Multidimensional Signal Processing

Research in multidimensional signal processing deals with issues such as those arising in automatic target detection and recognition problems, geophysical inverse problems, and medical estimation problems. Its goal is to develop methods to extract information from diverse data sources amid uncertainty. Research cited here was published or presented between January and September, 2014. It covers a range of subtopics including hidden communications channels, wave digital filters, SAR interferometry, and SAR tomography.

• Seleym, A, "High-rate Hidden Communications Channel: A Multi-Dimensional Signaling Approach," Integrated Communications, Navigation and Surveillance Conference (ICNS), 2014 , vol., no., pp.W4-1,W4-8, 8-10 April 2014. doi: 10.1109/ICNSurv.2014.6820026 Hidden communications is one recent method to provide reliable security in transferring information between entities. Data hiding in media carriers is a power limited and band-limited system, as a consequence, there is a tradeoff between the host media perceptual fidelity and the transferred data error rate. In this paper, a developed embedding approach is proposed by considering the altering process as a signaling communications problem. This approach uses a structured scheme of Multiple Trellis-Coded Quantization jointed with Multiple Trellis-Coded Modulation (MTCQ/MTCM) to generate the stego-cover space. The developed scheme allows transferring a high volume of information without causing a severe perceptual or statistical degradation, and also be robust to additive noise attacks. Keywords: quantisation (signal); steganography; trellis coded modulation; additive noise attack; data hiding; high rate hidden communications channel; host media perceptual fidelity; media carrier; multidimensional signaling; multiple trellis coded modulation; multiple trellis coded quantization; reliable security; signaling communications problem; stego cover space; Constellation diagram; Encoding; Noise; Nonlinear distortion; Quantization (signal); Vectors (ID#:14-2400) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6820026&isnumber=6819972

• Balasa, F.; Abuaesh, N.; Gingu, C.V.; Nasui, D.V., "Leakage-aware Scratch-Pad Memory Banking For Embedded Multidimensional Signal Processing," Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on , vol., no., pp.5026,5030, 4-9 May 2014. doi: 10.1109/ICASSP.2014.6854559 Partitioning a memory into multiple banks that can be independently accessed is an approach mainly used for the reduction of the dynamic energy consumption. When leakage energy comes into play as well, the idle memory banks must be put in a low-leakage `dormant' state to save static energy when not accessed. The energy savings must be large enough to compensate the energy overhead spent by changing the bank status from active to dormant, then back to active again. This paper addresses the problem of energy-aware on-chip memory banking, taking into account - during the exploration of the search space - the idleness time intervals of the data mapped into the memory banks. As on-chip storage, we target scratch-pad memories (SPMs) since they are commonly used in embedded systems as an alternative to caches. The proposed approach proved to be computationally fast and very efficient when tested for several data-intensive applications, whose behavioral specifications contain multidimensional arrays as main data structures. Keywords: embedded systems; power aware computing; signal processing; storage management; SPMs; data structures; dynamic energy consumption reduction; embedded multidimensional signal processing; embedded systems; energy-aware on-chip memory banking; leakage energy ;leakage-aware scratch-pad memory banking; low-leakage dormant state; multidimensional arrays; on-chip storage; Arrays; Banking; Energy consumption; Lattices; Memory management; Signal processing algorithms; System-on-chip memory banking; memory management; multidimensional signal processing; scratch-pad memory (ID#:14-2401) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854559&isnumber=6853544

