Showing papers on "Multidimensional signal processing published in 2012"

Kronecker Compressive Sensing

Abstract: Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement systems for such signals is complicated by their higher dimensionality. In this paper, we propose the use of Kronecker product matrices in CS for two purposes. First, such matrices can act as sparsifying bases that jointly model the structure present in all of the signal dimensions. Second, such matrices can represent the measurement protocols used in distributed settings. Our formulation enables the derivation of analytical bounds for the sparse approximation of multidimensional signals and CS recovery performance, as well as a means of evaluating novel distributed measurement schemes.

A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing

Abstract: Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is desired. In this paper, a proof-of-concept study is conducted on compressive sensing (CS) and unmixing for hyperspectral imaging. Specifically, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of CS. To decode the compressed data, we propose a numerical procedure to compute directly the unmixed abundance fractions of given end members, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variation of the abundance fractions subject to a preprocessed fidelity equation with a significantly reduced size and other side constraints. An augmented Lagrangian-type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidences obtained from this paper indicate that the proposed scheme has a high potential in real-world applications.

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

Abstract: We propose a cognitive radar network (CRN) system for the joint estimation of the target state comprising the positions and velocities of multiple targets, and the channel state comprising the propagation conditions of an urban transmission channel. We develop a measurement model for the received signal by considering a finite-dimensional representation of the time-varying system function which characterizes the urban transmission channel. We employ sequential Bayesian filtering at the receiver to estimate the target and the channel state. We propose a hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space. The feedback loop that embodies the radar environment and the receiver enables the transmitter to employ approximate greedy programming to find a suitable subset of antennas to be employed in each tracking interval, as well as the power transmitted by these antennas. We compute the posterior Cramer-Rao bound (PCRB) on the estimates of the target state and the channel state and use it as an optimization criterion for the antenna selection and power allocation algorithms. We use several numerical examples to demonstrate the performance of the proposed system.

A windowed graph Fourier transform

Abstract: The prevalence of signals on weighted graphs is increasing; however, because of the irregular structure of weighted graphs, classical signal processing techniques cannot be directly applied to signals on graphs. In this paper, we define generalized translation and modulation operators for signals on graphs, and use these operators to adapt the classical windowed Fourier transform to the graph setting, enabling vertex-frequency analysis. When we apply this transform to a signal with frequency components that vary along a path graph, the resulting spectrogram matches our intuition from classical discrete-time signal processing. Yet, our construction is fully generalized and can be applied to analyze signals on any undirected, connected, weighted graph.

Computer Generation of Hardware for Linear Digital Signal Processing Transforms

Abstract: Linear signal transforms such as the discrete Fourier transform (DFT) are very widely used in digital signal processing and other domains. Due to high performance or efficiency requirements, these transforms are often implemented in hardware. This implementation is challenging due to the large number of algorithmic options (e.g., fast Fourier transform algorithms or FFTs), the variety of ways that a fixed algorithm can be mapped to a sequential datapath, and the design of the components of this datapath. The best choices depend heavily on the resource budget and the performance goals of the target application. Thus, it is difficult for a designer to determine which set of options will best meet a given set of requirements.In this article we introduce the Spiral hardware generation framework and system for linear transforms. The system takes a problem specification as input as well as directives that define characteristics of the desired datapath. Using a mathematical language to represent and explore transform algorithms and datapath characteristics, the system automatically generates an algorithm, maps it to a datapath, and outputs a synthesizable register transfer level Verilog description suitable for FPGA or ASIC implementation. The quality of the generated designs rivals the best available handwritten IP cores.

On the Bandwidth of the Plenoptic Function

Abstract: The plenoptic function (POF) provides a powerful conceptual tool for describing a number of problems in image/video processing, vision, and graphics. For example, image-based rendering is shown as sampling and interpolation of the POF. In such applications, it is important to characterize the bandwidth of the POF. We study a simple but representative model of the scene where band-limited signals (e.g., texture images) are “painted” on smooth surfaces (e.g., of objects or walls). We show that, in general, the POF is not band limited unless the surfaces are flat. We then derive simple rules to estimate the essential bandwidth of the POF for this model. Our analysis reveals that, in addition to the maximum and minimum depths and the maximum frequency of painted signals, the bandwidth of the POF also depends on the maximum surface slope. With a unifying formalism based on multidimensional signal processing, we can verify several key results in POF processing, such as induced filtering in space and depth-corrected interpolation, and quantify the necessary sampling rates.

