National Institute of Technology, Meghalaya

About: National Institute of Technology, Meghalaya is a education organization based out in Shillong, India. It is known for research contribution in the topics: Control theory & Computer science. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

Unimolecular Dissociation of C6H6-C6Cl6 Complex and Effect of Mode-Mode Coupling.

Abstract: The unimolecular dissociation dynamics of the C6H6-C6Cl6 (Bz-HCB) complex is studied with initial excitation of all vibrational modes for a temperature range of 1000-2000 K and with mode-specific excitations at 1500 K. The results are compared with those of the C6H6-C6F6 [Bz- HFB] complex. When all modes of Bz-HCB are initially excited, the rate of dissociation is slower with respect to Bz-HFB. However, the rate of dissociation is faster when simulations with nonrandom excitation of the specific vibrational modes are performed. The rate of dissociation of Bz-HCB is found to become slower when a few intramolecular modes are excited along with all inter-fragment modes compared to the simulation when only inter-fragment modes of the same complex are excited. Such an energy-transfer dynamics is absent if both intramolecular and inter-fragment modes are not initially excited. Thus, a "stimulated" resonance energy-transfer dynamics is observed in Bz-HCB dissociation dynamics.

Quantum Dynamics of the (18)O + (36)O2 Collision Process.

Abstract: We report full quantum cross sections and rate constants for the (18)O + (36)O2 → (36)O2 + (18)O collision process. This constitutes to the best of our knowledge the first dynamical study of the (18)O(18)O(18)O system, with three identical (18)O oxygen atoms. We emphasize the comparison with the (16)O + (32)O2 collision as this latter presents the exact same features as the one treated here, except the consistent change of mass for all three atoms. We find very similar behaviors in the cross sections, and we confirm that the rates are faster when three identical nuclei are involved. In particular, we cannot dynamically study this system with classical trajectory methods, and we have to include properly the indistinguishability of the three (18)O nuclei; however, we note some slight differences with the (16)O(16)O(16)O benchmark system, and we focus our analysis on their origin.

Tracking control and synchronization of Bhalekar-Gejji chaotic systems using active backstepping control

Abstract: In this paper, active backstepping control technique is employed to design control laws for trajectory tracking and synchronization of Bhalekar-Gejji (BG) chaotic system. The designed controllers are capable of stabilizing the BG chaotic system at any position and also controlling it to track the desired trajectory which is smooth function of time. The designed controllers are effective to achieve the synchronization between BG chaotic systems excited from different initial conditions. Lyapunov's stability theory is used to ensure the stability and convergence of error dynamics. The results are simulated in the MATLAB. MATLAB simulation results reflect that the objectives are successfully achieved.

Hyperspectral Remote Sensing Images and Supervised Feature Extraction

Abstract: In the last three decade, one of the significant breakthrough in remote sensing is to introduce of hyperspectral sensors, which acquire a set of images from hundreds of narrow and contiguous wavelengths of the electromagnetic spectrum from visible to infrared regions. Images, which are captured by these sensors, have detailed information in the spectral domain to identify and distinguish spectrally unique materials. To recognize the objects present in hyperspectral images, classification/clustering task need to be performed. But due to the presence of huge number of attributes, classification technique becomes more complex. So, before performing the classification task, reduce the number of attributes (denoted by dimensionality of the data) is an important step where the aim is to discard the redundant attributes and make it less time consuming for classification. In this chapter, few supervised feature extraction techniques for hyperspectral images i.e., prototype space feature extraction (PSFE), modified Fisher’s linear discriminant analysis (MFLDA), maximum margin criteria (MMC) based and partitioned MMC based methods are explained. Experiments are conducted over different hyperspectral data set with different quantitative measures to analyze the performance of these feature extraction methods.