tested. At last‚ speaker signal files which are used in the experimental work‚ collected from Corbus database and some Arabic recorded files are used to test the algorithms and system. Matlab programming environment is utilized to design and run the system algorithms. The output results are plotted and tabulated using Matlab program. Conclusions are summarized and stated at the end of the paper. Future work concerning the point of research is also
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CANDIDATE’S DECLARATION I hereby certify that the work which is being presented in the project entitled “AIRCRAFT TAKE-OFF AND LANDING CONTROL SYSTEM USING FUZZY LOGIC CONTROLLER’’ in the partial fulfilment of requirements for the Award of degree of M.Tech (Electronics and Communication Engineering) submitted in the Department of Electronics and Communication Engineering at GURU NANAK DEV ENGINEERING COLLEGE‚ LUDHIANA under PUNJAB TECHNICAL UNIVERESITY‚ JALANDHAR is my work done to study aircraft
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caused the error. S= R[H]T=E H]T Where S= error syndrome R= received codeword E=error Algorithm: 1. Take generator matrix from the user. 2. Create the 4 bit data word‚ total of 16 data words‚ using two dimensional array in the matlab. 3. Then code word is generated utilizing the matrix multiplication formula for the code word. 4. Then Hamming distance is determined by the minimum of the number of ones among the individual code words. 5. Now to check for error‚ first
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Non-rectangular QAM constellations achieve marginally better bit-error rate (BER) but are harder to modulate and demodulate. Hence‚ below is the simulation of a rectangular QAM constellation. Simulation model in MATLAB Following is the methodology of simulation of the QAM modulator in MATLAB‚ 1) Generation of random binary sequence‚ this is the input which is to be modulated. 2) Assigning group of 4 bits to each 16-QAM constellation symbol per the Gray mapping 3) Addition of white Gaussian Noise 4)
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colour information to distinguish objects‚ food‚ places‚ materials‚ and even the time of the day Keywords— Face detection‚ Color histograms‚ Color segmentation‚ Training samples‚ Circularity as variance of “radius”. I. Introduction The presented MATLAB code detects the face in the colour image by locating regions of a colour image that is potentially a human face. First a training phase is undertaken to identify the nature of face pixels using samples from different people. It identify the pixel
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Implementation of Interpolation Process 2 Turbomachinery Institute of Technology & Sciences‚ Hyd. 319 Date:………… DSP Lab Manual ……… 1. INTRODUCTION TO MATLAB. GENERATION OF BASIC SIGNALS USING MATLAB AIM: Generation of Sine Wave & Illustration of the Sampling Process in the Time Domain EQUIPMENT REQUIRED: P – IV Computer Windows Xp SP2 MATLAB 7.0 THEORY: 3 Turbomachinery Institute of Technology & Sciences‚ Hyd. 319 Date:………… PROGRAM : DSP Lab Manual ……… %
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employed for a bridgeless interleaved boost converter for improving the power quality. The major control strategies discussed in this paper are: peak current‚ average current mode and borderline current control. These strategies are implemented in MATLAB/SIMULINK and the performance of the proposed converter is compared under open loop and closed loop operation. From the results‚ the input current waveform was close to sinusoidal implying high power factor and reduced harmonics for borderline control
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Argonne National Laboratory (Lee Teng Intern) June 2011- Aug 2011 Advisor: Dr. Lahsen Assoufid Argonne‚ IL Assisted in building a test bench to characterize a nanoradian slope sensor to be used in the new Long Trace Profiler at the APS and wrote Matlab programs to process images from a CCD camera. Resonant MEMS Group‚ Directed Study Advisor: Prof. Mina Rais-Zadeh Characterized wafer level transfer of epitaxial GaN thin films using eutectic bonding. Photonics Group‚ Undergraduate Research Opportunity
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healthy tissues as well. In this paper‚ we will develop a technique of SEGMENTATION of a brain tumor by using segmentation in conjunction with morphological operations. In our projects the MRI image of brain tumor is given as input to MATLAB (image processing toolbox) coding. It will extract the tumor portion and calculate its area within a small duration automatically. Here the quality of the detection is improved. 1.2 IMAGE : Images may be two-dimensional‚ such as a photograph
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an image; texture‚ colour‚ and shape. These features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features‚ are then explained. Our final result was a MatLab built software application‚ with an image database‚ that utilized texture and colour features of the images in the database as the basis of comparison and retrieval. The structure of the final software application is illustrated. Furthermore‚ the
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