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作者： G. Montalbano G.V. Serebryakov Dipartimento di Elettronica Politecnico di Turino Torino Italy Sophia Antipolis Institut Eurécom France Institute Applied Mathematics & Cybernetics University of Nizhni Novgorod Nizhny Novgorod Russia

Traditionally optimum-adaptive beamforming algorithms have been developed assuming fully coherent plane wavefronts, i.e., assuming a data model of point sources. In most applications this assumption is inappropriate, since the channel model has to account for different kinds of dispersion phenomena due to both the propagation environment and the array itself. Significant examples are sonar and underwater communication systems. Indeed, in such circumstances, the resulting wavefronts can be randomly distorted, usually suffering a loss of spatial coherence. Here, assuming a more realistic stochastic channel model, we analyze the performance of a traditional optimum adaptive beamformer for point sources, when the signal or the interference undergo a spatial coherence degradation. It is shown, with analytical details, that the same coherence loss, for the interference results in larger performance degradation than for the signal, Furthermore, we provide a theoretical comparison among different beamforming algorithms, based on the estimate of the channel parameters and on spatial smoothing methods.

关键词： Array signal processing signal processing algorithms Spatial coherence signal analysis Performance analysis Interference Degradation Data models Sonar Underwater communication

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Detection and classification of faults from helicopter vibration data using recently developed signal processing and neural network techniques

Detection and classification of faults from helicopter vibra...

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Asilomar Conference on signals, Systems & Computers

作者： M.R. Solorzano D.K. Ishii N.R. Nickolaisen W.Y. Huang Naval Ocean Systems Center Advanced Technology Development San Diego CA USA

The feasibility of using nontraditional methods of helicopter transmission fault classification is studied. Various signal processing and classifier techniques are investigated. All algorithms successfully classified the fault samples from a tail rotor transmission dataset. A hardware neural network system was designed and implemented. The relatively low resolution of the neural network circuitry required extensive preprocessing and scaling of the large dynamic range input signal. Results from the hardware system were similar to those achieved in simulation. It is pointed out that a true test of the techniques presented may require a dataset that is statistically richer.< >

关键词： Fault detection Helicopters Circuit faults signal processing algorithms Neural networks Tail Neural network hardware signal resolution Data preprocessing Dynamic range

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Digital signal processing solutions to 2-D finite support blind deconvolution problems

Digital signal processing solutions to 2-D finite support bl...

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IEEE International Conference on Image processing

作者： A.E. Yagle Department of EECS University of Michigan Ann Arbor MI USA

The 2-D discrete blind deconvolution problem is to reconstruct an image defined at integer coordinates and having known finite spatial extent from its 2-D convolution with a similar and also-unknown point-spread function. This is significantly more difficult than the typical image restoration problem of deconvolving a known blurring function. Many methods for solving this problem are iterative and alternate between the spatial and wavenumber domains. These algorithms are not POCS and they tend to stagnate. We present a completely novel approach that uses a battery of digital signal processing algorithms to produce an algorithm that comes close to expressing the solution in closed form. The only requirement on the image and point-spread functions are that they each have finite spatial extent and be roughly bandlimited, so that a fractional spatial shift also has finite spatial extent (this requirement is almost always met in practice).

关键词： Digital signal processing signal processing algorithms Iterative algorithms Deconvolution Image reconstruction Convolution Image restoration Iterative methods Batteries

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An array signal processing super-resolution fast algorithm

An array signal processing super-resolution fast algorithm

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International Conference on signal processing Proceedings (ICSP)

作者： Zhang Yongjun Han Ping Xu Feiwei Institute of Command and Technology COSTIND Beijing China

This paper provides array signal processing super-resolution fast algorithm. This algorithm is based on the eigenvalue shift of the covariance matrix and the power iteration of the shift covariance matrix. It does not need the inverse matrix. It converges quickly and only needs several iterations to converge to solution. Its architecture is very simple and easily implemented.

关键词： signal processing algorithms Array signal processing signal resolution Covariance matrix Eigenvalues and eigenfunctions Geometry signal processing Sonar applications Earth Radio astronomy

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Fourier series based nonminimum phase model for second- and higher-order statistical signal processing

Fourier series based nonminimum phase model for second- and ...

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IEEE signal processing Workshop on Higher-Order Statistics

作者： Chong-Yung Chi Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)0818680059

In the paper, a parametric Fourier series based model (FSBM) for or as an approximation to an arbitrary nonminimum-phase linear time-invariant (LTI) system is proposed for statistical signal processing applications where a model for LTI systems is needed. Based on the FSBM, a (minimum-phase) linear prediction error (LPE) filter for amplitude estimation of the unknown LTI system together with the Cramer Rao (CR) bounds is presented. Then an iterative algorithm for obtaining the optimum mean-square LPE filter with finite data is presented which is also an approximate maximum likelihood algorithm when the data are Gaussian. Then three iterative algorithms using higher-order statistics with finite non-Gaussian data are presented for estimating parameters of the FSBM followed by some simulation results to support the efficacy of the proposed algorithms. Finally, we draw some conclusions.

