the application of the real data collected from an experimental PBR system.

The frequency diverse array (FDA) radar has been extensively studied due to its unique range-angle dependent beampattern. Time-invariant spatial patterns have been reported for FDAs proposed so far. However, some recent studies indicate that the patterns obtained by using these methodologies neglect the time-range or frequency-phase relationship, and the definition of time in some equations are misinterpreted which results in erroneous conclusions. Taking the time-variant property of FDA beampatterns into consideration, in this study, the authors propose a short range FDA radar to generate a time-variant focused range-angle dependent transmit beampattern, where chirp waveform with multi-carrier FDA architecture is used. Both the fixed and time-modulated frequency offsets are considered to analyse the proposed FDA radar. The frequency offset employed across each element is generated by chaos sequence and sine function. By compensating the propagation delays of signals, the transmitted signals sum up constructively to focus on the desired range-angle sector only at a specific instant of time. Numerical results are implemented to verify the validity of the proposed schemes.

Precise diagnosis of brain tumour by experienced radiologists involves a complex set of processes including magnetic resonance imaging, magnetic resonance spectroscopy (MRS) data and histopathological evaluations. In this study, a new hybrid feature extraction method, called as aiMRS, based on the negative selection algorithm and clonal selection algorithm of artificial immune systems is developed on MRS data for the detection and classification of brain tumours. In the study, differentiation of benign and malignant brain tumours, classification of normal brain tissue and brain tumour, and detection of metastasis and primary brain tumours are performed with high precision using pattern recognition methods based on the proposed aiMRS method. According to the experimental results performed on a large data set created with the MRS data obtained from INTERPRET database, when the proposed feature extraction method applied, classification of normal brain tissue and brain tumours, benign and malignant brain tumours and metastasis and primary brain tumours is achieved with 100, 98.58 and 98.94% accuracy, respectively. These results show that this proposed system can be used as a secondary tool in physicians' decision-making processes for the classification of brain tumours.