Showing papers on "Fractional Fourier transform published in 2000"

Optical encryption by double-random phase encoding in the fractional Fourier domain.

Abstract: We propose an optical architecture that encodes a primary image to stationary white noise by using two statistically independent random phase codes. The encoding is done in the fractional Fourier domain. The optical distribution in any two planes of a quadratic phase system (QPS) are related by fractional Fourier transform of the appropriately scaled distribution in the two input planes. Thus a QPS offers a continuum of planes in which encoding can be done. The six parameters that characterize the QPS in addition to the random phase codes form the key to the encrypted image. The proposed method has an enhanced security value compared with earlier methods. Experimental results in support of the proposed idea are presented.

The discrete fractional Fourier transform

Abstract: We propose and consolidate a definition of the discrete fractional Fourier transform that generalizes the discrete Fourier transform (DFT) in the same sense that the continuous fractional Fourier transform generalizes the continuous ordinary Fourier transform. This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions. The definition is exactly unitary, index additive, and reduces to the DFT for unit order. The fact that this definition satisfies all the desirable properties expected of the discrete fractional Fourier transform supports our confidence that it will be accepted as the definitive definition of this transform.

Determination of Two-Dimensional Correlation Spectra Using the Hilbert Transform

Abstract: A computationally efficient numerical procedure to generate twodimensional (2D) correlation spectra from a set of spectral data collected at certain discrete intervals of an external physical variable, such as time, temperature, pressure, etc., is proposed. The method is based on the use of a discrete Hilbert transform algorithm which carries out the time-domain orthogonal transformation of dynamic spectra. The direct computation of a discrete Hilbert transform provides a definite computational advantage over the more traditional fast Fourier transform route, as long as the total number of discrete spectral data traces does not significantly exceed 40. Furthermore, the mathematical equivalence between the Hilbert transform approach and the original formal definition based on the Fourier transform offers an additional useful insight into the true nature of the asynchronous 2D spectrum, which may be regarded as a time-domain cross-correlation function between orthogonally transformed dynamic spectral intensity variations.

Closed-form discrete fractional and affine Fourier transforms

Abstract: The discrete fractional Fourier transform (DFRFT) is the generalization of discrete Fourier transform. Many types of DFRFT have been derived and are useful for signal processing applications. We introduce a new type of DFRFT, which are unitary, reversible, and flexible; in addition, the closed-form analytic expression can be obtained. It works in performance similar to the continuous fractional Fourier transform (FRFT) and can be efficiently calculated by the FFT. Since the continuous FRFT can be generalized into the continuous affine Fourier transform (AFT) (the so-called canonical transform), we also extend the DFRFT into the discrete affine Fourier transform (DAFT). We derive two types of the DFRFT and DAFT. Type 1 is similar to the continuous FRFT and AFT and can be used for computing the continuous FRFT and AFT. Type 2 is the improved form of type 1 and can be used for other applications of digital signal processing. Meanwhile, many important properties continuous FRFT and AFT are kept in the closed-form DFRFT and DAFT, and some applications, such as filter design and pattern recognition, are also discussed. The closed-form DFRFT we introduce has the lowest complexity among all current DFRFTs that is still similar to the continuous FRFT.

Image processing with the radial Hilbert transform: theory and experiments

Abstract: The Hilbert transform is useful for image processing because it can select which edges of an input image are enhanced and to what degree the edge enhancement occurs. However, the transform operation is one dimensional and is not applicable for arbitrarily shaped two-dimensional objects. We introduce a radially symmetric Hilbert transform that permits two-dimensional edge enhancement. We implement one-dimensional, two-dimensional, and radial Hilbert transforms with a programmable phase-only liquid-crystal spatial light modulator. Experimental results are presented.

