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Multidimensional Signal Processing

2015 (Part 1)


Multidimensional signal processing research deals with issues such as those arising in automatic target detection and recognition, 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 presented in 2015.

A. Helal and F. Balasa, “Multithreaded Signal-to-Memory Mapping Algorithm for Embedded Multidimensional Signal Processing,” 2015 20th International Conference on Control Systems and Computer Science, Bucharest, 2015, pp. 255-260. doi: 10.1109/CSCS.2015.22
Abstract: Many signal processing systems, particularly in the multimedia and telecommunication domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are multidimensional arrays. This paper proposes a memory management algorithm for mapping multidimensional signals (arrays) to physical memory blocs. The advantages of this novel technique are the following: (a) it can be applied to multilayer memory hierarchies, which makes it particularly useful in embedded systems design, (b) it provides metrics of quality for the overall memory allocation solution: the minimum data storage of each multidimensional signal in the behavioral specification (therefore, the optimal memory sharing between the elements of same arrays), as well as the minimum data storage for the entire specification (therefore, the optimal memory sharing between all the array elements and scalars in the code), (c) it is well-suited to a dynamic multithreading implementation, which makes it computationally efficient.
Keywords: multi-threading; signal processing; storage management; embedded multidimensional signal processing; high-level programming language; memory allocation; memory management algorithm; memory sharing; multidimensional signal mapping; multilayer memory hierarchy; multimedia domain; multithreaded signal-to-memory mapping algorithm; telecommunication domain; Arrays; Hardware; Indexes; Lattices; Memory management; Signal processing algorithms; dynamic multithreading; memory management; memory mapping; multidimensional signal processing (ID#: 16-9760)


F. Balasa, N. Abuaesh, C. V. Gingu and H. Zhu, “Optimization of Memory Banking in Embedded Multidimensional Signal Processing Applications,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015, pp. 2880-2883. doi: 10.1109/ISCAS.2015.7169288
Abstract: Hierarchical memory organizations are used in embedded systems to reduce energy consumption and improve performance by assigning the frequently-accessed data to the low levels of memory hierarchy. Within a given level of hierarchy, energy and access times can be further reduced by memory banking. This paper addresses the problem of banking optimization, presenting a dynamic programming approach that takes into account all three major design objectives — energy consumption, performance, and die area, letting the designers decide on their relative importance for a specific project. The time complexity is independent of the size of the storage access trace and of the memory size — a significant advantage in terms of computation speed when these two parameters are large.
Keywords: DRAM chips; cache storage; dynamic programming; signal processing; computation speed; die area; dynamic programming approach; embedded multidimensional signal processing applications; embedded systems; energy consumption reduction; frequently-accessed data assignment; hierarchical memory organizations; low memory hierarchy levels; memory banking optimization; memory size; off-chip DRAM; on-chip SPM; performance improvement; storage access trace; time complexity; Arrays; Banking; Dynamic programming; Energy consumption; Lattices; Memory management; Signal processing algorithms (ID#: 16-9761)


A. Madanayake, C. Wijenayake, Z. Lin and N. Dornback, “Recent Advances in Multidimensional Systems and Signal Processing: An Overview,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015, pp. 2365-2368. doi: 10.1109/ISCAS.2015.7169159
Abstract: In this paper, we present an overview of recent advances in multidimensional (MD) systems and signal processing. We focus on topics closely related to the four papers selected into the special session of “recent advances in multidimensional systems and signal processing” at ISCAS 2015. The paper starts with an overview of the theory of MD IIR digital filters and its applications ranging from image/videos of light-fields to microwave and mm-wave antenna array processing. State-space formulation for the realization of MD IIR notch filters is also discussed as applicable to image processing scenarios. Thereafter, new developments in visual tomography-based imaging systems that exploit MD signal processing towards safety and health applications are discussed. The paper also reviews new theoretical developments in modeling of physical systems. Recent advances in MD Kirchhoff circuit realizations for some physical systems including finite speed heat diffusion are reviewed.
Keywords: IIR filters; image processing; microwave antenna arrays; millimetre wave antenna arrays; notch filters; ISCAS 2015; MD IIR digital filters; MD Kirchhoff circuit realizations; MD signal processing; image processing scenarios; microwave antenna array processing; mm-wave antenna array processing; multidimensional systems; visual tomography-based imaging systems (ID#: 16-9762)


A. Madanayake, C. Wijenayake, L. Belostotski and L. T. Bruton, “An Overview of Multi-Dimensional RF Signal Processing for Array Receivers,” Moratuwa Engineering Research Conference (MERCon), 2015, Moratuwa, 2015, pp. 255-259. doi: 10.1109/MERCon.2015.7112355
Abstract: In this review paper, recent advancements in multidimensional (MD) spatio-temporal signal processing for highly-directional radio frequency (RF) antenna array based receivers are discussed. MD network-resonant beamforming filters having infinite impulse response (IIR) and recursive spatio-temporal signal flow graphs are reviewed. The concept of MD network-resonant pre-filtering is described as a modification to existing phased/timed array beamforming back-ends to achieve improved side-lobe performance in the array pattern, leading to better interference rejection capabilities. Both digital and analog signal processing models are described in terms of their system transfer functions and signal flow graphs. Example MD frequency response and RF antenna array pattern simulations are presented.
Keywords: IIR filters; antenna phased arrays; antenna radiation patterns; array signal processing; directive antennas; multidimensional signal processing; radio receivers; radiofrequency interference; recursive filters; signal flow graphs; spatiotemporal phenomena; IIR; MD network-resonant beamforming filter; MD spatiotemporal signal processing; RF antenna array based receiver; analog signal processing; digital signal processing; highly-directional radio frequency antenna array; infinite impulse response; interference rejection capability; multidimensional RF signal processing; recursive spatio-temporal signal flow graph; side-lobe performance; transfer function; Array signal processing; Arrays; Frequency response; Passband; Radio frequency; Receivers; Transfer functions; Multidimensional Signal Processing; Phased Arrays (ID#: 16-9763)


T. Randeny, A. Madanayake, A. Sengupta, Y. Li and C. Li, “Aperture-Array Directional Sensing Using 2-D Beam Digital Filters with Doppler-Radar Front-Ends,” Moratuwa Engineering Research Conference (MERCon), 2015, Moratuwa, 2015, pp. 265-270. doi: 10.1109/MERCon.2015.7112357
Abstract: A directional sensing algorithm is proposed employing doppler radar and low-complexity 2-D IIR spatially bandpass filters. The speed of the scatterer is determined by the frequency shift of the received signal following down-conversion. The downconversion is done by mixing it with the instantaneous transmitted signal. The direction of the scatterer is determined by the means of 2-D plane-wave spectral characteristics, using 2-D IIR beam filters. The proposed architecture was simulated for three scatterers at 10,° 30,° 60° from array broadside, traveling at speeds of 31 ms,-1 18 ms-1 and 27 ms -1, respectively. A doppler radar module was used to transmit and receive reflected signals, that has a carrier frequency of 2.4 GHz. Simulations show both direction and doppler information being enhanced.
Keywords: Doppler radar; IIR filters; UHF antennas; UHF filters; antenna arrays; aperture antennas; array signal processing; band-pass filters; directive antennas; radar antennas; radar signal processing; spatial filters; spectral analysis; two-dimensional digital filters; 2D IIR spatially band-pass filter; 2D beam digital filter; Doppler radar front-end; aperture-array directional sensing; array broadside; down conversion; scatterer direction; Arrays; Radar antennas; Radio frequency; Receivers; Sensors; 2-D IIR digital filters; Multidimensional signal processing; cyberphysical systems; directional sensing; doppler; radar (ID#: 16-9764)


M. Barjenbruch, F. Gritschneder, K. Dietmayer, J. Klappstein and J. Dickmann, “Memory Efficient Spectral Estimation on Parallel Computing Architectures,” Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015 IEEE, Salt Lake City, UT, 2015, pp. 337-340. doi: 10.1109/DSP-SPE.2015.7369576
Abstract: A method for spectral estimation is proposed. It is based on the multidimensional extensions of the RELAX algorithm. The fast Fourier transform is replaced by multiple Chirp-Z transforms. Each transform has a much shorter length than the transform in the original algorithm. This reduces the memory requirements significantly. At the same time a high degree of parallelism is preserved. A detailed analysis of the computational requirements is given. Finally, the proposed method is applied to automotive radar measurements. It is shown, that the multidimensional spectral estimation resolves multiple scattering centers on an extended object.
Keywords: Z transforms; estimation theory; fast Fourier transforms; parallel architectures; RELAX algorithm; fast Fourier transform; memory efficient spectral estimation; multidimensional extensions; multidimensional spectral estimation; multiple Chirp-Z transforms; multiple scattering centers; parallel computing architecture; Chirp; Estimation; Memory management; Signal processing algorithms; Transforms; Yttrium; Multidimensional signal processing; harmonic analysis; parallel algorithms; parameter estimation (ID#: 16-9765)


