Cognitive Radio Matlab Source Code clc close all t = 0:0.00001:0.001; Fc1 = 1000; Fc2 = 2000; Fc3 = 3000; Fc4 = 4000; Fc5 = 5000; Fs = 12000; y1 = 1; y2 = 0; y3 = 0; y4 = 0; y5 = 0; Y = 0; y = 0; x1 = cos(2*pi*1000*t); in_p = input(’\nDo you want to enter first primary user Y/N: ’‚’s’); if(in_p == ’Y’ | in_p == ’y’) y1 = ammod(x1‚Fc1‚Fs); end in_p = input(’Do you want to enter second primary user Y/N: ’‚’s’); if(in_p == ’Y’ | in_p == ’y’) y2 = ammod(x1
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The IEEE-30 bus system is used throughout this work to describe the proposed approach. It consists of six generating units‚ 41 transmission lines‚ 4 tap changing transformers and two VAR sources. The generator‚ bus‚ line‚ cost and emission data of the IEEE-30 bus system is taken from [27]. The one line diagram of this system is shown in Figure 5.1. The detailed system parameters are presented in the Appendix Tables A.1.Throughout all cases‚ the IEEE-30 bus system base MVA has assumed to be 100 MVA
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5023/EE4183 Wireless Information Systems Laboratory Lab #11 Software Defined Radio (SDR) I. Experimental Procedure Equipment List Universal Software Radio Peripheral (USRP) with Flex900 Radio Spectrum analyzer (Agilent E4411 or equivalent) PC with Matlab/Simulink R2011B CAT 6 Ethernet cable SMA(male)-to-BNC Adapter (for spectrum analyzer) 915 MHz Antenna (qty 2) A. INSTRUMENT CONNECTIONS: Connect the antenna to the SMA connector labeled RF1 port on the front of the USRP. Connect another antenna to
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ABSTRACT The high growth of the semiconductor industry over the past two decades has put Very Large Scale Integration in demand all over the world. Digital Signal Processing has played a great role in expanding VLSI device area. The recent rapid advancements in multimedia computing and high speed wired and wireless communications made DSP to grab increased attention. For an N-point transformation the direct computation of the Discrete Fourier Transform (DFT) requires N2 operations
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A General Strategic Capacity Planning Model under Demand Uncertainty Woonghee Tim Huh‚1 Robin O. Roundy‚2 Metin Çakanyildirim3 1 Department of Industrial Engineering and Operations Research‚ Columbia University‚ New York‚ New York 10027 2 School of Operations Research and Industrial Engineering‚ Cornell University‚ Ithaca‚ New York 14853 3 School of Management‚ University of Texas at Dallas‚ Richardson‚ Texas 75083 Received 29 October 2003; revised 24 August 2005; accepted 30 September
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B.Tech Project‚ 2011 Indian Institute Of Technology‚ Gandhinagar MODULATION CHARACTERISTIC OF A SEMICONDUCTOR LASER DIODE Project Guide Prof. Arup Lal Chakraborty Department of Electrical Engineering Submitted by Vivek Maheshwary (0800206) Department of Electrical Engineering 27th November‚ 2011 ACKNOWLEDGEMENT I would like to express my sincere gratitude to
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SOP 1 A Masters degree in your University will be the right launch pad to pursue my mission. It will help me develop technical as well as behavioral acumen with solid managerial‚ leadership and communication skills needed to become a successful engineer. Admitted into school at the age of 3‚ I had the opportunity to study in +2 school in India‚ Padma Seshadri Bala Bhavan Senior Secondary School and Junior College‚ Chennai. This laid a solid foundation for my education. The idea of taking up entrepreneurship
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STATEMENT OF PURPOSE NAME: SRIKANTHREDDY SANIKOMMU Firstly I would like to introduce myself as an undergraduate student of the Bachelor of Engineering program from Adam’s Engineering College‚ one of the most prestigious institutions of the Jawaharlal Nehru Technological University Hyderabad. I have completed my course of engineering in the field of ’Electronics and Communication Engineering’‚ which was for a period of four years. I graduated in the first class with distinction. Hence‚ having done
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Active Water-Wave Absorber Matthew Carney ME236: Control & Optimization of Distributed Parameters Professor Alex Bayen University of California‚ Berkeley 04.30.2007 UC Berkeley‚ Spring 2007 1 Contents • • • • • • Motivation Initial Idea Literature Search Focus Current Work Future Work www.emec.org.uk UC Berkeley‚ Spring 2007 2 Motivation • Significant amounts of energy is stored in the ocean • Large potential for extracting energy from waves • Control is the key to optimizing
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measured rainfall values. Data input for IHACRES were rainfall‚ runoff and temperature. The PC-IHACRES version 1.03 was used in this work. And Table 5.30 gives the calibration parameters for each study basin. Based on the parameters in Table 5.30‚ a Matlab code was written to simulate discharge data from measured rainfall and temperature data. The resultant data are shown in Tables 5.31 to 5.35 and the graphs are in Figures 5.26 to 5.30. The Figures show measured rainfall‚ measured flow and the simulated
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