Showing papers on "Multidimensional signal processing published in 2006"

Analog-to-Information Conversion via Random Demodulation

Abstract: Many problems in radar and communication signal processing involve radio frequency (RF) signals of very high bandwidth. This presents a serious challenge to systems that might attempt to use a high-rate analog-to-digital converter (ADC) to sample these signals, as prescribed by the Shannon/Nyquist sampling theorem. In these situations, however, the information level of the signal is often far lower than the actual bandwidth, which prompts the question of whether more efficient schemes can be developed for measuring such signals. In this paper we propose a system that uses modulation, filtering, and sampling to produce a low-rate set of digital measurements. Our "analog-to-information converter" (AIC) is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal having a sparse representation in some dictionary can be recovered from a small number of linear projections of that signal. We generalize the CS theory to continuous-time sparse signals, explain our proposed AIC system in the CS context, and discuss practical issues regarding implementation.

Random Sampling for Analog-to-Information Conversion of Wideband Signals

Abstract: We develop a framework for analog-to-information conversion that enables sub-Nyquist acquisition and processing of wideband signals that are sparse in a local Fourier representation The first component of the framework is a random sampling system that can be implemented in practical hardware The second is an efficient information recovery algorithm to compute the spectrogram of the signal, which we dub the sparsogram A simulated acquisition of a frequency hopping signal operates at 33times sub-Nyquist average sampling rate with little degradation in signal quality

Compressive Sampling for Signal Classification

Abstract: Compressive sampling (CS), also called compressed sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal. Further, the signal can be reconstructed from these "random projections," even when the number of observations is far less than the ambient signal dimension. The provable success of CS for signal reconstruction motivates the study of its potential in other applications. This paper investigates the utility of CS projection observations for signal classification (more specifically, m-ary hypothesis testing). Theoretical error bounds are derived and verified with several simulations.

Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter

Abstract: This paper presents a new approach to the time-frequency signal analysis and synthesis, using the eigenvalue decomposition method. It is based on the S-method, the time-frequency representation that can produce a distribution equal or close to a sum of the Wigner distributions of individual signal components. The new time-frequency signal decomposition method is evaluated on the simulated and experimental high-frequency surface-wave radar (HFSWR) data. Results demonstrate that it provides an effective way for analyzing and detecting maneuvering air targets with significant velocity changes, including target signal separation from the heavy clutter. The analysis shows that this method can provide additional insight into the interpretation and processing of radar signals, with respect to the traditional Fourier transform based methods currently used by the HFSWRs. The proposed method could also be used in other signal processing applications

An Eigenvector-Based Approach for Multidimensional Frequency Estimation With Improved Identifiability

Abstract: This paper presents an algebraic method for two-dimensional (2-D) and multidimensional frequency estimation by exploiting eigenvector structure. The algorithm is based on multidimensional smoothing and data folding, and offers significantly improved identifiability (ID) over existing algebraic approaches, thus is termed the improved multidimensional folding (IMDF) algorithm. The ID, performance, and computational complexity of the proposed algorithm are analyzed in detail. In the 2-D case, it is shown that with the IMDF algorithm, up to approximately 0.34M1(M2+1) 2-D frequencies can be uniquely resolved with probability one from an M1 by M2 data mixture (assuming M1gesM2), while the most relaxed ID bound offered by existing algebraic approaches is approximately M1M2/4. Unlike most eigenvalue techniques that usually require an extra frequency association step, the IMDF algorithm achieves automatic frequency pairing once an eigenvalue decomposition problem is solved because frequencies are estimated from the eigenvectors instead of the eigenvalues. Theoretical analysis and simulation results demonstrate its competitive performance compared to the Crameacuter-Rao bound (CRB)

Method and system for signal emulation

Abstract: A circuit can process a sample of a signal to emulate, simulate, or model an effect on the signal. Thus, an emulation circuit can produce a representation of a real-world signal transformation by processing the signal according to one or more signal processing parameters that are characteristic of the real-world signal transformation. The emulation circuit can apply analog signal processing and/or mixed signal processing to the signal. The signal processing can comprise feeding the signal through two signal paths, each having a different delay, and creating a weighted sum of the outputs of the two signal paths. The signal processing can also (or alternatively) comprise routing the signal through a network of delay elements, wherein a bank of switching or routing elements determines the route and thus the resulting delay.

