Multidimensional Systems and Signal Processing (MULTIDIM SYST SIGN P )

Publisher: Springer Verlag


Multidimensional Systems and Signal Processing is an archival peer-reviewed technical journal publishing survey and original papers spanning fundamentals as well as applicable research contributions. While the subject of multidimensional systems is concerned with mathematical issues designed to tackle a broad range of models its applications in signal processing have been known to cover spatial and temporal signals of diverse physical origin. The current problem faced due to the widely scattered nature of publications in this area will be circumvented through the unity of theme in this journal so that research is facilitated and expected with much reduced duplication of effort and much enhanced communication. Topics of current interest include but are not limited to: blurred and noisy image processing multidimensional signal reconstruction from partial or incomplete observations and projections signal modeling spectral analysis and transform techniques array processing linear and nonlinear prediction and filtering of multidimensional processes multidimensional spectrum estimation multivariate approximation multidimensional realization theory multidimensional sampling strategies interpolation and decimation schemes velocity filtering fast processing of remotely sensed multidimensional data multivariate polynomial and matrix factorization schemes computer algebra for symbolic and algebraic manipulations concurrent architecture for multidimensional signal processing visual communications neural networks and incorporation of artificial intelligence techniques in spatio temporal data processing

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    Multidimensional systems and signal processing (Online)
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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, an improved and simple approach for enhancement of dark and low contrast satellite image based on knee function and gamma correction using discrete wavelet transform with singular value decomposition (DWT–SVD) has been proposed for quality enhancement of feature. In addition, this method can also process the high resolution dark or very low contrast images, and offers best enhanced result using tuning parameter of Gamma. The technique decomposes the input image into four frequency subbands by using DWT and estimates the singular value matrix of the low–low subband image, and then compute the knee transfer function using gamma correction for further improvement of the LL component. Afterward, processed LL band image undergoes IDWT together with the unprocessed LH, HL, and HH subbands to generate an appropriate enhanced image. Although, various histogram equalization approaches has been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using knee function and gamma correction. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than the existing techniques.
    Multidimensional Systems and Signal Processing 01/2015;
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    ABSTRACT: This paper presents a new method for estimating the parameters of quarterplane two dimensional (2-D) autoregressive model based on the Levinson–Durbin algorithm. To achieve this aim, one-dimensional formulations related to Levinson–Durbin algorithm are extended to 2-D case. Online parameter estimation, capability of parameters variation detection, estimation improvement by using new data and less computational requirement are the significant advantages of the proposed method. Because of not involving complex and time consuming matrix computations, the presented method is computationally efficient. Numerical simulations are presented to show the efficiency of the proposed approach.
    Multidimensional Systems and Signal Processing 01/2015;
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    ABSTRACT: A biquaternion-based direction-finding algorithm for noncircular sources is presented. The covariance and conjugate covariance matrices of the array output are utilized symmetrically within a frame of biquaternions. The direction-of-arrivals are found where the biquaternion steering vectors are orthogonal to the noise subspace in the biquaternion domain. Simulations show the improved performance of the proposed method compared to its complex counterparts.
    Multidimensional Systems and Signal Processing 01/2015; 26(1):95-111.
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    ABSTRACT: In this paper, a color model for the Orthogonal Polynomials Transform and modifications to the generating function of the transform’s coefficients in order to enhance the speed of the transform have been proposed. Utilizing this simple and integer-based transform, a fast color image annotation and retrieval system is proposed. In the proposed retrieval system, images are represented as a mixture of Gaussians which are built from the transform’s partial coefficients. Annotation is performed by estimating the Kullback Leibler distance between the Gaussian distributions of the query and that of the database. This retrieval system is fast owing to the following reasons: 1) Usage of a computationally light transform 2) Sufficiency of partial decoding of the transform’s coefficients for building the image representation owing to its energy compaction property 3) Exploitation of the inherent symmetry of the point spread operator of transform which is useful for fast determination of the transform’s coefficients and 4) Non-usage of any time consuming weight assignment algorithm while fusing multiple features into the feature vector. The algorithm is validated on the COIL-100 database which has been categorized into six types for the purpose of analyzing the results better. An optimum number of extracted features and Gaussian mixtures that give a good annotation and retrieval performance is determined. The performance of the proposed system is compared with that of other recent compressed domain techniques and also with the feature set given by the local descriptors of SIFT.
