IEEE Organizations related to Discrete Fourier Transform

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Conferences related to Discrete Fourier Transform

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2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

he International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established as a biannual conference in 1991 and became an annual event in 2007. It is the premiere conference of the Australian Pattern Recognition Society (APRS).



Periodicals related to Discrete Fourier Transform

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Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Proceedings of the IEEE

The most highly-cited general interest journal in electrical engineering and computer science, the Proceedings is the best way to stay informed on an exemplary range of topics. This journal also holds the distinction of having the longest useful archival life of any EE or computer related journal in the world! Since 1913, the Proceedings of the IEEE has been the ...


Signal Processing Letters, IEEE

Rapid dissemination of new results in signal processing world-wide.


Signal Processing, IEEE Transactions on

The technology of transmission, recording, reproduction, processing, and measurement of speech; other audio-frequency waves and other signals by digital, electronic, electrical, acoustic, mechanical, and optical means; the components and systems to accomplish these and related aims; and the environmental, psychological, and physiological factors of thesetechnologies.



Most published Xplore authors for Discrete Fourier Transform

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Xplore Articles related to Discrete Fourier Transform

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Smoothing for random fields modeled by partial differential equations

C. E. Economakos; H. L. Weinert IEEE Transactions on Automatic Control, 1996

The authors present an efficient, numerically reliable smoothing algorithm for random fields modeled by linear, constant coefficient, partial differential equations. The estimate is computed from discrete measurements by using the discrete Fourier transform to convert the two-dimensional (2D) problem to a collection of uncoupled one-dimensional (1D) problems which are then solved using stable iterations


Image watermarking based on the two dimensional fully fledged fractional discrete fourier transform of type IV

Magdy Tawfik Hanna 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2014

An image watermarking technique is presented based on the two-dimensional fractional discrete Fourier transform of type IV (2D-FDFT-IV). The binary watermark image is first randomized by calling two functions in the NAG toolbox for Matlab with the objective of the pseudorandom permutation of the indices of the vector formed by the concatenation of the columns of the matrix representation of ...


Semiblind Turbo Equalization Scheme for LTE Uplink Receiver

Gideon Kutz; Amit Bar-Or; Dan Raphaeli IEEE Transactions on Vehicular Technology, 2012

An advanced receiver scheme for long-term evolution (LTE) uplink is proposed. The scheme combines semiblind channel estimation and turbo equalization and is based on an approximate expectation-maximization (EM) algorithm. The receiver iterates between demodulation of the data symbols using frequency-domain soft interference cancelation, turbo decoding, and semiblind channel estimation that utilizes the soft symbols extracted from the turbo decoder output. ...


Roundoff error in multidimensional generalized discrete transforms

O. Chan; E. Jury IEEE Transactions on Circuits and Systems, 1974

The analysis of rounding error in the one-dimensional fast Fourier transform (FFT) is extended to a class of generalized orthogonal transforms [1] with a common fast algorithm similar to the FFT algorithm. This class includes the BInary FOurier REpresentation (BIFORE) transform (BT) [2], the complex BT (CBT) [3], and the discrete Fourier transform (DFT). Expressions for the mean square error ...


Computing partial DFT for comb spectrum evaluation

Shousheng He; M. Torkelson IEEE Signal Processing Letters, 1996

A comb spectrum evaluation problem arises in the (de)modulation for orthogonal frequency division multiplexing-based (OFDM-based) multichannel communication system. Efficient algorithms for this special type of partial discrete Fourier transform (DFT) computation are studied. For an M-component comb spectrum evaluation with transform length N, it is shown that only O(N+MlogM) multiplications are needed, compared with O(NlogM) multiplications necessary for a narrowband ...


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Educational Resources on Discrete Fourier Transform

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eLearning

Smoothing for random fields modeled by partial differential equations

C. E. Economakos; H. L. Weinert IEEE Transactions on Automatic Control, 1996

The authors present an efficient, numerically reliable smoothing algorithm for random fields modeled by linear, constant coefficient, partial differential equations. The estimate is computed from discrete measurements by using the discrete Fourier transform to convert the two-dimensional (2D) problem to a collection of uncoupled one-dimensional (1D) problems which are then solved using stable iterations


Image watermarking based on the two dimensional fully fledged fractional discrete fourier transform of type IV

Magdy Tawfik Hanna 2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2014

An image watermarking technique is presented based on the two-dimensional fractional discrete Fourier transform of type IV (2D-FDFT-IV). The binary watermark image is first randomized by calling two functions in the NAG toolbox for Matlab with the objective of the pseudorandom permutation of the indices of the vector formed by the concatenation of the columns of the matrix representation of ...


Semiblind Turbo Equalization Scheme for LTE Uplink Receiver

Gideon Kutz; Amit Bar-Or; Dan Raphaeli IEEE Transactions on Vehicular Technology, 2012

An advanced receiver scheme for long-term evolution (LTE) uplink is proposed. The scheme combines semiblind channel estimation and turbo equalization and is based on an approximate expectation-maximization (EM) algorithm. The receiver iterates between demodulation of the data symbols using frequency-domain soft interference cancelation, turbo decoding, and semiblind channel estimation that utilizes the soft symbols extracted from the turbo decoder output. ...