Double random fractional Fourier domain encoding for optical security

Abstract: We propose a new optical encryption technique using the fractional Fourier transform. In this method, the data are encrypted to a stationary white noise by two statistically independent random phase masks in fractional Fourier domains. To decrypt the data correctly, one needs to specify the fractional domains in which the input plane, encryp- tion plane, and output planes exist, in addition to the key used for en- cryption. The use of an anamorphic fractional Fourier transform for the encryption of two-dimensional data is also discussed. We suggest an optical implementation of the proposed idea. Results of a numerical simulation to analyze the performance of the proposed method are pre- sented. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)01811-0)

Optical image encryption based on multifractional Fourier transforms

Abstract: We propose a new image encryption algorithm based on a generalized fractional Fourier transform, to which we refer as a multifractional Fourier transform. We encrypt the input image simply by performing the multifractional Fourier transform with two keys. Numerical simulation results are given to verify the algorithm, and an optical implementation setup is also suggested.

Oscillation and variation for the Hilbert transform

Abstract: It is well known that this limit exists a.e. for all f ∈ L, 1 ≤ p < ∞. In this paper, we will consider the oscillation and variation of this family of operators as goes to zero, which gives extra information on their convergence as well as an estimate on the number of λ-jumps they can have. For earlier results on oscillation and variation operators in analysis and ergodic theory, including some historical remarks and applications, the reader may look in [2], [3], [5], [4], and [6]. For each fixed sequence (ti) ↘ 0, we define the oscillation operator ( H∗f ) (x) = ( ∞ ∑

Fundamentals of Signals and Systems Using the Web and MATLAB

Abstract: Preface 1 FUNDAMENTAL CONCEPTS 1.1 Continuous-Time Signals 1.2 Discrete-Time Signals 1.3 Systems 1.4 Examples of Systems 1.5 Basic System Properties 1.6 Chapter Summary Problems 2 TIME-DOMAIN MODELS OF SYSTEMS 2.1 Input/Output Representation of Discrete-Time Systems 2.2 Convolution of Discrete-Time Signals 2.3 Difference Equation Models 2.4 Differential Equation Models 2.5 Solution of Differential Equations 2.6 Convolution Representation of Continuous-Time Systems 2.7 Chapter Summary Problems 3 THE FOURIER SERIES AND FOURIER TRANSFORM 3.1 Representation of Signals in Terms of Frequency Components 3.2 Trigonometric Fourier Series 3.3 Complex Exponential Series 3.4 Fourier Transform 3.5 Spectral Content of Common Signals 3.6 Properties of the Fourier Transform 3.7 Generalized Fourier Transform 3.8 Application to Signal Modulation and Demodulation 3.9 Chapter Summary Problems 4 FOURIER ANALYSIS OF DISCRETE-TIME SIGNALS 4.1 Discrete-Time Fourier Transform 4.2 Discrete Fourier Transform 4.3 DFT of Truncated Signals 4.4 FFT Algorithm 4.5 Application to Data Analysis 4.6 Chapter Summary Problems 5 FOURIER ANALYSIS OF SYSTEMS 5.1 Fourier Analysis of Continuous-Time Systems 5.2 Response to Periodic and Nonperiodic Inputs 5.3 Analysis of Ideal Filters 5.4 Sampling 5.5 Fourier Analysis of Discrete-Time Systems 5.6 Application to Lowpass Digital Filtering 5.7 Chapter Summary Problems 6 THE LAPLACE TRANSFORM AND THE TRANSFER FUNCTION REPRESENTATION 6.1 Laplace Transform of a Signal 6.2 Properties of the Laplace Transform 6.3 Computation of the Inverse Laplace Transform 6.4 Transform of the Input/Output Differential Equation 6.5 Transform of the Input/Output Convolution Integral 6.6 Direct Construction of the Transfer Function 6.7 Chapter Summary Problems 7 THE z-TRANSFORM AND DISCRETE-TIME SYSTEMS 7.1 z-Transform of a Discrete-Time Signal 7.2 Properties of the z-Transform 7.3 Computation of the Inverse z-Transform 7.4 Transfer Function Representation 7.5 System Analysis Using the Transfer Function Representation 7.6 Chapter Summary Problems 8 ANALYSIS OF CONTINUOUS-TIME SYSTEMS USING THE TRANSFER FUNCTION REPRESENTATION 8.1 Stability and the Impulse Response 8.2 Routh-Hurwitz Stability Test 8.3 Analysis of the Step Response 8.4 Response to Sinusoids and Arbitrary Inputs 8.5 Frequency Response Function 8.6 Causal Filters 8.7 Chapter Summary Problems 9 APPLICATION TO CONTROL 9.1 Introduction to Control 9.2 Tracking Control 9.3 Root Locus 9.4 Application to Control System Design 9.5 Chapter Summary Problems 10 DESIGN OF DIGITAL FILTERS AND CONTROLLERS 10.1 Discretization 10.2 Design of IIR Filters 10.3 Design of IIR Filters Using MATLAB 10.4 Design of FIR Filters 10.5 Design of Digital Controllers 10.6 Chapter Summary Problems 11 STATE REPRESENTATION 11.1 State Model 11.2 Construction of State Models 11.3 Solution of State equations 11.4 Discrete-Time Systems 11.5 Equivalent State Representations 11.6 Discretization of State Model 11.7 Chapter Summary Problems APPENDIX A BRIEF REVIEW OF COMPLEX VARIABLES APPENDIX B BRIEF REVIEW OF MATRICES BIBLIOGRAPHY INDEX