• Schwerdtfeger, T.; Kummert, A, "A Multidimensional Signal Processing Approach To Wave Digital Filters With Topology-Related Delay-Free Loops," Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on , vol., no., pp.389,393, 4-9 May 2014. doi: 10.1109/ICASSP.2014.6853624 To avoid the occurrence of noncomputable, delay-free loops, classic Wave Digital Filters (WDFs) usually exhibit a tree-like topology. For the realization of prototype circuits that contain ring-like subnetworks, prior approaches require the decomposition of the structure and thus neglect the notion of modularity of the original Wave Digital concept. In this paper, a new modular approach based on Multidimensional Wave Digital Filters (MDWDFs) is presented. For this, the contractivity property of WDFs is shown. On that basis, the new approach is studied with respect to possible side-effects and an appropriate modification is proposed that counteracts these effects and significantly improves the convergence behaviour. Keywords: digital filters; network topology; delay-free loops; multidimensional signal processing; multidimensional wave digital filter; ring-like subnetwork; structure decomposition; topology related loops; Convergence; Delays; Digital filters; Mathematical model; Ports (Computers); Prototypes; Topology; Bridged-T Model; Contractivity; Delay-Free Loop; Multidimensional; Wave Digital Filter (ID#:14-2402) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6853624&isnumber=6853544

• Holt, K.M., "Total Nuclear Variation and Jacobian Extensions of Total Variation for Vector Fields," Image Processing, IEEE Transactions on, vol.23, no.9, pp.3975,3989, Sept. 2014. doi: 10.1109/TIP.2014.2332397 We explore a class of vectorial total variation (VTV) measures formed as the spatial sum of a pixel-wise matrix norm of the Jacobian of a vector field. We give a theoretical treatment that indicates that, while color smearing and affine-coupling bias (often reported as gray-scale bias) are typically cited as drawbacks for VTV, these are actually fundamental to smoothing vector direction (i.e., smoothing hue and saturation in color images). In addition, we show that encouraging different vector channels to share a common gradient direction is equivalent to minimizing Jacobian rank. We thus propose total nuclear variation (TNV), and since nuclear norm is the convex envelope of matrix rank, we argue that TNV is the optimal convex regularizer for enforcing shared directions. We also propose extended Jacobians, which use larger neighborhoods than the conventional finite difference operator, and we discuss efficient VTV optimization algorithms. In simple color image denoising experiments, TNV outperformed other common VTV regularizers, and was further improved by using extended Jacobians. TNV was also competitive with the method of nonlocal means, often outperforming it by 0.25-2 dB when using extended Jacobians. Keywords: Color; Image color analysis; Image reconstruction; Jacobian matrices; Materials; TV; Vectors; Color imaging; convex optimization; denoising; image reconstruction; inverse problems; multidimensional signal processing; regularization; total variation; vector-valued images (ID#:14-2403) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6841619&isnumber=6862127

• Lombardini, F.; Cai, F., "Temporal Decorrelation-Robust SAR Tomography," Geoscience and Remote Sensing, IEEE Transactions on , vol.52, no.9, pp.5412,5421, Sept. 2014. doi: 10.1109/TGRS.2013.2288689 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. Keywords: array signal processing; decorrelation; forestry; image matching; image resolution; image restoration; optical tomography; radar imaging; ynthetic aperture radar; 2D MUSIC version; 3D imaging; MB SAR tomographic elevation beam forming; SAR; interferometric synthetic aperture radar method; multibase-line SAR tomographic elevation beam forming; nuisance temporal signal component; repeat-pass MB acquisition; space-time signature; spatial spectral estimation; superresolution tomography; temporal decorrelation-robust SAR tomography; volumetric forest scattering dynamic; Decorrelation; Estimation; Frequency estimation; Multiple signal classification; Synthetic aperture radar; Tomography; Decorrelation; electromagnetic tomography; multidimensional signal processing; radar interferometry; spectral analysis; synthetic aperture radar (SAR) (ID#:14-2404) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6679227&isnumber=6756973

