Autonomous Smart Device for COVID-19 Detection Using Artificial Intelligence

Corona is a pandemic disease and is spreading all over the world. There is also lack of corona virus detection machines. If it is detected at very early stages without pathological intervention, then further spreading of the disease can be controlled, and many of human lives can be saved. So, the proposed biomedical device can be used for fast and accurate prediction of COVID-19 from chest x-rays. X-ray can also be taken from anywhere and sent through any communication medium. Even if error is added, it can be removed using error concealment algorithms. Automated AI-based systems will be used for prediction of normal, COVID-19, and pneumonic cases from x-ray images. It makes detection of COVID-19 infection less costly and portable. This device can be stored in less stringent conditions, making it more effective.

Error concealment Pada Transmisi Video H.264/AVC dengan Frequency Selective Extrapolation

Video transmission is encoded by block-based techniques such as MPEG in sensitive environment. It’s very susceptible to noise which can cause block losses and even missing frames during transmission. A technique to overcome this problem is applied Error concealment (EC) on the decoder. There are two approaches used in this EC, spacial EC which utilizes information around the image and temporal EC by utilizing motion information. The method of EC that used in this research is Frequency Selective Extrapolation (FSE). The video is encoded by the H.264 / AVC standard (MPEG-Part10). The isolated block losses was added to the video as an error simulation, then applied the EC-FSE method on the decoder. There are two method of EC-FSE used, FSE-2D (two dimensions) and FSE-3D (three dimensions). The measurement results were observed by the PSNR and MOS values. The simulation results show that H.264 / AVC video concealed with FSE-3D has a better performance than FSE-2D.

MDC adaptive video streaming

Multiple Description Coding (MDC) is designed for multiple path video streaming with channel diversities. In this thesis, we investigate the performance of multi-path video streaming using the MDC technique. The MDC frame loss rate is one of the indicators of the real time video quality. A classification based framework for making mode decisions to minimize the MDC video frame transmission cost that may be defined in terms of the six parameters, number of sub-streams, number of transmission channels, GOP length, the I-frame positions, probability of network transmission states and probability of transmission changes. This thesis surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, and video coding. The focus of this thesis is on the video adaptive coding process to improve performance in terms of one or more of these factors. How to deliver a real-time MDC video from an end user over multi-channels is studied. The traffic is used to probe the network on determinig the network conditions and optimizing the coding algorithms appropriately. An efficient transmission statistical model Auto Regression (AR) to capture the properites of the region of interest is also introduced. Both the mode decisions and the error concealment require feedback from the network regarding the available bandwidth, loss probability, video coding methods and coding time spatial manners. The proposed algorithm works in a fully distributed environment, making it suitable for wireless ad hoc networks or other IP networks.

MDC adaptive video streaming

Multiple Description Coding (MDC) is designed for multiple path video streaming with channel diversities. In this thesis, we investigate the performance of multi-path video streaming using the MDC technique. The MDC frame loss rate is one of the indicators of the real time video quality. A classification based framework for making mode decisions to minimize the MDC video frame transmission cost that may be defined in terms of the six parameters, number of sub-streams, number of transmission channels, GOP length, the I-frame positions, probability of network transmission states and probability of transmission changes. This thesis surveys the current status of horizontal decomposition into distributed computation, and vertical decomposition into functional modules such as congestion control, routing, scheduling, random access, and video coding. The focus of this thesis is on the video adaptive coding process to improve performance in terms of one or more of these factors. How to deliver a real-time MDC video from an end user over multi-channels is studied. The traffic is used to probe the network on determinig the network conditions and optimizing the coding algorithms appropriately. An efficient transmission statistical model Auto Regression (AR) to capture the properites of the region of interest is also introduced. Both the mode decisions and the error concealment require feedback from the network regarding the available bandwidth, loss probability, video coding methods and coding time spatial manners. The proposed algorithm works in a fully distributed environment, making it suitable for wireless ad hoc networks or other IP networks.