Following its use in several applications, including video coding in wireless surveillance, moving object detection (MOD) has become a popular video analysis topic. Despite the considerable progress in the accuracy of MOD for video coding, its implementation in constrained sensors is a real challenge owing to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-theart techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.
Cardiovascular diseases (CVDs) are the most leading causes of death every year in the world. The threat of CVDs can be decreased and controlled with early diagnoses. Therefore, interpreting heart sounds is considered as one of the common ways to diagnose the cardiovascular system. Heart sound signal as known as phonocardiogram (PCG) provides useful information about the heart condition, which can be used in the diagnostic, and helps the physicians in the detection of several cardiovascular abnormalities. The technology development helped in the appearance of new diagnosis techniques, which combines new advanced signal processing techniques and deep learning algorithms. Thus, the heart sound classification is becoming a crucial task in the modern healthcare field. In this work a deep learning-based classification method was proposed. Using PCG database which contains five different classes taken from different cases of heart valve defects. Scalogram of heart sound signals was used as time-frequency representation to create a scalogram image database extracted from the PCG database. A convolutional neural network with Direct Acyclic Graph structure (DAG CNN) was used in the classification of the scalogram image database. The evaluation of the classification performance indicated that the accuracy was about 99,6%. A comparative results manifest that the proposed method had a better performance compared to other previous works in which the same database was used.
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