Multiplicity of fractional Fourier transforms and their relationships

Abstract: The multiplicity of the fractional Fourier transform (FRT), which is intrinsic in any fractional operator, has been claimed by several authors, but never systematically developed. The paper starts with a general FRT definition, based on eigenfunctions and eigenvalues of the ordinary Fourier transform, which allows us to generate all possible definitions. The multiplicity is due to different choices of both the eigenfunction and the eigenvalue classes. A main result, obtained by a generalized form of the sampling theorem, gives explicit relationships between the different FRTs.

Salem sets and restriction properties of fourier transforms

Abstract: Abstract. ((Without Abstract)).

Transform coding with backward adaptive updates

Abstract: The Karhunen-Loeve transform (KLT) is optimal for transform coding of a Gaussian source. This is established for all scale-invariant quantizers, generalizing previous results. A backward adaptive technique for combating the data dependence of the KLT is proposed and analyzed. When the adapted transform converges to a KLT, the scheme is universal among transform coders. A variety of convergence results are proven.

The discrete harmonic oscillator, Harper's equation, and the discrete fractional Fourier transform

Abstract: Certain solutions to Harper's equation are discrete analogues of (and approximations to) the Hermite-Gaussian functions. They are the energy eigenfunctions of a discrete algebraic analogue of the harmonic oscillator, and they lead to a definition of a discrete fractional Fourier transform (FT). The discrete fractional FT is essentially the time-evolution operator of the discrete harmonic oscillator.

Orthonormal finite ridgelet transform for image compression

Abstract: A finite implementation of the ridgelet transform is presented. The transform is invertible, non-redundant and achieved via fast algorithms. Furthermore we show that this transform is orthogonal hence it allows one to use non-linear approximations for the representation of images. Numerical results on different test images are shown. Those results conform with the theory of the ridgelet transform in the continuous domain-the obtained representation can represent efficiently images with linear singularities. Thus it indicates the potential of the proposed system as a new transform for coding of images.

On fractional Fourier transform moments

Abstract: Based on the relation between the ambiguity function represented in a quasipolar coordinate system and the fractional power spectra, the fractional Fourier transform (FT) moments are introduced. Important equalities for the global second order fractional FT moments are derived, and their applications for signal analysis are discussed. The connection between the local moments and the angle derivative of the fractional power spectra is established. This permits us to solve the phase retrieval problem if only two close fractional power spectra are known.

Method for subpixel registration of images

Abstract: Methods for registering first and second images which are offset by an x and/or y displacement in sub-pixel locations are provided. A preferred implementation of the methods includes the steps of: multiplying the first image by a window function to create a first windowed image; transforming the first windowed image with a Fourier transform to create a first image Fourier transform; multiplying the second image by the window function to create a second windowed image; transforming the second windowed image with a Fourier transform to create a second image Fourier transform; computing a collection of coordinate pairs from the first and second image Fourier transforms such that at each coordinate pair the values of the first and second image Fourier transforms are likely to have very little aliasing noise; computing an estimate of a linear Fourier phase relation between the-first and second image Fourier transforms using the Fourier phases of the first and second image Fourier transforms at the coordinate pairs in a minimum-least squares sense; and computing the displacements in the x and/or y directions from the linear Fourier phase relationship. Also provided are a computer program having computer readable program code and program storage device having a program of instructions for executing and performing the methods of the present invention, respectively.