T. A. Palka and R. J. Vaccaro, “Asymptotically Efficient Estimators for Multidimensional Harmonic Retrieval Based on the Geometry of the Stiefel Manifold,” 2015 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2015, pp. 1691-1695. doi: 10.1109/ACSSC.2015.7421437
Abstract: A variety of signal processing applications require multidimensional harmonic retrieval on regular arrays and R-dimensional subspace-based methods (e.g; R-D Unitary ESPRIT ) are often used for this task. The conventional subspace estimation step via an SVD is the MLE for the unconstrained assumption but is suboptimal here since the harmonic signal structure or constraint is not exploited. Subspace estimation methods such as F/B averaging, HO-SVD, and SLS, which make use of the structure to varying degrees, yield improved performance but remain suboptimal in the sense that they do not satisfy a maximum likelihood criterion. Using a modified complex Stiefel manifold for the domain of the likelihood function we derive a quadratic ML criterion with a geometric constraint for the R- dimensional problem. This constraint is expressed in terms of the tangent space of an appropriate submanifold. For the special case when the submanifold satisfies a shift-invariance condition, we present a stand-alone estimation algorithm that computes the submanifold tangent space as the null space of a matrix that represents the linearized form of a geometric constraint. The estimator's performance is compared to existing approaches and to the intrinsic subspace CRB for highly stressful scenarios of closely spaced and highly correlated sources.
Keywords: maximum likelihood estimation; multidimensional signal processing; singular value decomposition; F/B averaging; R-dimensional problem; R-dimensional subspace; SVD; asymptotically efficient estimators; geometric constraint; harmonic signal structure; intrinsic subspace CRB; likelihood function; maximum likelihood criterion; modified complex Stiefel manifold; multidimensional harmonic retrieval; quadratic ML criterion; regular arrays; shift-invariance condition; signal processing; stand-alone estimation; subspace estimation; Covariance matrices; Geometry; Harmonic analysis; Manifolds; Maximum likelihood estimation; Niobium
(ID#: 16-9766)


R. Gierlich, “Joint Estimation of Spatial and Motional Radar Target Parameters by Multidimensional Spectral Analysis,” 2015 16th International Radar Symposium (IRS), Dresden, 2015, pp. 95-101. doi: 10.1109/IRS.2015.7226282
Abstract: In this paper we investigate the suitability of modern multidimensional spectral analysis techniques for the estimation of radar target parameters. A short survey of both classical and alternative methods is given. The latter comprise high- and superresolution spectral analysis techniques like 2D-MUSIC, 2D-Minimum Variance, and recently published 2D-AR spectrum estimators. The performance characteristics are evaluated by systematic benchmarks, including spectral resolution, high signal dynamics, and multi-tone capacity. Field trials with a military C-band surveillance radar demonstrate the high potential of the alternative multidimensional spectrum estimators under real-world conditions with regard to the joint estimation of spatial and motional radar target parameters.
Keywords: military radar; multidimensional signal processing; parameter estimation; radar resolution; search radar; high signal dynamics; high-resolution spectral analysis technique; military C-band surveillance radar; multidimensional spectral analysis; multitone capacity; spatial and motional radar target parameter joint estimation; spectral resolution; superresolution spectral analysis technique; Benchmark testing; Discrete Fourier transforms; Multiple signal classification; Signal resolution; Signal to noise ratio; Spectral analysis (ID#: 16-9767)


J. Zhu, J. J. Bellanger, H. Shu and R. Le Bouquin Jeannès, “Investigating Bias in Non-Parametric Mutual Information Estimation,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015,
pp. 3971-3975. doi: 10.1109/ICASSP.2015.7178716
Abstract: In this paper, our aim is to investigate the control of bias accumulation when estimating mutual information from nearest neighbors non-parametric approach with continuously distributed random data. Using a multidimensional Taylor series expansion, a general relationship between the estimation bias and neighborhood size for plug-in entropy estimator is established without any assumption on the data for two different norms. When applied with the maximum norm, our theoretical analysis explains experimental simulation tests drawn in existing literature. In the experiments, two different strategies are tested and compared to estimate mutual information on independent and dependent simulated signals.
Keywords: entropy; multidimensional signal processing; nonparametric statistics; series (mathematics); bias accumulation control; multidimensional Taylor series expansion; nearest neighbors nonparametric approach; nonparametric mutual information estimation; plug-in entropy estimator; Approximation methods; Entropy; Estimation; Joints; Mutual information; Random variables; Entropy estimation; bias reduction; independence test; mutual information (ID#: 16-9768)


S. Gupta and C. Caloz, “Multi-Dimensional Real-Time Spectrum Analysis for High-Resolution Signal Processing,” Electromagnetics in Advanced Applications (ICEAA), 2015 International Conference on, Turin, 2015, pp. 1412-1415. doi: 10.1109/ICEAA.2015.7297351
Abstract: Two types of RTSAs are presented, which spectrally decompose a broadband electromagnetic signal in space in real-time. The first system is based on an array of LWA fed using an array of phasers, where the dispersion of the phasers in conjunction with the natural frequency scanning of the leaky-wave antenna enable the 2-D frequency scan. The second system is a purely spatial system based on dispersive metasurface which operate on a incident wave and performs 2-D spectral decomposition. Their principle and basic characteristics are discussed in details.
Keywords: antenna arrays; leaky wave antennas; multidimensional signal processing; spectral analysis; 2D frequency scan; 2D spectral decomposition; LWA; broadband electromagnetic signal; dispersive metasurface; high-resolution signal processing; Incident wave; leaky-wave antenna; multidimensional real-time spectrum analysis; natural frequency scanning; purely spatial system; Arrays; Broadband antennas; Broadband communication; Dispersion; Leaky wave antennas; Real-time systems; Spectral analysis
(ID#: 16-9769)


Yuanxin Li, Yingsheng He, Yuejie Chi and Y. M. Lu, “Blind Calibration of Multi-Channel Samplers Using Sparse Recovery,” Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 IEEE 6th International Workshop on, Cancun, 2015, pp. 33-36. doi: 10.1109/CAMSAP.2015.7383729
Abstract: We propose an algorithm for blind calibration of multi-channel samplers in the presence of unknown gains and offsets, which is useful in many applications such as multi-channel analog-to-digital converters, image super-resolution, and sensor networks. Using a subspace-based rank condition developed by Vandewalle et al., we obtain a set of linear equations with respect to complex harmonics whose frequencies are determined by the offsets, and the coefficients of each harmonic are determined by the discrete-time Fourier transforms of outputs of each of the channels. By discretizing the offsets over a fine grid, this becomes a sparse recovery problem where the signal of interest is sparse with an additional structure, that in each block there is only one nonzero entry. We propose a modified CoSaMP algorithm that takes this structure into account to estimate the offsets. Our algorithm is scalable to large numbers of channels and can also be extended to multi-dimensional signals. Numerical experiments demonstrate the effectiveness of the proposed algorithm.
Keywords: Fourier transforms; analogue-digital conversion; calibration; compressed sensing; image resolution; multidimensional signal processing; signal sampling; CoSaMP algorithm; blind calibration; complex harmonics; discrete-time Fourier transforms; image superresolution; linear equations; multichannel analog-to-digital converters; multichannel samplers; multidimensional signals; sensor networks; sparse recovery problem; subspace-based rank condition; Calibration; Conferences; Discrete Fourier transforms; Harmonic analysis; Image resolution; Signal resolution; Yttrium; CoSaMP; multi-channel sampling; sparse recovery (ID#: 16-9770)


S. Ono, I. Yamada and I. Kumazawa, “Total Generalized Variation for Graph Signals,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015, pp. 5456-5460. doi: 10.1109/ICASSP.2015.7179014
Abstract: This paper proposes a second-order discrete total generalized variation (TGV) for arbitrary graph signals, which we call the graph TGV (G-TGV). The original TGV was introduced as a natural higher-order extension of the well-known total variation (TV) and is an effective prior for piecewise smooth signals. Similarly, the proposed G-TGV is an extension of the TV for graph signals (G-TV) and inherits the capability of the TGV, such as avoiding staircasing effect. Thus the G-TGV is expected to be a fundamental building block for graph signal processing. We provide its applications to piecewise-smooth graph signal inpainting and 3D mesh smoothing with illustrative experimental results.
Keywords: graph theory; multidimensional signal processing; piecewise constant techniques; smoothing methods; 3D mesh smoothing; G-TGV; graph TGV; graph signal processing; piecewise-smooth graph signal inpainting; second-order discrete total generalized variation; Convex functions; Graph theory; Optimization; Signal processing; Smoothing methods; TV; Three-dimensional displays; Graph signal processing; proximal splitting; total generalized variation (TGV) (ID#: 16-9771)