• Hao Fang; Vorobyov, S.A; Hai Jiang; Taheri, O., "Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals," Signal Processing, IEEE Transactions on , vol.62, no.1, pp.196,210, Jan.1, 2014. doi: 10.1109/TSP.2013.2284762 Traditional compressed sensing considers sampling a 1D signal. For a multidimensional signal, if reshaped into a vector, the required size of the sensing matrix becomes dramatically large, which increases the storage and computational complexity significantly. To solve this problem, the multidimensional signal is reshaped into a 2D signal, which is then sampled and reconstructed column by column using the same sensing matrix. This approach is referred to as parallel compressed sensing, and it has much lower storage and computational complexity. For a given reconstruction performance of parallel compressed sensing, if a so-called acceptable permutation is applied to the 2D signal, the corresponding sensing matrix is shown to have a smaller required order of restricted isometry property condition, and thus, lower storage and computation complexity at the decoder are required. A zigzag-scan-based permutation is shown to be particularly useful for signals satisfying the newly introduced layer model. As an application of the parallel compressed sensing with the zigzag-scan-based permutation, a video compression scheme is presented. It is shown that the zigzag-scan-based permutation increases the peak signal-to-noise ratio of reconstructed images and video frames. Keywords: compressed sensing; matrix algebra; parallel processing; 2D sparse signals; computational complexity; image reconstruction; isometry property; multidimensional signal; parallel compressed sensing; peak signal-to-noise ratio; sensing matrix; video compression scheme; video frames; zigzag scan based permutation; Compressed sensing; Computational complexity; Educational institutions; Image reconstruction; Sensors; Size measurement; Sparse matrices; Compressed sensing; multidimensional signal processing; parallel processing; permutation (ID#:14-2405) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6619412&isnumber=6678249

• Lyons, S.M.J.; Sarkka, S.; Storkey, AJ., "Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes," Signal Processing, IEEE Transactions on , vol.62, no.6, pp.1514,1524, March15, 2014. doi: 10.1109/TSP.2014.2303430 In this paper, we describe a novel application of sigma-point methods to continuous-discrete filtering. The nonlinear continuous-discrete filtering problem is often computationally intractable to solve. Assumed density filtering methods attempt to match statistics of the filtering distribution to some set of more tractable probability distributions. Filters such as these are usually decompose the problem into two sub-problems. The first of these is a prediction step, in which one uses the known dynamics of the signal to predict its state at time tk+1 given observations up to time tk. In the second step, one updates the prediction upon arrival of the observation at time tk+1. The aim of this paper is to describe a novel method that improves the prediction step. We decompose the Brownian motion driving the signal in a generalised Fourier series, which is truncated after a number of terms. This approximation to Brownian motion can be described using a relatively small number of Fourier coefficients, and allows us to compute statistics of the filtering distribution with a single application of a sigma-point method. Assumed density filters that exist in the literature usually rely on discretisation of the signal dynamics followed by iterated application of a sigma point transform (or a limiting case thereof). Iterating the transform in this manner can lead to loss of information about the filtering distribution in highly non-linear settings. We demonstrate that our method is better equipped to cope with such problems. Keywords: Fourier series; Kalman filters; approximation theory; iterative methods; nonlinear filters; statistical distributions; Brownian motion approximation; Fourier coefficients; assumed density filtering methods; assumed density filters; diffusion processes; generalised Fourier series; nonlinear Kalman filtering; nonlinear continuous-discrete filtering problem; series expansion approximations; sigma-point methods; signal dynamic discretisation; tractable probability distributions; Approximation methods; Differential equations; Kalman filters; Mathematical model; Noise; Stochastic processes; Transforms; Kalman filters; Markov processes; multidimensional signal processing; nonlinear filters (ID#:14-2406) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6728679&isnumber=6744712

• Xuefeng Liu; Bourennane, S.; Fossati, C., "Reduction of Signal-Dependent Noise From Hyperspectral Images for Target Detection," Geoscience and Remote Sensing, IEEE Transactions on , vol.52, no.9, pp.5396,5411, Sept. 2014. doi: 10.1109/TGRS.2013.2288525 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. Keywords: Wiener filters; geophysical image processing; hyperspectral imaging; image denoising; interference suppression; multidimensional signal processing; object detection; random noise;singular value decomposition; statistical analysis; tensors;HSI distortion; HYDICE; HYNE method ;MWF method; PARAFAC decomposition; PARAFACSD method; PARAFACSI method; SD noise removal; SI noise removal; airborne HSI; hybrid method; hyperspectral digital imagery collection experiment; hyperspectral image; hyperspectral noise estimation; image denoising; multidimensional Wiener filter; multilinear algebra model; noise variance; parallel factor analysis; parametric model; random noise component reduction; residual SD component reduction; signal dependent noise reduction; signal independent noise; statistical property; target detection; tensor decomposition-based method; Covariance matrices; Hyperspectral sensors; Noise; Noise reduction; Silicon; Tensile stress; Vectors;Denoising; PARAFAC; hyperspectral image (HSI);signal-dependent (SD) noise ;target detection (ID#:14-2407) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6675784&isnumber=6756973

