Multidimensional Systems and Signal Processing (MULTIDIM SYST SIGN P)

Publisher: Springer Verlag

Journal description

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

Current impact factor: 1.58

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.578
2012 Impact Factor 0.857
2011 Impact Factor 0.953
2010 Impact Factor 0.822
2009 Impact Factor 0.524
2008 Impact Factor 0.486
2007 Impact Factor 0.545
2006 Impact Factor 0.588
2005 Impact Factor 0.722
2004 Impact Factor 0.278
2003 Impact Factor 0.441
2002 Impact Factor 0.938
2001 Impact Factor 0.676
2000 Impact Factor 0.385
1999 Impact Factor 0.49
1998 Impact Factor 0.135
1997 Impact Factor 0.121
1996 Impact Factor 0.256
1995 Impact Factor 0.267
1994 Impact Factor 0.419
1993 Impact Factor 0.167
1992 Impact Factor 0.366

Impact factor over time

Impact factor

Additional details

5-year impact 0.80
Cited half-life 9.00
Immediacy index 0.20
Eigenfactor 0.00
Article influence 0.38
Website Multidimensional Systems and Signal Processing website
Other titles Multidimensional systems and signal processing (Online)
ISSN 0923-6082
OCLC 38267214
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

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    • Author's pre-print on pre-print servers such as
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In the seventies, the study of transfer matrices of linear time-invariant systems led to the development of the polynomial approach (Kailath, Rosenbrock, et al.). In this approach, univariate polynomial matrices play a central role (e.g., Hermite and Smith normal forms, Bezout equations etc.).When generalizing linear systems given by ordinary differential/difference equations to differential time-delay systems, systems with parameters, 2-D or 3-D filters and circuits, one had to face the case of systems described by means of matrices with entries in multivariate polynomial rings. These new systems were called 2-D or 3-D and, more generally, multidimensional systems (Bose, Lin, Pommaret, Oberst, Youla, Wood, et al.). For such systems, no normal forms such as Hermite and Smith forms exist. In order to handle these problems, the concept of Gröbner bases (developed by Buchberger) was introduced in multidimensional systems theory. In many ways, the computation of these bases can be seen as ...
    Multidimensional Systems and Signal Processing 04/2015; 26(2). DOI:10.1007/s11045-015-0313-z
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    ABSTRACT: In this paper, we formalize two related but different notions for state-space realization of multidimensional ( \(n\hbox {D}\) ) single-input–single-output discrete systems in \(n\hbox {D}\) Roesser model, namely the “absolutely minimal realization” and the “minimal realization”. We then focus our study mainly on first-degree 2D and 3D causal systems. A necessary and sufficient condition for absolutely minimal realizations is given for first-degree 2D systems. It is then shown that first-degree 2D systems that do not admit absolutely minimal realizations always admit minimal realizations of order 3. A Gröbner basis approach is also proposed which leads to a sufficient condition for the absolutely minimal realizations of some higher-degree 2D systems. We then present a symbolic method that gives simple necessary conditions for the existence of absolutely minimal realizations for first-degree 3D systems. A two-step approach to absolutely minimal realizations for first-degree 3D systems is then presented, followed by techniques for minimal realizations of first-degree 3D systems. Throughout the paper, several non-trivial examples are illustrated with the aim of helping the reader to apply the realization methods proposed in this paper.
    Multidimensional Systems and Signal Processing 04/2015; 26(2). DOI:10.1007/s11045-014-0297-0
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    ABSTRACT: In this paper, we investigate super-resolution image restoration from multiple images, which are possibly degraded with large nonparametric motion blur. The blur kernel for each input image is separately estimated. This is unlike many existing super-resolution algorithms, which assume identical blur kernel for all input images. We also do not make any restrictions on the motion field among images; that is, we estimate dense motion field without simplifications such as parametric motion. We present a two-step algorithm: In the first step, each input image is deblurred using its estimated blur kernel. In the second step, multi-frame super-resolution restoration is applied to the deblurred images. Because the estimated blur kernels may not be accurate, we propose a weighted cost function for the super-resolution restoration step, where a weight associated with an input image reflects the reliabilities of the corresponding kernel estimate and deblurred image. We provide experimental results with both simulated and real data, and show the effectiveness and robustness of the proposed method compared to some alternative approaches and state-of-the-art methods.
    Multidimensional Systems and Signal Processing 02/2015; DOI:10.1007/s11045-015-0322-y
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    ABSTRACT: Focus stacking and high dynamic range (HDR) imaging are two paradigms of computational photography. Focus stacking aims to produce an image with greater depth of field (DOF) from a set of images taken with different focus distances; HDR imaging aims to produce an image with higher dynamic range from a set of images taken with different exposure values. In this paper, we present an algorithm which combines focus stacking and HDR imaging in order to produce an image with both extended DOF and dynamic range from a set of differently focused and exposed images. The key step in our algorithm is focus stacking regardless of the differences in exposure values of input images. This step includes photometric and spatial registration of images, and image fusion to produce all-in-focus images. This is followed by HDR radiance estimation and tonemapping. We provide experimental results with real data to illustrate the algorithm.
    Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0315-x
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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; DOI:10.1007/s11045-014-0305-4
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    ABSTRACT: In this paper, we proffer an explicit representation of solutions for a specific class of linear repetitive processes with smoothing. This representation is used to obtain direct controllability and observability criteria of this same class of discrete time 2-D systems. Not only classical controllability properties are considered, where control of the system is obtained by choosing its inhomogeneity appropriately, but also controllability of the system by steering it through boundary data control. From the point of view of technical applications, for instance in high pressure gas network modelling (see Azevedo-Perdicoúlis and Jank in Proceedings of n-DS, international workshop on multidimensional systems, Thessaloniki. 2009), it seems to be more reliable to consider boundary data controls. Therefore, in this paper we emphasise boundary data control properties of the system. A disturbed optimal boundary control problem with a quadratic criterion is also solved.
