https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/5 This community include all BIT Students thesis Wed, 15 Apr 2026 00:03:40 GMT 2026-04-15T00:03:40Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4108 Title: LU and QR Factorization on GPUs for High-performance Authors: AMODHA, M.A.C Abstract: ABSTRACT GPUs have become very interesting, especially with the General Purpose Graphics Processing Units. With the ability to program the GPUs, their computation capabilities with the processing power and their competitive low cost have enabled the development of numerous kinds of interesting GPGPU application programs resulting in substantial accomplishments in terms of the performance. The LU and QR factorizations represent an underlying process of a large number of scientific application programs with complex and computationally expensive modules. But in here, the solution process has a high impact on the matrix size for the performance because of the costly computations. Proposed methodology for the GPU only LU and QR factorization algorithms were implemented using block matrix factorization where the input matrix is considered as multiple matrices when performing the factorization steps. GPU only factorization algorithms are implemented on a NVIDIA MX130 GPU. For LU factorization, the suggested GPU only algorithm implementation starts to perform well with the square matrix 6144 and upwards. With the suggested GPU only QR factorization implementation, it was possible to execute matrix sizes up-to 1024x1024. The evaluation of the implemented algorithms clearly depicted that the output matrices are accurate when computed and compared with the input matrix. Finally, it is believed that the work accomplished through this research work has facilitated for the betterment of the learning community as well as the parallel computing and computer science research community. Tue, 01 Jan 2019 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4108 2019-01-01T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3940 Title: Enhancing the Onion Name System for Darknet Authors: Maduranga, G. A. A. Abstract: Abstract The surface net as a whole does not provide the capabilities for an individual to share all kinds of information without having to take responsibility for the content posted by the individual. In a place where surface net fails, darknet prospers. Individuals with the need to share information with plausible deniability use darknet to achieve this end of the goal. However, the usability issues of the darknet act as a continuous hindrance to the use and growth of darknet. Thus, there is a requirement for a secure, decentralized name system for darknet, to overcome the above issue. In this document, the Onion Name System (OnioNS) is considered as a possible candidate to overcome the above-mentioned issues. Primarily the security concerns OnioNS introduces to the Tor network is analysed. Special concern is given to the possibility of time analyses attacks that could be carried out on the Tor network due to restrictions imposed by the OnioNS. The concerns raised are addressed in this dissertation and a method to overcome them is introduced. A novel hash tree data structure is introduced as the core component of the proposed solution. The proposed solution is analysed in order to guarantee that it is capable of implementing all the features of the OnioNS while minimizing the security threats the existing system imposes on Tor network. Further analysis of the novel solution is discussed in order to identify if the solution introduced has implemented any additional vulnerabilities to the Onion Name System or the Tor network. Sun, 01 Jan 2017 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3940 2017-01-01T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3939 Title: A Framework for Secure Software Engineering: A Knowledge Modeling based Approach for inferring Association between Source Code and Software Design Artifacts Authors: Abeyrathna, K.A.I.; Dahanayake, B.N.; Samarage, C.S. Abstract: Abstract The popular approaches in securing software systems are operating system security, anti-virus, and firewalls. These approaches build security around the software system instead of integrating within the software system. However, it is not adequate since the root cause of software vulnerabilities reside within the software system. As a result, current approaches for Software Development have given a major focus on the integration of Security with the development process to develop secure and reliable software systems. Secure Software Engineering process integrates security in each phase of the software development lifecycle. A disconnected set of security-specific practices and tools are available to be used in each phase. Architecture-level security flaws arise in the design phase while security specific bugs are caused in the implementation level. Whenever a security issue in one phase is not resolved, it can be propagated to security ramifications in another phase. The unresolved architecture-level security flaws will create security bugs at the implementation level. A connectivity between the security bugs and architecture-level security flaws needs to be identified to solve the root cause of the security bug arise as a ramification. This dissertation proposes a semi-automated approach to infer the association between security bugs and architecture-level security flaws by implementing a framework named Conexus as a proof of concept. The proposing approach uses static code analysis to identify the security bugs with respect to OWASP Top 10 vulnerability types and threat modeling to identify the architecture-level security flaws with respect to STRIDE threat categorization model. The identified security bugs and architecture-level security flaws are used as the input to the Conexus framework and the association between the two categories is derived using a Knowledge modeling based mechanism. The security controls violated by each STRIDE threat category and OWASP Top 10 vulnerability type are used in the Knowledge Base to identify the association between threat categories and bug categories through a semantic similarity matching model. Depending on the results generated from the Conexus framework, a software developer can revise the design to make a secure design followed by a secure code to eliminate and reduce security vulnerabilities in a software application. Sun, 01 Jan 2017 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3939 2017-01-01T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3938 Title: A Sound Processing Pipeline for Robust Feature Extraction to Detect Elephant Rumbles Authors: Silva, M. B. C. K. Abstract: Abstract A signi cant number of human and elephant lives have been lost due to the humanelephant con ict in Sri Lanka. To save lives of humans and elephants, it is therefore essential to minimize encounters between them. An early warning system, which detects and localize the presence of elephants through their infrasonic emissions is a viable solution to mitigate such con icts. The high cost of infrasonic detectors is an important challenge to the realworld deployment of such localization systems, in particular in developing countries where the human-elephant con ict occurs. ElOC is a system developed as a part of inventing a lowcost automatic elephant detection and localization system. Which is capable of localizing the infrasonic source within a ten-meter accuracy. In this dissertation, a novel approach is proposed to extend the ELOC to identify the elephant infrasound automatically. The novelty of this approach is the capability of distinguishing the infrasonic emissions from the elephant on top of the low-cost, resource-limited hardware platform of the ELOC. The approach rst applies a sequence of operations to reduce the e ect of noise contained in the infrasonic signal captured by the ELOC node. Then the spectral features of the infrasonic signal are extracted with wavelet-based signal reconstruction to analyze the signal more precisely. Finally, the extracted features are feed to the pre-trained classi er to distinguish the infrasound emissions from the elephants. This study is able to classify elephant rumbles with an accuracy of 82%. Thereby the proposed approach exhibits promising results in elephant detection and capable of operating on the resource-limited hardware platform of the ELOC. This study also contributes to the domain of digital signal processing since the study is the rst attempt of wavelet-based feature extraction in the domain of infrasound elephant rumble detection. Sun, 01 Jan 2017 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3938 2017-01-01T00:00:00Z