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Process Neural Network (PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum...
This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary power signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of power signal analysis, yielding...
Identifying reoccurring patterns within a signal set often yields important information about the signal. Fourier analysis is typically used as a method to determine periodic sinusoidal elements of a given signal; however, it is not able to properly identify near-periodic patterns with small instantaneous changes in the periodicity of the input waveform. To that end, a new transform has been developed...
The theory of families of odd-periodic perfect complementary sequence pairs(OPCSPF) is given. OPCSPF can be derived from the odd-perfect almost binary sequence and sequence pairs. Some characteristics of OPCSPF are discussed. The Fourier spectrum characteristics of OPCSPF are studied, and the necessary conditions for existence of OPCSPF are given. The method for constructing an OPCSPF ii given and...
In this paper, we present a method for digital image stabilization based on Fourier-Mellin transform and phase correlation. We acquire the rotating angle and scaling factor firstly by phase correlation between Fourier-Mellin transform images of the reference and observed images. After rotating and scaling the observed image, implement the phase correlation again to compute the spacial translation...
A fast and robust method for visual odometry based on the Fourier-Mellin Invariant (FMI) descriptor is presented. It extends previous FMI based approaches in two ways. First, a logarithmic representation of the spectral magnitude of the FMI descriptor is used. Second, a filter on the frequency where the shift is supposed to appear is applied. It is shown with experiments with an Unmanned Aerial Vehicle...
Logo or Trademark is of high importance because it carries the goodwill of the company and the product. Products are mostly recognized by their brand logos. Their recognition is a major problem. A number of techniques are there for logo recognition. In this paper, a number of invariant techniques are compared to find out their effectiveness on various categories of brand logos. Techniques which were...
We present a novel method to compute the normalized cross-correlation (NCC) of spherical signals such as omnidirectional images for the purpose of image alignment, i.e. orientation estimation, or template tracking. Expanding the reference image and the search template in the spherical harmonics basis, we use the Fourier representation of SO(3) as introduced by Kostelec and Rockmore. We show that the...
Image preprocessing and corner detection are of importance in 3-dimension measurement based on computer technology. A novel image preprocessing method by combining the deconvolution based on Fourier transform with the white Gaussian denosing method based on finite Ridgelet transform is proposed, and a corner detector is given according to the cornerness of the preprocessed image. The simulation and...
The phase of an audio/speech signal conveys critical information, which is exploited in a variety of applications. Accurate phase information extraction is a prerequisite for applications such as coding, synchronization, synthesis or recognition. However, signal phase extraction is not a straightforward procedure, mainly due to the discontinuities appearing in it (phase 'wrapping' effect). In previously...
In 2001 Lee, Rajan and Park introduced (among other things) the concept of extended complex reverse jacket (CRJ) transform over finite field. In this paper, we prove that this transform is equivalent to Galois field Fourier (GFF) transform and possesses an additional feature of having a matrix with specific border. We also, present examples of Jacket transforms over finite fields applicable to odd...
This paper studies the uncertainty principle of the second-order local polynomial Fourier transform (LPFT). It shows that the uncertainty product of the LPFT is time-independent when the Gaussian window is used to segment the signal. Meanwhile when the extra parameter is estimated correctly, the uncertainty product of the LPFT becomes a constant. Compared to the short-time Fourier transform and the...
Based on discrete Hermite-Gaussian-like functions, a discrete fractional Fourier transform (DFRFT), which provides sample approximations of the continuous fractional Fourier transform, was defined and investigated recently. In this paper, we propose a novel nearly tridiagonal matrix, which commutes with the generalized discrete Fourier transform (GDFT) matrix. It doesn't has repeated eigenvalue. We...
A novel algorithm for stitching images, which is faster and more robust than standard approaches like the Scale Invariant Feature Transform (SIFT) is presented. The algorithm is particularly suited for Autonomous Underwater Vehicles (AUV), namely for the online generation of photo maps that can be the basis of intelligent onboard functionalities. The photo maps can be generated just based on registration,...
This paper presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Specifically, we studied Fourier, angular radial transform and image moment descriptors for shape representation. Since shape is one of the most widely used image feature exploited in content-based image retrieval systems, we studied for each descriptor, the...
In this paper, we consider the problem of reconstructing a signal with constant envelope property in the time domain from its Fourier transform magnitude. The constant envelope property makes the signal reconstruction problem difficult. In this context, several convex-set like signal properties are used to generate projection based iterative reconstruction algorithms possessing error reduction property.
R.V. Hartley disclosed a real-valued transform closely related to the Fourier transform in 1942. Besides having interesting properties of its own, the transform introduced by Hartley allows an indirect computation of the Fourier power spectrum of a given function only using real arithmetic. In the last decade some new discrete real-valued orthogonal transforms have been proposed, which are Hartley-related...
Two-sided spectra of patterns based on classes of orthogonal matrices are introduced and their main properties discussed. In particular, the ldquomosaicnessrdquo of patterns is studied. It is shown that this property of patterns may easily be recognized in the spectral domain, albeit the mosaic structure cannot be unambiguously determined. 2D-Dirichlet kernels based on non-Abelian groups can however...
This paper provides a novel method to obtain the eigenvectors of discrete Fourier transform (DFT), which are accurate approximations to the continuous Hermite-Gaussian functions (HGFs). The proposed method uses a generating matrix and an initial eigenvector. By multiplying the initial eigenvector with the generating matrix, we can derive a new eigenvector. Repeating this procedure we can acquire all...
The algebraic theory of finite groups appears in signal processing problems involving the statistical analysis of ranked data and the construction of invariants for pattern recognition. Standard signal processing techniques involving spectral analysis are, in theory, possible for data defined on finite groups by using the Fourier transform provided by group representations. However, one such technique,...
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