关键词： Fourier series signal processing Autoregressive processes signal processing algorithms Iterative algorithms Deconvolution Phase estimation Nonlinear filters Amplitude estimation Parameter estimation

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Performance Improvement Through Error Fine Tuning In Interferometric SAR signal processing

Performance Improvement Through Error Fine Tuning In Interfe...

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IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Zahid Hasan Bawar Long Teng Tao Zeng School of Information Engineering Beijing Institute of Technology Beijing China

Efforts are continuously in hand in the world to improve upon the performance of the SAR interferometric signal processing. The quality of the InSAR products has been improved drastically in the last few years. However, in order to enhance the accuracy of the InSAR products and obtain results close to the real parameters further tuning of the InSAR processing is needed. In an innovative way, in this paper error evaluation at every step in the InSAR signal processing has been carried out and small algorithms have been incorporated to inverse the errors, thus fine tuning the InSAR signal processing. Methods and relationships have been developed to enable retrieve the accurate results as much as possible. The relevant processing steps have been identified and recommendations have been made to implement the results with better understanding of different InSAR applications. The necessary steps involved in processing the received data to the geometrically compensated digital models have been explained in an easy and comprehensible way

关键词： signal processing Synthetic aperture radar interferometry signal processing algorithms Spaceborne radar Radar imaging Pixel Surface topography Fluid dynamics Phase measurement Time measurement

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Towards Low-Cost, Real-Time, Distributed signal and Data processing for Artificial Intelligence Applications at Edges of Large Industrial and Internet Networks

Towards Low-Cost, Real-Time, Distributed Signal and Data Pro...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Emmanuel Oyekanlu Kevin Scoles Electrical and Computer Engr. Department Drexel University Philadelphia Pennsylvania USA

ISBN: (纸本)9781538695562;9781538660843

Digital signal Processors (DSP) are vital system components in industrial Artificial Intelligence (AI) applications. In this paper, FIR filters that could be used for industrial AI applications are designed from the Spline Biorthogonal 1.5 (SB1.5) mother wavelet using a real-time, low-cost, generic industrial IoT (IIoT) hardware: the C28x DSP and low-level, Embedded C, system software. Our contribution in this paper is the first reported application of the C28x for SB1.5 wavelet construction. The significance of this approach is to be able to repurpose low-cost, readily available hardware for distributed AI applications. Our approach is different from the state of the art, in which specialized hardware are always manufactured for implementing AI applications at large network edges. Our approach supports low-cost and fast single-stage FIR implementation suitable for use in real-time, distributed AI application at network edges, since in our case, successive recursion of FIR filters leading to a full implementation of Pyramid Algorithm is not implemented. The designed FIR filter is evaluated and found capable of both low-pass and high pass filtering operations. Results of this paper indicate that the C28x real-time DSP, which exists in many IoT devices, could have improved scalability by being deployed for other important AI and IoT network edge analytic applications, different from its present uses.

关键词： Artificial intelligence Finite impulse response filters Real-time systems Wavelet transforms signal processing algorithms Multiresolution analysis Splines (mathematics)

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Comparison of different development kits and its suitability in signal processing education

Comparison of different development kits and its suitability...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Dong Yuan Shi Woon-Seng Gan School of Electrical and Electronic Engineering Nanyang Technological University SINGAPORE

ISBN: (纸本)9781479999897

With the availability of many low-cost programmable development kits in the market, real-time signal processing projects can now be readily introduced into today's signal processing and embedded system course curriculum. In this paper, we group these popular development kits in terms of their cost, hardware architecture, development methodology and software resource. We further illustrate the programming efforts in implementing a real-time digital signal processing algorithm using different types of programmable development kits.

关键词： Programming Hardware signal processing algorithms Digital signal processing Real-time systems Clocks

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Source Separation and Applications

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IEEE signal processing MAGAZINE 2014年第3期31卷 16-17页

作者： Adali, Tuelay Jutten, Christian Yeredor, Arie Cichocki, Andrzej Moreau, Eric University of Maryland Baltimore MD 21250 United States Department of Electronics and Communication Engineering National Institute of Technology Durgapur West Bengal India Department of ECE NITMAS Dum Dum West Bengal India University of Toulon 83957 France

Presents the guest editorial for this issue of the publication.

关键词： Special issues and sections Source separation signal processing algorithms Matrix decomposition Heuristic algorithms Blind source separation

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The comparison of the constrained and unconstrained frequency-domain block-LMS adaptive algorithms

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IEEE TRANSACTIONS ON signal processing 1996年第7期44卷 1813-1816页

作者： Li, XH Jenkins, WK Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana-Champaign IL USA

Convergence properties of the constrained and the unconstrained frequency-domain block LMS algorithms are analyzed, Comparisons based on both a theoretical analysis and computer simulation are given, It is shown that the unconstrained algorithm has a slower convergence rate and smaller stable range of step size than that of the constrained algorithm.

关键词： Adaptive algorithm signal processing algorithms Frequency domain analysis Least squares approximation Algorithm design and analysis Convergence Computer simulation Filtering algorithms Adaptive filters Computational complexity

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