Plane-wave ultrasound beamforming using a nonuniform fast fourier transform

Abstract: Beamforming of plane-wave ultrasound echo signals in the Fourier domain provides fast and accurate image reconstruction. Conventional implementations perform a k-space interpolation from the uniform sampled grid to a nonuniform acoustic dispersion grid. In this paper, we demonstrate that this step can be replaced by a nonuniform Fourier transform. We study the performance of the nonuniform fast Fourier transform (NUFFT) in terms of signal-to-noise ratio and computational cost, and show that the NUFFT offers an advantage in the trade-off between speed and accuracy, compared with other frequency-domain beamforming strategies.

Generalized State Coherence Transform for Multidimensional TDOA Estimation of Multiple Sources

Abstract: According to the physical meaning of the frequency-domain blind source separation (FD-BSS), each mixing matrix estimated by independent component analysis (ICA) contains information on the physical acoustic propagation related to each source and then can be used for localization purposes. In this paper, we analyze the Generalized State Coherence Transform (GSCT) which is a non-linear transform of the space represented by the whole demixing matrices. The transform enables an accurate estimation of the propagation time-delay of multiple sources in multiple dimensions. Furthermore, it is shown that with appropriate nonlinearities and a statistical model for the reverberation, GSCT can be considered an approximated kernel density estimator of the acoustic propagation time-delay. Experimental results confirm the good properties of the transform and its effectiveness in addressing multiple source TDOA detection (e.g., 2-D TDOA estimation of several sources with only three microphones).

Method and system for demodulating a digital pulse recognition signal in a light based positioning system using a fourier transform

Abstract: In one aspect, the present disclosure relates to a method for demodulating a modulated light signal in a light based positioning system. In some embodiments, the method includes receiving a modulated light signal at a device, the modulated light signal transmitting an arbitrary identifier of a light source, recording the modulated light signal using a rolling shutter image sensor to create a raw image having alternating spaced apart stripes, subtracting background information from the raw image to create an isolated image, taking a Fourier transform of an image representative of the isolated image to calculate a Fourier transformed signal, and determining the arbitrary identifier of the light source based on the Fourier transformed signal.

Contemporary ultrasonic signal processing approaches for nondestructive evaluation of multilayered structures

Abstract: Various signal processing techniques have been used for the enhancement of defect detection and defect characterisation. Cross-correlation, filtering, autoregressive analysis, deconvolution, neural network, wavelet transform and sparse signal representations have all been applied in attempts to analyse ultrasonic signals. In ultrasonic nondestructive evaluation (NDE) applications, a large number of materials have multilayered structures. NDE of multilayered structures leads to some specific problems, such as penetration, echo overlap, high attenuation and low signal-to-noise ratio. The signals recorded from a multilayered structure are a class of very special signals comprised of limited echoes. Such signals can be assumed to have a sparse representation in a proper signal dictionary. Recently, a number of digital signal processing techniques have been developed by exploiting the sparse constraint. This paper presents a review of research to date, showing the up-to-date developments of signal processing t...

Design and Simulation of 32-Point FFT Using Radix-2 Algorithm for FPGA Implementation

Abstract: The Fast Fourier Transform (FFT) is one of the rudimentary operations in field of digital signal and image processing. Some of the very vital applications of the fast fourier transform include Signal analysis, Sound filtering, Data compression, Partial differential equations, Multiplication of large integers, Image filtering etc. Fast Fourier transform (FFT) is an efficient implementation of the discrete Fourier transform (DFT). This paper concentrates on the development of the Fast Fourier Transform (FFT), based on Decimation-In-Time (DIT) domain, Radix-2 algorithm, this paper uses VHDL as a design entity, and their Synthesis by Xilinx Synthesis Tool on Vertex kit has been done. The input of Fast Fourier transform has been given by a PS2 KEYBOARD using a test bench and output has been displayed using the waveforms on the Xilinx Design Suite 12.1. The synthesis results show that the computation for calculating the 32-point Fast Fourier transform is efficient in terms of speed.

