Research Papers - Dept of Information Technology

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    Techniques of Enhancing Synchronization Efficiency of Distributed Real Time Operating Systems
    (IEEE, 2022-02-23) Rajapaksha, S; Alagalla, H
    Distributed Operating Systems is one of the most modernizing concepts of the world. When it comes to Distributed Networks and Operating Systems, Real-Time Operating systems are highly crucial for the developers to achieve desired Tasks. To make the concept into the working functionality, Synchronization is playing a huge role. Synchronization has experimented with many techniques by researchers. Still, there is a lack of analysis to find the most optimized and most effective technique to achieve the goal of enhancing the accuracy and efficiency of Real-Time Operating Systems (RTOS) by synchronization context. Apart from that research is analytically produces the explanation about existing synchronization platforms in the computing world including backup Synchronization, On-Chip Memory Handling, Location-Based Network Systems Configuration, and Hardware Oriented Synchronization. Especially this research focuses on the problems and challenges of the existing RTOS and Distributed RTOS (DRTOS) Systems. Further, this paper will elaborate on the best-suited solutions and techniques to follow during the development of Distributed Node Network with Multi-Core Processors. It will propose multiple techniques to enhance the efficiency by using various algorithms comprising lock-based, lock-free, semaphore based, Mutex based, and Hardware related high accuracy criteria. Also, this will be highly beneficial for the people who are interested in Internet of Thing-based Distributed Networks to build more supportive and high-performance processing systems using desired featured objectives.
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    Real time deception detection for criminal investigation
    (IEEE, 2019-10-08) Lakshan, I; Wickramasinghe, L; Disala, S; Chandrasegar, S; Haddela, P. S
    Deception Detection System (PREDICTOR) is a solution to support the criminal investigation process by providing a technological analysis in justifying the guilt of an accused criminal in the investigation process. This study gives guidelines to substantiate decision making in the interrogation. In judicature, the importance of a platform that is capable of analyzing the genuineness and the (a) reliability of a lie and a truth, (b) emotion of the suspect and the (c) attentiveness has been recognized for a long period. The feasibility of using Machine Learning (ML) techniques to build such platforms has been explored before. However, no known platform could identify the suspect's authenticity, emotion, and attentiveness. The goal is to analyze the brain waves and build a real-time deception detection application to analyze lie/truth, emotion and the attentiveness, which will support the investigation process in a wide range of angles to decision making. Electroencephalogram (EEG) based real-time lie detection, emotion detection, and attention detection will be implemented using ML tools and techniques along with the help of special hardware equipment called MUSE 2 headband. Especially this equipment is required for the data acquisition as well as the creation of the final application. The outcome of this system is a solution to be used during the criminal investigation process as a deception detection system for lie, emotion and attentiveness of the suspect. This is more effective in the questioning process to get an idea of the suspect. This system will have a major impact on the Police Department, Criminal Investigation Department, and Judicial System to ensure the real criminal and reduce the workload of Criminal Investigation officers.