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In this paper, the direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array (also known as full-dimension MIMO) is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. Based on the variational Bayesian framework, a novel iterative algorithm for subspace identification operating on tensor represented data is proposed with integrated features of effective bad data mitigation and automatic source enumeration. The subspace recovered from the proposed algorithm not only enables existing 2D DOA estimators to be readily applied, if the number of signal paths is less than the number of horizontal antennas and vertical antennas, the subspaces in elevation and azimuth domains can be separately estimated, from which one dimensional (1D) DOA estimators can be utilized, thus further lowering the complexity. Simulation results are presented to illustrate the excellent performance of the proposed subspace recovery method and subsequent DOA estimation in terms of accuracy and robustness. [ABSTRACT FROM PUBLISHER]

In this letter, we propose HOG-Dot, a method for the direct computation of the polar image gradients coordinates from the pixels values. The proposed algorithm, to be used as the first step of the Histogram of Oriented Gradient (HOG) pipeline, approximates the exact gradient with its projection onto a versor chosen among the projection plane set. Instead of non linear computations, the HOG-Dot method exploits linear operations while introducing a bounded approximation error with respect to other HOG approaches, thus resulting a more suitable solution for embedded devices. Concerning the state of the art, it also achieves improved accuracy with the mathematical spatial gradient formulation . [ABSTRACT FROM PUBLISHER]

Pixelated two-dimensional (2-D) optical wireless communication systems (OWCS), with intensity-modulation and direct-detection (IM/DD), modulate the data to be transmitted, into image intensities at the transmitter. The image is passed through an optical channel, and is captured at the receiver, then demodulated into received data. In traditional one-dimensional (1-D) IM/DD OWCS, the proposed schemes modulate the output signal either in the time-domain, such as in single-carrier modulation, or in the frequency-domain, such as in orthogonal frequency-division multiplexing (OFDM). In contrast to 1-D systems, the previously proposed 2-D pixelated OWCS, use frequency-domain modulation (FDM) only, overlooking space-domain modulation (SDM). FDM benefits from the ability to control the output signal's spectrum, though suffers from high peak-to-average power ratio (PAPR), and high computational complexity at the transmitter. In this study, we propose a new SDM-based, 2-D pixelated OWCS, which maintains low PAPR and low computational-complexity at the transmitter side, while enabling efficient spectral-control of its output signal. After a definition of a general scheme for the proposed SDM, we present three signal examples, suitable for three typical channels. Analysis of the spectral content of those output signals is performed, demonstrating the spectral control ability, and their resulted lower PAPR. Simulation results of the proposed SDM methods, as well as a common reference FDM method, the spatial asymmetrically-clipped optical OFDM, are presented. Although showing minor performance disadvantage for typical average power limited channels, the proposed SDM methods show significant performance advantage for peak limited channels, while maintaining, in all cases, flexibility in dimensions, to meet the target screen size, significantly lower PAPR, and lower transmitter side computational complexity. [ABSTRACT FROM PUBLISHER]

Recursive, causal and non-causal, multidimensional digital filters, with infinite impulse responses and maximally flat magnitude and delay responses in the low-frequency region, are designed to negate correlated clutter and interference in the ‘background’ and to accumulate power due to dim targets in the ‘foreground’ of a surveillance sensor. Expressions relating mean impulse-response duration, frequency selectivity and group delay, to low-order linear-difference-equation coefficients are derived using discrete Laguerre polynomials and discounted least-squares regression, then verified through simulation. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) cyclic codes are presented which correct any single occurrence of known 2D error patterns within a 2D array of bits. Applications for this type of codes include storage and display devices. The code construction begins with a generation of distinct syndrome sets for all targeted 2D error patterns. A method to refine the syndrome sets is then presented for making each syndrome set to contain distinct members, thereby guaranteeing full correction capability for the given list of known error patterns. Using an example construction, the effectiveness of the proposed coding approach is demonstrated versus the maximum-distance-separable (MDS) random-error-correcting code and known 2D burst-correcting codes for a 2D intersymbol interference (ISI) channel that yields a few dominant, but relatively large error patterns. [ABSTRACT FROM PUBLISHER]