From linear adaptive filtering to nonlinear information processing - The design and analysis of information processing systems

Abstract: Recent advances in computing capabilities and the interest in new challenging signal processing problems that cannot be successfully solved using traditional techniques have sparked an interest in information-theoretic signal processing techniques. Adaptive nonlinear filters that process signals based on their information content have become a major focus of interest. The design and analysis of such nonlinear information processing systems is demonstrated in this paper. Theoretical background on necessary information theoretic concepts are provided, nonparametric sample estimators for these quantities are derived and discussed, the use of these estimators for various statistical signal processing problems have been illustrated. These include data density modeling, system identification, blind source separation, dimensionality reduction, image registration, and data clustering

Statistical models of reconstructed phase spaces for signal classification

Abstract: This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics.

Optical signal processing in Radar systems

Abstract: Opto-electronic components and their performances are well suited to be integrated in radar systems. In this paper, two optical architectures illustrate functions that are specific to optical processing of microwave signals, i.e., time-delay-based processing and arbitrary waveform generation of large frequency bandwidth signals.

Polarization analysis and polarization filtering of three-component signals with the time–frequency S transform

Abstract: SUMMARY From basic Fourier theory, a one-component signal can be expressed as a superposition of sinusoidal oscillations in time, with the Fourier amplitude and phase spectra describing the contribution of each sinusoid to the total signal. By extension, three-component signals can be thought of as superpositions of sinusoids oscillating in the x-, y-, and z-directions, which, when considered one frequency at a time, trace out elliptical motion in three-space. Thus the total three-component signal can be thought of as a superposition of ellipses. The information contained in the Fourier spectra of the x-, y-, and z-components of the signal can then be re-expressed as Fourier spectra of the elements of these ellipses, namely: the lengths of their semi-major and semi-minor axes, the strike and dip of each ellipse plane, the pitch of the major axis, and the phase of the particle motion at each frequency. The same type of reasoning can be used with windowed Fourier transforms (such as the S transform), to give time-varying spectra of the elliptical elements. These can be used to design signal-adaptive polarization filters that reject signal components with specific polarization properties. Filters of this type are not restricted to reducing the whole amplitude of any particular ellipse; for example, the ‘linear’ part of the ellipse can be retained while the ‘circular’ part is rejected. This paper describes the mathematics behind this technique, and presents three examples: an earthquake seismogram that is first separated into linear and circular parts, and is later filtered specifically to remove the Rayleigh wave; and two shot gathers, to which similar Rayleigh-wave filters have been applied on a trace-by-trace basis.

Algebraic Signal Processing Theory

Abstract: This paper presents an algebraic theory of linear signal processing. At the core of algebraic signal processing is the concept of a linear signal model defined as a triple (A, M, phi), where familiar concepts like the filter space and the signal space are cast as an algebra A and a module M, respectively, and phi generalizes the concept of the z-transform to bijective linear mappings from a vector space of, e.g., signal samples, into the module M. A signal model provides the structure for a particular linear signal processing application, such as infinite and finite discrete time, or infinite or finite discrete space, or the various forms of multidimensional linear signal processing. As soon as a signal model is chosen, basic ingredients follow, including the associated notions of filtering, spectrum, and Fourier transform. The shift operator is a key concept in the algebraic theory: it is the generator of the algebra of filters A. Once the shift is chosen, a well-defined methodology leads to the associated signal model. Different shifts correspond to infinite and finite time models with associated infinite and finite z-transforms, and to infinite and finite space models with associated infinite and finite C-transforms (that we introduce). In particular, we show that the 16 discrete cosine and sine transforms are Fourier transforms for the finite space models. Other definitions of the shift naturally lead to new signal models and to new transforms as associated Fourier transforms in one and higher dimensions, separable and non-separable. We explain in algebraic terms shift-invariance (the algebra of filters A is commutative), the role of boundary conditions and signal extensions, the connections between linear transforms and linear finite Gauss-Markov fields, and several other concepts and connections.

