Artificial Intelligence for Healthcare Systems of Developing World: Opportunities and Risks

Artificial Intelligence (AI) is now everywhere. It refers to systems that demonstrate intelligent behavior, such as the ability to analyze their environment to take action that is typically displayed by humans and animals.  AI-embedded devices possess the ability to learn through example scenarios and past data presented to the system. The word AI was first introduced to the research community in 1956 at a conference at Dartmouth College in the United States [1].  With time, the AI industry expanded from data to information, then to knowledge, and ultimately to intelligence. Due to the depth and breadth of learning capacity associated with AI technologies, today it has become one of the hottest research areas, finding applications in but not limited to computer vision, marketing, industrial automation, big data, and the Internet of things (IOT)

Brain Tumor Boundary Segmentation of MR Imaging using Spatial Domain Image Processing

Extracting information for medical purposes from magnetic resonance imaging is critically important for diagnostic and treatment plans. In this paper, a simple algorithm for tumor segmentation of Magnetic resonance imaging (MRI) is introduced. The novelty incorporates, preserving fine details of the input image while detecting the boundary accurately. Tumor segmentation is carried out by set of pre processing steps followed by morphological operations. Rough contour of the tumor is localized to reduce the search space for the boundary. Line drawing algorithm in cooperated with pixel selection criteria is used to detect the accurate boundary. The algorithm is evaluated in terms of the performance and accuracy with radiologist labelled ground truth MRI scans. Simulation results show that the proposed algorithm provides better identification with above 95% of accuracy, for clearly distinguishable tumors in relation to conventional contour detection methods

Advanced Driver-Assistance System with Traffic Sign Recognition for Safe and Efficient Driving

Advanced Driver-Assistance Systems (ADAS) coupled with traffic sign recognition could lead to safer driving environments. This study presents a sophisticated, yet robust and accurate traffic sign detection system using computer vision and ML, for ADAS. Unavailability of large local traffic sign datasets and the unbalances of traffic sign distribution are the key bottlenecks of this research.  Hence, we choose to work with support vector machines (SVM) with a custom-built unbalance dataset, to build a lightweight model with excellent classification accuracy.  The SVM model delivered optimum performance with the radial basis kernel, C=10, and gamma=0.0001. In the proposed method, same priority was given to processing time (testing time) and accuracy, as traffic sign identification is time critical. The final accuracy obtained was 87% (with confidence interval 84%-90%) with a processing time of 0.64s (with confidence interval of 0.57s-0.67s) for correct detection at testing, which emphasizes the effectiveness of the proposed method

Error Robust Video Transmisson Using Redundant Data.

Video communication is a popular topic for research in the modem communication field. It is widely used in mobile television broadcasting, teleconferencing, gaming applications and online streaming. However, due to the unpredictable nature of transmission network, a bit stream transmitted through a noisy channel may suffer heavy loss of data. This hinders user satisfaction, through artefacts created by lost data fragments. Hence, error resilient techniques are introduced to the transmitted data stream to improve the error robustness. The research presented here, is mainly focused on the error resilient tool called redundant coding and the possible adaptability of this tool to channel conditions. Also, this thesis discusses the error resilience tools of both 2D and 3D video coding paradigms, focusing on the bottlenecks associated with the existing techniques. One of the key objectives of this research is to find an efficient trade-off among the redundant information present, bandwidth utilisation and the capacity of error robustness. An unequal error protection (UEP) based redundant motion information coding method is outlined. The prioritisation achieved through UEP helps to mitigate the amount of redundant information present while maintaining a good primary picture quality. Also, the possibility of replacing the residual information with turbo coded parity bits is discussed. This particular proposal employs the unidirectional distributed video coding architecture to generate Wyner-Ziv parity bits to improve the motion compensated frame of the redundant stream. Another section of the thesis presents a new coding architecture where encoder based H.264/AVC compatible motion compensated frames and Wyner-Ziv parity bits are used to generate a robust primary data stream, which has the capacity to withstand a large number of bit errors in WiMAX environments, specifically for low motion sequences. Finally, in the last section of the thesis, error resilience tools are investigated for 3D video coding. Much needed redundant data transmission based error resilient tools for multi-view coding are presented. The two techniques described, employ the disparity information and region of interest coding to generate the redundant information respectively to achieve the target

Error Robust Video Transmisson Using Redundant Data.

Video communication is a popular topic for research in the modem communication field. It is widely used in mobile television broadcasting, teleconferencing, gaming applications and online streaming. However, due to the unpredictable nature of transmission network, a bit stream transmitted through a noisy channel may suffer heavy loss of data. This hinders user satisfaction, through artefacts created by lost data fragments. Hence, error resilient techniques are introduced to the transmitted data stream to improve the error robustness. The research presented here, is mainly focused on the error resilient tool called redundant coding and the possible adaptability of this tool to channel conditions. Also, this thesis discusses the error resilience tools of both 2D and 3D video coding paradigms, focusing on the bottlenecks associated with the existing techniques. One of the key objectives of this research is to find an efficient trade-off among the redundant information present, bandwidth utilisation and the capacity of error robustness. An unequal error protection (UEP) based redundant motion information coding method is outlined. The prioritisation achieved through UEP helps to mitigate the amount of redundant information present while maintaining a good primary picture quality. Also, the possibility of replacing the residual information with turbo coded parity bits is discussed. This particular proposal employs the unidirectional distributed video coding architecture to generate Wyner-Ziv parity bits to improve the motion compensated frame of the redundant stream. Another section of the thesis presents a new coding architecture where encoder based H.264/AVC compatible motion compensated frames and Wyner-Ziv parity bits are used to generate a robust primary data stream, which has the capacity to withstand a large number of bit errors in WiMAX environments, specifically for low motion sequences. Finally, in the last section of the thesis, error resilience tools are investigated for 3D video coding. Much needed redundant data transmission based error resilient tools for multi-view coding are presented. The two techniques described, employ the disparity information and region of interest coding to generate the redundant information respectively to achieve the target

Error robust video transmission using redundant data

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