• Xun Chen; Aiping Liu; McKeown, M.J.; Poizner, H.; Wang, Z.J., "An EEMD-IVA Framework for Concurrent Multidimensional EEG and Unidimensional Kinematic Data Analysis," Biomedical Engineering, IEEE Transactions on , vol.61, no.7, pp.2187,2198, July 2014. doi: 10.1109/TBME.2014.2319294 Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson's disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications. Keywords: biomechanics; blind source separation; data analysis; diseases; electroencephalography; kinematics; medical signal processing; multidimensional signal processing; numerical analysis; EEMD-IVA framework; Parkinson disease; concurrent multidimensional EEG; electroencephalogram; ensemble empirical mode decomposition; independent vector analysis ;joint blind source separation; kinematic data joint recording; multidimensional datasets; multiple datasets; numerical simulations; reaching movements; real-world biomedical signal processing applications; unidimensional kinematic data analysis; unidimensional kinematic datasets; Data analysis; Data mining;Electroencephalography;Joints;Kinematics;Noise;Vectors;Data fusion; EEG; EEMD; IVA; JBSS; unidimensional (ID#:14-2408) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6803885&isnumber=6835114

• Paskaleva, B.S.; Godoy, S.E.; Woo-Yong Jang; Bender, S.C.; Krishna, S.; Hayat, M.M., "Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks," Image Processing, IEEE Transactions on , vol.23, no.5, pp.2315,2327, May 2014. doi: 10.1109/TIP.2014.2315154 Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector. Keywords: data compression; edge detection; image colour analysis; infrared imaging; multidimensional signal processing; HySPADE edge detectors; MCG edge detector; airborne hyperspectral imager; data compression; dots-in-a-well midinfrared imager; edge signature; hyperspectral-spatial detection of edges; isoluminant edges; model based edge detector; multicolor gradient;s parse joint spatiospectral nonlinear operator; sparse spatiospectral masks; spatial mask; spectral band ratio; spectral imagery; Detectors; Gray-scale; Hyperspectral imaging; Image color analysis; Image edge detection; Materials; Standards; Edge detection; classification ;isoluminant edge; multicolor edge detection; spatio-spectral mask; spectral ratios (ID#:14-2409) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6781601&isnumber=6779706

• Kamislioglu, B.; Karaboga, N., "Design of FIR QMF Bank Using Windowing Functions," Signal Processing and Communications Applications Conference (SIU), 2014 22nd , vol., no., pp.95,99, 23-25 April 2014. doi: 10.1109/SIU.2014.6830174 The past over the years, single or multi-dimensional signal processing applications, communication systems, biomedical signal processing, word coding, sub-band coding in applications such as efficient use filter banks; single filter instead of multiple custom filters come together with being designed. In this study, two-channel filter banks a special case known as the QMF (Quadrature Mirror Filter - Quarter-mirror filter) bank for the design of Kaiser, Chebyshev and Hanning windowing methods with the filter's cutoff frequency on the optimization of a design based were made. QMF bank design, failure to peak reconstruction error (Peak Reconstruction Error-PRA) is based. As a result of the ongoing applications designed filter banks belonging to the numerical results and comparisons are given. Keywords: Chebyshev approximation; channel bank filters; quadrature mirror filters; Chebyshev methods ;FIR QMF bank design; Hanning windowing methods; Kaiser design; QMF bank design; biomedical signal processing; communication systems; design optimization; filter banks; filter cutoff frequency; multidimensional signal processing; peak reconstruction error; quadrature mirror filter quarter-mirror filter bank; subband coding; two-channel filter banks; windowing functions; word coding; Chebyshev approximation; Conferences; Encoding; Filter banks; Finite impulse response filters; Mirrors (ID#:14-2410) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6830174&isnumber=6830164