Signal Processing in Analytical Chemistry

Abstract: Signal processing refers to a variety of operations that can be carried out on a continuous (analog) or discrete (digital) sequence of measurements in order to enhance the quality of information it is intended to convey. In the analog domain, electronic signal processing can encompass such operations as amplification, filtering, integration, differentiation, modulation/demodulation, peakdetection, and analog-to-digital (A/D) conversion. Digital signal processing can include a variety of filtering methods (e.g. polynomial least-squares smoothing, differentiation, median smoothing, matched filtering, boxcar averaging, interpolation, decimation, and Kalman filtering) and domain transformations (e.g. Fourier transform (FT), Hadamard transform (HT), and wavelet transform (WT)). Generally the objective is to separate the useful part of the signal from the part that contains no useful information (the noise) using either explicit or implicit models that distinguish these two components. Signal processing at various stages has become an integral part of most modern analytical measurement systems and plays a critical role in ensuring the quality of those measurements.

Image signal processing apparatus, camera system and image signal processing method

Abstract: An image signal processing apparatus, the method and a camera system are provided, by which highly efficient processing close to 1-path processing can be performed without deteriorating a picture quality or taking too much time for the processing, a capacity required for a compression rate, band and memory in the worst case can be assured and a random accessing property is not impaired when compressing image data: wherein a data signal processing unit performs a compression processing by dividing input image data to a plurality of bit resolution parts and applying a predetermined compression method to each divided part.

Processing of ND NMR spectra sampled in polar coordinates: a simple Fourier transform instead of a reconstruction.

Abstract: In order to reduce the acquisition time of multidimensional NMR spectra of biological macromolecules, projected spectra (or in other words, spectra sampled in polar coordinates) can be used. Their standard processing involves a regular FFT of the projections followed by a reconstruction, i.e. a non-linear process. In this communication, we show that a 2D discrete Fourier transform can be implemented in polar coordinates to obtain directly a frequency domain spectrum. Aliasing due to local violations of the Nyquist sampling theorem gives rise to base line ridges but the peak line-shapes are not distorted as in most reconstruction methods. The sampling scheme is not linear and the data points in the time domain should thus be weighted accordingly in the polar FT; however, artifacts can be reduced by additional data weighting of the undersampled regions. This processing does not require any parameter tuning and is straightforward to use. The algorithm written for polar sampling can be adapted to any sampling scheme and will permit to investigate better compromises in terms of experimental time and lack of artifacts.

Wavelets with applications in signal and image processing

Abstract: Wavelets with applications in signal and image processing , Wavelets with applications in signal and image processing , کتابخانه دیجیتال دانشگاه علوم پزشکی اصفهان

Digital image coding system having self-adjusting selection criteria for selecting a transform function

Abstract: In a digital signal processing system, a method for selecting a transform function to apply to an input signal based on characteristics of the signal, and for self-adjusting criteria which are used in selecting a transform function to apply to a subsequent signal. Characteristics are obtained from the signal. The characteristics are compared to adjustable criteria which are used in selecting a transform function. Differing criteria are maintained for the different selectable transform functions. A record is maintained of transform functions selected and the particular characteristics that caused the selection. Based on the ability of a transform function to minimally define the coded signal, an inverse transform function is selected to decode the signal. The criteria used in selecting a transform function to apply to a subsequent signal are adjusted based on a quality measure of the decoded signal and the record of selected transform functions.

