SDM College of Engineering and Technology

About: SDM College of Engineering and Technology is a based out in . It is known for research contribution in the topics: Diesel fuel & Combustion. The organization has 350 authors who have published 351 publications receiving 2399 citations.

Classification of archaeological monuments for different art forms with an application to CBIR

Abstract: Until now, Content Based Image Retrieval (CBIR) techniques barely contributed to the archaeological domain. The use of these techniques can support archaeologists in their assessment and classification of archaeological finds. Museums and art galleries deal in inherently visual objects. The ability to identify objects sharing some aspect of visual similarity can be useful both to researchers trying to trace historical influences, and to art lovers looking for further examples of paintings or sculptures appealing to their taste. This paper illustrates the use of CBIR techniques for automatic classification of archaeological monuments using visual features shape and texture to study the art form and retrieve the similar images from reference collection. Shape based features are extracted using morphological operators and texture features are extracted using gray level co-occurrence matrix (GLCM). Robust feature set is built to retrieve the similar images. Experiments have been conducted on database consists of 500 images with 5 categories. Results of proposed method are compared with Canny and Sobel methods. Results demonstrate the efficiency of proposed method.

Artificial Neural Networks for the Prediction of Wear Properties of Al6061-TiO2 Composites

Abstract: The exceptional performance of composite materials in comparison with the monolithic materials have been extensively studied by researchers. Among the metal matrix composites Aluminium matrix based composites have displayed superior mechanical properties. The aluminium 6061 alloy has been used in aeronautical and automotive components, but their resistance against the wear is poor. To enhance the wear properties, Titanium dioxide (TiO2) particulates have been used as reinforcements. In the present investigation Back propagation (BP) technique has been adopted for Artificial Neural Network [ANN] modelling. The wear experimentations were carried out on a pin-on-disc wear monitoring apparatus. For conduction of wear tests ASTM G99 was adopted. Experimental design was carried out using Taguchi L27 orthogonal array. The sliding distance, weight percentage of the reinforcement material and applied load have a substantial influence on the height damage due to wear of the Al6061 and Al6061-TiO2 filled composites. The Al6061 with 3 wt% TiO2 composite displayed an excellent wear resistance in comparison with other composites investigated. A non-linear relationship between density, applied load, weight percentage of reinforcement, sliding distance and height decrease due to wear has been established using an artificial neural network. A good agreement has been observed between experimental and ANN model predicted results.

Implementation of Fingerprint Based Biometric System Using Optimized 5/3 DWT Architecture and Modified CORDIC Based FFT

Abstract: The real-time biometric systems are used to authenticate persons for wide range of security applications. In this paper, we propose implementation of fingerprint-based biometric system using Optimized 5/3 DWT architecture and Modified CORDIC-based Fast Fourier Transform (FFT). The Optimized 2D-DWT architecture is designed using Optimized 1D-DWT architectures, Memory Units and novel Controller Unit which is used to scan rows and columns of an image. The database fingerprint image is applied to the proposed Optimized 2D-DWT architecture to obtain four sub-bands of LL, LH, HL and HH. The efficient architecture of FFT is designed by using Modified CORDIC processor which generates twiddle factor angles of range $$0^{\circ }$$ – $$360^{\circ }$$ using Pre-processing Unit and Comparator Block. Further, the LL sub-band coefficients are applied to the Modified CORDIC based FFT to generate final fingerprint features. The test fingerprint features are obtained by repeating the same procedure and are used to match the database fingerprint image features using Euclidean Distance. The performance parameters of proposed architecture in terms of area utilization, speed and accuracy is compared with existing architecture to validate the obtained results.