In this letter, we present a new 3D statistical method for surface detection which provides improvements over competitive methods both in terms of noise suppression and detection of complete surfaces. The methods are applied to both synthetically created image volumes, and MRI data. Accuracy against a ground truth is assessed using the quantitative figure ofmerit performance measure, with the statistical methods outperforming both a 3D implementation of the gradient Canny operator and a 3D optimal steerable filter method. The results also confirm how 3D surface detection methods avoid missing surface information by successfully locating complete boundaries irrespective of the object orientation and plane of image capture.We conclude that the statistical 3D methods are capable of producing more accurate surface maps in textured images, that reflect the 3D boundary information, improving on current 2D and 3D standards. [ABSTRACT FROM AUTHOR]

Distance functions are at the core of important data analysis and processing tools, e.g., PCA, classification, vector median filter, and mathematical morphology. Despite its key role, a distance function is often used without careful consideration of its underlying assumptions and mathematical construction. With the objective of identifying a suitable distance function for hyperspectral images so as to maintain the accuracy of hyperspectral image processing results, we compare existing distance functions and define a suitable set of selection criteria. Bearing in mind that the selection of distance functions is highly related to the actual definition of the spectrum, we also classify the existing distance functions based on how they inherently define a spectrum. Theoretical constraints and behavior, as well as numerical tests are proposed for the evaluation of distance functions. With regards to the evaluation criteria, Euclidean distance of cumulative spectrum (ECS) was found to be the most suitable distance function. [ABSTRACT FROM PUBLISHER]

Multidimensional signals are generated by subjecting molecules to sequences of short optical pulses and recording correlation plots related to the various controlled delay periods. These techniques which span all the way from the THz to the x-ray regimes provide qualitatively new structural and dynamical molecular information not available from conventional one-dimensional techniques. This issue surveys the recent experimental and theoretical progresses in this rapidly developing 20 year old field which illustrates the novel insights provided by multidimensional techniques into electronic and nuclear motions. It should serve as a valuable source for experts in the field and help introduce newcomers to this exciting and challenging branch of nonlinear spectroscopy. [ABSTRACT FROM AUTHOR]

A three-dimensional (3-D) spatiotemporal prediction-error filter (PEF) is used to enhance foreground/background contrast in (real and simulated) sensor image sequences. Relative velocity is utilized to extract point targets that would otherwise be indistinguishable with spatial frequency alone. An optical-flow field is generated using local estimates of the 3-D autocorrelation function via the application of the fast Fourier transform (FFT) and inverse FFT. Velocity estimates are then used to tune in a background-whitening PEF that is matched to the motion and texture of the local background. Finite impulse response (FIR) filters are designed and implemented in the frequency domain. An analytical expression for the frequency response of velocity-tuned FIR filters, of odd or even dimension with an arbitrary delay in each dimension, is derived. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is an important topic for a wireless communication area. So far, the multiple signal classification (MUSIC) algorithm is the most popular one in the field of DOA estimation because it is noticeably simple but effective. However, the resolution becomes low when the number of snapshots is not appropriate for the received signal power. Therefore, this paper proposesan adaptive snapshot technique to solve the aforementioned problem. The idea is to have a DOA finder which is able to adapt the snapshot numbers according to the received power to maintain the accuracy of the DOA estimation. A DOA finder prototype is also constructed to validate the proposed idea. [ABSTRACT FROM AUTHOR]

A closed-form analytic representation of multidimensional periodic functions with a finite Fourier spectrum is obtained as a modified interpolation series with a finite number of terms. The corresponding theorem is formulated, and the multidimensional periodic kernel of the expansion is analyzed. It is shown that such a representation makes it possible to calculate values of the derivatives of an arbitrary order of a periodic function with a finite spectrum from the samples of this function. [ABSTRACT FROM AUTHOR]

Much interest is continuing to grow in advanced interferometric synthetic aperture radar (SAR) methods for full 3-D imaging, particularly of volumetric forest scatterers. Multibaseline (MB) SAR tomographic elevation beam forming, i.e., spatial spectral estimation, is a promising technique in this framework. In this paper, the important effect of temporal decorrelation during the repeat-pass MB acquisition is tackled, analyzing the impact on superresolution (MUSIC) tomography with limited sparse data. Moreover, new tomographic methods robust to temporal decorrelation phenomena are proposed, exploiting the advanced differential tomography concept that produces “space-time” signatures of scattering dynamics in the SAR cell. To this aim, a 2-D version of MUSIC and a generalized MUSIC method matched to nonline spectra are applied to decouple the nuisance temporal signal components in the spatial spectral estimation. Simulated analyses are reported for different geometrical and temporal parameters, showing that the new concept of restoring tomographic performance in temporal decorrelating forest scenarios through differential tomography is promising. [ABSTRACT FROM PUBLISHER]