Roundoff error in multidimensional generalized discrete transforms

O. Chan; E. Jury IEEE Transactions on Circuits and Systems, 1974

The analysis of rounding error in the one-dimensional fast Fourier transform (FFT) is extended to a class of generalized orthogonal transforms [1] with a common fast algorithm similar to the FFT algorithm. This class includes the BInary FOurier REpresentation (BIFORE) transform (BT) [2], the complex BT (CBT) [3], and the discrete Fourier transform (DFT). Expressions for the mean square error ...


Computing partial DFT for comb spectrum evaluation

Shousheng He; M. Torkelson IEEE Signal Processing Letters, 1996

A comb spectrum evaluation problem arises in the (de)modulation for orthogonal frequency division multiplexing-based (OFDM-based) multichannel communication system. Efficient algorithms for this special type of partial discrete Fourier transform (DFT) computation are studied. For an M-component comb spectrum evaluation with transform length N, it is shown that only O(N+MlogM) multiplications are needed, compared with O(NlogM) multiplications necessary for a narrowband ...


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IEEE.tv Videos

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IEEE-USA E-Books

  • Image Formation

    This chapter contains sections titled: Image Coordinate System Linear Systems Point Source and Impulse Functions Probability and Random Variable Functions Image Formation Pin-hole Imaging Fourier Transform Radon Transform Sampling Discrete Fourier Transform Wavelet Transform Exercises References

  • Measurements of Frequency Response Functions

    This chapter contains sections titled: Introduction An Introduction to the Discrete Fourier Transform Spectral Representations of Periodic Signals Analysis of FRF Measurements Using Periodic Excitations Reducing FRF Measurement Errors for Periodic Excitations FRF Measurements Using Random Excitations FRF Measurements of Multiple Input, Multiple Output Systems Guidelines for FRF Measurements Conclusion Exercises Appendixes

  • Introduction to Orthogonal Frequency Division Multiplexing

    This chapter contains sections titled: Introduction Principles of QAM-OFDM Modulation by Discrete Fourier Transform [553, 554] Transmission via Bandlimited Channels Generalised Nyquist Criterion Basic OFDM Modem Implementations Cyclic OFDM Symbol Extension Reducing MDI by Compensation [151] Adaptive Channel Equalisation OFDM Bandwidth Efficiency Summary

  • No title

    This book is Volume IV of the series DSP for MATLAB™ and LabVIEW™. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEWThis book is Volume IV of the series DSP for MATLAB™ and LabVIEW™. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEWis book is Volume IV of the series DSP for MATLAB™ and LabVIEW™. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEW book is Volume IV of the series DSP for MATLAB™ and LabVIEW™. Volume IV is an introductory treatment of LMS Adaptive Filtering and applications, and covers cost functions, performance surfaces, coefficient perturbation to estimate the gradient, the LMS algorithm, response of the LMS algorithm to narrow-band signals, and various topologies such as ANC (Active Noise Cancelling) or system modeling, Noise Cancellation, Interference Cancellation, Echo Cancellation (with single- and dual-H topologies), and Inverse Filtering/Deconvolution. The entire series consists of four volumes that collectively cover basic digital signal processing in a practical and accessible manner, but which nonetheless include all essential foundation mathematics. As the series title implies, the scripts here will run on both MATLAB™ and LabVIEW™. The text for all volumes contains many examples, and many useful computational scripts, augmented by demonstration scripts and LabVIEW&# 482; Virtual Instruments (VIs) that can be run to illustrate various signal processing concepts graphically on the user's computer screen. Volume I consists of four chapters that collectively set forth a brief overview of the field of digital signal processing, useful signals and concepts (including convolution, recursion, difference equations, LTI systems, etc), conversion from the continuous to discrete domain and back (i.e., analog-to-digital and digital-to-analog conversion), aliasing, the Nyquist rate, normalized frequency, sample rate conversion and Mu-law compression, and signal processing principles including correlation, the correlation sequence, the Real DFT, correlation by convolution, matched filtering, simple FIR filters, and simple IIR filters. Chapter 4 of Volume I, in particular, provides an intuitive or "first principle" understanding of how digital filtering and frequency transforms work. Volume II provides detailed coverage of discrete frequency transforms, includi g a brief overview of common frequency transforms, both discrete and continuous, followed by detailed treatments of the Discrete Time Fourier Transform (DTFT), the z-Transform (including definition and properties, the inverse z-transform, frequency response via z-transform, and alternate filter realization topologies including Direct Form, Direct Form Transposed, Cascade Form, Parallel Form, and Lattice Form), and the Discrete Fourier Transform (DFT) (including Discrete Fourier Series, the DFT-IDFT pair, DFT of common signals, bin width, sampling duration, and sample rate, the FFT, the Goertzel Algorithm, Linear, Periodic, and Circular convolution, DFT Leakage, and computation of the Inverse DFT). Volume III covers digital filter design, including the specific topics of FIR design via windowed-ideal-lowpass filter, FIR highpass, bandpass, and bandstop filter design from windowed-ideal lowpass filters, FIR design using the transition-band-optimized Frequency Sampling technique (impleme ted by Inverse-DFT or Cosine/Sine Summation Formulas), design of equiripple FIRs of all standard types including Hilbert Transformers and Differentiators via the Remez Exchange Algorithm, design of Butterworth, Chebyshev (Types I and II), and Elliptic analog prototype lowpass filters, conversion of analog lowpass prototype filters to highpass, bandpass, and bandstop filters, and conversion of analog filters to digital filters using the Impulse Invariance and Bilinear Transform techniques. Certain filter topologies specific to FIRs are also discussed, as are two simple FIR types, the Comb and Moving Average filters. Table of Contents: Introduction To LMS Adaptive Filtering / Applied Adaptive Filtering

