Showing papers on "Multidimensional signal processing published in 2004"

From frequency to quefrency: a history of the cepstrum

Abstract: The idea of the log spectrum or cepstral averaging has been useful in many applications such as audio processing, speech processing, speech recognition, and echo detection for the estimation and compensation of convolutional distortions. To suggest what prompted the invention of the term cepstrum, this article narrates the historical and mathematical background that led to its discovery. The computations of earlier simple echo representations have shown that the spectrum representation domain results does not belong in the frequency or time domain. Bogert et al. (1963) chose to refer to it as quefrency domain and later termed the spectrum of the log of a time waveform as the cepstrum. The article also recounts the analysis of Al Oppenheim in relation to the cepstrum. It was in his theory for nonlinear signal processing, referred to as homomorphic systems, that the realization of the characteristic system of homomorphic convolution was reminiscent of the cepstrum. To retain both the relationship to the work of Bogart et al. and the distinction, the term power cepstrum was eventually applied to the nonlinear mapping in homomorphic deconvolution . While most of the terms in the glossary have faded into the background, the term cepstrum has survived and has become part of the digital signal processing lexicon.

Differentiation of discrete multidimensional signals

Abstract: We describe the design of finite-size linear-phase separable kernels for differentiation of discrete multidimensional signals. The problem is formulated as an optimization of the rotation-invariance of the gradient operator, which results in a simultaneous constraint on a set of one-dimensional low-pass prefilter and differentiator filters up to the desired order. We also develop extensions of this formulation to both higher dimensions and higher order directional derivatives. We develop a numerical procedure for optimizing the constraint, and demonstrate its use in constructing a set of example filters. The resulting filters are significantly more accurate than those commonly used in the image and multidimensional signal processing literature.

An introduction to statistical signal processing

Abstract: This volume describes the essential tools and techniques of statistical signal processing. At every stage, theoretical ideas are linked to specific applications in communications and signal processing. The book begins with an overview of basic probability, random objects, expectation, and second-order moment theory, followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the text.

Spiral: A Generator for Platform-Adapted Libraries of Signal Processing Algorithms

Abstract: SPIRAL is a generator for libraries of fast software implementations of linear signal processing transforms. These libraries are adapted to the computing platform and can be re-optimized as the hardware is upgraded or replaced. This paper describes the main components of SPIRAL: the mathematical framework that concisely describes signal transforms and their fast algorithms; the formula generator that captures at the algorithmic level the degrees of freedom in expressing a particular signal processing transform; the formula translator that encapsulates the compilation degrees of freedom when translating a specific algorithm into an actual code implementation; and, finally, an intelligent search engine that finds within the large space of alternative formulas and implementations the "best" match to the given computing platform. We present empirical data that demonstrate the high performance of SPIRAL generated code.

Comparative analysis of radio occultation processing approaches based on Fourier integral operators

Abstract: [1] We analyze and compare two approaches to processing radio occultation data: (1) canonical transform method and (2) full spectrum inversion method. We show that these methods are closely related and can be explained from two view points: (1) both methods apply a Fourier transform like operator to the entire radio occultation signal, and the derivative of the phase of the transformed signal is used for the computation of bending angles, and (2) they can be explained from a signal processing view point as the location of multiple tones constituting the complete signal. The full spectrum inversion method is a composition of phase correction and Fourier transform, which makes the numerical algorithm computationally more efficient as compared to the canonical transform method. We investigate the relative performance of the two methods in simulations using a wave optics propagator. We use simple analytical models of the atmospheric refractivity as well as radiosonde data in order to reproduce complex multipath situations. The numerical simulations as well as the analytical estimations indicate that a resolution of 60 m (or even higher) can be achieved.

Design of signal-adapted multidimensional lifting scheme for lossy coding

Abstract: This paper proposes a new method for the design of lifting filters to compute a multidimensional nonseparable wavelet transform Our approach is stated in the general case, and is illustrated for the 2-D separable and for the quincunx images Results are shown for the JPEG2000 database and for satellite images acquired on a quincunx sampling grid The design of efficient quincunx filters is a difficult challenge which has already been addressed for specific cases Our approach enables the design of less expensive filters adapted to the signal statistics to enhance the compression efficiency in a more general case It is based on a two-step lifting scheme and joins the lifting theory with Wiener's optimization The prediction step is designed in order to minimize the variance of the signal, and the update step is designed in order to minimize a reconstruction error Application for lossy compression shows the performances of the method

Reference structure tomography.

