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: 1.62

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.617
2013 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
Year

Additional details

5-year impact 1.43
Cited half-life 5.40
Immediacy index 0.19
Eigenfactor 0.00
Article influence 0.34
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

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as arXiv.org
    • 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: Due to their uniqueness and high value commercially, logos/trademarks play a key role in e-business based global marketing. However, existing trademark/logo retrieval techniques and content-based image retrieval methods are mostly designed for generic images, which cannot provide effective retrieval of trademarks/logos. Although color and spatial features have been intensively investigated for logo image retrieval, in most cases they were applied separately. When these are combined in a fused manner, a fixed weighting is normally used between them which cannot reflect the significance of these features in the images. When the image quality is degraded by various reasons such as noise, the reliability of color and spatial features may change in different ways, such that the weights between them should be adapted to such changes. In this paper, adaptive fusion of color and spatial descriptors is proposed for colored logo/trademark image retrieval. First, color quantization and k-means are combined for effective dominant color extraction. For each extracted dominant color, a component-based spatial descriptor is derived for local features. By analyzing the image histogram, an adaptive fusion of these two features is achieved for more effective logo abstraction and more accurate image retrieval. The proposed approach has been tested on a database containing over 2300 logo/trademark images. Experimental results have shown that the proposed methodology yields improved retrieval precision and outperforms three state-of-the-art techniques even with added Gaussian, salt and pepper, and speckle noise.
    No preview · Article · Feb 2016 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Due to the complexity and extensive application of wireless systems, fading channel modeling is of great importance for designing a mobile network, especially for high speed environments. High mobility challenges the speed of channel estimation and model optimization. In this study, we propose a single-hidden layer feedforward neural network (SLFN) approach to modelling fading channels, including large-scale attenuation and small-scale variation. The arrangements of SLFN in path loss (PL) prediction and fading channel estimation are provided, and the information in both of them is trained with extreme learning machine (ELM) algorithm and a faster back-propagation (BP) algorithm called Levenberg-Marquardt algorithm. Computer simulations show that our proposed SLFN estimators could obtain PL prediction and the instantaneous channel transfer function of sufficient accuracy. Furthermore, compared with BP algorithm, the ability of ELM to provide millisecond-level learning makes it very suitable for fading channel modelling in high speed scenarios.
    No preview · Article · Jan 2016 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Texture feature extraction methods have been improved greatly in recent years. It is widely known that the local texture feature descriptor can achieve desired performance under the change of image geometric size, different poses and complex illumination conditions. In this paper, a novel local texture descriptor is proposed, named as the multi-degrees improved local difference binary (ILDB). Local difference binary is an promising feature description method, while it only computes the intensity and gradient difference on pairwise grid cells and ignores the image grid inherent texture gradient difference. ILDB can represent difference and texture information of the grid cells intensity and gradient simultaneously. In addition, the multiple-degree strategy is adopted to achieve richer texture description. At the same time, the optimized mutual information is proposed to capture more discriminant feature selection and reduce the dimensionality of the ILDB. Experimental results demonstrate that the proposed method is highly efficient and distinctive compared with several state-of-the-art approaches. Due to good performance of ILDB, it is expected that ILDB has a potential for widespread application in many computer vision fields.
    No preview · Article · Jan 2016 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Due to the lack of post-processing resistance, traditional forensic methods are vulnerable to cascade image manipulations, e.g. copy-and-paste operation followed by high compression. Different from these traditional methods, a new forensic method that has the ability to resist multiple types of post-processing, is proposed by using white balance from the EXchangeable Image File format (EXIF) header. We first extract image quality metrics between each two combination of one original image and twelve re-balanced images. By regularizing the eigen spectrum of image quality metrics, the compact set of image eigen features is then selected for recognizing different EXIF-white balance modes via the SVM classifier. The experimental results show that the proposed method has the ability to resist the influence of high compression or heavy downsampling in both theoretical and realistic scenarios. Furthermore, thanks to image eigen features affected by cascade image operations, it is possible to lead to a wrong white balance mode. Thus, we use the EXIF-white balance parameter as a manipulator indicator for forgery detection. Based on the forgery photos in practice, the proposed evidence can detect cascade manipulated images which are subject to copy-and-paste followed by different white balance post-processing operations, high compression or heavy downsampling.