  • Some Probability and Stochastic Convergence Fundamentals

    This chapter contains sections titled: Notations and Definitions The Covariance Matrix of a Function of a Random Variable Sample Variables Mixing Random Variables Preliminary Example Definitions of Stochastic Limits Interrelations between Stochastic Limits Properties of Stochastic Limits Laws of Large Numbers Central Limit Theorems Properties of Estimators Cramér-Rao Lower Bound How to Prove Asymptotic Properties of Estimators? Pitfalls Preliminary Example - Continued Properties of the Noise after a Discrete Fourier Transform Exercises Appendixes

  • Generation and Analysis of Excitation Signals

    This chapter contains sections titled: Introduction The Discrete Fourier Transform (DFT) Generation and Analysis of Multisines and Other Periodic Signals Generation of Optimized Periodic Signals Generating Signals Using The Frequency Domain Identification Toolbox (Fdident) Generation of Random Signals Differentiation, Integration, Averaging, and Filtering of Periodic Signals

  • Index

    You can immediately have the power to perform electromagnetic simulation. If you have a fundamental understanding of electromagnetic theory and the knowledge of at least one high-level computer language, you can begin writing simple electromagnetic simulation programs after reading the first chapter of this book. Electromagnetic Simulation Using the FDTD Method describes the power and flexibility of the finite-difference time-domain method as a direct simulation of Maxwell's equations. The FDTD method takes advantage of today's advanced computing power because its computational requirements increase linearly with the size of the simulation problem. This book begins with a simple one-dimensional simulation and progresses to a three-dimensional simulation. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Projects that increase in complexity are included, ranging from simulations in free space to propagation in dispersive media. Peripheral topics that are pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transform, are also covered. Electromagnetic Simulation Using the FDTD Method is written for anyone who would like to learn electromagnetic simulation using the finite-difference time-domain method. Appropriate as both a textbook and for self-study, this tutorial-style book will provide all the background you will need to begin research or other practical work in electromagnetic simulation.

  • Appendix B: Fourier Transform

    This chapter contains sections titled: Properties of the Fourier Transformation Spectrum of Example Time Domain Signals Transformation of Sampled Time Signals Short Time Fourier Transform of Continuous Signals Discrete Fourier Transform

  • About the Author

    You can immediately have the power to perform electromagnetic simulation. If you have a fundamental understanding of electromagnetic theory and the knowledge of at least one high-level computer language, you can begin writing simple electromagnetic simulation programs after reading the first chapter of this book. Electromagnetic Simulation Using the FDTD Method describes the power and flexibility of the finite-difference time-domain method as a direct simulation of Maxwell's equations. The FDTD method takes advantage of today's advanced computing power because its computational requirements increase linearly with the size of the simulation problem. This book begins with a simple one-dimensional simulation and progresses to a three-dimensional simulation. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Projects that increase in complexity are included, ranging from simulations in free space to propagation in dispersive media. Peripheral topics that are pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transform, are also covered. Electromagnetic Simulation Using the FDTD Method is written for anyone who would like to learn electromagnetic simulation using the finite-difference time-domain method. Appropriate as both a textbook and for self-study, this tutorial-style book will provide all the background you will need to begin research or other practical work in electromagnetic simulation.

  • Some Probability and Stochastic Convergence Fundamentals

    This chapter contains sections titled: Notations and Definitions The Covariance Matrix of a Function of a Random Variable Sample Variables Mixing Random Variables Preliminary Example Definitions of Stochastic Limits Interrelations between Stochastic Limits Properties of Stochastic Limits Laws of Large Numbers Central Limit Theorems Properties of Estimators Cramér-Rao Lower Bound How to Prove Asymptotic Properties of Estimators? Pitfalls Preliminary Example - Continued Properties of the Noise after a Discrete Fourier Transform Exercises Appendixes



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