IEEE GEOSCIENCE AND REMOTE SENSING LETTERS‚ VOL. 7‚ NO. 2‚ APRIL 2010 333 Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition Hasan Demirel‚ Cagri Ozcinar‚ and Gholamreza Anbarjafari Abstract—In this letter‚ a new satellite image contrast enhancement technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The technique decomposes the input image into the four frequency subbands by using
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at ) sin ( at ) - at cos ( at ) cos ( at ) - at sin ( at ) sin ( at + b ) sinh ( at ) e at sin ( bt ) e at sinh ( bt ) t ne at ‚ n = 1‚ 2‚3‚K uc ( t ) = u ( t - c ) Heaviside Function F ( s ) = L { f ( t )} 1 s n! s n +1 Table of Laplace Transforms f ( t ) = L -1 {F ( s )} F ( s ) = L { f ( t )} 1 s-a G ( p + 1) s p +1 1 × 3 × 5L ( 2n - 1) p 2n s 2 s 2 s + a2 s2 - a2 2 n+ 1 2. 4. 6. 8. 2 e at t p ‚ p > -1 t n- 1 2 p 2s a 2 s + a2 2as 2 3 2 ‚ n = 1‚ 2‚3‚K cos ( at
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Several Variables Exam 2009 -2 - Question 1 Use the Tables of Laplace transforms‚ along with the operational theorems‚ to find the Laplace transform of the following functions: (a) t(t + cos t)e−3t (b) (c) t 0 e−u sinh 2u du 4 − 4e2t t [4+2+2=8 marks] Question 2 (a) Write f (t) = 2t + 3‚ 0 ≤ t < 9 in terms of Heaviside functions. Find the Laplace −2‚ t≥9 transform of the function. (b) Use Laplace transforms to determine the solution y(t) of the following initial-value problem y −
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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 signal
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SCIENTIA Series A: Mathematical Sciences‚ Vol. 22 (2012)‚ 129-151 Universidad T´cnica Federico Santa Mar´ e ıa Valpara´ ıso‚ Chile ISSN 0716-8446 c Universidad T´cnica Federico Santa Mar´ 2012 e ıa The integrals in Gradshteyn and Ryzhik. Part 22: Bessel-K functions Larry Glasser‚ Karen T. Kohl‚ Christoph Koutschan‚ Victor H. Moll‚ and Armin Straub Abstract. The table of Gradshteyn and Ryzhik contains many integrals that can be evaluated using the modified Bessel function. Some examples
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APPLICATION NOTE AN014 Understanding FFT Windows The Fast Fourier Transform (FFT) is the Fourier Transform of a block of time data points. It represents the frequency composition of the time signal. Figure 2 shows a 10 Hz sine waveform (top) and the FFT of the sine waveform (bottom). A sine wave is composed of one pure tone indicated by the single discrete peak in the FFT with height of 1.0 at 10 Hz. Introduction FFT based measurements are subject to errors from an effect known as leakage
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have understood: -Basic signal analysis (mostly continuous-time) -Basic system analysis (also mostly continuous systems) -Time-domain analysis (including convolution) -Laplace Transform and transfer functions -Fourier Series (revision) and Fourier Transform -Sampling Theorem and signal reconstructions -Basic z-transform PLD Autumn 2012 Thursday‚ 27 September 12 Signals and Linear Systems Lecture 1 Slide 3 About the course •Lectures - 15-16 hours over 10 weeks •Problem Classes – 8 hours
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Analog Communication Theory: A Text for EE501 Michael P. Fitz The Ohio State University fitz.7@osu.edu Fall 2001 2 Note to Students. This text is an evolving entity. Please help make an OSU education more valuable by providing me feedback on this work. Small things like catching typos or big things like highlighting sections that are not clear are both important. My goal in teaching communications (and in authoring this text) is to provide students with 1. the required theory‚ 2. an insight
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OXFORD UNIVERSITY PRESS R-5 INTRODUCTION TO SIGNALS AND SYSTEMS DOUGLAS K. LINDNER 12th R-6 SIGNALS AND LINEAR SYSTEMS ROBERT A. GABEL‚ RICHARD A. ROBERTS 3rd JOHN WILEY & SONS R-7 SIGNALS AND SYSTEMS ANALYSIS USING TRANSFORM METHODS AND MATLAB M. J. ROBERTS 8th TMH R-8 SIGNALS AND SYSTEMS I. J. NAGRATH‚ S. N. 1st SHARAN‚ R. RANJAN‚ S. KUMAR TMH R-9 ANALOG AND DIGITAL SIGNAL PROCESSING ASHOK AMBARDAR BROOKE/COLE PUBLISHING COMPANY
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wavelet histogram and wavelet moment of image‚ etc. [12]. Wavelet transform can be used to characterize textures using statistical properties of the gray levels of the pixels comprising a surface image [13]. The wavelet transform is a tool that cuts up data or functions or operators into different frequency components and then studies each component with a resolution matched to its scale. In this paper‚ we used D4 and Haar wavelet transforms to decompose color images into multilevel scale and wavelet coefficients
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