    No preview · Article · Jan 2016 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Hole and crack filling is the most important issue in depth-image-based rendering (DIBR) algorithms for generating virtual view images when only one view image and one depth map are available. This paper proposes a priority patch inpainting algorithm for hole filling in DIBR algorithms by generating multiple virtual views. A texture-based interpolation method is applied for crack filling. Then, an inpainting-based algorithm is applied patch by patch for hole filling. A prioritized method for selecting the critical patch is also proposed to reduce computation time. Finally, the proposed method is realized on the compute unified device architecture parallel computing platform which runs on a graphics processing unit. Simulation results show that the proposed algorithm is 51-fold faster for virtual view synthesis and achieves better virtual view quality compared to the traditional DIBR algorithm which contains depth preprocessing, warping, and hole filling.
    No preview · Article · Dec 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: One key task in forensic science is to perform criminal investigation through image database retrieval. Of the various images, tire pattern is an important type of image data for crime scene investigation. However, different rotation and direction of tire patterns are often encountered and is insufficient to use the conventional multi-scale texture feature extraction method which is not rotational invariant. To alleviate this problem, the paper proposed two new texture feature extraction methods based on the Radon transform and Curvelet transform. The experiments were conducted using a tire pattern database containing 400 images. The results show that the proposed methods effectively overcome the influences of rotation and significantly improve the retrieval efficiency.
    No preview · Article · Dec 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Melasma image segmentation plays a fundamental role for computerized melasma severity assessment. A method of hybrid threshold optimization between a given image and its local regions is proposed and used for melasma image segmentation. An analytic optimal hybrid threshold solution is obtained by minimizing the deviation between the given image and its segmented outcome. This optimal hybrid threshold comprises both local and global information around image pixels and is used to develop an optimal hybrid thresholding segmentation method. The developed method is firstly evaluated based on synthetic images and subsequently used for melasma segmentation and severity assessment. Statistical evaluations of experimental results based on real-world melasma images show that the proposed method outperforms other state-of-the-art thresholding segmentation methods for melasma severity assessment.
    No preview · Article · Dec 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Underground pipeline network surveillance system attracts increasingly attentions recently due to severe breakages caused by external excavation equipments in the mainland of China. In this paper, we study excavation equipments classification algorithm based on acoustic signal processing and machine learning algorithms. A cross-layer microphone array with four elements is designed to collect the acoustic database of representative excavation equipments on real construction sites. The generalized sidelobe canceller algorithm is employed for background noise reduction. The improved spectrum dynamic feature extraction algorithm is then implemented for the benchmark acoustic feature database construction of excavation equipments. To perform classification and background noise identification, the single hidden layer feedforward neural network is employed as the classifier. An improved algorithm based on the popular extreme learning machine (ELM) is proposed for classifier learning. The leave-one-out cross validation strategy is adopted for the regularization parameter optimization in ELM. Comprehensive experiments are conducted to test the effectiveness of the proposed algorithm. Comparisons with state-of-art classifiers and the Mel-frequency cepstrual coefficients acoustic features are also provided to demonstrate the superiority of our approach.
    No preview · Article · Dec 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the proposed scheme, generation of the pseudo-random sensing matrix offers a natural method for data encryption in DCS, allowing for joint recovery of multiparty data at legal users’ side. Experimental analysis and results indicate that the secure signal processing and recovery in DCS domain is feasible, and requires fewer measurements than the achievable approach of separate CS and Nyquist processing. The proposed scheme can be also extended to other cloud-based collaborative secure signal processing and data-mining applications.
    No preview · Article · Dec 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Target detection in remote sensing images (RSIs) is a fundamental yet challenging problem faced for remote sensing images analysis. More recently, weakly supervised learning, in which training sets require only binary labels indicating whether an image contains the object or not, has attracted considerable attention owing to its obvious advantages such as alleviating the tedious and time consuming work of human annotation. Inspired by its impressive success in computer vision field, in this paper, we propose a novel and effective framework for weakly supervised target detection in RSIs based on transferred deep features and negative bootstrapping. On one hand, to effectively mine information from RSIs and improve the performance of target detection, we develop a transferred deep model to extract high-level features from RSIs, which can be achieved by pre-training a convolutional neural network model on a large-scale annotated dataset (e.g. ImageNet) and then transferring it to our task by domain-specifically fine-tuning it on RSI datasets. On the other hand, we integrate negative bootstrapping scheme into detector training process to make the detector converge more stably and faster by exploiting the most discriminative training samples. Comprehensive evaluations on three RSI datasets and comparisons with state-of-the-art weakly supervised target detection approaches demonstrate the effectiveness and superiority of the proposed method.