Tensor-decomposition-based methods for reducing random noise components in hyperspectral images (HSIs), both dependent and independent from signal, are proposed. In this paper, noise is described by a parametric model that accounts for the dependence of noise variance on the signal. This model is thus suitable for the cases where photon noise is dominant compared with the electronic noise contribution. To denoise HSIs distorted by both signal-dependent (SD) and signal-independent (SI) noise, some hybrid methods, which reduce noise by two steps according to the different statistical properties of those two types of noise, are proposed in this paper. The first one, named as the PARAFACSI- PARAFACSD method, uses a multilinear algebra model, i.e., parallel factor analysis (PARAFAC) decomposition, twice to remove SI and SD noise, respectively. The second one is a combination of the well-known multiple-linear-regression-based approach termed as the HYperspectral Noise Estimation (HYNE) method and PARAFAC decomposition, which is named as the HYNE-PARAFAC method. The last one combines the multidimensional Wiener filter (MWF) method and PARAFAC decomposition and is named as the MWF-PARAFAC method. For HSIs distorted by both SD and SI noise, first, most of the SI noise is removed from the original image by PARAFAC decomposition, the HYNE method, or the MWF method based on the statistical property of SI noise; then, the residual SD components can be further reduced by PARAFAC decomposition due to its own statistical property. The performances of the proposed methods are assessed on simulated HSIs. The results on the real-world airborne HSI Hyperspectral Digital Imagery Collection Experiment (HYDICE) are also presented and analyzed. These experiments show that it is worth taking into account noise signal-dependence hypothesis for processing HYDICE data. [ABSTRACT FROM PUBLISHER]

Abstract: The core contribution of this paper is to introduce a general neat scheme based on soft vector clustering for the dithering of multidimensional signals that works in any space of arbitrary dimensionality, on arbitrary number and distribution of quantization centroids, and with a computable and controllable quantization noise. Dithering upon the digitization of one-dimensional and multi-dimensional signals disperses the quantization noise over the frequency domain which renders it less perceptible by signal processing systems including the human cognitive ones, so it has a very beneficial impact on vital domains such as communications, control, machine-learning, etc. Our extensive surveys have concluded that the published literature is missing such a neat dithering scheme. It is very desirable and insightful to visualize the behavior of our multidimensional dithering scheme; especially the dispersion of quantization noise over the frequency domain. In general, such visualization would be quite hard to achieve and perceive by the reader unless the target multidimensional signal itself is directly perceivable by humans. So, we chose to apply our multidimensional dithering scheme upon encoding true-color images – that are 3D signals – with palettes of limited sets of colors to show how it minimizes the visual distortions – esp. contouring effect – in the encoded images. [Copyright &y& Elsevier]

Highlights: [•] We introduce a general dithering scheme for multidimensional signals. [•] This dithering scheme is based on stochastic (soft) vector quantization. [•] We provide the formulae of the upper-bound of the quantization noise of this dithering. [•] We provide a visual instance of the behavior of this dithering with true-color images. [•] This is also a new method of the optimal encoding of true-color images with a limited palette. [ABSTRACT FROM AUTHOR]

Highlights: [•] Buildings account for almost 40% of the total energy use in Europe. [•] ICT based cost effective systems are needed for building renovation. [•] Current BMS do not have the capabilities of energy monitoring and optimization. [•] Our system can integrate, store, analyze complex data sets from multiple sources. [•] This provides actionable information for the energy optimized intelligent control. [ABSTRACT FROM AUTHOR]

We present an error-diagnostic validation method for posterior distributions in Bayesian signal inference, an advancement of a previous work. It transfers deviations from the correct posterior into characteristic deviations from a uniform distribution of a quantity constructed for this purpose. We show that this method is able to reveal and discriminate several kinds of numerical and approximation errors, as well as their impact on the posterior distribution. For this we present four typical analytical examples of posteriors with incorrect variance, skewness, position of the maximum, or normalization. We show further how this test can be applied to multidimensional signals. [ABSTRACT FROM AUTHOR]

