Cooperative Spectrum Sensing for Cognitive Radio Networks using Firefly Algorithm
Abstract: The work presented in this paper is the Fusion technique for hybrid cooperative spectrum sensing using AND fusion of three secondary users for decision making. Energy detection based spectrum sensing method is the most common technique due to its simple operation and efficient detection rate for signals with higher SNR values. When the SNR level degrades, the performance of energy detector fails to give proper detection rate. This problem led towards development of an efficient spectrum sensing algorithm. In this regard, this research work includes the development of efficient and reliable spectrum sensing algorithm for cognitive radio network with the help of soft computing techniques in cooperative scenario. A Firefly Algorithm (FA) optimized model for conventional SVD based cooperative spectrum sensing has been presented.

Authors: Chunmun Singh, Dharmendra Kumar Singh

File Name: Chunmun_61000-17-101.pdf
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Iris Recognition using GLCM and Wavelet Moments with Neural Network Classifier
Abstract: The recognition of the iris is one of the most booming biometric modalities in recent years, due to its unique character as a biometric and biological feature, which makes identification and verification systems based in iris are one of the most accurate and very difficult to impersonate. We present a modular neuronal network architecture for a system of recognition of people through the biometric measurement of the human iris. In this system, a database of the human iris is processed by means of image processing methods. The coordinates of the center and radius of the iris were obtained for then perform a cut of the area of interest eliminating the noise around the iris. The inputs to the modular neural network architecture were the processed iris images and the exit is the number of the person identified. The integration of the modules was done with the integrator of the gate network type. This paper proposes the hybridization of features like GLCM and wavelet moments for training to neural network (NN). Calculated accuracy of hybrid based approach claims 97.1% with 60:40 ratio of training and testing respectively.

Authors: Taapas Jain

File Name: Taapas_61000-17-103.pdf
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A Review on Artificial Intelligence Based Relay Node Localization of Wireless Sensor Network
Abstract: Wireless sensor networks (WSNs) are quickly becoming an integral part of our daily life. WSN is widely used in national defense, military, environmental monitoring, traffic management, medical and health care, manufacturing etc. In this Paper we discussed Artificial Intelligence based localization along with it the algorithms for wireless sensor network. We will be shortly discussing about the routing techniques and comparing the results between the localization algorithms. Specifically, we are examining their performance and accordingly check the accuracy of wireless sensor network. We co-jointly discussed the advancement that can be made to get a better accuracy results. This paper gives an overview of different approach of node localization discovery in wireless sensor networks. Various overviews of the schemes proposed by different authors for the improvement of localization in WSN are also highlighted.

Authors: Gurneet Singh Makked, Nitin Jain

File Name: Gurneet_61000-17-105.pdf
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