M. A. Sedaghat and R. Müller, “Multi-Dimensional Continuous Phase Modulation in Uplink of MIMO Systems,” Signal Processing Conference (EUSIPCO), 2015 23rd European, Nice, 2015, pp. 2446-2450. doi: 10.1109/EUSIPCO.2015.7362824
Abstract: Phase Modulation on the Hypersphere (PMH) is considered in which the instantaneous sum power is constant. It is shown that for an i.i.d. Gaussian channel, the capacity achieving input distribution is approximately uniform on a hypersphere when the number of receive antennas is much larger than the number of transmit antennas. Moreover, in the case that channel state information is not available at the transmitter, it is proven that the capacity achieving input distribution is exactly uniform on a hypersphere. Mutual information between input and output of PMH with discrete constellation for an i.i.d. Gaussian channel is evaluated numerically. Furthermore, a spherical spectral shaping method for PMH is proposed to have Continuous Phase Modulation on the Hypersphere (CPMH). In CPMH, the continuous time signal has a constant instantaneous sum power. It is shown that using a spherical low pass filter in spherical domain followed by a Cartesian filter results in very good spectral properties.
Keywords: Gaussian channels; MIMO communication; antenna arrays; continuous phase modulation; low-pass filters; multidimensional signal processing; receiving antennas; transmitting antennas; CPMH; Cartesian filter; MIMO systems; channel state information; continuous time signal; hypersphere; i.i.d. Gaussian channel; multidimensional continuous phase modulation; mutual information; receive antennas; spectral properties; spherical domain; spherical low pass filter; spherical spectral shaping method; transmit antennas; MIMO; Mutual information; Peak to average power ratio; Pulse shaping methods; Radio transmitters; Receiving antennas; Phase modulation; continuous phase modulation (CPM); multiple-input multiple-output (MIMO) systems; peak-to-average power ratio (PAPR); single-RF transmitters; spherical filtering (ID#: 16-9772)


A. K. Bhoi, K. S. Sherpa, D. Phurailatpam, J. S. Tamang and P. K. Giri, “Multidimensional Approaches for Noise Cancellation of ECG Signal,” Communications and Signal Processing (ICCSP), 2015 International Conference on, Melmaruvathur, 2015,
pp. 0066-0070. doi: 10.1109/ICCSP.2015.7322569
Abstract: In many situations, the Electrocardiogram (ECG) is recorded during ambulatory or strenuous conditions such that the signal is corrupted by different types of noise, sometimes originating from another physiological process of the body. Hence, noise removal is an important aspect of signal processing. Here five different filters i.e. median, Low Pass Butter worth, FIR, Weighted Moving Average and Stationary Wavelet Transform (SWT) with their filtering effect on noisy ECG are presented. Comparative analyses among these filtering techniques are described and statically results are evaluated.
Keywords: electrocardiography; medical signal processing; multidimensional signal processing; signal denoising; wavelet transforms; ECG signal; FIR; ambulatory conditions; electrocardiogram; filtering techniques; low-pass butter; multidimensional approaches; noise cancellation; physiological process; signal processing; stationary wavelet transform; strenuous conditions; weighted moving average; Discrete wavelet transforms; Finite impulse response filters; Noise measurement; Active noise reduction; Electrocardiography; Filters; Noise cancellation (ID#: 16-9773)


I. Dokmanić, J. Ranieri and M. Vetterli, “Relax and Unfold: Microphone Localization with Euclidean Distance Matrices,” Signal Processing Conference (EUSIPCO), 2015 23rd European, Nice, 2015, pp. 265-269. doi: 10.1109/EUSIPCO.2015.7362386
Abstract: Recent methods for microphone position calibration work with sound sources at a priori unknown locations. This is convenient for ad hoc arrays, as it requires little additional infrastructure. We propose a flexible localization algorithm by first recognizing the problem as an instance of multidimensional unfolding (MDU) - a classical problem in Euclidean geometry and psychometrics - and then solving the MDU as a special case of Euclidean distance matrix (EDM) completion. We solve the EDM completion using a semidefinite relaxation. In contrast to existing methods, the semidefinite formulation allows us to elegantly handle missing pairwise distance information, but also to incorporate various prior information about the distances between the pairs of microphones or sources, bounds on these distances, or ordinal information such as “microphones 1 and 2 are more apart than microphones 1 and 15”. The intuition that this should improve the localization performance is justified by numerical experiments.
Keywords: acoustic generators; acoustic signal processing; calibration; mathematical programming; matrix algebra; microphone arrays; multidimensional signal processing; source separation; EDM completion; Euclidean distance matrices; Euclidean geometry; MDU; ad hoc arrays; flexible localization algorithm; localization performance improvement; microphone localization; microphone position calibration; missing pairwise distance information handling; multidimensional unfolding; psychometrics; semidefinite relaxation; sound sources; Calibration; Euclidean distance; Europe; Geometry; Microphones; Noise measurement; Symmetric matrices; Euclidean distance matrix; Microphone localization; array calibration; microphone array  (ID#: 16-9774)


A. Pedrouzo-Ulloa, J. R. Troncoso-Pastoriza and F. Pérez-González, “Multivariate Lattices for Encrypted Image Processing,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015, pp. 1707-1711. doi: 10.1109/ICASSP.2015.7178262
Abstract: Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.
Keywords: cryptography; image processing; telecommunication security; cryptographic hard problem; encrypted image processing; lattice cryptosystem; m-RLWE; multidimensional signal processing; multivariate lattice; multivariate ring learning with error; privacy-preserving solution; Ciphers; Encryption; Image processing; Lattices; Polynomials; Homomorphic Processing; Image Encryption; Lattice Cryptography; Security (ID#: 16-9775)


A. Madanayake, “Keynote Address: Multi-Dimensional RF Signal-Processing and Analog/Digital Circuits,” Moratuwa Engineering Research Conference (MERCon), 2015, Moratuwa, Sri Lanka, 2015, pp. xxi-xxii. doi: 10.1109/MERCon.2015.7112306
Abstract: The Advanced Signal Processing Circuits (ASPC) laboratory at the University of Akron was established in 2010, to conduct basic research involving antenna array signal processing, reconfigurable systems, and multidimensional filters. The target applications span communications, cognitive radio, radio astronomy, microwave imaging, and radar. In this talk, we will discuss our areas of investigation, starting with an overview of the impending spectral scarcity problem. The accelerating growth of wireless systems is rapidly leading to scarcity of electromagnetic spectral bandwidth. The spectrum is a finite natural resource that is subject to oversubscription. Cognitive radio (CR) is an approach for mitigating spectral scarcity. In a CR, situational awareness is provided to a wireless network, allowing intelligent decisions on the use of electromagnetic spectral resources without being limited by licensing schemes. The technologies and approaches that would enable an unprecedented increase in wireless system capacity, data rates and connectivity is known as the 1000 x Game.
Keywords:  (not provided) (ID#: 16-9776)


W. Hong, “Research Progress on Multidimensional Space Joint-Observation SAR,” 2015 40th International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz), Hong Kong, 2015, pp. 1-1. doi: 10.1109/IRMMW-THz.2015.7327714
Abstract: Summary form only given. With the application requirement and technology development, the necessity and tendency of Synthetic Aperture Radar (SAR) imaging within the framework of multidimensional space joint-observation, which are polarimetry, frequency, angle, time series and etc., evoke catholic interests in SAR imaging research nowadays. Recent research progress on the Multidimensional Space Joint-observation SAR (MSJosSAR) in the National Key Lab of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of, Sciences(MITL-IECAS) is reported in this talk, where the a sphere cluster cordinate system is defined as the modeling basis on the demand of information fusion for SAR multidimensional space joint-observation. Further more, the advantage of MSJosSAR is revealed by using Kronecker product decomposition for better understanding of target scattering mechanisms, with the hypothesis and basic framework on which the MSJosSAR signal processing relies on. Tentative studies on multi-layer material with PolinSAR technique, anisotropic scattering mechanisms with multi-directional observation (cuverture or circular SAR technique), and instantaneous time-variant target with array SAR technique are demonstrated as the initial verification of the above defined hypothesis and framework. Finally, the value of joint observation space numbers for typical SAR configurations is enumerated, followed by the perspective discussions on the future work for MSJosSAR study.
Keywords: decomposition; image fusion; radar imaging; radar polarimetry; spaceborne radar; synthetic aperture radar; Chinese Academy of Sciences  Institute of Electronics; Kronecker product decomposition; MITL-IECAS; MSJosSAR imaging; National Key Lab of Microwave Imaging Technology; PolinSAR technique; anisotropic scattering mechanisms; circular SAR technique; cuverture SAR technique; instantaneous time-variant target; multidimensional space joint-observation SAR imaging; multidirectional observation; multilayer material; polarimetry; signal processing; sphere cluster cordinate system; synthetic aperture radar imaging; target scattering mechanism; time series; Aerospace electronics; Microwave imaging; Microwave theory and techniques; Radar imaging; Scattering; Synthetic aperture radar (ID#: 16-9777)


