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In the recent era, the utilization of the H.264 encoder has been increasing due to its outstanding performance in video compression. However, compressing video with reduced power is still a challenging issue faced by H.264 encoders. Thus, the proposed study intends to minimize the power consumption of H.264 encoders on FPGA by optimizing the basic components of H.264, thereby enhancing performance. For this purpose, the elements like Motion Estimation, intra-prediction, transform unit and entropy encoder are optimized through the effective schemes introduced in the proposed work. Initially, the Motion Estimation unit can be alternated by optimizing the fundamental components of Block Matching Algorithms. To design the Block Matching Algorithms, the proposed study introduces low-power arithmetic units like an add-one circuit-based Carry SeLect Adder and Sum of Absolute Difference. With the help of these methods, the Block Matching Algorithms has designed, and the Motion Estimation Unit can be effectively optimized. Then, by adopting a comparator-less reusing method, the intra-prediction unit is optimized. Next, the transform unit is optimized by proposing a Steerable Discrete Cosine Transform and finally, the entropy encoders are optimized by combining Golomb and Rice entropy encoders. The proposed study uses the schemes above to improve the efficiency of H.264 encoders on FPGA. The experimental analysis in the proposed study is done using Xilinx software. The simulation results show that the proposed work obtained higher power, LUTs, delay, PSNR, frequency and MSE than other competing methods. [ABSTRACT FROM AUTHOR]

Highlights • Feature sets robust against force level variations in pattern recognition based control of myoelectric upper limb prosthesis have been proposed. • Significance of using non-linear techniques to analyze complex signals like EMG, which are both non-linear and non-stationary has been highlighted here. • With a significant improvement in classification performance of about 7% to 9%, the proposed feature sets, FFS and PSEAR outperformed all the other well established feature sets for contraction level invariant myoelectric control, considered in this paper. • Improvement in performance was observed not only for flexion movements but also for prehension and power grip movements. • The proposed feature sets can be potential candidates to make the clinical implementation of intuitive PR-based myoelectric prosthetic control possible in the future. Abstract In spite of the tremendous progress of upper limb myoelectric prosthetic control in the field of rehabilitation engineering, there still exist several real world challenges to be met, before realizing it as a good substitute for a natural arm. Incompetence of the system to accommodate variations in contraction levels of muscle movements has been identified as one of the significant challenges, as these variations have a subsequent impact on the performance of pattern recognition based myoelectric control. Non-linear techniques are more suited to characterize myoelectric signals since one of their major properties is nonlinearity. Based on this we propose two feature combinations which can lead to a reliable control scheme that is robust against contraction level variations. The performance of our proposed features when tested on nine transradial amputees for six motion classes at three different force levels outweighed other established feature extraction methods meant for contraction variation independent control. Significant improvement of around 8% in average classification performance was achieved across all subjects and force levels, subjected to training, both with all force levels and with unseen force levels. Moreover, these features achieved superior performance in classifying flexion as well as grip movements. [ABSTRACT FROM AUTHOR]

The survival rate of preterm infants has been increased by the development in treatment methods, still premature birth increases globally. These infants are at risk of medical condition that affects the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. Better understanding of preterm birth can be utilized for the prevention of such birth. This paper explores the significance of entropy features and classification using machine learning approach. Classification of spectral energy entropy feature using Elman neural network shows better result with specificity 99.85%, sensitivity 99.92% and accuracy of 99.8875%. [ABSTRACT FROM AUTHOR]