Optimal Signal Processing in Ground-Based Forward Scatter Micro Radars

Abstract: The received signal in forward scatter radar (FSR) depends on the target's electrical size and trajectory, which are unknown a priori. As a result, in practical situations, it is impossible to obtain the accurate reference function at the reception side, and adaptation of optimal filtering is therefore proposed for this case. This paper presents a signal processing algorithm for ground target detection using FSR, which includes the construction of the adaptive reference functions and the identification of target velocity and its observation time. Furthermore, the algorithm performance is analytically determined under practical motion trajectories such as different motion directions and baseline crossing points, which indicate the effectiveness of the proposed algorithm in a practical case for FSR. The effectiveness of the algorithm is shown using both simulated and experimental data. Finally, the resolution in convoy targets in ground-based FSR is analytically obtained for the first time; the resolution is totally different from the resolution in conventional radar theory because of the target signature characteristics in ground-based FSR. The majority of the analytical results are verified experimentally.

A Geometric Construction of Multivariate Sinc Functions

Abstract: We present a geometric framework for explicit derivation of multivariate sampling functions (sinc) on multidimensional lattices. The approach leads to a generalization of the link between sinc functions and the Lagrange interpolation in the multivariate setting. Our geometric approach also provides a frequency partition of the spectrum that leads to a nonseparable extension of the 1-D Shannon (sinc) wavelets to the multivariate setting. Moreover, we propose a generalization of the Lanczos window function that provides a practical and unbiased approach for signal reconstruction on sampling lattices. While this framework is general for lattices of any dimension, we specifically characterize all 2-D and 3-D lattices and show the detailed derivations for 2-D hexagonal body-centered cubic (BCC) and face-centered cubic (FCC) lattices. Both visual and numerical comparisons validate the theoretical expectations about superiority of the BCC and FCC lattices over the commonly used Cartesian lattice.

Algebraic Signal Processing Theory: 1-D Nearest Neighbor Models

Abstract: We present a signal processing framework for the analysis of discrete signals represented as linear combinations of orthogonal polynomials. We demonstrate that this representation implicitly changes the associated shift operation from the standard time shift to the nearest neighbor shift introduced in this paper. Using the algebraic signal processing theory, we construct signal models based on this shift and derive their corresponding signal processing concepts, including the proper notions of signal and filter spaces, z-transform, convolution, spectrum, and Fourier transform. The presented results extend the algebraic signal processing theory and provide a general theoretical framework for signal analysis using orthogonal polynomials.

Power Quality Disturbance Classification Using S-transform and Hidden Markov Model

Abstract: In this article, a combination method based on S-transform and the hidden Markov model is presented for power quality disturbance classification. S-transform is a new method that has been used in signal processing and power quality disturbance classification applications. S-transform not only has the advantages of both wavelet transform and fast Fourier transform but also characteristics superior than both mentioned transform methods. Besides, the hidden Markov model is a powerful and effective method on signal processing applications. This method computes the maximum likelihood probability between training and testing data signals for identification. The proposed method makes the classification more simple and accurate. The identification procedure has two major steps. First, features are extracted by S-transform, and in the second step, classification is done using a decision tree structure of hidden Markov models. Simulation results by simulated and experimental test signals reveal the robustn...