W. Yi, Z. Wang and J. Wang, “Multiple Antenna Wireless Communication Signal Blind Recovery Based on PARAFAC Decomposition,” Network and Information Systems for Computers (ICNISC), 2015 International Conference on, Wuhan, 2015, pp. 137-140. doi: 10.1109/ICNISC.2015.13
Abstract: Multiple antenna signals in wireless communication system form into a kind of multiple dimension array signal from the view of time, frequency and space factors which can be modelled and processed by tensor analysis method. This paper focuses on the application of PARAFAC-based tensor decomposition method in solving the problem of signal blind recovery in MIMO-OFDM wireless communication system. The received signal of MIMO-OFDM system can be viewed as a multidimensional array. PARAFAC decomposition is applied for blind recovery of the received signal with unknown CSI (Channel State Information) and CFO (Carrier Frequency Offset). Simulation results show that the proposed scheme performs better under high SNR and small symbol collection, which turns out to verify the blind recovery method based on the PARAFAC decomposition.
Keywords: MIMO communication; OFDM modulation; antenna arrays; array signal processing; decomposition; radio networks; tensors; wireless channels; CFO; CSI; MIMO-OFDM wireless communication system; PARAFAC-based tensor decomposition method; SNR; carrier frequency offset; channel state information; multiple antenna wireless communication signal blind recovery; multiple dimension array signal; MIMO; OFDM; Receiving antennas; Tensile stress; Transmitting antennas; Wireless communication; PARAFAC decomposition; blind recovery; multiple antenna; wireless communication (ID#: 16-9778)


J. Velten, A. Kummert, A. Gavriilidis and F. Boschen, “2-D Signal Theoretic Investigation of Background Elimination in Visual Tomographic Reconstruction for Safety and Enabling Health Applications,” 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, 2015, pp. 2377-2380.  doi: 10.1109/ISCAS.2015.7169162
Abstract: Visual tomography is a relatively new method for 3D scene reconstruction. It is adopted from medical tomography and based on multiple images from different viewpoints of a scene. In this context, multidimensional spectra and filtering techniques are the key technology for the reconstruction process. Visual tomography differs from classical tomography in several aspects which leads to new challenges with respect to mathematical description. The present paper examines the influence of image background on reconstruction quality. This background problem does not appear in classical medical tomography applications. In particular, the influence of multidimensional sampling and restrictions with respect to the number of view angles can be analyzed by using multidimensional signal theoretical concepts. The differences between ideal (no background) and real acquisition conditions are examined. Visual tomography has the potential for innovative new fields of applications, where Enabling Technologies for Societal Challenges are the focus of our considerations. Demographic change leads to a high interest for enabling mobility for elderly people with physical disabilities. Walking frames equipped with such technologies will be able to assist such people in real day environments.
Keywords: computerised tomography; image reconstruction; mathematical analysis; medical image processing; 2D signal theoretic investigation; 3D scene reconstruction; background elimination; classical medical tomography applications; elderly people; filtering techniques; health applications; mathematical description; medical tomography; multidimensional spectra; physical disabilities; reconstruction process; reconstruction quality; visual tomographic reconstruction; Biomedical imaging; Cameras; Image reconstruction; Legged locomotion; Radio frequency; Tomography; Visualization (ID#: 16-9779)


M. Cvijetic and I. B. Djordjevic, “Multidimensional Aspects of Ultra High Speed Optical Networking,” 2015 17th International Conference on Transparent Optical Networks (ICTON), Budapest, 2015, pp. 1-4. doi: 10.1109/ICTON.2015.7193472
Abstract: Multidimensional approach in optical channel construction and parallelization in signal processing are the key factors in enabling high spectral efficiency of optical transmission links and the overall throughput increase in optical networks. Multidimensionality is mainly related to employment of advanced modulation and multiplexing schemes operating in combination with the advanced coding and detection techniques. In this paper we discuss the key multidimensional principles that can be used not only of the information capacity increase, but also as enablers of the elastic and dynamic networking.
Keywords: channel capacity; multiplexing; optical fibre networks; optical information processing; optical modulation; spectral analysis; dynamic networking; elastic networking; information capacity; multidimensional approach; multiplexing scheme; optical channel construction; optical channel parallelization; optical modulation; optical signal processing; optical transmission link spectral efficiency; ultra high speed optical networking; Modulation; OFDM; Optical fiber networks; Optical fibers; Optical polarization; Optical signal processing (ID#: 16-9780)


Y. C. Lee, W. H. Fang and Y. T. Chen, “Improved HISS Technique for Multidimensional Harmonic Retrieval Problems,” Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, Chengdu, 2015, pp. 109-112. doi: 10.1109/ChinaSIP.2015.7230372
Abstract: This paper presents an accurate and efficient algorithm for multidimensional harmonic retrieval (MHR) problems. The new algorithm improves the previously addressed hierarchical signal separation (HISS) technique by using more robust constrained filtering instead of the projection matrices in the signal separation process. Thereby, the new algorithm not only requires lower complexity, but it can provide even superior performance, especially in low signal-to-noise ratio (SNR) scenarios. Moreover, the pairing of the estimated parameters is automatically achieved without extra overhead. While the new algorithm, as HISS, calls for low complexity as only one-dimensional (1-D) parameters are estimated in each stage, it, as shown in the simulations, provides competing performance compared with the main state-of-the-art works.
Keywords: filtering theory; matrix algebra; source separation; HISS technique; MHR problem; SNR; constrained filtering; hierarchical signal separation; multidimensional harmonic retrieval problem; projection matrix; signal-to-noise ratio; Manganese; Nickel; Silicon
(ID#: 16-9781)


G. Desodt, C. Adnet, A. Martin and R. Castaing, “Extract Before Detect, Coherent Extraction Based on Gridless Compressed Sensing,” Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), 2015 3rd International Workshop on, Pisa, 2015, pp. 174-178. doi: 10.1109/CoSeRa.2015.7330287
Abstract: The goal of radar processing chain is to extract a few target echoes from their noisy sum made of thousands of complex numbers. Current radar chains are composed of matched filters (Pulse Compression, Doppler filters, Digital Beam Forming), noise/clutter estimation and thresholding, then extraction (hits clustering, location estimation). In this paper, extraction function is performed thanks to a Compressed Sensing approach. An important difference with current extraction function is that it performs a real “coherent extraction” (complex subtraction in each burst) of targets from the observed signals. This is a key factor to increase extraction capacity: coherent extraction can extract a higher number of target echoes than non-coherent extraction. This paper considers a multidimensional target domain and multidimensional input signals that are ambiguous in range and radial velocity, where target echoes fluctuate from burst to burst. The algorithm used to recover the sparse representation is Orthogonal Matching Pursuit (OMP) where the dictionary matrix is continuous over the target domain, therefore overcoming the grid problem: “gridless OMP”.
Keywords: array signal processing; compressed sensing; iterative methods; matched filters; pulse compression; radar clutter; radar detection; radar signal processing; signal representation; time-frequency analysis; Doppler filters; dictionary matrix; digital beam forming; gridless OMP; gridless compressed sensing; multidimensional input signals; multidimensional target domain; noise-clutter estimation; noncoherent extraction; orthogonal matching pursuit; pulse compression; radar processing chain; sparse representation; target echoes; Coherence; Compressed sensing; Conferences; Matching pursuit algorithms; Radar remote sensing; OMP; ambiguity solving; block; complex; extract before detect; extraction; gridless; off the grid; radar (ID#: 16-9782)


J. A. T. Machado and A. M. Lopes, “Analysis and Visualization of Complex Phenomena,” Computational Intelligence and Informatics (CINTI), 2015 16th IEEE International Symposium on, Budapest, 2015, pp. 135-140. doi: 10.1109/CINTI.2015.7382909
Abstract: In this paper we study natural and man-made complex phenomena by means of signal processing and fractional calculus. The outputs of the complex phenomena are time series to be interpreted as manifestations of the system dynamics. In a first step we use the Jensen-Shannon divergence to compare real-world signals. We then adopt multidimensional scaling and visualization tools for data clustering and analysis. Classical and generalized (fractional) Jensen-Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand distinct phenomena.
Keywords: data analysis; data visualisation; pattern clustering; signal processing; time series; complex phenomena visualization; data clustering; fractional calculus; generalized fractional Jensen-Shannon divergence; man-made complex phenomena; multidimensional scaling; natural complex phenomena; signal processing; system dynamics; time series; visualization tools; Data visualization; Economic indicators; Electrocardiography; Entropy; IP networks; Indium tin oxide; Internet (ID#: 16-9783)


