Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Influence of TiC on dry sliding wear and mechanical properties of in situ synthesized AA5052 metal matrix composites

Abstract: In this paper, aluminium metal matrix composites were synthesized through in situ process in which aluminium alloy 5052 (AA5052) and titanium carbide were used as matrix and reinforcement materials...

A Fuzzy Rule Base Approach for Intelligent Protection of Microgrids

Abstract: The proposed work develops a decision tree-induced fuzzy rule base intelligent protection scheme for fault detection and classification in a microgrid with multiple distributed generation interfaces. The proposed protection scheme retrieves one cycle post-fault current signal samples of each phase from fault inception at bus ends of the respective feeder to derive some differential features. The retrieved current samples are pre-processed using S-transform to obtain a time–frequency contour. The statistical features, such as energy, mean, standard deviation, and entropy, are computed from the time–frequency contour, which is further used to calculate the differential features. The differential features are used to build the fault classification tree. From the decision tree classification boundaries, the fuzzy membership functions are drawn, and further, the corresponding fuzzy rule base is generated for the final relaying decision. The proposed scheme is developed on a MATLAB/SIMULINK (The MathWor...

Efficient Localization of Hot Spots in Proteins Using a Novel S-Transform Based Filtering Approach

Abstract: Protein-protein interactions govern almost all biological processes and the underlying functions of proteins. The interaction sites of protein depend on the 3D structure which in turn depends on the amino acid sequence. Hence, prediction of protein function from its primary sequence is an important and challenging task in bioinformatics. Identification of the amino acids (hot spots) that leads to the characteristic frequency signifying a particular biological function is really a tedious job in proteomic signal processing. In this paper, we have proposed a new promising technique for identification of hot spots in proteins using an efficient time-frequency filtering approach known as the S-transform filtering. The S-transform is a powerful linear time-frequency representation and is especially useful for the filtering in the time-frequency domain. The potential of the new technique is analyzed in identifying hot spots in proteins and the result obtained is compared with the existing methods. The results demonstrate that the proposed method is superior to its counterparts and is consistent with results based on biological methods for identification of the hot spots. The proposed method also reveals some new hot spots which need further investigation and validation by the biological community.

Retrieval of colour and texture images using local directional peak valley binary pattern

Abstract: Many content-based image retrieval (CBIR) methods are being developed to store more and more information about images in shorter feature vectors and to improve image retrieval rate. In the proposed method, two-step approach to CBIR has been developed. The first step generates an image mask from local binary pattern (LBP). This LBP mask is then utilized to draw comparison between the centre pixel and the eight surrounding pixels. The second step involves drawing the peak and valley patterns of local directional binary pattern for each image which is then combined with the colour histogram to retrieve similar images. Existing methods suffer from lower average image retrieval accuracy even with larger feature vectors. The proposed method overcomes such problems through shorter feature vectors that can store more information about the image. As illustrated through experimental results, the proposed method produces promising results with shorter feature vector of length 56 and improved image retrieval rate of about 5–10%. Our method outperforms similar techniques when tested with public data sets.