Discrete fractional Hilbert transform

Abstract: The Hilbert transform plays an important role in the theory and practice of signal processing. A generalization of the Hilbert transform, the fractional Hilbert transform, was recently proposed, and it presents physical interpretation in the definition. In this paper, we develop the discrete fractional Hilbert transform, and apply the proposed discrete fractional Hilbert transform to the edge detection of digital images.

Hamiltonian description of Vlasov dynamics: Action-angle variables for the continuous spectrum

Abstract: The linear Vlasov-Poisson system for homogeneous, stable equilibria is solved by means of a novel integral transform that is a generalization of the Hilbert transform. The integral transform provid...

Fractional Fourier transforms in two dimensions.

Abstract: We analyze the fractionalization of the Fourier transform (FT), starting from the minimal premise that repeated application of the fractional Fourier transform (FrFT) a sufficient number of times should give back the FT. There is a qualitative increase in the richness of the solution manifold, from U(1) (the circle S1) in the one-dimensional case to U(2) (the four-parameter group of 2 × 2 unitary matrices) in the two-dimensional case [rather than simply U(1)×U(1)]. Our treatment clarifies the situation in the N-dimensional case. The parameterization of this manifold (a fiber bundle) is accomplished through two powers running over the torus T2=S1×S1 and two parameters running over the Fourier sphere S2. We detail the spectral representation of the FrFT: The eigenvalues are shown to depend only on the T2 coordinates; the eigenfunctions, only on the S2 coordinates. FrFT’s corresponding to special points on the Fourier sphere have for eigenfunctions the Hermite–Gaussian beams and the Laguerre–Gaussian beams, while those corresponding to generic points are SU(2)-coherent states of these beams. Thus the integral transform produced by every Sp(4, R) first-order system is essentially a FrFT.

Generalized fourier transform processing system

Abstract: Improved Fourier transform processing systems for a data transmission system are disclosed. The improved Fourier transform processing systems efficiently performs Fourier transform signal processing. In addition, the improved Fourier transform processing can perform address transformations to better and more efficiently use a memory system for in-place processing. The address transformations are provided by a generalized address translation algorithm that works for any size Fourier transform, in any radix, and with various memory architectures. The processing system can also be pipelined. The invention is particularly well suited for performing in-place processing in a data transmission system.

Applications of nonuniform fast transform algorithms in numerical solutions of differential and integral equations

Abstract: We review our efforts to apply the nonuniform fast Fourier transform (NUFFT) and related fast transform algorithms to numerical solutions of Maxwell's equations in the time and frequency domains. The NUFFT is a fast algorithm to perform the discrete Fourier transform of data sampled nonuniformly (NUDFT). Through oversampling and fast interpolation, the forward and inverse NUFFTs can be achieved with O(N log/sub 2/ N) arithmetic operations, asymptotically the same as the regular fast Fourier transform (FFT) algorithms. Using the NUFFT scheme, we develop nonuniform fast cosine transform (NUFCT) and fast Hankel transform (NUFHT) algorithms. These algorithms provide an efficient tool for numerical differentiation and integration, the key in the solutions to differential equations and volume integral equations. We present sample applications of these nonuniform fast transform algorithms in the numerical solution to Maxwell's equations.

An industrially useful means for decomposition and differentiation of harmonic components of periodic waveforms

Abstract: This paper presents efficient methods to estimate the spectral content of (noisy) periodic waveforms that are common in industrial processes The techniques presented, which are based on the recursive discrete Fourier transform, are especially useful in computing high-order derivatives of such waveforms Unlike conventional differentiating techniques, the methods presented differentiate in the frequency domain and thus are quite immune to uncorrelated measurement noise This paper also shows the theoretical relationship between the proposed methods and those of well-known resonant filters

