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.

Different Energy Saving Schemes in Wireless Sensor Networks: A Survey

Abstract: Wireless sensor networks (WSNs) are one of the very active research area. They have many applications like military, health care, environmental monitoring and industrial monitoring. The sensor nodes have limited energy source. Since in many cases the nodes are deployed in unreachable areas, hence recharging or replacing the battery of the sensor nodes is not an option. Therefore, one must employ techniques to conserve the energy by reducing the energy consumption by the nodes. In this paper, we discuss about the different energy saving schemes investigated by different research community in WSNs to reduce the energy consumption of the nodes and thereby improving the lifetime of the overall network. Energy saving protocols such as duty cycle, energy efficient routing, energy efficient medium access control (MAC), data aggregation, cross layer design and error control code (ECC) are discussed. Sleep/wake up method is adopted by the duty cycle approach to reduce the active time of the nodes and conserve their energy. The routing and MAC protocols use suitable energy efficient algorithms for saving energy. The data aggregation aims to save energy by reducing the number of transmissions. On the other side, cross layer approach looks for a cross layer optimization solution to improve the energy efficiency of the network. ECC reduces energy consumption by virtue of coding gain it offers which allows lower signal-to-noise ratio (SNR) to achieve the same bit error rate (BER) as an uncoded system. Some techniques such as use of directional antennas, topology control and transmission power control which have been widely investigated for other ad-hoc networks for energy conservation are also discussed in brief in this paper.

Optimal Energy Management of Microgrids Using Quantum Teaching Learning Based Algorithm

Abstract: Quantum inspired computational intelligence is gaining momentum in the interest of enhancing the performance of existing metaheuristic optimization while solving multi-dimensional nonlinear problems. The microgrid optimal energy scheduling is one such problem that involves multiple distributed energy resources (DER) with volatile characteristics and proficient energy management is essential for their coordination and reducing global carbon emissions. Relatively very few works in the existing literature have attempted to solve this problem using quantum-based algorithms. In this article, a stochastic framework associated with the Quantum Teaching Learning-based optimization (QTLBO) algorithm is devised for the first time to optimize energy flow in the microgrids. Four scenarios concerning seasonal variations are chosen to address the uncertainties related to generated power from DERs with better accuracy. The day-ahead optimum power scheduling configuration of DERs is evaluated for each scenario. The performance of QTLBO is assessed on a grid-connected microgrid network and compared with existing metaheuristic algorithms such as the Real-coded Genetic Algorithm, Differential Evolution, and TLBO. The obtained simulation results prove the superiority of QTLBO in terms of convergence and achieving a global optimum solution by overcoming premature convergence. Further, the proposed stochastic framework is helpful to attain techno-economic benefits to both customers and market operators.

Vision-based hand gesture recognition of alphabets, numbers, arithmetic operators and ASCII characters in order to develop a virtual text-entry interface system

Abstract: Hand gesture recognition can substitute the use of text-entry interface for human computer interaction. However, it is a challenging task to develop a virtual text-entry interface covering a large number of gesture-based characters. In this paper, 18 new ASCII printable characters have been introduced along with some of the previously introduced characters [A–Z alphabets, 0–9 numbers and four arithmetic operators (add, minus, multiply, divide)]. In addition to some of the efficient existing features, three new features of 15 dimensions have been incorporated to enhance the performance of the system, which are normalized distance between direction extreme, close figure test and direction change ratio. These features are measured for single-stroke as well as multistroke gestures. An experimental analysis has been carried out for selection of optimal features using the statistical analysis techniques such as one-way analysis of variance test, Kruskal–Wallis test, Friedman test in combination with incremental feature selection technique. Furthermore, a comparative study has been carried out for classification of 58 gestures with the new list of features. A comparative analysis has been performed using five classifiers, namely SVM, kNN, Naive Bayes, ANN and ELM. It has been observed that maximum accuracy achieved using the combination of existing and proposed features is 96.95%, as compared to 94.60% accuracy achieved using existing features for classification of 58 gestures.

Hand gesture recognition using two-level speed normalization, feature selection and classifier fusion

Abstract: Hand gesture recognition provides an alternative way to many devices for human computer interaction. In this work, we have developed a classifier fusion based dynamic free-air hand gesture recognition system to identify the isolated gestures. Different users gesticulate at different speed for the same gesture. Hence, when comparing different samples of the same gesture, variations due to difference in gesturing speed should not contribute to the dissimilarity score. Thus, we have introduced a two-level speed normalization procedure using DTW and Euclidean distance-based techniques. Three features such as `orientation between consecutive points', `speed' and `orientation between first and every trajectory points' were used for the speed normalization. Moreover, in feature extraction stage, 44 features were selected from the existing literatures. Use of total feature set could lead to overfitting, information redundancy and may increase the computational complexity due to higher dimension. Thus, we have tried to overcome this difficulty by selecting optimal set of features using analysis of variance and incremental feature selection techniques. The performance of the system was evaluated using this optimal set of features for different individual classifiers such as ANN, SVM, k-NN and Naive Bayes. Finally, the decisions of the individual classifiers were combined using classifier fusion model. Based on the experimental results it may be concluded that classifier fusion provides satisfactory results compared to other individual classifiers. An accuracy of 94.78 % was achieved using the classifier fusion technique as compared to baseline CRF (85.07 %) and HCRF (89.91 %) models.