Did you mean: Search also for broader subjects Search also for related subjects Search also for narrower subjects Research Design. Design Research Case Study Research : design and methods Bordens, Kenneth S. Research design and methods. Creswell, John W. Qualitative inquiry & research design. National Research Council (u.S.). Committee for the Study of Relationships between Vehicle Configurations and Highway Design. Architectural design Research Nuclear energy Research Laboratories Design and construction Research parks Switzerland Basel Design and construction Building Research: Design, Construction and Technologies Experimental Design & Research Methods 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-MECH-84 MSTHESIS ABSTRACT. Reconfigurable Manufacturing Systems (RMS) effectively respond to fluctuating market needs and customer demands for finished product. Diagnosability is a supporting characteristic of RMS that has a say in the quality of finished product. Cost and time taken for manufacturing are also considerably affected if proper diagnosability measures are not taken. Previous studies on Diagnosability of RMS have been studied from Axiomatic System Theory as such Design For Diagnosability (DFD). Nevertheless Diagnosability remains to be the least studied characteristic of RMS. With the availability of digitized data, Machine Learning approaches to advance manufacturing have proven to be considerably effective. A research gap existed for the application of Machine Learning techniques in improving the Diagnosability of RMS. A framework of Machine Learning has been proposed to address this gap. The working of the framework has been illustrated by two demonstrations from the available datasets, one in identifying proper signals in semi-conductor manufacturing to predict excursions, and the second in predicting machine failures due to a variety of factors. The framework is rendered in a concurrent-engineering fashion. The framework is tested against two available manufacturing datasets. Increase in Diagnosability will decrease the cost and time taken to production. Key Words: Reconfigurable Manufacturing Systems, Machine Learning, Artificial Intelligence, Preventive Maintenance, Intelligent Manufacturing