Cognitive Radio: Computing Techniques, Network Security and Challenges

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Chapter 1 A Framework for Identification of Vehicular Traffic Accident Hotspots in Complex Networks
	1.1 Introduction
	1.2 Overview on Vehicular Traffic Accident Hotspot Techniques
	1.3 Kernel Density Estimation
	1.4 K-Means Clustering
	1.5 Point Density Method
	1.6 Line Density Method
	1.7 Interpolation Method
	1.8 Kriging Method
	1.9 Spline Method
	1.10 Natural Neighborhood Method
	1.11 Mapping Cluster
	1.12 Moran’s I Method
	1.13 Getis-Ord GI* Method
	1.14 Earlier Studies
	1.15 Research Directions
	1.16 Research Framework
	1.17 Conclusions
	References
Chapter 2 Traffic Safety Management Using Spatial Analysis and Clustering Methods
	2.1 Introduction
	2.2 Earlier Studies
	2.3 Accident Hotspot Methods—An Overview
	2.4 Getis-Ord Gi* Method
	2.5 Nearest Neighborhood Hierarchical Method
	2.6 CrimeStat Software
	2.7 Research Methodology
	2.8 Case Study
	2.9 Analysis
	2.10 Results and Discussions
	2.11 Conclusion
	References
Chapter 3 Machine-Learning Algorithms on Cognitive Multisensor Image Application
	3.1 Introduction about Machine Learning
	3.2 Introduction to Remote Sensing and Satellite Image Processing
	3.3 Multispectral/Hyperspectral Satellite Images
	3.4 Dimensional Reduction Technique
	3.5 Results and Discussion
	3.6 Conclusion and Future Scope
	References
Chapter 4 Performance Evaluation of Video Compression Techniques: x.263, x.264 and x.265 to Improve Video Streaming Quality
	4.1 Introduction
		4.1.1 Video Formats
	4.2 Selected Codecs
		4.2.1 H.264
		4.2.2 H.265
		4.2.3 Evaluation Metrics
			4.2.3.1 Quality
			4.2.4 PSNR
			4.2.5 H.266
			4.2.6 Evaluation Method
				4.2.6.1 Results
	4.3 Conclusion
	References
Chapter 5 Design of CMOS Circuits for Cognitive Radio Application with Power Analysis
	5.1 Introduction
	5.2 Active Power Dissipation or Switching Power Dissipation
	5.3 Short Circuit Power Dissipation
	5.4 Leakage Power Dissipation
		5.4.1 Junction Reverse Bias Leakage Current (I[sub(rev)])
		5.4.2 Subthreshold Conduction Leakage Current (I[sub(sub)])
		5.4.3 Drain-Induced Barrier Lowering Leakage Currents(IDIBL)
		5.4.4 Gate-Induced Drain Leakage Current (IGIDL)
		5.4.5 Punch through Leakage Current (IPT)
		5.4.6 Gate Oxide Tunneling Current (I[sub(ox)])
		5.4.7 Hot Carrier Leakage Current (IHOT)
	5.5 Proposed Theoretical Analysis of Parameters in CMOS Circuits
		5.5.1 Calculation of CMOS NOT Gate Dynamic Power
		5.5.2 Gate Capacitance per Unit Area (C[sub(ox)])
		5.5.3 Gate Capacitance (Cg)
		5.5.4 Leakage Power (V[sub(DD)].I[sub(leak)])
	5.6 Conclusion
	References
Chapter 6 A Novel Design of 16 Bit MAC Unit Based on Vedic Mathematics Using FPGA Hardware for Cognitive Radio Application
	6.1 Introduction
	6.2 Literature Survey
		6.2.1 “Hybrid Multiplier-based Optimized MAC Unit” Kavindra Dwivedi, R.K. Sharma and Ajay Chunduri
		6.2.2 ”Parallel Multiplier Stake Element Built On Vedic Calculation” Jithin S. and Prabhu E.
		6.2.3 ”Proposal in Addition Optimization of 16×16 Bit Multiplier by Vedic Arithmetic” Sheetal N. Gadakh and Amitkumar Khade
		6.2.4 “Proposal of Mackintosh Element in Artificial Neural System Construction with Verilog HDL“ L. Ranganath, P.G. Scholor, D. Jay Kumar and P. Siva Nagendra Reddy
	6.3 Existing Method Vedic Arithmetic
		6.3.1 Straightforward Multiply Accumulator Circuit (MAC)
		6.3.2 Carry-Save Adder
	6.4 Proposed Method and Performance and Simulation Results
		6.4.1 Adders
		6.4.2 Proposed Block Diagram
		6.4.3 Pre-Processing
		6.4.4 Gray Cell
		6.4.5 Last Computation
		6.4.6 Proposed Diagram
		6.4.7 Hardware Requirement
			6.4.7.1 General Integrated Circuits
	6.5 Conclusion
	References
Chapter 7 Hybrid Optimization Technique Using Particle Swarm Optimization and Firefly Algorithm using the Chord Protocol
	7.1 Introduction
	7.2 Proposed Methodology
		7.2.1 Firefly Algorithm
		7.2.2 Proposed Hybrid “Particle Swarm Optimization-Firefly“ (PSO-FF) Algorithm
	7.3 Performance and Simulation Results
	7.4 Conclusion
	References
Chapter 8 MESH-DHT Approach for Efficient Resource Sharing in P2P-Based Wireless Mesh Cognitive Networks
	8.1 Introduction
		8.1.1 Methodology
	8.2 Proposed Work
	8.3 Performance and Simulation Results and Discussion
	8.4 Conclusion
	References
Chapter 9 Design of the VLSI Technology for Cognitive Radio
	9.1 Introduction
	9.2 Enhanced Flexible Range Detection System
	9.3 VLSI Structural Design Used for RDDC
	9.4 Performance of FIR Filter
	9.5 Energy Detection
		9.5.1 Performance of Energy Detection within an Instance Area
	9.6 Experimental Result
	9.7 Conclusion
	References
Index