The integer transforms analogous to discrete trigonometric transforms

Abstract: The integer transform (such as the Walsh transform) is the discrete transform that all the entries of the transform matrix are integer. It is much easier to implement because the real number multiplication operations can be avoided, but the performance is usually worse. On the other hand, the noninteger transform, such as the DFT and DCT, has a good performance, but real number multiplication is required. W derive the integer transforms analogous to some popular noninteger transforms. These integer transforms retain most of the performance quality of the original transform, but the implementation is much simpler. Especially, for the two-dimensional (2-D) block transform in image/video, the saving can be huge using integer operations. In 1989, Cham had derived the integer cosine transform. Here, we will derive the integer sine, Hartley, and Fourier transforms. We also introduce the general method to derive the integer transform from some noninteger transform. Besides, the integer transform derived by Cham still requires real number multiplication for the inverse transform. We modify the integer transform introduced by Cham and introduce the complete integer transform. It requires no real number multiplication operation, no matter what the forward or inverse transform. The integer transform we derive would be more efficient than the original transform. For example, for the 8-point DFT and IDFT, there are in total four real numbers and eight fixed-point multiplication operations required, but for the forward and inverse 8-point complete integer Fourier transforms, there are totally 20 fixed-point multiplication operations required. However, for the integer transform, the implementation is simpler, and many of the properties of the original transform are kept.

The analysis of multiple linear chirp signals

Abstract: This paper presents a technique for the analysis and extraction of multiple linear chirps in a time signal. These signals occur in nature and are becoming increasingly important in sonar, geophysical, ultrasonic and radar applications. The work is based on and closely related to the fractional Fourier transform (FrFT). The paper considers discrete analysis and synthesis of complex signals containing linear chirps with various characteristics. We show how individual chirps in a mixture of chirps (not necessarily linear) can be extracted and investigate the filtering and reconstruction of mixed chirp signals. Examples are presented to illustrate the concepts using both synthetic signals and real data. A visual interpretation of the magnitude and phase of the analytic results is introduced allowing a range of transform orders to be viewed simultaneously. The filtering of linear and 'near-linear' chirp signals is also discussed alongside the efficient pulse compression characteristics of the FrFT.

Simplified fractional Fourier transforms

Abstract: The fractional Fourier transform (FRFT) has been used for many years, and it is useful in many applications. Most applications of the FRFT are based on the design of fractional filters (such as removal of chirp noise and the fractional Hilbert transform) or on fractional correlation (such as scaled space-variant pattern recognition). In this study we introduce several types of simplified fractional Fourier transform (SFRFT). Such transforms are all special cases of a linear canonical transform (an affine Fourier transform or an ABCD transform). They have the same capabilities as the original FRFT for design of fractional filters or for fractional correlation. But they are simpler than the original FRFT in terms of digital computation, optical implementation, implementation of gradient-index media, and implementation of radar systems. Our goal is to search for the simplest transform that has the same capabilities as the original FRFT. Thus we discuss not only the formulas and properties of the SFRFT’s but also their implementation. Although these SFRFT’s usually have no additivity properties, they are useful for the practical applications. They have great potential for replacing the original FRFT’s in many applications.

Linear and Quadratic Time-Frequency Representations

Abstract: : This report is reviewing both linear and quadratic time-frequency representations. The linear representations discussed are Short-Time Fourier Transform and S-transform. The quadratic representation discussed is Wigner distribution. We outline the motivations, interpretations, mathematical fundamentals, properties, and applications of these linear and quadratic time-frequency representations. We also compare these three different time-frequency analysis techniques and show that each technique has its strengths and drawbacks. The simulated data sets have been used for the comparison. The choice of the particular time-frequency representation depends upon the specific area of application and what we aim to achieve with a local frequency analysis. We show that time-frequency analysis methods should enable us to classify signals with a considerably greater interpretation of the physical situation than can be achieved by the conventional Fourier Transform method alone.

Selection of the wavenumbers k using an optimization method for the inverse Fourier transform in 2.5D electrical modelling

Abstract: An optimization method is used to select the wavenumbers k for the inverse Fourier transform in 2.5D electrical modelling. The model tests show that with the wavenumbers k selected in this way the inverse Fourier transform performs with satisfactory accuracy.