Abstract: Reference structure tomography (RST) uses multidimensional modulations to encode mappings between radiating objects and measurements. RST may be used to image source-density distributions, estimate source parameters, or classify sources. The RST paradigm permits scan-free multidimensional imaging, data-efficient and computation-efficient source analysis, and direct abstraction of physical features. We introduce the basic concepts of RST and illustrate the use of RST for multidimensional imaging based on a geometric radiation model.

Blind I/Q imbalance parameter estimation and compensation in low-IF receivers

Abstract: The rejection of the image signal is a problem inherent to all receiver architectures. One of the benefits of the low-IF receiver is, that image rejection is realized by I/Q signal processing instead of a fixed analog filter, making it highly reconfigurable and cost-efficient. However, unavoidable imbalances between the I- and Q-branch lead to a limited image attenuation. In this paper a novel I/Q imbalance compensation scheme is presented, in which the unknown analog imbalance parameters are estimated digitally without the need for any calibration or training signal. Based on these estimates the interference by the image signal is effectively compensated, upgrading the vulnerable ordinary low-IF receiver to a powerful advanced receiver architecture, where the mean value of the image-to-signal ratio never exceeds a pre-defined maximum value, regardless of the image signal power.

Robust and frequency-adaptive measurement of peak value

Abstract: A new approach for measuring the peak value of the fundamental component of a distorted sinusoidal signal for power system applications is presented. The method is applicable to single-phase as well as three-phase systems. While maintaining structural simplicity, the proposed approach is highly robust with respect to noise and distortion due to disturbances and unbalanced conditions of the system. The method is also highly tolerant of uncertainties in the setting of its internal parameters. The salient feature of the proposed approach is its capability of adapting to the variations in the center frequency of the input signal. The method is suitable for environments that frequency excursions are experienced and conventional discrete Fourier transform (DFT)-based methods do not provide satisfactory results. Speed and accuracy of the response can also be controlled. Structural simplicity and robustness of the proposed scheme make it well suited for digital implementation on software and hardware platforms. Performance of the proposed method is presented based on simulation studies in the MATLAB environment and an experimental setup.

Signal processing apparatus, signal processing method, correction value generation apparatus, correction value generation method, and display apparatus manufacturing method

Abstract: A signal processing apparatus supplied with a signal obtained by performing orthogonal transform on a video signal corresponding to a plurality of pixels includes a correction circuit for correcting the orthogonal-transformed signal, by using correction values obtained by performing orthogonal transform equivalent to the orthogonal transform on measured values obtained by measuring nonuniformity of display characteristics of a display device, which performs display on the basis of a signal processed by the signal processing apparatus, and an inverse orthogonal transform device for performing inverse orthogonal transform on a signal corrected by the correction circuit and thereby obtaining a corrected video signal.

Rotation estimation from spherical images

Abstract: Robotic navigation algorithms increasingly make use of the panoramic field of view provided by omnidirectional images to assist with localization tasks. Since the images taken by a particular class of omnidirectional sensors can be mapped to the sphere, the problem of attitude estimation arising from 3D rotations of the camera can be treated as a problem of estimating rotations between spherical images. Recently, it has been shown that direct signal processing techniques are effective tools in handling rotations of the sphere, but are limited when the signal is altered by larger rotations of omnidirectional cameras. We present an effective solution to the attitude estimation problem under large rotations. Our approach utilizes a shift theorem for the spherical Fourier transform to produce a solution in the spectral domain.

OFDM receiver, semiconductor integrated circuit and OFDM method for receiving a signal

Abstract: An OFDM receiver includes a converter configured to generate a transform signal by Fourier transform of a received signal. A first interpolator is configured to detect a pilot signal from the transform signal, and to provide time interpolation to the pilot signal. An interference detector is configured to provide arithmetic processing to the time interpolated pilot signal, and to detect interference by comparing a result of the arithmetic processing with a threshold. A second interpolator is configured to provide frequency interference interpolation with respect to the interference detected pilot signal, and to provide frequency interpolation to the pilot signal after the interference interpolation. A demodulator is configured to perform demodulation based on the transform signal and the frequency interpolated pilot signal.

