KEYWORDS: Video, Video coding, Computer programming, Video processing, Signal processing, Local area networks, Video compression, Internet, Standards development, Operating systems
Although dynamic adaptive video streaming over http (DASH) has developed as a most subtle technology that can be used for the transmission of live and on-demand audio and video content over any IP network, the design of video segment size is an important aspect, as it varies from one technology to another. We proposed a method to investigate the effect of changing the buffer size, as it was configured to be dynamically adapted to the segment size. Our proposed method also retrieves the most appropriate video representation based on the available bandwidth compared to the size of the video representations. We expose an empirical study for different segment sizes (i.e. 1,2,5,10,15 and 20 seconds) striving for the best available quality. An objective evaluation was carried out in relation to study the impact of the segment size while streaming video. From the tests carried out, the larger segment size, the better PSNR value; however, it produces higher Initial delay. In our obtained results, segment size of 20 seconds has the highest PSNR value at 45.7dB; whereas segment size of 1 second has the lowest initial delay at 1.2 seconds.
Falls are the most critical health problem for elderly people, which are often, cause significant injuries. To tackle a serious risk that made by the fall, we develop an automatic wearable fall detection system utilizing two devices (mobile phone and wireless sensor) based on three axes accelerometer signals. The goal of this study is to find an effective machine learning method that distinguish falls from activities of daily living (ADL) using only a single triaxial accelerometer. In addition, comparing the performance results for wearable sensor and mobile device data .The proposed model detects the fall by using seven different classifiers and the significant performance is demonstrated using accuracy, recall, precision and F-measure. Our model obtained accuracy over 99% on wearable device data and over 97% on mobile phone data.
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