Y. Li and T. Yu, “EEG-based Hybrid BCIs and Their Applications,” Brain-Computer Interface (BCI), 2015 3rd International Winter Conference on, Sabuk, 2015, pp. 1-4. doi: 10.1109/IWW-BCI.2015.7073035
Abstract: In this paper, we presented two hybrid brain computer interfaces (BCIs), one combing motor imagery (MI) and P300 and another combing P300 and steady state visual evoked potential (SSVEP), and their applications. An important issue in BCI research is multidimensional control. Potential applications include BCI controlled computer mouse, document and email processing, web browser, wheelchair and neuroprosthesis. The challenge for EEG-based multidimensional control is to obtain multiple independent control signals from the noisy EEG data. For this purpose, hybrid BCIs may yield better performance than BCIs those use only one type of brain pattern. In this project, we first developed a hybrid system for 2-D cursor control. In our system, two independent signals based on MI and P300 were produced from EEG for the vertical and horizontal movement control of the cursor respectively, and the cursor can be moved from an arbitrary initial position to a randomly given target position. Furthermore, a hybrid feature was extracted for selecting the target-of-interest and rejecting the target-of-no-interest, as fast and accurate as possible. In this way, a BCI mouse was implemented. Then an internet browser and a mail client were developed based on the BCI mouse. Moreover, we extended this hybrid BCI system to control a virtual car/wheelchair. On the other hand, we also developed a P300 and SSVEP-based hybrid BCI not only to improve the classification performance, but also to validate the possibility of clinical applications, e.g., detection of residual cognitive function and covert awareness in patients with disorders of consciousness.
Keywords: brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; visual evoked potentials; 2D cursor control; BCI controlled computer mouse; BCI mouse; EEG-based hybrid BCI; EEG-based multidimensional control; internet browser; MI; P300; SSVEP; classification performance improvement; clinical applications; consciousness disorder patients; covert awareness detection; horizontal movement control; hybrid brain computer interfaces; hybrid feature extraction; mail client; motor imagery; multiple independent control signals; noisy EEG data; residual cognitive function detection; steady state visual evoked potential; target position; target-of-interest selection; target-of-no-interest rejection; vertical movement control; virtual car; virtual wheelchair; Accuracy; Brain-computer interfaces; Browsers; Control systems; Mice; Postal services; Wheelchairs; Hybrid BCIs; Multidimentional control; Steady state visual evoked potential; awareness detection (ID#: 16-9784)


Y. Zhang, L. Comerford, M. Beer and I. Kougioumtzoglou, “Compressive Sensing for Power Spectrum Estimation of Multi-Dimensional Processes Under Missing Data,” 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), London, 2015, pp. 162-165. doi: 10.1109/IWSSIP.2015.7314202
Abstract: A compressive sensing (CS) based approach is applied in conjunction with an adaptive basis re-weighting procedure for multi-dimensional stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. By relying on signal sparsity, and the assumption that multiple records are available upon which to produce a spectral estimate, it has been shown that a re-weighted CS approach succeeds in estimating power spectra with satisfactory accuracy. Of key importance in this paper is the extension from one-dimensional vector processes to a broader class of problems involving multidimensional stochastic fields. Numerical examples demonstrate the effectiveness of the approach when records are subjected to up to 75% missing data.
Keywords: compressed sensing; spectral analysis; stochastic processes; adaptive basis reweighting procedure; compressive sensing; missing data; multidimensional process; multidimensional stochastic fields; multidimensional stochastic process; one-dimensional vector process; power spectrum estimation; Compressed sensing; Estimation; Frequency-domain analysis; Sensors; Sparse matrices; Spectral analysis; Stochastic processes; Compressive Sensing; Missing Data; Power Spectrum; Stochastic Process (ID#: 16-9785)


D. Misra, S. Deb and S. Joardar, “Efficient Design of Quadrature Mirror Filter Bank for Audio Signal Processing Using Craziness based Particle Swarm Optimization Technique,” Computer, Communication and Control (IC4), 2015 International Conference on, Indore, 2015, pp. 1-5. doi: 10.1109/IC4.2015.7375563
Abstract: In this paper, a superior version of Particle Swarm Optimization called Craziness based Particle Swarm Optimization (CRPSO) Technique is demonstrated for designing two-channel Quadrature Mirror Filter (QMF) Bank so as to process an audio signal with nearly perfect reconstructed output. Apart from achieving a better control on cognitive and social components of standard PSO, the proposed CRPSO dictates better implementation due to incorporation of a fresh craziness parameter, in the velocity equation of PSO, to ensure that the particle would have a predefined craziness probability to maintain the diversity of the particles. This mutation in the velocity equation not only ensures the faster searching in the multidimensional search space but also the solution produced is nearly accurate to the global optimal solution. The algorithm's performance is studied with the comparison of traditional PSO. Simulation results articulate that the proposed CRPSO algorithm outperforms its counterparts(PSO) not only in terms of quality output, i.e. sharpness at cut-off, pass band ripple and stop band attenuation but also in convergence speed with assured fidelity.
Keywords: audio signal processing; channel bank filters; particle swarm optimisation; CRPSO algorithm; QMF Bank; cognitive components; craziness based particle swarm optimization technique; craziness probability; global optimal solution; multidimensional search space; pass band ripple; quadrature mirror filter bank; social components; standard PSO; stop band attenuation; two-channel quadrature mirror filter bank; Filter banks; Finite impulse response filters; IIR filters; Mirrors; Particle swarm optimization; Power filters; CRPSO; FIR; PSO; QMF (ID#: 16-9786)


J. Velten, A. Kummert, A. Gavriilidis and K. Galkowski, “Application Specific Stability of 3-D Roesser-like Model Realizations,” Multidimensional (nD) Systems (nDS), 2015 IEEE 9th International Workshop on, Vila Real, 2015, pp. 1-6. doi: 10.1109/NDS.2015.7332651
Abstract: Stability of multidimensional (k-D) systems is still a challenging field of work. Well known and established stability measures may lead to complex mathematical problems, while simple tests are restricted to special cases of n-D systems. A new stability test for certain discrete 3-D system realizations given in a Roesser-like model description is proposed. This test is suitable for signals bounded with respect to all three coordinate directions, like spatio temporal video image signals. The 3-D system is observed in real operation, i.e. considering a sequence of processing, which leads to a 1-D state space description, allowing for application of a 1-D stability test. Since application of 1-D stability tests to higher dimensional problems is not a completely new approach, main contribution of this paper is the regular and well structured decomposition of a discrete 3-D system description into a classical 1-D state space description.
Keywords: discrete systems; stability; state-space methods; 3D Roesser-like model realization; discrete 3D system realization; multidimensional k-D system stability; state space description; Computational modeling; Electronic mail; MIMO; Mathematical model; Solid modeling; Stability criteria (ID#: 16-9787)


V. M. Chubich, O. S. Chernikova and E. A. Beriket, “Specificities of the Design of Input Signals for Models Gaussian Linear Systems,” Control and Communications (SIBCON), 2015 International Siberian Conference on, Omsk, 2015, pp. 1-6. doi: 10.1109/SIBCON.2015.7147271
Abstract: Some theoretical and applied aspects of the design input signals of the multidimensional stochastic linear discrete and continuous-discrete systems described by state space models are under consideration. Original results are obtained in the case that the parameters of mathematical models to be estimated appear the covariance matrices of the process noise and measurement noise. It is shown that in such a case, occurrence of the unknown parameters of the Fisher information matrix is constant and design of experiment not useful.
Keywords: Gaussian processes; covariance analysis; discrete systems; stochastic processes; Fisher information matrix; Gaussian linear systems; continuous-discrete systems; covariance matrices; design input signals; mathematical models; measurement noise; multidimensional stochastic linear discrete systems; process noise; state space models; Covariance matrices; Kalman filters; Linear systems; Mathematical model; Noise; Noise measurement; Stochastic processes; continuous - discrete system; discrete system; experiment design (ID#: 16-9788)


T. Liu, I. B. Djordjevic and Mo Li, “Multidimensional Signal Constellation Design for Channel Dominated with Nonlinear Phase Noise,” Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), 2015 12th International Conference on, Nis, 2015, pp. 133-136. doi: 10.1109/TELSKS.2015.7357754
Abstract: Multidimensional signal constellation sets suitable for nonlinear phase noise dominated channel are proposed, where the cumulative log-likelihood-function is used as the optimization criterion. Also, we demonstrate that our proposed signal constellation sets significantly outperform polarization switched QPSK and sphere packing constellations in 8-ary case and 16-ary case.
Keywords: optimisation; phase noise; signal processing; cumulative log likelihood function; multidimensional signal constellation; nonlinear phase noise; optimization criterion; polarization switched QPSK; sphere packing constellations; Algorithm design and analysis; Constellation diagram; Optical fiber communication; Optical fiber polarization; Parity check codes; Phase noise; Fiber optics and optical communications; Forward error correction; Low-density parity-check codes; Modulations; Optimal signal constellation design (ID#: 16-9789)


