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 & Electric power system. The organization has 503 authors who have published 1062 publications receiving 6818 citations. The organization is also known as: NIT Meghalaya & NITM.

Modeling of turbulent dissipation and its validation in periodically stratified region in the Liverpool Bay and in the North Sea

Abstract: The present work explores the applicability of an alternative eddy viscosity formulation in numerical models dealing with the dynamics of the coastal ocean. The formulation is based on the Reynolds stress anisotropy–anisotropy being an important tool for capturing turbulent mixing. Initially idealized entrainment scenarios are evaluated that are typical for shelf seas viz. entrainment in linearly stratified and two-layer fluids caused by surface wind stress or barotropic pressure gradient-driven bottom stress. An attempt is made to simulate the realistic semi-diurnal cycle of turbulent dissipation in Liverpool Bay Region of Freshwater Inflow (ROFI) in the Irish Sea characterized by strong horizontal gradients and interactions with tidal flow. Turbulent dissipation cycles with a 25-h period using free-falling light yo-yo (FLY) dissipation profiler exhibits a strong asymmetry between ebb and flood. The above dynamics involving tidal straining during the ebb and mixing during the flood has been simulated using k– $$ \varepsilon $$ and the alternative formulated turbulence scheme in a one-dimensional (1-D) dynamic model. The model is forced with observed tidal flow and horizontal gradients of temperature and salinity. Simulated dissipation cycles show good agreement with observation. The present work also involves a comparison of dissipation rate measurements in northern North Sea using the abovementioned turbulence schemes—the measurements being taken using free-falling shear probes and CTD (conductivity, temperature, and depth) sensors. The main forcing provided for the upper and bottom boundary layers are atmospheric forcing and tides, respectively. To compare the observations and model results, quantitative error measurements have also been studied which reveal the applicability of the alternative turbulence scheme.

Integration of service oriented WSN and IoT for E-Commerce

Abstract: Wireless sensor networks (WSN) generate huge volume of the heterogeneous data that can be made open source and linkedfor the different Enterprise IT applications. In order to use the services and data generated by the sensors, it should be in the standardized format. This homogeneity of defining the data and services is provided by the new emerging technology known as Internet of things. It has the power of Resource Description Framework, which links URIs and thus provides easy access from different sources to users through Internet. In this paper, we have studied different technologies that are using for integration of Service Oriented WSN and IoT for E-Commerce. Paper also discussed the current issues and challenges of the integration. By studying E-Commerce scenario, we also propose mathematical formulation of the problem for healthy transportation between seller and receiver.

Blind speech separation using canonical correlation and performance analysis

Abstract: Several methods have been explained for blind source separation (BSS) in the literature. Those methods fail when considered for separation of speech signals. This paper mainly focuses on blind speech signal separation from the observations using canonical correlation. The performance of the proposed method is evaluated in terms of signal to interference ratio (SIR) and time domain waveforms of separated speech signals. It is found that proposed technique will improve the SIR values compared with principal component analysis (PCA) and independent component analysis (ICA) based algorithms.

Predictive approaches for the UNIX command line: curating and exploiting domain knowledge in semantics deficit data

Abstract: The command line has always been the most efficient method to interact with UNIX flavor based systems while offering a great deal of flexibility and efficiency as preferred by professionals. Such a system is based on manually inputting commands to instruct the computing machine to carry out tasks as desired. This human-computer interface is quite tedious especially for a beginner. And hence, the command line has not been able to garner an overwhelming reception from new users. Therefore, to improve user-friendliness and to mark a step towards a more intuitive command line system, we propose two predictive approaches that can benefit all kinds of users specially the novice ones by integrating into the command line interface. These methods are based on deep learning based predictions. The first approach is based on the sequence to sequence (Seq2seq) model with joint learning by leveraging continuous representations of a self-curated exhaustive knowledge base (KB) comprising an all-inclusive command description to enhance the embedding employed in the model. The other is based on the attention-based transformer architecture where a pretrained model is employed. This allows the model to dynamically evolve over time making it adaptable to different circumstances by learning as the system is being used. To reinforce our idea, we have experimented with our models on three major publicly available Unix command line datasets and have achieved benchmark results using GLoVe and Word2Vec embeddings. Our finding is that the transformer based framework performs better on two different datasets of the three in our experiment in a semantic deficit scenario like UNIX command line prediction. However, Seq2seq based model outperforms bidirectional encoder representations from transformers (BERT) based model on a larger dataset.

Induced Homeomorphism and Atsuji Hyperspaces

Abstract: Given uniformly homeomorphic metric spaces $$X$$ and $$Y$$ , it is proven that hyperspaces $$C(X)$$ and $$C(Y)$$ are uniformly homeomorphic, where $$C(X)$$ denotes the collection of all nonempty closed subsets of $$X$$ , and is endowed with Hausdorff distance. Gerald Beer has proved that hyperspace $$C(X)$$ is an Atsuji space when $$X$$ is either compact or uniformly discrete. An Atsuji space is a generalization of compact metric spaces as well as of uniformly discrete spaces. In this paper, we investigate space $$C(X)$$ when $$X$$ is an Atsuji space, and a class of Atsuji subspaces of $$C(X)$$ is obtained. Using the results, some fixed point results for continuous maps on Atsuji spaces are obtained.