Wavelet-Transform-Based Algorithm for Harmonic Analysis of Power System Waveforms Javid Akhtar‚ Md Imran Ali Baig Ghousia College of Engineering Ramanagaram-571511 mirzaimranalibaig@gmail.com Abstract The concept is to develop an approach based on wavelet transform for the evaluation of harmonic contents of power system waveforms. This new algorithmic approach can simultaneously identify all harmonics including integer‚ non-integer and sub-harmonics. This algorithm presents features that
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YOUR ANSWERS BRIEF AND TO THE POINT. DON’T WASTE TIME WRITING LONG DISCUSSIONS. SOME Useful Facts • xn = f0 G(nf0 )‚ where xn are the Fourier Series coefficients of periodic signal x(t)‚ and G(f ) is the Fourier Transform of a single period of x(t). • “Convolution of a signal of width w1 with a signal of width w2 results in a signal of width w1 + w2 .” • From the Fourier Transform table: k=∞ n=∞ w(t) = k=−∞ δ(t − kT ) ⇐⇒ W (f ) = 1/T n=−∞ δ(f − n/T ) 2 1. 10% dB problem: The output
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fendpaper.qxd 11/4/10 12:05 PM Page 2 Systems of Units. Some Important Conversion Factors The most important systems of units are shown in the table below. The mks system is also known as the International System of Units (abbreviated SI )‚ and the abbreviations sec (instead of s)‚ gm (instead of g)‚ and nt (instead of N) are also used. System of units Length Mass Time Force cgs system centimeter (cm) gram (g) second (s) dyne mks system meter (m) kilogram (kg) second (s) newton
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PROPERTIES OF DISCRETE TIME FOURIER TRANSFORMS ABSTRACT In mathematics‚ the discrete Fourier transform (DFT) converts a finite list of equally-spaced samples of a function into the list of coefficients of a finite combination of complex sinusoids‚ ordered by their frequencies‚ that has those same sample values. It can be said to convert the sampled function from its original domain (often time or position along a line) to the frequency domain. INTRODUCTION The input samples are complex numbers
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ANNA UNIVERSITY CHENNAI :: CHENNAI – 600 025 B.E / B.TECH. DEGREE EXAMINATIONS – I YEAR ANNUAL PATTERN MODEL QUESTION PAPER MA 1X01 - ENGINEERING MATHEMATICS - I (Common to all Branches of Engineering and Technology) Regulation 2004 Time : 3 Hrs Answer all Questions PART – A (10 x 2 = 20 Marks) Maximum: 100 Marks ⎡ 3 −1 1 ⎤ 1. Find the sum and product of the eigen values of the matrix ⎢− 1 5 − 1⎥ ⎢ ⎥ ⎢ 1 −1 3 ⎥ ⎣ ⎦ 2. If x = r cosθ‚ y = r sinθ‚ find ∂ ( r ‚θ ) ∂( x‚ y ) 3. Solve (D3+D2+4D+4)y
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basis vectors are orthogonal and the transform is extremely useful in image processing. If the vector x gives the intensities along a row of pixels‚ its cosine series ck vk has the coefficients ck = (x‚ vk )/N . They are quickly computed from a Fast Fourier Transform. But a direct proof of orthogonality‚ by calculating inner products‚ does not reveal how natural these cosine vectors are. We prove orthogonality in a different way. Each DCT basis contains the eigenvectors of a symmetric “second difference”
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still with the signal processing application in mind. Contents 1 Introduction : : : : : : : : : : : : : : : : : : : : : : : 2 Mathematical motivations for the Hilbert transform : 2.1 The Cauchy integral : : : : : : : : : : : : : : : 2.2 The Fourier transform : : : : : : : : : : : : : : 2.3 The §¼=2 phaseshift : : : : : : : : : : : : : : : 3 Properties of the Hilbert transform : : : : : : : : : : 3.1 Linearity : : : : : : : : : : : : : : : : : : : : : : 3.2 Multiple Hilbert transforms and their
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Recently due to internet growth‚ lots of medical digital images are being shared between medical experts and hospitals for better and more precise diagnosis‚as well as for research and educational purpose and also for many other commercial and non-commercial applications. In modern times now‚ all the integrated health care systems like Hospital Information System (HIS) and Picture Archiving and Communication System (PACS) allow easy distribution of medical imageswherethe whole or the region of interest(ROI)
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the abstract of the document here. The abstract is typically a short summary of the contents of the document.] | Section 2: The following code was used to calculate perform the DFT Function in Matlab: function sw = dft(st) % DFT - Discrete Fourier Transform M = length(st); N = M; WN = exp(2*pi*j/N); %Main Loop for n=0:N-1 temp = 0; for m=0:M-1 s = st(m+1); temp = temp + (s* (WN ^ (-n*m))); end sw(n+1) = temp;
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Q-A. Find the Laplace transform of the following functions 1. f (t) = t − 1‚ 0 < t < 3; 7‚ t > 3. 2. f (t) = cos t − 0‚ 2π 3 ‚ 0 2π . 3 2π ; 3 4‚ 0 < t < 1; −2‚ 1 < t < 3; 3. f (t) = 5‚ t > 3. 5. f (t) = 3t3 + e−2t + t 3 7. f (t) = cos3 2t 9. f (t) = sin (3t + 5) 11. f (t) = e−3t sin2 t 13. f (t) = 7T 15. f (t) = e−3t (cos (4t) + 3 sin (4t)) 17. f (t) = teat 19. f (t) = t sin2 3t 21. f (t) = t2 e−2t cos t 23. f (t) = t cos (7t + 9) 25. f (t) = 27. f (t) = sin2 t t e−t sin t t 1 2
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