V. C. K. Cheung, K. Devarajan, G. Severini, A. Turolla and P. Bonato, “Decomposing Time Series Data by a Non-Negative Matrix Factorization Algorithm with Temporally Constrained Coefficients,” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 3496-3499. doi: 10.1109/EMBC.2015.7319146
Abstract: The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a less-than-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures.
Keywords: electromyography; matrix decomposition; medical signal processing; numerical analysis; time series; vectors; Akaike Information Criterion; NMF update rules; coefficient temporal constraint; human upper limb; independently distributed data dimensions; independently distributed data samples; multidimensional electromyographic data sets; muscle synergy identification; nonnegative basis vectors; nonnegative matrix factorization algorithm; temporal correlations; temporally constrained coefficients; time series data analysis; time series data decomposition; vector coefficient; Data mining; Data models; Distributed databases; Electromyography; Matrix decomposition; Muscles; Time series analysis (ID#: 16-9790)


T. D. Pham and M. Oyama-Higa, “Photoplethysmography Technology and Its Feature Visualization for Cognitive Stimulation Assessment,” Industrial Technology (ICIT), 2015 IEEE International Conference on, Seville, 2015, pp. 1735-1740. doi: 10.1109/ICIT.2015.7125348
Abstract: Therapeutic communication is recognized as an alternative cognitive stimulation for people with mental disorders. It is important to measure the effectiveness of such therapeutic treatments. In this paper, we present the use of photoplethysmography (PPG) technology to synchronize communication signals between the care-giver and people with dementia. To gain insights into the communication effect, the largest Lyapunov exponents are extracted from the PPG signals, which are then analyzed by multidimensional scaling to visualize the signal similarity/dissimilarity between the care-giver and participants. Experimental results show that the proposed approach is promising as a useful tool for visual assessment of the influence of the therapy over participants.
Keywords: cognition; data visualisation; feature extraction; medical disorders; medical signal processing; patient care; patient treatment; photoplethysmography; PPG signals; alternative cognitive stimulation; care-giver; cognitive stimulation assessment; communication effect; communication signals; dementia; feature visualization; largest Lyapunov exponents; mental disorders; multidimensional scaling; photoplethysmography technology; signal dissimilarity; signal similarity; therapeutic communication; therapeutic treatments; visual assessment; Data visualization; Dementia; Feature extraction; Mathematical model; Senior citizens; Synchronization; Data visualization; Largest Lyapunov exponent; Mental health; Multidimensional scaling; Photoplethysmography; Synchronized signal processing (ID#: 16-9791)


V. Vinoth, M. Muthaiah and M. Chitra, “An Efficient Pipeline Inspection on Heterogeneous Relay Nodes in Wireless Sensor Networks,” Communications and Signal Processing (ICCSP), 2015 International Conference on, Melmaruvathur, 2015, pp. 0730-0734. doi: 10.1109/ICCSP.2015.7322586
Abstract: Wireless sensor networks (WSNs) have become an effective technique in monitoring water, oil, and gas pipelines. Wireless sensor nodes have played a greater role in multidimensional applications such as tactical monitoring, weather monitoring, and battlefield detecting. Combined wireless nodes create a network in a distributed manner. They can provide accurate results in both aboveground and underground. However, there are some challenges in propagating the signal in the underground. In underground pipeline communications, sensor nodes detect the signal and forward it to the relay node, which is placed in aboveground. Our study is designed to reduce the propagating delay and to allocate channel in optimal relay node selection by using a heterogeneous network.
Keywords: channel allocation; computerised monitoring; inspection; pipelines; relay networks (telecommunication); signal detection; underground communication; wireless channels; wireless sensor networks; WSN; gas pipeline; heterogeneous relay node; multidimensional application; oil pipeline; pipeline inspection; signal propagation; underground pipeline communication; water pipeline; wireless sensor network; Delays; Media; Monitoring; Pipelines; Relays; Reliability; Wireless sensor networks; heterogeneous; leak detection; pipelines (ID#: 16-9792)


Asit Kumar Subudhi, Subhranshu Sekhar Jena and S. K. Sabut, “Delineation of Infarct Lesions by Multi-Dimensional Fuzzy C-Means of Acute Ischemic Stroke Patients,” Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015 International Conference on, Visakhapatnam, 2015, pp. 1-5. doi: 10.1109/EESCO.2015.7253655
Abstract: Lesion size in diffusion weighted imaging (DWI) of magnetic resonance (MR) images is an important clinical parameter to assess the lesion area in ischemic stroke. Manual delineation of stroke lesion is time-consuming, highly user-dependent and difficult to perform in areas of indistinct borders. In this paper we present a segmentation process to detect lesion which separates non-enhancing brain lesion from healthy tissues in DWI MR images to aid in the task of tracking lesion area over time. Lesion segmentation by Fast Fuzzy C-means was performed in DWI images obtained from patients following ischemic stroke. The lesions are delineated and segmented by Multi- dimensional Fuzzy C-Means (FCM). A high visual similarity of lesions was observed in segmented images obtained by this method. The key elements are the accurate segmenting brain images from stroke patients and measuring the size of images in pixel-wise for defining areas with hypo- or hyper-intense signals. The relative area of the affected lesion is also measured with respect to normal brain image.
Keywords: biodiffusion; biological tissues; biomedical MRI; brain; fuzzy set theory; image segmentation; medical image processing; DWI MR images; FCM; acute ischemic stroke patients; brain images; diffusion weighted imaging; healthy tissues; hyper-intense signals; hypointense signals; infarct lesions; lesion segmentation; lesion size; magnetic resonance images; manual delineation; multidimensional fuzzy C-means; nonenhancing brain lesion; segmentation process; stroke lesion; Brain; Clustering algorithms; Image segmentation; Lesions; Magnetic resonance imaging; Visualization; DWI; Lesion; Magnetic resonance imaging; ischemic stroke (ID#: 16-9793)


M. Mardani and G. B. Giannakis, “Online Sketching for Big Data Subspace Learning,” Signal Processing Conference (EUSIPCO), 2015 23rd European, Nice, 2015, pp. 2511-2515. doi: 10.1109/EUSIPCO.2015.7362837
Abstract: Sketching (a.k.a. subsampling) high-dimensional data is a crucial task to facilitate data acquisition process e.g., in magnetic resonance imaging, and to render affordable 'Big Data' analytics. Multidimensional nature and the need for realtime processing of data however pose major obstacles. To cope with these challenges, the present paper brings forth a novel real-time sketching scheme that exploits the correlations across data stream to learn a latent subspace based upon tensor PARAFAC decomposition 'on the fly.' Leveraging the online subspace updates, we introduce a notion of importance score, which is subsequently adapted into a randomization scheme to predict a minimal subset of important features to acquire in the next time instant. Preliminary tests with synthetic data corroborate the effectiveness of the novel scheme relative to uniform sampling.
Keywords: Big Data; data acquisition; learning (artificial intelligence); tensors; Big Data analytics; Big Data subspace learning; PARAFAC decomposition; data acquisition process; data processing; importance score; latent subspace; online subspace updates; randomization scheme; realtime sketching scheme; Big data; Europe; Magnetic resonance imaging; Matrix decomposition; Real-time systems; Signal processing; Tensile stress; Tensor; randomization; streaming data; subspace learning (ID#: 16-9794)


F. A. B. Hamzah, T. Yoshida, M. Iwahashi and H. Kiya, “Channel Scaling for Rounding Noise Reduction in Minimum Lifting 3D Wavelet Transform,” 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Hong Kong, 2015, pp. 888-891. doi: 10.1109/APSIPA.2015.7415398
Abstract: An integer transform is used in lossless-lossy coding since it can reconstruct an input signal without any loss at output of the backward transform. Recently, its number of lifting steps is reduced as well as delay from input to output introducing multi-dimensional memory accessing. However it has a problem that quality of the reconstructed signal in lossy coding has its upper bound in the rate distortion curve. This is because the noise generated by rounding operations in each lifting step inside the integer transform does not contribute to data compression. This paper tries to reduce the rounding noise observed at output of the integer transform introducing channel scaling inside the transform. As a result of experiments, it was observed that the proposed method improves quality of the decoded signal in lossy coding mode.
Keywords: channel coding; data compression; rate distortion theory; signal denoising; signal reconstruction; wavelet transforms; backward transform; channel scaling; data compression; input signal reconstruction; integer transform; lossless-lossy coding; minimum lifting 3D wavelet transform; multidimensional memory access; rate distortion curve; rounding noise reduction; upper bound; Bit rate; Decision support systems (ID#: 16-9795)


