A unimodal biometric system based Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) is developed to recognize the facial of 40 subjects. The matching process is implemented using three classifiers: Euclidean distance, Manhattan distance, and Cosine distance. The maximum accuracy (100%) is satisfied when GLCM and LBP are applied with Euclidean distance. The accuracy result of these two methods is advanced the Principle Component Analysis (PCA) and Fourier Descriptors (FDs) recognition rate. The ORL database is considered for constructing the proposed biometric system. 

Super-Gaussian Based Bayesian Estimators plays significant role in noise reduction. However, the traditional Bayesian Estimators process only DFT spectral amplitude of noisy speech and the phase is left unprocessed. While deriving Bayesian estimators, consideration of phase information provides improved results. The main objective of this paper is twofold. Firstly, the Super-Gaussian based Complex speech coefficients given Uncertain Phase (CUP) based Bayesian estimators are compared under different noise conditions like White noise, Babble noise, Pink noise, Modulated Pink noise, Factory noise, Car noise, Street noise, F16 noise and M109 noise. Secondly, a novel speech enhancement method is proposed by combining CUP estimators with different NMF approaches and online bases updation. The statistical estimators show less effective results under completely non-stationary assumptions. Non-negative Matrix Factorization (NMF) based algorithms show better performance for non stationary noises. The drawback of NMF is, it requires training and/or requires clean speech and noise signals. This drawback can be overcome by taking the advantages of both statistical approaches and NMF approaches. Such approaches like Posteriori Regularized NMF (PR-NMF), Weibull Rayleigh NMF (WR-NMF), Nakagami Rayleigh (NR-NMF), CUP estimator with Gamma and Generalized Gamma distributions + NMF + Online bases Update (CUP-GG + NMF + OU) and CUP-GG + WR-NMF / NR-NMF + OU are considered for comparison. The objective of this paper is to analyze the performance of speech enhancement methods using Bayesian estimators, NMF approaches, Combination of statistical and NMF approaches. The objective performance measures Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), Signal to Noise Ratio (SNR), Signal to Distortion Ratio (SDR), Segmental SNR (Seg SNR) are considered for comparison. 

Recently there has been an increase in the use of thermal-visible conjunction technique in the field of surveillance applications due to complementary advantages of both. An amalgamation of these for tracking requires a reasonable scientific procedure that can efficiently make decisions with sound accuracy and excellent precision. The proposed research presents a unique idea for obtaining a robust track estimate with the thermo-visual fusion in the context of fundamental template matching. This method firstly introduces a haphazard transporting control mechanism for individual modality tracking that avoids unexpected estimates. Then it brings together an efficient computation procedure for providing the weighted output using minimal information from the individual trackers. Experiments performed on publically available datasets mark the usefulness of the proposed idea in the context of accuracy, precision and process time in comparison with the state of art methods.

The main disadvantage of the classical direct torque control is high torque and flux ripples. This is due to hysteresis comparators suffer from a variable switching frequency and a high torque ripple. Besides, a hybrid strategy; Direct Torque Control with Space Vector Modulation (DTC -SVM) is established using Interval Type-2 Fuzzy Logic Controller (IT2FLC) for enhancing control performance parameters to reducing torque and flux ripple. In this work, a IT2FLC is applied to the DTC-SVM of Double Stator Induction Machine (DSIM). Simulation results are shown to present the robustness and efficiency of the recommended control strategy. 

Soft Computing becomes visible in the field of computer science. The soft computing (SC) comprises of several basic methods such as Fuzzy logic (FL), Evolutionary Computation (EC) and Machine Learning (ML). Soft computing has many real-world applications in domestic, commercial and industrial situations. Edge detection in image processing is the most important applications where soft computing becomes popular. Edge detection decreases the measure of information and filters out undesirable information and gives the desirable information in an image. In image processing edge detection is a fundamental step. For this, high level Computational Intelligence based edge detections methods are required for different images. Computational Intelligence deals with ambiguous and low cost solution. The mind of the human is the key factor of the soft computing. In this paper, we included Binary particle Swarm Optimization (BPSO), Distinct Particle Swarm Optimization (DPSO), Genetic Algorithm (GA) and Ant Colony optimization (ACO) techniques. The ground truth images are taken as reference edge images and all the edge images acquired by different computational intelligent techniques for edge detection systems are contrasted with reference edge image with ascertain the Precision, Recall and F-Score. The techniques are tested on 100 test images from the BSD500 datasets. Experimental results show that the BPSO provides promising results in comparison with the other techniques such as DPSO, GA and ACO. 

This paper presents a filter bank summation method to perform spectral splitting of input signal for binaural dichotic presentation along with dynamic range compression coupled with noise reduction algorithm based on wiener filter. This helps to compensate the effect of spectral masking, reduced dynamic range, and improves speech perception for moderate sensorineural hearing loss in the adverse listening conditions. We have considered cascaded structure of noise reduction technique; Filter Bank Summation (FBS) based amplitude compression and spectral splitting. Wiener filter produces the enhanced signal by removing unwanted noise. The signal is split into eighteen frequency bands, ranging from 0-5KHz, based on auditory critical bandwidths. To reduce the dynamic range, amplitude compression is carried out using constant compression factor in each of the bands. Subjective and objective assessment based on Mean Opinion Score (MOS) and Perceptual Evaluation of Speech Quality (PESQ) scores, respectively, are used to test the Perceived quality of speech for different Signal-to-Noise Ratio (SNR) conditions. Vowel Consonant Vowel (VCV) syllable /aba/ and sentences were used as the test material. The results of the listening tests showed MOS scores for processed speech sentence “sky that morning was clear and bright blue” (4.41, 4.2, 3.96, 3.6, 3.08 and 2.66) as compared with unprocessed speech MOS scores ( 4.53, 1.21, 1.16, 1.06, 0.8, 0.483) for SNR values of ∞, +6, +3, 0, -3 and -6 dB respectively, and PESQ values (Left Channel: 2.6192, 2.5355, 2.5646, 2.5513, 2.5221, and 2.4309; Right Channel: 2.5889, 2.3001, 2.3714, 2.4710, 2.3636, and 2.4712) for SNR values of ∞, +6, +3, 0, -3 and -6 dB respectively, indicating the improvement in the perceived quality for different SNR conditions. To evaluate the intelligibility of the perceived speech, listening test was carried out for hearing impaired (moderate Sensorineural Hearing Loss (SNHL)) persons in the presence of background noise using Modified Rhyme Test (MRT).The test material consists 50 sets of monosyllabic words of consonant-vowel-consonant (CVC) form with six words in each set. Each subject responded for a total of 1800 presentations (300 words x 6 different SNR conditions). Results of the listening tests (using MRT) showed maximum improvement of (27.299%, 23.95%, 24.503%, 23.602%, and 23.498%) in the speech recognition scores at SNR values of   (-6dB, -3dB, 0dB, +3dB, +6dB) compared to unprocessed speech recognition scores. Reductions in response times compared to unprocessed speech response times at lower SNR values were observed. The decrease in response times at the SNR values of -6, -3, 0, +3 and+6 dB were 1.581, 1.41, 1.329, 1.279, and 1.01s, respectively, indicating improvement in intelligibility of the speech at lower SNR values.