    Multidimensional Systems and Signal Processing 10/2014; 25(4).
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    ABSTRACT: We focus on the design of the measurement schemes in the compressed sensing (CS) method for direction-of-arrival estimation, and three stochastic measurement schemes are considered. In the perspectives of average apertures and incoherences, we give a detailed mathematical analysis for these schemes. The superiorities in incoherences for these schemes are illustrated, compared with the popularly used random Gaussian measurement scheme. Then we demonstrate that the newly used Poisson disk sampling scheme and uniform jittered sampling scheme can obtain relatively large average apertures by using a proposed computational method. Finally, several simulations are implemented to evaluate the performances of the CS methods with these stochastic measurement schemes.
    Multidimensional Systems and Signal Processing 10/2014; 25(4).
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    ABSTRACT: In inverse synthetic aperture radar (ISAR) imaging, translation compensation should be done before range-Doppler imaging process, and range alignment is the first step for translation compensation. In order to remove the limitation of integer range bin and align the echoes precisely for ISAR range alignment, combining with the advantage of array signal processing at fractional unit delay compensation, we propose a novel range alignment method based on linear transmitting/receiving (T/R) array. Firstly the ISAR imaging system is modeled as a linear T/R array. Then based on the snapshot imaging model of linear T/R array, range alignment is accomplished by wave path difference compensation which is transformed into the phase difference compensation in frequency domain between adjacent array elements. The phase difference compensation consists of integer range bin alignment and decimal time delay compensation which is implemented by the phase rotation’s estimation and compensation in frequency domain. Finally, the results of simulation data and real radar data are provided to demonstrate the effectiveness of the proposed method.
    Multidimensional Systems and Signal Processing 10/2014;
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    ABSTRACT: The Synthetic Aperture Focusing Technique (SAFT) is an algorithm applied in non-destructive ultrasonic testing which provides an image of flaws within a specimen. The image is reconstructed from A-scans measured at different positions. Reliable evaluation of the images obtained by the SAFT-algorithm, however, depends on the representation of the reconstructed data, which is initially given in terms of positive and negative local values only. A suitable way of processing this data for evaluation is to calculate the envelope, which can be achieved by means of the analytic signal. The extension of this concept to the multidimensional case is neither trivial nor unique and although extensive work on this subject has been carried out in the past, a correct envelope calculation in multidimensional data remains difficult since it depends on an additional condition, namely the separability of the signal. In this paper, the concept of analytic signals with single-quadrant spectra is applied to process 2-dimensional data obtained by the SAFT-algorithm. Furthermore, we present a procedure to overcome the limitations of that approach by selecting local magnitude values from a number of rotated frames after evaluating the signal’s separability in each frame, which is briefly validated against synthetic and experimental data.
    Multidimensional Systems and Signal Processing 10/2014;
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    ABSTRACT: This paper addresses the two-dimensional (2-D) linear inequalities based robust Iterative Learning Control (ILC) for nonlinear discrete systems with time delays. The proposed two-gain ILC rule has a rectifying action to iterative initial error and external disturbances. It guarantees a reduced bound of the ILC tracking error against the iteration-varying initial error and disturbances. For the iteration-invariant initial error and disturbances, the ILC tracking error can be driven to convergence, and a complete tracking to reference trajectory beyond the initial time point is even achieved as the control gain is specifically selected. An example is used to validate the proposed ILC method.
    Multidimensional Systems and Signal Processing 10/2014;
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    ABSTRACT: A multiplierless architecture based on algebraic integer representation for computing the Daubechies 4-tap wavelet transform for 1-D/2-D signal processing is proposed. This architecture improves on previous designs in a sense that it minimizes the number of parallel 2-input adder circuits. The algorithm was achieved using numerical optimization based o exhaustive search over the algebraic integer representation. The proposed architecture furnishes exact computation up to the final reconstruction step, which is the operation that maps the exactly computed filtered results from algebraic integer representation to fixed-point. Compared to Madishetty et al. (IEEE Trans Circuits Syst I (Accepted, In Press), 2012a), this architecture shows a reduction of \(10\cdot n-3\) adder circuits, where \(n\) is the number of wavelet decomposition levels. Standard \(512\times 512\) images Mandrill, Lena, and Cameraman were submitted to digital realizations of both proposed algebraic integer based as well as fixed-point schemes, leading to quantifiable comparisons. The design is physically implemented for a 4-level 2-D decomposition using a Xilinx Virtex-6 vcx240t-1ff1156 FPGA device operating at up to a maximum clock frequency of 263.15 MHz. The FPGA implementation is tested using hardware co-simulation using an ML605 board with clock of 100 MHz. A 45 nm CMOS synthesis shows improved clock frequency of better than 500 MHz for a supply voltage of 1.1 V.