K. Konopko, Y. P. Grishin and D. Jańczak, “Radar Signal Recognition Based on Time-Frequency Representations and Multidimensional Probability Density Function Estimator,” Signal Processing Symposium (SPSympo), 2015, Debe, 2015, pp. 1-6. doi: 10.1109/SPS.2015.7168292
Abstract: A radar signal recognition can be accomplished by exploiting the particular features of a radar signal observed in presence of noise. The features are the result of slight radar component variations and acts as an individual signature. The paper describes radar signal recognition algorithm based on time frequency analysis, noise reduction and statistical classification procedures. The proposed method is based on the Wigner-Ville Distribution with using a two-dimensional denoising filter which is followed by a probability density function estimator which extracts the features vector. Finally the statistical classifier is used for the radar signal recognition. The numerical simulation results for the P4-coded signals are presented.
Keywords: Wigner distribution; probability; radar signal processing; Wigner-Ville distribution; multidimensional probability density function estimator; noise reduction; radar signal recognition; statistical classifier; time-frequency representations; two-dimensional denoising filter; Algorithm design and analysis; Feature extraction; Noise; Noise reduction; Radar; Signal processing algorithms; Time-frequency analysis; Wigner-Ville Distribution; time-frequency analysis (ID#: 16-9796)


S. Kumar and K. Rajawat, “Velocity-Assisted Multidimensional Scaling,” 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Stockholm, 2015, pp. 570-574. doi: 10.1109/SPAWC.2015.7227102
Abstract: This paper considers the problem of cooperative localization in mobile networks. In static networks, node locations can be obtained from pairwise distance measurements using the classical multidimensional scaling (MDS) approach. This paper introduces a modified MDS framework that also incorporates relative velocity measurements available in mobile networks. The proposed cost function is minimized via a provably convergent, low complexity majorization algorithm similar to SMACOF. The algorithm incurs low computational and communication cost, and allows practical constraints such as missing measurements and variable node velocities. Simulation results corroborate the performance gains obtained by the proposed algorithm over state-of-the-art localization algorithms.
Keywords: cooperative communication; mobile communication; cooperative localization; mobile networks; node locations; pairwise distance measurements; relative velocity measurements; velocity-assisted multidimensional scaling; Accuracy; Doppler effect; Measurement uncertainty; Noise measurement; Stress; Velocity measurement; Wireless sensor networks; Localization; iterative majorization-minimization; mobile sensor networks; multidimensional scaling (MDS) (ID#: 16-9797)


G. Shishkov, N. Popova, K. Alexiev and P. Koprinkova-Hristova, “Investigation of Some Parameters of a Neuro-Fuzzy Approach for Dynamic Sound Fields Visualization,” Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on, Madrid, 2015, pp. 1-8. doi: 10.1109/INISTA.2015.7276769
Abstract: The present paper presents detailed investigation of some parameters of our recently proposed approach for multidimensional data clustering aimed at dynamic sound fields' visualization. These include the following: number of direction selective cells (MT neurons) applied as filters at the first step of feature extraction from the raw data; size of ESN reservoir used at the second step for feature extraction; selection criteria for proper 2D projection of the original multidimensional data; number of clusters into which data are separated. The tests were performed using real experimental data collected by a microphone array (called further “acoustic camera”) build from 18 microphones placed irregularly on a wheel antenna with a photo camera at its center. Using our approach we created dynamic “sound pictures” of the data collected by acoustic camera and compared them with the static “sound picture” created by the original software of the equipment. During investigations we also discovered that our algorithm is able to distinguish among two sound sources — a task that was not that well performed by the original software of the acoustic camera.
Keywords: acoustic signal processing; data visualisation; feature extraction; fuzzy neural nets; microphone arrays; 2D projection; ESN reservoir; MT neuron; acoustic camera; direction selective cell; dynamic sound field visualization; dynamic sound fields visualization; microphone array; multidimensional data clustering; neuro-fuzzy approach; original multidimensional data; photo camera; raw data; selection criteria; sound source; static sound picture; wheel antenna; Acoustics; Cameras; Feature extraction; Heuristic algorithms; Microphones; Neurons; Reservoirs; Echo state networks (ESN); Fuzzy C-means (FCM) clustering; acoustic camera; direction selective cells (MT neurons) (ID#: 16-9798)


M. Xu, J. Shen and H. Yu, “Multimodal Data Classification Using Signal Quality Indices and Empirical Similarity-Based Reasoning,” 2015 Computing in Cardiology Conference (CinC), Nice, 2015, pp. 1197-1200. doi: 10.1109/CIC.2015.7411131
Abstract: All bedside monitors are prone to heterogeneity and mis-labeled data, yet each multimodal sample data contains different sets of multi-dimensional attributes. To reduce the incidence of false alarms in the Intensive Care Unit (ICU), a new interactive classifier was proposed. In the algorithm, case was represented with signal quality Indices (SQIs) and RR interval features. With the function wabp, the annotations were obtained from the target signal after preprocessing. Five features were used as the inputs to a case-based reasoning classifier, retrieving the cases with empirical similarity. With the posted 750 records of the PhysioNet/CinC 2015 Challenge, the classifier was trained for answering the alarm types of the query segments. Compared with conventional threshold-based alarm algorithms, the performance of our proposed algthom reduces the maximum number of false alarms while avoiding the suppression of true alarms. Evaluated with the hidden test dataset, both real-time and retrospective, the results show that the overall TPR is 83% and 82% respectively; and TNR 44% and 43% respectively. This algorithm offers a new way of thinking about retrieving heterogeneity patients with multimodal data and classifying the alarm types in the context of mislabeled cases.
Keywords: electrocardiography; haemodynamics; inference mechanisms; medical signal processing; patient care; photoplethysmography; signal classification; ECG wavefonns; ICU; Intensive Care Unit; PPG; PhysioNet/CinC 2015 Challenge; RR interval features; SQI; arterial blood pressure; bedside monitors; case-based reasoning classifier; empirical similarity-based reasoning; interactive classifier; multidimensional attributes; multimodal data classification;  signal quality Indices; signal quality indices; Chlorine; Classification algorithms; Physiology; Robustness (ID#: 16-9799)


K. Xiao, B. Xiao, S. Zhang, Z. Chen and B. Xia, “Simplified Multiuser Detection for SCMA with Sum-Product Algorithm,” Wireless Communications & Signal Processing (WCSP), 2015 International Conference on, Nanjing, 2015, pp. 1-5. doi: 10.1109/WCSP.2015.7341328
Abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access technique, which fully exploits the shaping gain of multi-dimensional codewords. However, the lack of simplified multiuser detection algorithm prevents further implementation due to the inherently high computation complexity. In this paper, general SCMA detector algorithms based on Sum-product algorithm are elaborated. Then two improved algorithms are proposed, which simplify the detection structure and curtail exponent operations quantitatively in logarithm domain. Furthermore, to analyze these detection algorithms fairly, we derive theoretical expression of the average mutual information (AMI) of SCMA (SCMA-AMI), and employ a statistical method to calculate SCMA-AMI associated with specific detection algorithms. Simulation results show that the performance is almost as well as the based message passing algorithm in terms of both BER and AMI while the complexity is significantly decreased,compared to the traditional Max-Log approximation method.
Keywords: communication complexity; error statistics; message passing; multi-access systems; multiuser detection; statistical analysis; AMI; Max-Log approximation method; SCMA detector algorithm; average mutual information; computational complexity; message passing algorithm; multidimensional codeword; non-orthogonal multiple access technique; simplified multiuser detection algorithm; sparse code multiple access technique; statistical method; sum-product algorithm; Approximation algorithms; Complexity theory; Detectors; Least squares approximations; Message passing; Multiuser detection; Sum product algorithm (ID#: 16-9800)


B. Balasingam, M. Baum and P. Willett, “MMOSPA Estimation with Unknown Number of Objects,” Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, Chengdu, 2015, pp. 706-710. doi: 10.1109/ChinaSIP.2015.7230496
Abstract: We consider the problem of estimating unordered sets of objects, which arises when the object labels are irrelevant. The widely used minimum mean square error (MMSE) estimators are not applicable for the estimation of unordered objects. Recently, a new type of estimator, known as the minimum mean OSPA (MMOSPA) estimator, which minimizes the optimal sub-pattern assignment (OSPA) metric, was proposed. Unfortunately, the MMOSPA estimator is unable to deliver a closed form solution when the objects, represented as a random finite set (RFS), are multidimensional or when the underlying posterior density is non-Gaussian; also, the existing MMOSPA estimators have not bee used to estimate unknown numbers of objects. In this paper, we derive a particle-based algorithm for the estimation of unknown number of objects which is optimal in the MMOSPA sense; also, the proposed algorithm is not limited by the dimension of the RFS or the requirement of Gaussian posterior density.
Keywords: Gaussian processes; estimation theory; minimisation; object tracking; MMOSPA estimation; OSPA metric minimization; RFS; minimum mean OSPA; nonGaussian posterior density; optimal subpattern assignment metric; particle-based algorithm; random finite set; unordered object estimation; Conferences; Cost function; Estimation; Radar tracking; Signal processing algorithms; Target tracking; Minimum mean OSPA (MMOSPA) estimate; Multi-object filtering; Multi-object systems; Multitarget tracking; Optimal sub-pattern assignment (OSPA); Point processes; Random finite sets (RFS); Wasserstein distance (ID#: 16-9801)