Highlights: [•] Noninvasive diagnosis of melanoma from clinical color image. [•] Novel feature extraction based on tensor decomposition and nonlinear transformation. [•] Interesting alternative and competitive performance in comparison with state-of-the-art methods. [•] Diagnosis from color RGB image possibly enabling design of cost-effective prescreening devices. [ABSTRACT FROM AUTHOR]

In this paper, the idea of multi-split adaptive filter is applied to a Decision Feedback Equalizer (DFE). The feed-forward and feedback sections in the DFE are divided into parallel sub-filters by imposing separately the symmetry and antisymmetry conditions on the impulse responses of filters with the use of appropriate and distinct sets of linear constraints. The objective of this work is to obtain a new structure that gives better performance than the conventional structure and to study its convergence behavior. The performance of multi-split DFE is tested with different algorithms (LMS, RLS, and DCT-LMS) and compared with the performance of multi-split linear transversal Equalizer. We show also by simulation that the feedback section of DFE need not be split. [ABSTRACT FROM AUTHOR]

The first task to be performed in most hyperspectral unmixing chains is the estimation of the number of endmembers. Several techniques for this problem have already been proposed, but the class of fractal techniques for intrinsic dimensionality estimation is often overlooked. In this paper, we study an intrinsic dimensionality estimation technique based on the known scaling behavior of nearest-neighbor distance ratios, and its performance on the spectral unmixing problem. We present the relation between intrinsic manifold dimensionality and the number of endmembers in a mixing model, and investigate the effects of denoising and the statistics on the algorithm. The algorithm is compared with several alternative methods, such as Hysime, virtual dimensionality, and several fractal-dimension based techniques, on both artificial and real data sets. Robust behavior in the presence of noise, and independence of the spectral dimensionality, is demonstrated. Furthermore, due to its construction, the algorithm can be used for non-linear mixing models as well. [ABSTRACT FROM PUBLISHER]

Consider the task of sampling and reconstructing a bandlimited spatial field in \BBR^2 using moving sensors that take measurements along their path. It is inexpensive to increase the sampling rate along the paths of the sensors but more expensive to increase the total distance traveled by the sensors per unit area, which we call the path density. In this paper, we introduce the problem of designing sensor trajectories that are minimal in path density subject to the condition that the measurements of the field on these trajectories admit perfect reconstruction of bandlimited fields. We study various possible designs of sampling trajectories. Generalizing some ideas from the classical theory of sampling on lattices, we obtain necessary and sufficient conditions on the trajectories for perfect reconstruction. We show that a single set of equispaced parallel lines has the lowest path density from certain restricted classes of trajectories that admit perfect reconstruction. We then generalize some of our results to higher dimensions. We first obtain results on designing sampling trajectories in higher dimensional fields. Further, interpreting trajectories as 1-D manifolds, we extend some of our ideas to higher dimensional sampling manifolds. We formulate the problem of designing \kappa-dimensional sampling manifolds for d-dimensional spatial fields that are minimal in manifold density, a natural generalization of the path density. We show that our results on sampling trajectories for fields in \BBR^2 can be generalized to analogous results on d-1-dimensional sampling manifolds for d-dimensional spatial fields. [ABSTRACT FROM PUBLISHER]

A novel approach to symbol spaced adaptive multiple-input–multiple-output (MIMO) equalization is proposed. Different functionalities are defined for static versus adaptive equalizers, to minimize noise enhancement. Static equalizers are used for anti-aliasing filtering and bulk CD compensation, and a newly proposed modified-LMS-based adaptive MIMO equalizer is used to compensate for fiber random impairments such as residual CD and PMD. In addition, the proposed technique enables the use of low-cost reduced bandwidth components in both transmit and receive sides, and supports aggressive in-line optical filtering in the existing WDM network. Moreover, the proposed technique perfectly suits coherent metro applications, allowing significant power savings in application specific integrated circuit implementation. [ABSTRACT FROM PUBLISHER]

In this paper, we present two generators for the group of symmetries of the generic (m+1)-dimensional periodic Costas arrays over elementary abelian (\BBZp)^{m} groups: one that is defined by multiplication on m dimensions and the other by shear (addition) on m dimensions. Through exhaustive search, we observe that these two generators characterize the group of symmetries for the examples we were able to compute. Following the results, we conjecture that these generators characterize the group of symmetries of the generic (m+1)-dimensional periodic Costas arrays over elementary abelian (\BBZp)^{m} groups. [ABSTRACT FROM AUTHOR]