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MS-COMPUTER ENGINEERING-2017 MSTHESIS ABSTRACT. The use and vast implementation of Discrete Fourier Transform has revolutionized the world and allowed the researchers to think of the modern world from a different perspective. The discovery of Fast Fourier Transform has laid the foundation of an entirely new dimension to the modern world. Keeping in view its utmost importance in the future industry researchers tried to design its hardware architecture as per the requirement of the application. Several architectures have been proposed time to time with new inventions in the previous designs. Some architectures consider clock rate, some take architectural area into consideration, some focuses on parallel execution of the algorithm, so on and so forth. Considering all these inputs to the industry that has been a part to modern world time to time, this research presents an empirical model based upon the optimal architectures for Fast Fourier Transform algorithm for n-bits m-points input. This empirical model is obtained by making several architectures and their respective characteristics are obtained. The data obtained is then passed through a machine learning algorithm known as Regression Algorithm. Linear, quadratic and cubic regression technique is applied to achieve the hierarchy of the designed architectural parameters and this intern will provide us with the empirical models of the architecture. This model will provide us with the specifications of the futuristic architecture that mainly depends upon the one's requirement i.e. either one considers a single parameter or a tradeoff between different hardware parameters. The parameters that are mainly considered are number of Slice LUT's, LUT FF Pairs, clock rate, number of processing elements and number of clock cycles required. This proposed methodology can be applied to any hardware architectural designs for analysis and generation of empirical models. Key Words: Discrete Fourier Transform, Fast Fourier Transform, Processing Element, Butterfly Architecture, n Radix FFT, Permutation Matrix, Kronecker Product
MS-CSE-15 MSTHESIS ABSTRACT. Database management Systems (DBMS) are one the most critical component of a software application. Searching data from DBMS is an enormous part in software performance. Text search engines are also used for searching, but these engines lack sophisticated DBMS features. Relational database management systems (RDBMS) are not quite compatible with modern objectoriented languages. To overcome the complexity of data and object-oriented programming, modern development practices adopted Object Relation Mapping frameworks (ORM). ORM bears a layer of abstraction between object models and database. This layer automatically bridges objects in OOP languages to database records, which results in significantly reducing custom mapping code complexity. ORM has its advantages but on the other side it comes with be some challenges too. In process of mapping objects and data, ORM keeps the relations between objects intact and that results in retrieval of multiple objects from multiple tables. When the data is big and have a hieratical structure, data retrieval or search becomes more complex. Database performance for the retrieval of data are optimized by adding indexing to each table. Indexing makes search significantly fast but also makes other processes slow because tables are required to be re-index every time a record is changed. Hence an optimized solution is required to resolve this problem in ORM search process. To overcome this problem, this research proposes a java-based framework that can interact between ORM and search engine. It consumes search engine web APIs to provide a layer that can convert and search objects to/from XML. It makes search process faster and support ORM with its object-oriented methodology. Moreover, this framework not only reduces performance load on databases but also makes search queries simpler when implemented in development process. The results have been validated by two case studies, which were carried out by implementing each approach. 1000 similar search queries were processed on each framework and results shows 30 to 40 % improvement in query time. Keywords: DBMS Search, Indexing, Text Search Engines, Solr inedexes, Object oriented programming (OOP), Object Relation Mapping (ORM), Search optimization, Information Retrieval, database indexing