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: 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 12/2014;
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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;
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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;
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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: 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;
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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;
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    ABSTRACT: We define the controllability of the title algebraically and characterise it by a corresponding concatenability of trajectories. The main result is motivated by and extends the characterisation of standard controllability by Wood and Zerz (Notes on the definition of behavioural controllability, Syst Control Lett 37(1):31–37, 1999). Negligibility is defined with respect to a suitably chosen Serre category of modules over a given operator domain. A behaviour is called negligible if its module belongs to this category. Standard examples of negligible behaviours with respect to suitably chosen Serre categories are asymptotically stable and nilpotent behaviours according to Bisiacco and Valcher. A behaviour is called controllable up to negligible trajectories if its factor behaviour modulo its largest controllable subbehaviour is negligible. This controllability notion generalises multidimensional stabilisability and coincides with it in dimension one. The preceding definitions also apply to continuous systems. The domain of the independent discrete variables of the considered discrete behaviours is an arbitrary finitely generated submonoid of a free abelian group.
    Multidimensional Systems and Signal Processing 07/2014;
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    ABSTRACT: Adaptive detection of moving targets on the sea is important for radar seekers. Recently, more attention has been paid to the deleterious effect of clutter heterogeneity on space-time adaptive processing (STAP) for pulse Doppler radar. Since secondary samples are no longer statistically independent and identically distributed (IID) in heterogeneous environments, this is subjected to a great challenge to target detection for radar seekers. Due to the fact that chaff jamming severely affects the performance degradation of target detection, the hybrid detection algorithm is proposed to suppress the sea clutter and chaff jamming. Firstly, the range cells can be classified into two regions according to the power, namely clutter region and hybrid region. Then we propose different algorithms to process two regions. The fixed point (FP) estimator is used to estimate the clutter covariance matrix in clutter region. While the power selected training (PST) algorithm is used to select the homogeneous secondary samples, and an algorithm based on two-step subspace projection for hybrid interference suppression is presented in hybrid region. Finally, the proposed Pareto-based generalized likelihood ratio test (PBGLRT) detector can detect the slowly moving targets in heterogeneous interference. Simulation results show that the PBGLRT detector outperforms both the low rank normalized adaptive match filter (LRNAMF) and normalized adaptive match filter (NAMF) detectors against interference heterogeneity.
    Multidimensional Systems and Signal Processing 07/2014;
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    ABSTRACT: Traditional inverse synthetic aperture radar (ISAR) imaging algorithms can not obtain focused images when the target undergoes complex three-dimensional (3D) rotation. An imaging algorithm to obtain two dimensional (2D) images or 3D distributions of scattering centers is proposed in this paper for targets undergoing complex rotation in a small angular extent. Firstly, the phase histories of different scattering centers are extracted by signal decomposition and they are arranged into a phase history matrix. Then, the singular value decomposition is carried out for the phase matrix to reveal the rotation characters. 3D rotations and 2D rotations are identified from the singular values and these two cases are treated separately. When target undergoes 2D rotation, the focused ISAR image can be obtained by resampling the received signals according to the first column of the right singular matrix. When target undergoes 3D rotation, the distorted 3D scattering center model can be obtained directly from the first and second columns of the left singular matrix. The distortion and ambiguity for the extracted 3D scattering center model are also analyzed theoretically. Simulations and experimental results verify the effectiveness of the algorithm.
    Multidimensional Systems and Signal Processing 07/2014;