G. Kaur ¹, H. Sharma ¹

Affiliations
1 Department of Computer Science and Engineering, Lovely Professional University, Jalandhar -144411,Punjab, IN

Abstract

For the improvement in underwater acoustic sensor networks, vector based technique is used to increase the efficiency of network and reduce the chances of link failure. It is also improve robustness and scalability of the network. Underwater acoustic are used sensor nodes and autonomous vehicles to communicate from one place to another under the ocean and also used a surface station. They create a link to on-shore control center. UANs are different from terrestrial networks. These are used in different fields like military and commercial fields. Underwater networks are connected with number of vehicles and sensors which are used to monitor the underwater activities. This paper evaluates the research problems of underwater acoustic networks like energy consumption and different layers are used to solve the issues which occur in UANs.

Keywords

Different Layers Design Energy Efficiency Robustness, Scalability,Underwater Acoustic Sensor Networks, Vector Pipeline Technique.

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G. Kaur ¹, S. Bhushan ², D. Singh ³

Affiliations
1 Department of Computer Science and Engineering, I.K. Gujral Punjab Technical University, Kapurtala – 144603, Punjab, IN
2 Department of CSE, Chandigarh Group of Colleges, Landran – 140307, Punjab, IN
3 Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh – 160019, IN

Abstract

Objectives: The use of multimodal biometric has been introduced recently owing to use of multiple biometric modalities. Here we perform in-depth review of the various methods used for multimodal biometric technology. Methods/ Statistical Analysis: Here we present a systematic review of various methods used for fusing multiple biometric modalites. Specifically, fusing at various levels such as, before matching and after matching. Score level, feature level, rank level and decision fusion is followed by feature optimization using methods such as genetic algorithms and artificial neural networks. Findings: Single biometric based methods suffer from lack of security and efficiency. This leads to advent of multimodal biometric systems. However, fusing various biometric modalities is being persued with very high interest. We describe the granular nature of several methods used to fuse multiple biometric modalities. A wide range of methods are being employed to fuse biometric data. These methods vary in efficiency and are highly dependant upon the selection of type of biometric chosen for fusion. Application/Improvements: As computational efficiency increases, there increase in more secure and efficient biometric systems that use multiple sources of biometric identification and access authorization.

Keywords

Biometric Modality, Fusion, Multimodal Biometric, Optimization

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