Signal Processing in Radar Systems

Abstract: Introduction Part I Design of Radar Digital Signal Processing and Control Algorithms Principles of Systems Approach to Design Complex Radar Systems Methodology of Systems Approach Main Requirements to Complex Radar Systems Problems of System Design for Automated Complex Radar Systems Radar Signal Processing System as an Object of Design Signal Processing by Digital Generalized Detector in Complex Radar Systems Analog to Digital Signal Conversion: Main Principles Digital Generalized Detector for Coherent Impulse Signals Convolution in Time Domain Convolution in Frequency Domain Examples of Some DGD Types Digital Interperiod Signal Processing Algorithms Digital Moving-Target Indication Algorithms DGD for Coherent Impulse Signals with Known Parameters DGD for Coherent Impulse Signals with Unknown Parameters Digital Measurers of Target Return Signal Parameters Complex Generalized Algorithms of Digital Interperiod Signal Processing Algorithms of Target Range Track Detection and Tracking Main Stages and Signal Reprocessing Operations Target Range Track Detection Using Surveillance Radar Data Target Range Tracking Using Surveillance Radar Data Filtering and Extrapolation of Target Track Parameters Based on Radar Measure Initial Conditions Process Representation in Filtering Subsystems Statistical Approach to Solution of Filtering Problems of Stochastic (Unknown) Parameters Algorithms of Linear Filtering and Extrapolation under Fixed Sample Size of Measurements Recurrent Filtering Algorithms of Undistorted Polynomial Target Track Parameters Adaptive Filtering Algorithms of Maneuvering Target Track Parameters Logical Flowchart of Complex Radar Signal Reprocessing Algorithm Principles of Control Algorithm Design for Complex Radar System Functioning at Dynamical Mode Configuration and Flowchart of Radar Control Subsystem Direct Control of Complex Radar Subsystem Parameters Scan Control in New Target Searching Mode Power Resource Control under Target Tracking Distribution of Power Resources of Complex Radar System under Combination of Target Searching and Target Tracking Modes Part II Design Principles of Computer System for Radar Digital Signal Processing and Control Algorithms Design Principles of Complex Algorithm Computational Process in Radar Systems Design Considerations Complex Algorithm Assignment Evaluation of Work Content of Complex Digital Signal Processing Algorithm Realization by Microprocessor Subsystems Paralleling of Computational Process Design Principles of Digital Signal Processing Subsystems Employed by Complex Radar System Structure and Main Engineering Data of Digital Signal Processing Subsystems Requirements for Effective Speed of Operation Requirements for RAM Size and Structure Selection of Microprocessor for Designing the Microprocessor Subsystems Structure and Elements of Digital Signal Processing and Complex Radar System Control Microprocessor Subsystems High-Performance Centralized Microprocessor Subsystem for Digital Signal Processing of Target Return Signals in Complex Radar Systems Programmable Microprocessor for Digital Signal Preprocessing of Target Return Signals in Complex Radar Systems Digital Signal Processing Subsystem Design (Example) General Statements Design of Digital Signal Processing and Control Subsystem Structure Structure of Coherent Signal Preprocessing Microprocessor Subsystem Structure of Noncoherent Signal Preprocessing Microprocessor Subsystem Signal Reprocessing Microprocessor Subsystem Specifications Structure of Digital Signal Processing Subsystem Global Digital Signal Processing System Analysis Digital Signal Processing System Design Analysis of "n - 1 - 1" MTI System Analysis of "n - n - 1" MTI System Analysis of "n - m - 1" MTI System Comparative Analysis of Target Tracking Systems Part III Stochastic Processes Measuring in Radar Systems Main Statements of Statistical Estimation Theory Main Definitions and Problem Statement Point Estimate and Its Properties Effective Estimations Loss Function and Average Risk Bayesian Estimates for Various Loss Functions Estimation of Mathematical Expectation Conditional Functional Maximum Likelihood Estimate of Mathematical Expectation Bayesian Estimate of Mathematical Expectation: Quadratic Loss Function Applied Approaches to Estimate the Mathematical Expectation Estimate of Mathematical Expectation at Stochastic Process Sampling Mathematical Expectation Estimate under Stochastic Process Amplitude Quantization Optimal Estimate of Varying Mathematical Expectation of Gaussian Stochastic Process Varying Mathematical Expectation Estimate under Stochastic Process Averaging in Time Estimate of Mathematical Expectation by Iterative Methods Estimate of Mathematical Expectation with Unknown Period Estimation of Stochastic Process Variance Optimal Variance Estimate of Gaussian Stochastic Process Stochastic Process Variance Estimate under Averaging in Time Errors under Stochastic Process Variance Estimate Estimate of Time-Varying Stochastic Process Variance Measurement of Stochastic Process Variance in Noise Estimation of Probability Distribution and Density Functions of Stochastic Process Main Estimation Regularities Characteristics of Probability Distribution Function Estimate Variance of Probability Distribution Function Estimate Characteristics of the Probability Density Function Estimate Probability Density Function Estimate Based on Expansion in Series Coefficient Estimations Measurers of Probability Distribution and Density Functions: Design Principles Estimate of Stochastic Process Frequency-Time Parameters Estimate of Correlation Function Correlation Function Estimation Based on its Expansion in Series Optimal Estimation of Gaussian Stochastic Process Correlation Function Parameter Correlation Function Estimation Methods Based on Other Principles Spectral Density Estimate of Stationary Stochastic Process Estimate of Stochastic Process Spike Parameters Mean-Square Frequency Estimate of Spectral Density Notation Index Index Chapters include a summary and discussion as well as references.

Efficient phase retrieval of sparse signals

Abstract: We consider the problem of one dimensional (1D) phase retrieval, namely, recovery of a 1D signal from the magnitude of its Fourier transform. This problem is ill-posed since the Fourier phase information is lost. Therefore, prior information on the signal is needed in order to recover it. In this work we consider the case in which the prior information on the signal is that it is sparse, i.e., it consists of a small number of nonzero elements. We propose a fast local search method for recovering a sparse 1D signal from measurements of its Fourier transform magnitude. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that the proposed algorithm is fast and more accurate than existing techniques.