    Multidimensional Systems and Signal Processing 10/2014; 25(4).
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    ABSTRACT: A computationally efficient two-dimensional (2-D) direction-of-arrival (DOA) estimation method for uniform rectangular arrays is presented. A preprocessing transformation matrix is first introduced, which transforms both the complex-valued covariance matrix and the complex-valued search vector into real-valued ones. Then the 2-D DOA estimation problem is decoupled into two successive real-valued one-dimensional (1-D) DOA estimation problems with real-valued computations only. All these measures lead to significantly reduced computational complexity for the proposed method.
    Multidimensional Systems and Signal Processing 10/2014; 25(4).
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    ABSTRACT: The structured singular value $\mu$ was introduced independently by Doyle and Safanov as a tool for analyzing robustness of system stability and performance in the presence of structured uncertainty in the system parameters. While the structured singular value provides a necessary and sufficient criterion for robustness with respect to a structured ball of uncertainty, it is notoriously difficult to actually compute. The method of diagonal (or simply "D") scaling, on the other hand, provides an easily computable upper bound (which we call $\hat \mu$) for the structured singular value, but provides an exact evaluation of $\mu$ (or even a useful upper bound for $\mu$) only in special cases. However it was discovered in the 1990s that a certain enhancement of the uncertainty structure (i.e., letting the uncertainty parameters be freely noncommuting linear operators on an infinite-dimensional separable Hilbert space) resulted in the $D$-scaling procedure leading to an exact evaluation of $\mu_{\text{enhanced}}$ ($\mu_{\text{enhanced}} = \hat \mu$), at least for the tractable special cases which were analyzed in complete detail. On the one hand this enhanced uncertainty has some appeal from the physical point of view: one can allow the uncertainty in the plant parameters to be time-varying, or more generally, one can catch the uncertainty caused by the designer's decision not to model the more complex (e.g. nonlinear) dynamics of the true plant. On the other hand, the precise mathematical formulation of this enhanced uncertainty structure makes contact with developments in the growing theory of analytic functions in freely noncommuting arguments and associated formal power series in freely noncommuting indeterminates. In this article we obtain the $\widetilde \mu = \hat \mu$ theorem for a more satisfactory general setting.
    Multidimensional Systems and Signal Processing 08/2014;
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    ABSTRACT: A novel 3D watermarking algorithm by combining use of computational integral imaging (CII) and cellular automata transform (CAT) is proposed in this paper. In this proposed scheme, first, the original image signal is decomposed into three resolution levels by using the level-3 2D CAT, and meanwhile, the middle-frequency domains are obtained. Then, an elemental images (EIs) array is generated by recording the information of rays of light coming from an object through a pinhole array in the CII system. The EIs array is encrypted by linear maximum-length cellular automata as the encrypted watermark embedded into the CAT middle-frequency domains. Finally, the watermarked image is obtained by using the level-3 2D inverse CAT. To verify the usefulness of the proposed algorithm, we carry out the computational experiments and present the experimental results for various attacks. Experimental results show that this proposed watermarking system provides excellent results in unobtrusiveness and robustness.
    Multidimensional Systems and Signal Processing 07/2014; 25(3).
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    ABSTRACT: This paper is concerned with the problem of global asymptotic stability of a class of nonlinear uncertain two-dimensional (2-D) discrete systems described by the Fornasini-Marchesini second local state-space model with time-varying state delays. The class of systems under investigation involves norm bounded parameter uncertainties, interval-like time-varying delays and various combinations of quantisation and overflow nonlinearities. A linear matrix inequality-based delay-dependent criterion for the global asymptotic stability of such systems is proposed. An example is given to illustrate the effectiveness of the proposed method.
    Multidimensional Systems and Signal Processing 07/2014;