• Deyun Wei; Yuanmin Li, "Reconstruction of Multidimensional Bandlimited Signals From Multichannel Samples In Linear Canonical Transform Domain," Signal Processing, IET , vol.8, no.6, pp.647,657, August 2014. doi: 10.1049/iet-spr.2013.0240 The linear canonical transform (LCT) has been shown to be a powerful tool for optics and signal processing. In this study, the authors address the problem of signal reconstruction from the multidimensional multichannel samples in the LCT domain. Firstly, they pose and solve the problem of expressing the kernel of the multidimensional LCT in the elementary functions. Then, they propose the multidimensional multichannel sampling (MMS) for the bandlimited signal in the LCT domain based on a basis expansion of an exponential function. The MMS expansion which is constructed by the ordinary convolution structure can reduce the effect of the spectral leakage and is easy to implement. Thirdly, based on the MMS expansion, they obtain the reconstruction method for the multidimensional derivative sampling and the periodic non-uniform sampling by designing the system filter transfer functions. Finally, the simulation results and the potential applications of the MMS are presented. Especially, the application of the multidimensional derivative sampling in the context of the image scaling about the image super-resolution is discussed. Keywords: signal processing ;transforms; LCT; MMS; bandlimited signal; image scaling; image super resolution ;linear canonical transform domain; multichannel samples; multidimensional bandlimited signal reconstruction; multidimensional multichannel samples; multidimensional multichannel sampling; optics processing; signal processing; transfer functions (ID#:14-2411) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6869171&isnumber=6869162

• Wen-Long Chin; Chun-Wei Kao; Hsiao-Hwa Chen; Teh-Lu Liao, "Iterative Synchronization-Assisted Detection of OFDM Signals in Cognitive Radio Systems," Vehicular Technology, IEEE Transactions on , vol.63, no.4, pp.1633,1644, May 2014. doi: 10.1109/TVT.2013.2285389 Despite many attractive features of an orthogonal frequency-division multiplexing (OFDM) system, the signal detection in an OFDM system over multipath fading channels remains a challenging issue, particularly in a relatively low signal-to-noise ratio (SNR) scenario. This paper presents an iterative synchronization-assisted OFDM signal detection scheme for cognitive radio (CR) applications over multipath channels in low-SNR regions. To detect an OFDM signal, a log-likelihood ratio (LLR) test is employed without additional pilot symbols using a cyclic prefix (CP). Analytical results indicate that the LLR of received samples at a low SNR can be approximated by their log-likelihood (LL) functions, thus allowing us to estimate synchronization parameters for signal detection. The LL function is complex and depends on various parameters, including correlation coefficient, carrier frequency offset (CFO), symbol timing offset, and channel length. Decomposing a synchronization problem into several relatively simple parameter estimation subproblems eliminates a multidimensional grid search. An iterative scheme is also devised to implement a synchronization process. Simulation results confirm the effectiveness of the proposed detector. Keywords: OFDM modulation; cognitive radio; fading channels; iterative methods; multipath channels; parameter estimation; signal detection; synchronisation; LLR; OFDM signal detection; SNR; carrier frequency offset; cognitive radio systems; correlation coefficient; cyclic prefix ;iterative synchronization; log likelihood functions ;log-likelihood ratio; multidimensional grid search; multipath channels; multipath fading channels; orthogonal frequency division multiplexing; parameter estimation subproblems; signal-to-noise ratio; synchronization problem; Correlation; Detectors; OFDM; Signal to noise ratio; Synchronization; Cognitive radio; Cognitive radio (CR);cyclic prefix; cyclic prefix (CP); orthogonal frequency-division multiplexing; orthogonal frequency-division multiplexing (OFDM); synchronization (ID#:14-2412) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6627985&isnumber=6812142