Coarse beamforming techniques for multi-beam satellite networks

Abstract: Coarse beamforming is a space processing scheme which allows for efficient use of the available spectral resources on the feeder link of a multi-beam broadband satellite system. In this framework, spectral occupancy of the multiplexed antenna signals that must be exchanged between the satellite and the gateway is a critical issue, up to the point that a costly multiple gateway infrastructure could be required to restrain bandwidth usage. Alternatively, a hybrid on board/on ground processing architecture is desirable, where the effect of space processing allows to project feed signals on a subspace, thus reducing the required feeder link bandwidth. This foresees a fixed processing scheme on board the satellite, which we refer to as Coarse Beamforming, yielding an overall system which relies on reasonable payload complexity, together with on ground processing flexibility. We explore two Coarse Beamforming techniques for the return link of a multi beam satellite system, and we evaluate the effect of compression in terms of reconstructed signal degradation. We show how, without significant distortion, bandwidth occupancy can be considerably reduced.

On robust phase retrieval for sparse signals

Abstract: Recovering signals from their Fourier transform magnitudes is a classical problem referred to as phase retrieval and has been around for decades. In general, the Fourier transform magnitudes do not carry enough information to uniquely identify the signal and therefore additional prior information is required. In this paper, we shall assume that the underlying signal is sparse, which is true in many applications such as X-ray crystallography, astronomical imaging, etc. Recently, several techniques involving semidefinite relaxations have been proposed for this problem, however very little analysis has been performed. The phase retrieval problem can be decomposed into two tasks — (i) identifying the support of the sparse signal from the Fourier transform magnitudes, and (ii) recovering the signal using the support information. In earlier work [13], we developed algorithms for (i) which provably recovered the support for sparsities upto O(n1/3−∊). Simulations suggest that support recovery is possible upto sparsity O(n1/2−∊). In this paper, we focus on (ii) and propose an algorithm based on semidefinite relaxation, which provably recovers the signal from its Fourier transform magnitude and support knowledge with high probability if the support size is O(n1/2−∊).

Statistical Signal Processing: Frequency Estimation

Abstract: Signal processing may broadly be considered to involve the recovery of information from physical observations. The received signal is usually disturbed by thermal, electrical, atmospheric or intentional interferences. Due to the random nature of the signal, statistical techniques play an important role in analyzing the signal. Statistics is also used in the formulation of the appropriate models to describe the behavior of the system, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Statistical signal processing basically refers to the analysis of random signals using appropriate statistical techniques. The main aim of this book is to introduce different signal processing models which have been used in analyzing periodic data, and different statistical and computational issues involved in solving them. We discuss in detail the sinusoidal frequency model which has been used extensively in analyzing periodic data occuring in various fields. We have tried to introduce different associated models and higher dimensional statistical signal processing models which have been further discussed in the literature. Different real data sets have been analyzed to illustrate how different models can be used in practice. Several open problems have been indicated for future research.

A new signal processing method for video: reproduce the frequency spectrum exceeding the Nyquist frequency

Abstract: A new signal processing method to improve video image quality is proposed in this paper. Improving video image resolution has been researched for many years. The method of edge enhancing is usually known as Sharpness Circuit (SC), Enhancer or Unsharp Mask,. It is widely used since it works in real time and it is cost efficient. However, edge enhancing does not actually improve the degree of resolution but increases perceived resolution. It is almost impossible to create components exceeding the Nyquist frequency using conventional linear signal processing methods. Super Resolution (SR) is a highly interesting research field and many ideas and methods have been proposed, some of which have the potential to actually enhance the resolution. However, these ideas have not been widely discussed in the frequency domain. A new signal processing method that creates components exceeding the Nyquist frequency is proposed. The simulation results are discussed with regard to time domain as well as to the frequency domain.

Multiscale Signal Analysis and Modeling

Abstract: Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory.

Application of MATLAB in Digital Signal Processing

Abstract: The digital filter is one of the most significant applications of digital signal processing (DSP). The design process is very complex involving the model approximation, parameter selection, computer simulation and a series of work. This paper introduces an efficient design method for the digital filter (IIR and FIR) based on the Signal Processing Toolbox of MATLAB, which makes design easy, fast and greatly reduces the amount of design work, and then proves it by practical examples.

