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Aim and background: The aim of this study was to investigate Kinematics and kinetics Symmetry of Lower Limbs during running at a constant speed. Methods: 28 Elite runner (age: 34.75±6.63years) participated in this study. Running at constant speed was conducted by each subject at 2/5 m.s-1 on treadmill while kinematic and kinetic data were captured at 150 Hz and 300 Hz, respectively. The peak of joint moments and flexion angles in sagittal plane were calculated. The normality and homogeneity of variances assumptions of the dependent variables was tested using Bartlett and Leven’s test. Independent t tests were conducted to examine the symmetry of hip, knee and ankle moments between dominant and non-dominant joints during stance phase of running (p<0.05). Results: results of the present study showed no significant difference exists between dominant and non-dominant lower joints regarding the peak moments of Hip, Knee and ankle in sagittal plane during stance phase (p>0.05) as well as between peak flexion angles during running. Conclusion: Symmetry exists in lower joints sagittal moments and flexion angle during running at the constant speed. According to the results dominant and non- dominant lower joints play propulsive and absorbent roles cooperatively.

Most real-time speech signals are frequently disrupted by noise such as traffic, babbling, and background noises, among other things. The goal of speech denoising is to extract the clean speech signal from as many distorted components as possible. For speech denoising, many researchers worked on sparse representation and dictionary learning algorithms. These algorithms, however, have many disadvantages, including being overcomplete, computationally expensive, and susceptible to orthogonality restrictions, as well as a lack of arithmetic precision due to the usage of double-precision. We propose a greedy technique for dictionary learning with sparse representation to overcome these concerns. In this technique, the input signal's singular value decomposition is used to exploit orthogonality, and here the ℓ1-ℓ2 norm is employed to obtain sparsity to learn the dictionary. It improves dictionary learning by overcoming the orthogonality constraint, the three-sigma rule-based number of iterations, and the overcomplete nature. And this technique has resulted in improved performance as well as reduced computing complexity. With a bit-precision of Q7 fixed-point arithmetic, this approach is also used in resource-constrained embedded systems, and the performance is considerably better than other algorithms. The greedy approach outperforms the other two in terms of SNR, Short-Time Objective Intelligibility, and computing time.

The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes a significant development in terms of new three-dimensional representation of binary population for GA as well as significant new modification to the GA operators. The efficacy of the scheme is evident from the results presented in the paper for dataset under consideration.

Purpose: The research aims to visualize and analyze co-word network, and thematic clusters of the intellectual structure in the field of digital content management during 2010-2020. Method: The study is applied research with a descriptive approach which is conducted by techniques of co-word, and social network analysis. Data analysis and visualization of the co-word network were represented by SPSS, UCINet, and Python programming language. Findings: 8 main clusters are identified. The cluster multimedia content management & retrieval is the most mature and central thematic cluster. The USA and various sub-categories of Computer Science are located in the top ranks of WOS in the field. Most productions were published in 2020. Generally, the Clusters were labeled in two contexts of health and LAM (Libraries, Archives, Museums, and cultural heritage). Conclusion: Content-based management and retrieval are focused on artificial intelligence, decision-supported, knowledge-based and ontological techniques which are conducted as novel approaches and underlying trends in the field.