• Alvarez-Perez, J.L., "A Multidimensional Extension of the Concept of Coherence in Polarimetric SAR Interferometry," Geoscience and Remote Sensing, IEEE Transactions on, vol.PP, no.99, pp.1, 14, July 2014. doi: 10.1109/TGRS.2014.2336805 Interferometric synthetic aperture radar (InSAR) is a phase-based radar signal processing technique that has been addressed from a polarimetric point of view since the late 1990s, starting with Cloude and Papathanassiou's foundational work. Polarimeric InSAR (PolInSAR) has consolidated as an active field of research in parallel to non-PolInSAR. Regarding the latter, there have been a number of issues that were discussed in an earlier paper from which some other questions related to Cloude's PolInSAR come out naturally. In particular, they affect the usual understanding of coherence and statistical independence. Coherence involves the behavior of electromagnetic waves in at least a pair of points, and it is crucially related to the statistical independence of scatterers in a complex scene. Although this would seem to allow PolInSAR to overcome the difficulties involving the controversial confusion between statistical independence and polarization as present in PolSAR, Cloude's PolInSAR originally inherited the idea of separating physical contributors to the scattering phenomenon through the use of singular values and vectors. This was an assumption consistent with Cloude's PolSAR postulates that was later set aside. We propose the introduction of a multidimensional coherence tensor that includes PolInSAR's polarimetric interferometry matrix $Omega_{12}$ as its 2-D case. We show that some important properties of the polarimetric interferometry matrix are incidental to its bidimensionality. Notably, this exceptional behavior in 2-D seems to suggest that the singular value decomposition (SVD) of $Omega_{12}$ does not provide a physical insight into the scattering problem in the sense of splitting different scattering contributors. It might be argued that Cloude's PolInSAR in its current form does not rely on the SVD of $Omega_{12}$ but on other underlying optimization sch- mes. The drawbacks of such ulterior developments and the failure of the maximum coherence separation procedure to be a consistent scheme for surface topography estimation in a two-layer model are discussed in depth in this paper. Nevertheless, turning back to the SVD of $Omega_{12}$, the use of the singular values of a prewhitened version of $Omega_{12}$ is consistent with a leading method of characterizing coherence in modern Optics. For this reason, the utility of the SVD of $Omega_{12}$ as a means of characterizing coherence is analyzed here and extended to higher dimensionalities. Finally, these extensions of the concept of coherence to the multidimensional case are tested and compared with the 2-D case by numerically simulating the scattered electromagnetic field from a rough surface. Keywords: Coherence; Interferometry; Matrix decomposition; Tensile stress; Vectors; Coherence; electromagnetic scattering; polarimetric synthetic aperture radar interferometry (PolInSAR)} (ID#:14-2413) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6868983&isnumber=4358825

• Di Franco, Carmelo; Franchino, Gianluca; Marinoni, Mauro, "Data Fusion For Relative Localization Of Wireless Mobile Nodes," Industrial Embedded Systems (SIES), 2014 9th IEEE International Symposium on, vol., no., pp.58,65, 18-20 June 2014. doi: 10.1109/SIES.2014.6871187 Monitoring teams of mobile nodes is becoming crucial in a growing number of activities. When it is not possible to use fix references or external measurements, a practicable solution is to derive relative positions from local communication. In this work, we propose an anchor-free Received Signal Strength Indicator (RSSI) method aimed at small multi-robot teams. Information from Inertial Measurement Unit (IMU) mounted on the nodes and processed with a Kalman Filter are used to estimate the robot dynamics, thus increasing the quality of RSSI measurements. A Multidimensional Scaling algorithm is then used to compute the network topology from improved RSSI data provided by all nodes. A set of experiments performed on data acquired from a real scenario show the improvements over RSSI-only localization methods. With respect to previous work only an extra IMU is required, and no constraints are imposed on its placement, like with camera-based approaches. Moreover, no a-priori knowledge of the environment is required and no fixed anchor nodes are needed. Keywords: Accuracy; Channel models; Covariance matrices; Equations; Estimation; Mobile nodes; Sensors (ID#:14-2414) URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6871187&isnumber=6871170

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