Advanced Signal Processing and Command Synthesis for Memory-Limited Complex Systems

Abstract: This paper presents advanced signal processing methods and command synthesis for memory-limited complex systems. For accurate measurements performed on limited time interval, some specific methods should be added. For signal processing, a robust filtering and sampling procedure performed on a specific working interval is required, so as the influence of low-amplitude and high-frequency fluctuations to be diminished. This study shows that such a signal processing method for the case of memory-limited complex systems requires the use of certain differentiation/integration procedures performed by oscillating systems, so as robust results suitable for efficient command synthesis to be available. A brief comparison with uncertainty aspects in modern physics (where quantum aspects can be considered as features of complex systems) is also presented.

Method and device for bandwidth extension

Abstract: The present invention relates to a method and device for extending the signal bandwidth of a voice or audio signal. The bandwidth extension method according to the present invention comprises the steps of: generating a first transformed signal by subjecting an input signal to a MDCT (Modified Discrete Cosine Transform); generating a second transformed signal and a third transformed signal based on the first transformed signal; generating respective normal components and energy components from the first transformed signal, the second transformed signal and the third transformed signal; generating an extended normal component from the respective normal components, and generating an extended energy component from the respective energy components; generating an extended transformed signal based on the extended normal component and the extended energy component; and subjecting the extended transformed signal to IMDCT (Inverse MDCT).

Audio Processing Device, System, Use and Method

Abstract: AUDIO PROCESSING DEVICE, SYSTEM, USE AND METHOD The application relates to an audio processing device comprising a) an input unit for converting a time domain input signal to a number Ni of input frequency bands and b) an output unit for converting a number No of output frequency bands to a time domain output signal. The application further relates to the use of such device and to a method of processing an input audio signal. The object of the present application is to provide a flexible audio processing scheme, e.g. adapted to characteristics of the input signal. The problem is solved in that c) a signal processing unit adapted to process the input signal in a number Np of processing channels, the number Np of processing channels being smaller than the number Ni of input frequency bands, d) a frequency band allocation unit for allocating input frequency bands to processing channels, e) a frequency band redistribution unit for redistributing processing channels to output frequency bands, and f) a control unit for dynamically controlling the allocation of input frequency bands to processing channels and the redistribution of processing channels to output frequency bands. This has the advantage of allowing the audio processing to be optimized to a particular acoustic environment and/or to a user's needs (e.g. hearing impairment) with a view to minimizing power consumption and/or processing frequency resolution. The invention may e.g. be used in applications where processing resources are limited, e.g. in portable devices subject to size and/or power consumption constraints. (Fig. 3 should be published) IFBNI OFBNO IN -1-. IU C-BC&PU OU OUT IFB1 X OFB, oX-CNT FIG. 1a IN - U BC&PU OU OUT IFB Nc TCNT OFB, X-CNT FIG. 1b IFBNI OFBNO IN U BC&PU OU OUT IFB, Nc CNT OFB, X-CNT FIG. 1c

EEG signal processing based on proper orthogonal decomposition

Abstract: Proper orthogonal decomposition (POD) is proposed to decompose and reconstruct multidimensional electroencephalo-graph (EEG) signal. With this method, EEG signal can be represented by orthonomal basis, eigenfuction mode and main coordinate component. The eigenfuction mode is deterministic functions of spatial information, and the main coordinate component is the random function of real time information. In this modal decomposition way, EEG signal can not only be compressed in a certain least squares optimal sense, but also be predicted for any random scalp position.

Fractional Fourier Transform for Ultrasonic Chirplet Signal Decomposition

Abstract: A fractional fourier transform (FrFT) based chirplet signal decomposition (FrFT-CSD) algorithm is proposed to analyze ultrasonic signals for NDE applications. Particularly, this method is utilized to isolate dominant chirplet echoes for successive steps in signal decomposition and parameter estimation. FrFT rotates the signal with an optimal transform order. The search of optimal transform order is conducted by determining the highest kurtosis value of the signal in the transformed domain. A simulation study reveals the relationship among the kurtosis, the transform order of FrFT, and the chirp rate parameter in the simulated ultrasonic echoes. Benchmark and ultrasonic experimental data are used to evaluate the FrFT-CSD algorithm. Signal processing results show that FrFT-CSD not only reconstructs signal successfully, but also characterizes echoes and estimates echo parameters accurately. This study has a broad range of applications of importance in signal detection, estimation, and pattern recognition.