    Multidimensional Systems and Signal Processing 01/2015; 26(1):145-158. DOI:10.1007/s11045-013-0241-8
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    ABSTRACT: It has been proved that the performance of a person-specific active appearance model (AAM) built to model the appearance variation of a single person across pose, illumination, and expression is substantially better than the performance of a generic AAM built to model the appearance variation of many faces. However, it is not practical to build a personal AAM before tracking an unseen subject. A virtual person-specific AAM is proposed to tackle the problem. The AAM is constructed from a set of virtual personal images with different poses and expressions which are synthesized from the annotated first frame via regressions. To preserve personal facial details on the virtual images, a poison fusion strategy is designed and applied to the virtual facial images generated via bilinear kernel ridge regression. Furthermore, the AAM subspace is sequentially updated during tracking based on sequential Karhunen-Loeve algorithm, which helps the AAM adaptive to the facial context variation. Experiments show the proposed virtual personal AAM is robust to facial context changes during tracking, and outperforms other state-of-the-art AAM on facial feature tracking accuracy and computation cost.
    Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0326-7
  • Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0324-9
  • Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-014-0312-5
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    ABSTRACT: In this paper, we show that every discrete 2D autonomous system, that is described by a set of linear partial difference equations with constant real coefficients, admits a finite union of parallel lines as a characteristic set. In order to prove our claim, we first look at a special class of scalar discrete 2D systems and provide such characteristic sets for systems in this class. This special class has the property that systems in this class have their quotient rings to be finitely generated modules over a one-variable Laurent polynomial subring of the original two-variable Laurent polynomial ring in the shift operators. We show that such systems always admit a finite collection of horizontal lines for a characteristic set. We then extend this result to non-scalar discrete 2D autonomous systems. We achieve this in two steps: first, we show that every scalar discrete 2D system can be converted into a system in the above-mentioned class by a coordinate transformation on the independent variables set, \(\mathbb {Z}^2\) . Using this we then show that characteristic sets for the original system can be found by applying the inverse coordinate transformation on characteristic sets of the transformed system. Since the transformed system, by virtue of being in the special class, admits a finite union of horizontal lines as a characteristic set, the original system is guaranteed to admit a characteristic set that is a coordinate transformation applied to a finite union of horizontal lines. The coordinate transformation maps this union of horizontal lines to a union of parallel, but possibly tilted, lines. In the next step, we generalize the scalar case to the general vector case: that is, systems with more than one dependent variables. The main motivation for studying characteristic sets that are unions of finitely many parallel lines is that, arguably, such sets can be called “thin” in \(\mathbb {Z}^2\) in comparison to the prevalent notions of convex cones and half-spaces as characteristic sets (see “Appendix 1”).
    Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0330-y
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    ABSTRACT: In this paper, we study a blind deconvolution problem by using an image decomposition technique. Our idea is to make use of a cartoon-plus-texture image decomposition procedure into the deconvolution problem. Because cartoon and texture components can be represented differently in images, we can adapt suitable regularization methods to restore their components. In particular, the total variational regularization is used to describe the cartoon component, and Meyer’s G-norm is employed to model the texture component. In order to obtain the restored image automatically, we also use the generalized cross validation method efficiently and effectively to estimate their corresponding regularization parameters. Experimental results are reported to demonstrate that the visual quality of restored images by using the proposed method is very good, and is competitive with the other testing methods.
    Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0318-7
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    ABSTRACT: In this paper, a new spatially spread electromagnetic vector sensor (SS-EMVS) is proposed by a two-step design. In addition, a novel DOA estimator with coarse-fine estimate combination is presented for the proposed array. The first step aims to make the configurations of SS-EMVS satisfy the “vector cross-product” estimator, leading to a coarse estimation of three direction-cosines. The second step focuses on extending the two dimensional (2-D) array apertures of SS-EMVS, resulting in two fine but ambiguous estimations on the direction-cosines by extracting inter-sensor phase-delay. Combination the coarse and fine estimations, the high-accuracy 2-D DOA estimation can be obtained by using the coarse estimation to disambiguate the fine estimation. The three- dipoles and loops of the proposed configuration are located separately, which are found to reduce mutual coupling as compared with collocated EMVS. Moreover, the new configuration is able to extend 2-D array aperture to improve the accuracy of 2-D direction-finding. Numerical Simulations are conducted to demonstrate the effectiveness of the proposed algorithm.
    Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0327-6
  • Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0325-8
  • Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0317-8
  • Multidimensional Systems and Signal Processing 01/2015; DOI:10.1007/s11045-015-0316-9
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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. DOI:10.1007/s11045-013-0238-3