F. Mandanas and C. Kotropoulos, “A Maximum Correntropy Criterion for Robust Multidimensional Scaling,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015, pp. 1906-1910. doi: 10.1109/ICASSP.2015.7178302
Abstract: Multidimensional Scaling (MDS) refers to a class of dimensionality reduction techniques applied to pairwise dissimilarities between objects, so that the interpoint distances in the space of reduced dimensions approximate the initial pairwise dissimilarities as closely as possible. Here, a unified framework is proposed, where the MDS is treated as maximization of a correntropy criterion, which is solved by half-quadratic optimization in a multiplicative formulation. The proposed algorithm is coined as Multiplicative Half-Quadratic MDS (MHQMDS). Its performance is assessed for potential functions associated to various M-estimators, because the correntropy criterion is closely related to the Welsch M-estimator. Three state-of-the-art MDS techniques, namely the Scaling by Majorizing a Complicated Function (SMACOF), the Robust Euclidean Embedding (REE), and the Robust MDS (RMDS), are implemented under the same conditions. The experimental results indicate that the MHQMDS, relying on the M-estimators, performs better than the aforementioned state-of-the-art competing techniques.
Keywords: entropy; quadratic programming; MHQMDS; REE; RMDS; SMACOF; Welsch M-estimator; correntropy criterion maximization; dimensionality reduction technique; half-quadratic optimization; maximum correntropy criterion; multiplicative formulation; multiplicative half-quadratic MDS robust Euclidean embedding; robust MDS; robust multidimensional scaling; scaling by majorizing a complicated function; Computer integrated manufacturing; Kernel; Minimization; Optimization; Robustness; Signal processing; Stress; M-estimators; Multidimensional scaling; correntropy; robustness (ID#: 16-9802)


B. Gajic, S. Nováczki and S. Mwanje, “An Improved Anomaly Detection in Mobile Networks by Using Incremental Time-Aware Clustering,” 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), Ottawa, ON, 2015, pp. 1286-1291. doi: 10.1109/INM.2015.7140483
Abstract: With the increase of the mobile network complexity, minimizing the level of human intervention in the network management and troubleshooting has become a crucial factor. This paper focuses on enhancing the level of automation in the network management by dynamically learning the mobile network cell states and improving the anomaly detection on the individual cell level taking into consideration not just the multidimensionality of cell performance indicators, but also the sequence of cell states that have been traversed over time. Our evaluation based on the real network data shows very good performance of such a learning model being able to capture the cell behavior in time and multidimensional space. Such knowledge can improve the detection of different types of anomalies in cell functionality and enhance the process of cell failure mitigation.
Keywords: mobile communication; mobile computing; mobility management (mobile radio); pattern clustering; anomaly detection; cell failure mitigation process; incremental time-aware clustering; learning model; mobile network cell; mobile network complexity; mobile network management; multidimensional space; network troubleshooting; Clustering algorithms; Mobile communication; Mobile computing; Quantization (signal); Sun; Testing; Training (ID#: 16-9803)


T. Qu and Z. Cai, “A Fast Multidimensional Scaling Algorithm,” 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, 2015, pp. 2569-2574. doi: 10.1109/ROBIO.2015.7419726
Abstract: Classical multidimensional scaling (CMDS) is a common method for dimensionality reduction and data visualization. Aimed at the problem of slow speed of CMDS, a divide-and-conquer based MDS (dcMDS) algorithm is put forward in this paper. In this algorithm, the distance matrix between samples is divided along its main diagonal into several submatrices, which are solved respectively. By isometric transformation, the solutions of the submatrices can be integrated to form the solution of the whole matrix. The solution of dcMDS is the same as that of CMDS. Moreover, when the intrinsic dimension of the samples is much smaller than the number of samples, the speed of dcMDS is significantly improved than CMDS. In this paper, a detailed theoretical analysis of dcMDS is presented, and its efficiency is verified by experiments.
Keywords: data visualisation; CMDS; classical multidimensional scaling; data visualization; dimensionality reduction; divide-and-conquer based MDS; isometric transformation; multidimensional scaling algorithm; Algorithm design and analysis; Euclidean distance; Matrix decomposition; Nickel; Niobium; Principal component analysis; Signal processing algorithms (ID#: 16-9804)


M. Ahadi, M. Vempa and S. Roy, “Efficient Multidimensional Statistical Modeling of High Speed Interconnects in SPICE via Stochastic Collocation Using Stroud Cubature,” Electromagnetic Compatibility and Signal Integrity, 2015 IEEE Symposium on, Santa Clara, CA, 2015, pp. 350-355. doi: 10.1109/EMCSI.2015.7107713
Abstract: In this paper, a novel stochastic collocation approach for the efficient statistical analysis of high-speed interconnect networks within a SPICE environment is proposed. This approach employs the Stroud cubature rules to locate the sparse grid of collocation nodes within the random space where the deterministic SPICE simulations of the network are performed. The major advantage of this approach lies in the fact that the number of collocation nodes scales optimally (i.e. linearly) with the number of random dimensions unlike the exponential or polynomial scaling exhibited by the conventional tensor product grids or the Smolyak sparse grids respectively. This enables the quantification of the statistical moments for interconnect networks involving large random spaces at only a fraction of the typical CPU cost. The validity of this methodology is demonstrated using a numerical example.
Keywords: SPICE; integrated circuit design; integrated circuit interconnections; stochastic processes; Stroud cubature rules; collocation nodes; high speed interconnect; multidimensional statistical modeling; sparse grid location; statistical moment quantification; stochastic collocation; Algorithm design and analysis; Computational modeling; Integrated circuit interconnections; Numerical models; Polynomials; Stochastic processes; Cubature rules; interconnect networks; statistical moments; transient analysis (ID#: 16-9805)


T. Merritt, J. Latorre and S. King, “Attributing Modelling Errors in HMM Synthesis by Stepping Gradually from Natural to Modelled Speech,” 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, 2015, pp. 4220-4224. doi: 10.1109/ICASSP.2015.7178766
Abstract: Even the best statistical parametric speech synthesis systems do not achieve the naturalness of good unit selection. We investigated possible causes of this. By constructing speech signals that lie in between natural speech and the output from a complete HMM synthesis system, we investigated various effects of modelling. We manipulated the temporal smoothness and the variance of the spectral parameters to create stimuli, then presented these to listeners alongside natural and vocoded speech, as well as output from a full HMM-based text-to-speech system and from an idealised 'pseudo-HMM'. All speech signals, except the natural waveform, were created using vocoders employing one of two popular spectral parameterisations: Mel-Cepstra or Mel-Line Spectral Pairs. Listeners made 'same or different' pairwise judgements, from which we generated a perceptual map using Multidimensional Scaling. We draw conclusions about which aspects of HMM synthesis are limiting the naturalness of the synthetic speech.
Keywords: hidden Markov models; speech synthesis; vocoders; voice equipment; HMM synthesis; Mel-Cepstra pairs; Mel-Line Spectral pairs; hidden Markov model; modelled speech; modelling errors; natural speech; speech naturalness; speech synthesis systems; vocoded speech; Hidden Markov models; Lead; Smoothing methods; Speech; hidden Markov modelling; vocoding
(ID#: 16-9806)


V. Seneviratne, A. Madanayake and N. Udayanga, “Wideband 32-element 200-MHz 2-D IIR Beam Filters Using ROACH-2 Virtex-6 sx475t FPGA,” Multidimensional (nD) Systems (nDS), 2015 IEEE 9th International Workshop on, Vila Real, 2015, pp. 1-5. doi: 10.1109/NDS.2015.7332641
Abstract: Two-dimensional (2-D) IIR beam filter applications operating in ultra wide-band (UWB) radio frequency (RF) range requires hardware capable of handling high speed real-time processing due to its operation bandwidth lies in megahertz or gigahertz range. Two-dimensional IIR beam forming is used mainly for applications such as communications, radars and detection of directional sensing. A systolic architecture is proposed for the real-time implementation of the 2-D IIR beam filter. This the first attempt of evaluating the prospect of practical implementation of such a beam filter capable in ROACH-2 hardware platform which is equipped with a Xilinx Virtex-6 sx475t FPGA chip, widely used in the field of radio astronomy reaching up to 200 MHz operating frequency.
Keywords: IIR filters; array signal processing; field programmable gate arrays; ultra wideband communication; 2D IIR beam forming; ROACH-2 Virtex-6 sx475t FPGA; UWB radio frequency; directional sensing; frequency 200 MHz; radio astronomy; ultra wideband radio frequency; wideband 32-element 2-D IIR beam filters; Antenna arrays; Array signal processing; Computer architecture; Field programmable gate arrays; Hardware; Radio frequency; Real-time systems; Analog; arrays; beamfilter; beamforming (ID#: 16-9807)


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