National Institute of Technology, Silchar

About: National Institute of Technology, Silchar is a education organization based out in Silchar, Assam, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1934 authors who have published 4219 publications receiving 41149 citations. The organization is also known as: NIT Silchar.

Non-casual linear prediction based adaptive filter for removal of high density impulse noise from color images

Abstract: In this paper, a non-causal linear prediction based adaptive vector median filter is proposed for removal of high density impulse noise from color images. Generally, when an image is affected by high density of impulse noise, homogeneity amongst the pixels is distorted. In the proposed method, if the pixel under operation is found to be corrupted, the filtering operation will be carried out. The decision about a particular pixel of being corrupted or not depends on the linear prediction error calculated from the non-causal region around the pixel under operation. If the error of the central pixel of the kernel exceeds some predefined threshold value, adaptive window based vector median filtering operation will be performed. The size of adaptive window will depend on the level of error according to the predefined threshold. The proposed filter improves the peak signal to noise ratio (PSNR) than that of modified histogram based fuzzy filter (MHFC) by approximately 4.5 dB. The results of structural similarity index measure (SSIM) suggest that the image details are maintained significantly better in the proposed method as compared to earlier approaches. It may be observed from subjective evaluation that the proposed method outperforms some of the existing filters.

Halide perovskite two-terminal analog memristor capable of photo-activated synaptic weight modulation for neuromorphic computing

Abstract: Emulation of biological signal processing, learning and memory functions is essential for the development of artificial learning circuitry. Two terminal artificial synapses are supposed to be more feasible with biological system in terms of energy efficiency and processing. Here, we report on the fabrication of organic–inorganic hybrid perovskite based two-terminal artificial synapse in which the synaptic plasticity is modified by both voltage pulses and light illumination. The device emulates important synaptic characteristics, including analog memory switching, short-term plasticity, and long-term plasticity, analogous to the biological system. The change in conductance is attributed to the ion migration under external electric field. In addition, the improved post-synaptic current in optical exposer could be related to the generation of excitons and lowered Schottky barrier at perovskite/electrode interface under external electric field.

Classification of benign and malignant masses in mammograms using multi-resolution analysis of oriented patterns

Abstract: The paper proposes a novel approach for the classification of breast masses as benign and malignant using multi-resolution analysis of oriented patterns of tissues in mammograms. Since, the oriented structures of normal breast near the mass region may be changed in presence of masses, three regions are defined, first, for the analysis. Statistical features are then extracted using two angle co-occurrence matrices derived at different resolution levels of each region with Haar-wavelet transform to quantify the joint occurrences of different angle pairs of oriented patterns. The experiments show best classification accuracy of 81.23% and area under the receiver operating characteristic curve of 0.86 with 433 images from the DDSM database using artificial neural network and tenfold cross-validation method.