    No preview · Article · Nov 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: This work addresses the off-grid issue for DOA and frequency estimations when the dictionary based sparse signal recovery concept is adopted. By off-grid, we mean that the true values of signal, angles or frequencies in this case, are not exactly on the sampling grid created by utilizing the discrete dictionary technique. To handle this problem, off-grid is remodelled such that it is represented by an offset matrix that is a sparse matrix. And then, a direct estimate of the offset matrix is developed to compensate the off-grid by utilizing the fact that the offset matrix is a sparse matrix. Finally, by exploring the sparse property of DOAs/frequencies and offset matrix, a joint estimation approach is devised under optimization framework. Numerical studies demonstrate the effectiveness of the proposed approach.
    No preview · Article · Nov 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: This paper addresses the problem of direction finding in multiple-input multiple-output radars with unknown transmitter and/or receiver gains and phases. Three different cases are considered and the corresponding methods for determining the direction-of-arrivals (DOAs) of multiple targets are presented. In the first case, the transmitter is well calibrated, but the receiver is uncalibrated with unknown gains and phases. On the contrary, the second case assumes that the receiver is well calibrated, whereas the transmitter is not. In the third case, the transmitter is uncalibrated, and the receiver is partly calibrated, i.e. only a portion of the receiver array is well calibrated and the remaining sensor elements have gain and phase uncertainties. For the first two cases, it is shown that a type of determinant-based or eigenvalue-based spectral can be utilized to determine the DOAs. For the third one, an ESPRIT-like algorithm is developed. Numerical examples are provided to validate the proposed methods and the results show that these methods are insensitive to the unknown gains and phases.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: We propose an image segmentation model that is derived from reaction–diffusion equations and level set methods. In our model, a diffusion term is used for regularization of a level set function, and a reaction term has the desired sign property to force the level set function to move up or down and finally identify an object and its background. Our level set function can be initialized to any bounded function (e.g., a constant function). The proposed model can be applied to a wider range of images with promising results, especially for real images that have high noise and blurred boundaries. This study gives a new method for the further investigations of reaction–diffusion equations directly for segmentation.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: In this paper, a fusion scheme is presented to combine the useful information present in magnetic resonance and positron emission tomography images. The proposed scheme utilizes image pre-processing, local feature (fractal dimension), weighted fusion and improved guided filter to extract and combine information present at different scales/frequencies. The fusion scheme assist radiologist in better analysis and diagnosis of different diseases. Visual and quantitative analysis reveals the significance of proposed image fusion scheme, as compared to state of the art techniques.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: A multidimensional (MD) linear phase biorthogonal filter bank (LPBOFB) with higher order feasible (HOF) building blocks is reported. Basically, there are two ways to design filter banks with large filter supports. One way is to use a cascade of degree-1 building blocks, and the other way is to use a cascade of order-1 building blocks. Unfortunately, both methods have high implementation costs in terms of the number of parameters, especially for the multidimensional case. A previously reported HOF building block has now been applied to MD LPBOFBs. Their generalized structural design supports both an even and odd number of channels. It is shown that the HOF structure cannot be factored into a cascade of order-1 building blocks. The proposed MD LPBOFB has larger filters and uses fewer building blocks than the traditional degree-1 and order-1 structures.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: In this paper, we propose a novel interpolation method to expand the decimated stereoscopic 3D (S3D) video to the original size. The basic approach for our interpolation is to exploit key-point correspondences between the stereoscopic left and right images. Since the rectified left and right frames of the S3D videos are aligned, a simple key-point detection method can be employed without considering the scale and transformation invariances. After detecting matched key-point pairs between the left and right images, we can interpolate the decimated pixels by exploiting the corresponding key-points in the opposite view as well as their neighboring pixels in the current view. The merit of our method is that no side information overhead is required for the interpolation. Nevertheless, the proposed method yields similar or even better PSNR performance than the previous side information method.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    ABSTRACT: Multiwatermarking embeds multiple watermarks into the media content imperceptibly. It is regarded as the potential means to protect the media contents’ copyright and/or trace illegal redistributors in multi-user environments, e.g., collaborative media content production or layered media content distribution. However, there are still some open issues in designing a good multiwatermarking scheme, including the combination between multiple embedding steps, the combination between multiple watermarks and the security of embedding schemes. In this paper, novel multiwatermarking schemes are proposed, which are based on hybrid multi-bit multiplicative rules controlled by secret keys. Two hybrid multiplicative multiwatermarking decoders, i.e., optimum and locally optimum, are proposed, which are based on the minimum Bayesian risk criterion and the DWT coefficients are modeled as the generalized Gaussian distribution. The BER (average bit error rate) as the evaluation index of the performance of optimum hybrid decoders is exactly analyzed. Finally, experimental results are shown to confirm the validity of the theoretical and empirical analysis.
    No preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing
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    Preview · Article · Oct 2015 · Multidimensional Systems and Signal Processing