Multidimensional Localization of Multiple Sound Sources Using Blind Adaptive MIMO System Identification

Abstract: The TDOA-based acoustic source localization approach is a powerful and widely-used method which can be applied for one source in several dimensions or several sources in one dimension. However the localization turns out to be more challenging when multiple sound sources should be localized in multiple dimensions, due to a spatial ambiguity phenomenon which requires to perform an intermediate step after the TDOA estimation and before the calculation of the geometrical source positions. In order to obtain the required set of TDOA estimates for the multidimensional localization of multiple sound sources, we apply a recently presented TDOA estimation method based on blind adaptive multiple-input-multiple-output (MIMO) system identification. We demonstrate that this localization method also provides valuable side information which allows us to resolve the spatial ambiguity without any prior knowledge about the source positions. Furthermore we show that the blind adaptive MIMO system identification allows a high spatial resolution. Experimental results for the localization of two sources in a two-dimensional plane show the effectiveness of the proposed scheme

Spectral analysis of a signal driven sampling scheme

Abstract: This work is a part of a drastic revolution in the classical signal processing chain required in mobile systems. The system must be low power as it is powered by a battery. Thus a signal driven sampling scheme based on level crossing is adopted, delivering non-uniformly spaced out in time sampled points. In order to analyse the non-uniformly sampled signal obtained at the output of this sampling scheme a new spectral analysis technique is devised. The idea is to combine the features of both uniform and non-uniform signal processing chains in order to obtain a good spectrum quality with low computational complexity. The comparison of the proposed technique with General Discrete Fourier transform and Lomb's algorithm shows significant improvements in terms of spectrum quality and computational complexity.

Optimal combined word-length allocation and architectural synthesis of digital signal processing circuits

Abstract: In this brief, we address the combined application of word-length allocation and architectural synthesis of linear time-invariant digital signal processing systems. These two design tasks are traditionally performed sequentially, thus lessening the overall design complexity, but ignoring forward and backward dependencies that may lead to cost reductions. Mixed integer linear programming is used to formulate the combined problem and results are compared to the two-step traditional approach.

Spectral Analysis: Parametric and Non-Parametric Digital Methods (Digital Signal and Image Processing series)

Abstract: Preface. Specific Notations. Part 1: Tools for spectral analysis. Chapter 1. Fundamentals, Francis Castanie. 1.1 Classes of Signals. 1.2 Representations of Signals. 1.3 Spectral Analysis: Position of the Problem. 1.4 Bibliography. Chapter 2. Digital processing of signals, Eric Le Carpentier. 2.1 Introduction. 2.2 Transform Properties. 2.3 Windows. 2.4 Examples of Application. 2.5 Bibliography. Chapter 3. Estimation in spectral analysis, Olivier Besson and Andre Ferrari. 3.1 Introduction to Estimation. 3.2 Estimation of 1st and 2nd Order Moments. 3.3 Periodogram Analysis. 3.4 Analysis of Estimators based on cxx (m). 3.5 Conclusion. 3.6 Bibliography. Chapter 4. Time-series models, Francis Castanie. 4.1 Introduction. 4.2 Linear Models. 4.3 Exponential Models. 4.4 Non-linear Models. 4.5 Bibliography. Part 2: Non-parametric methods. Chapter 5. Non-parametric methods, Eric Le Carpentier. 5.1 Introduction. 5.2 Estimation of the Power Spectral Density. 5.3 Generalization to Higher Order Spectra. 5.4 Bibliography. Part 3: Parametric methods. Chapter 6. Modeling of stationary time series, Corinne Mailhes and Francis Castanie. 6.1 Parametric Models. 6.2 Estimation of Model Parameters. 6.3 Properties of Spectral Estimators Produced. 6.4 Bibliography. Chapter 7. Minimum variance, Nadine Martin. 7.1 Principle of the MV Method. 7.2 Properties of the MV Estimator. 7.3 Link with the Fourier estimators. 7.4 Link with a Maximum Likelihood Estimator. 7.5 Lagunas Methods: Normalized and Generalized MV. 7.6 The CAPNORM Estimotor. 7.7 Bibliography. Chapter 8. Sub-space based estimators, Sylvie Marcos. 8.1 Model, Concept of Subspace, Definition of High Resolution. 8.2 MUSIC. 8.3 Determination Criteria of the Number of Complex Sine Waves. 8.4 The MinNorm Method. 8.5 "Linear" Subspace Methods. 8.6 The ESPRIT Method. 8.7 Illustration of Subspace-based Methods Performance. 8.8 Adaptive Research of Subspaces. 8.9 Bibliography. Chapter 9. Spectral analysis of random non-stationary signals, Corinne Mailhes and Francis Castanie. 9.1 Evolution Spectra. 9.2 Non-parametric Spectral Estimation. 9.3 Parametric Spectral Estimation. 9.4 Bibliography. List of Authors. Index.

Multi-scale enveloping spectrogram signal processing for condition monitoring and the like

Abstract: A signal processing technique (30 of figure 2) that decomposes complex, dynamically changing non-stationary signals from machine components such as bearings into different scales by means of a continuous wavelet transform (34 of figure 2). The envelope signal in each scale is then calculated from the modulus of the wavelet coefficients (36 of figure 2). Subsequently, Fourier transform (38 of figure 2) is performed repetitively on the envelope of the signal at each scale, resulting in an 'envelope spectrum' of the original signal at the various scales. The final output (42 of figure 2) is a three-dimensional scale-frequency map that indicates the intensity and location of the defect- related frequency lines. The technique is generic in nature, and applicable not only to machine condition monitoring, but also to the health monitoring of a wide range of dynamic systems, including human beings.

Multidimensional systems and signal processing

Abstract: ▶ Publishes surveys and research papers ranging from the fundamentals to important new findings ▶ Offers unity of theme, reduced duplication of effort, and greatly enhanced communication among researchers and practitioners in the field ▶ Addresses such topics as blurred and noisy image processing; multidimensional signal reconstruction from partial or incomplete observations and projections; signal modeling; spectral analysis and transform techniques; array processing; etc.

Impact of signal constellation expansion on the achievable diversity of pragmatic bit-interleaved space-time codes

Abstract: This letter studies the effect of signal constellation expansion on the achievable diversity of pragmatic bit-interleaved space-time codes in quasistatic multiple antenna channels. Signal constellation expansion can be obtained either by increasing the size of the constellation in the complex plane or by using multidimensional linear mappings. By means of two simple constructions, we provide a comparison of the two options with message passing decoding. We show that multidimensional expansion achieves some performance advantage over complex-plane expansion at the cost of significantly higher decoding complexity and larger peak-to-average power ratio of the transmitted signals

Fast acquisition system for digital holograms and image processing for three-dimensional display with data manipulation

Abstract: A three-dimensional (3D) digital holographic display system with image processing is presented. By use of phase-shifting digital holography, we obtain the complex amplitude of a 3D object at a recording plane. Image processing techniques are introduced to improve the quality of the reconstructed 3D object or manipulate 3D objects for elimination and addition of information by modifying the complex amplitude. The results show that the information processing is effective in such manipulations of 3D objects. We also show a fast recording system of 3D objects based on phase-shifting digital holography for display with image processing. The acquisition of 3D object information at 500 Hz is demonstrated experimentally.

Signal processing in digital communications

Abstract: Preface. Introduction. Deterministic Signal Processing. Probability, Random, and Stochastic Signal Processing. Channel Characterization and Distortion. Channel Estimation and Blind Identification. Adaptive Equalization. Multichannel Modulation, DMT and OFDM. Discrete-Time Synchronization. Appendixes. About the Author. Index.

Filtered Dynamics and Fractal Dimensions for Noisy Speech Recognition

Abstract: We explore methods from fractals and dynamical systems theory for robust processing and recognition of noisy speech. A speech signal is embedded in a multidimensional phase-space and is subsequently filtered exploiting aspects of its unfolded dynamics. Invariant measures (fractal dimensions) of the filtered signal are used as features in automatic speech recognition (ASR). We evaluate the new proposed features as well as the previously proposed multiscale fractal dimension via ASR experiments on the Aurora 2 database. The conducted experiments demonstrate relative improved word accuracy for the fractal features, especially at lower signal-to-noise ratio, when they are combined with the mel-frequency cepstral coefficients

System and method for iterative reconstruction using parallel processing

Abstract: A method and system are provided for processing an acquired image signal in parallel to generate a reconstructed image signal. In one embodiment, a processing component is provided comprising one or more field-programmable gate arrays configured as co-processors. Other aspects of the present technique provide a pipelined processor configured to forward- and back-project image data using the same data path and arithmetic units.