Binaural signal enhancement system

Abstract: A signal processing system, such as a hearing aid system, adapted to enhance binaural input signals is provided The signal processing system is essentially a system with a first signal channel having a first filter and a second signal channel having a second filter for processing first and second channel inputs and producing first and second channel outputs, respectively Filter coefficients of at least one of the first and second filters are adjusted to minimize the difference between the first channel input and the second channel input in producing the first and second channel outputs The resultant signal match processing of the signal processing system gives broader regions of signal suppression than using the Wiener filters alone for frequency regions where the interaural correlation is low, and may be more effective in reducing the effects of interference on the desired speech signal Modifications to the algorithms can be made to accommodate sound sources located to the sides as well as the front of the listener Processing artifacts can be reduced by using longer averaging time constants for estimating the signal power and cross-spectra as the signal-to-noise ratio decreases A stability constant can also be incorporated in the transfer functions of the first and second filters to increase the stability of the signal processing system

Estimation of multiple local orientations in image signals

Abstract: Local orientation estimation can be posed as the problem of finding the minimum grey level variance axis within a local neighbourhood. In 2D image signals, this corresponds to the eigensystem analysis of a 2 /spl times/ 2-tensor, which yields valid results for single orientations. We describe extensions to multiple overlaid orientations, which may be caused by transparent objects, crossings, bifurcations, corners etc. Multiple orientation detection is based on the eigensystem analysis of an appropriately extended tensor, yielding so-called mixed orientation parameters. These mixed orientation parameters can be regarded as another tensor built from the sought individual orientation parameters. We show how the mixed orientation tensor can be decomposed into the individual orientations by finding the roots of a polynomial. Applications are, e.g., in directional filtering and interpolation, feature extraction for corners or crossings, and signal separation.

Fixed-point error analysis of CORDIC processor based on the variance propagation formula

Abstract: This paper presents a fixed-point mean-square error (MSE) analysis of coordinate rotation digital computer (CORDIC) processors based on the variance propagation method, whereas the conventional approaches provide only the error bound which results in large discrepancy between the analysis and actual implementation. The MSE analysis is aimed at obtaining a more accurate analysis of digital signal processing systems with CORDIC processor, especially when the design specification is given by the signal-to-noise ratio or MSE. For the MSE analysis, the error source and models are first defined and the output error is derived in terms of MSE in the rotation mode of the conventional CORDIC processor. It is shown that the proposed analysis can also be applied to the modified CORDIC algorithms. As an example of practical application, a fast Fourier transform processor using the CORDIC processor is presented in this paper, and its output error variance is analyzed with respect to the wordlength of CORDIC. The results show a close match between the analysis and simulation.

Fast algorithm for the 3-D DCT-II

Abstract: Recently, many applications for three-dimensional (3-D) image and video compression have been proposed using 3-D discrete cosine transforms (3-D DCTs). Among different types of DCTs, the type-II DCT (DCT-II) is the most used. In order to use the 3-D DCTs in practical applications, fast 3-D algorithms are essential. Therefore, in this paper, the 3-D vector-radix decimation-in-frequency (3-D VR DIF) algorithm that calculates the 3-D DCT-II directly is introduced. The mathematical analysis and the implementation of the developed algorithm are presented, showing that this algorithm possesses a regular structure, can be implemented in-place for efficient use of memory, and is faster than the conventional row-column-frame (RCF) approach. Furthermore, an application of 3-D video compression-based 3-D DCT-II is implemented using the 3-D new algorithm. This has led to a substantial speed improvement for 3-D DCT-II-based compression systems and proved the validity of the developed algorithm.

Adaptive detector for multiple-data-path systems

Abstract: An adaptive, reduced-complexity soft-output maximum-likelihood detector that is operable to process data by adaptively selecting a processing scheme based on a determination of signal quality. The signal quality is derived as a function of the noise, the modulation format, the channel (the communication environment), the transmit signal power and the receive signal power. If the signal quality is low, the signal is processed using a maximum likelihood detector. If, however, the signal quality is high, a simpler sub-optimal detector is used. By estimating the signal quality and choosing an appropriate detection method, the present invention ensures accurate detection of incoming data signals in a MIMO communication system while maintaining the highest possible processing speed.

Algorithms for hardware-based pattern recognition

Abstract: Nonlinear spatial transforms and fuzzy pattern classification with unimodal potential functions are established in signal processing. They have proved to be excellent tools in feature extraction and classification. In this paper, we will present a hardware-accelerated image processing and classification system which is implemented on one field-programmable gate array (FPGA). Non-linear discrete circular transforms generate a feature vector. The features are analyzed by a fuzzy classifier. This principle can be used for feature extraction, pattern recognition, and classification tasks. Implementation in radix-2 structures is possible, allowing fast calculations with a computational complexity of O(N)up to O(N ċ ld(N)). Furthermore, the pattern separability properties of these transforms are better than those achieved with the well-known method based on the power spectrum of the Fourier Transform, or on several other transforms. Using different signal flow structures, the transforms can be adapted to different image and signal processing applications.

Particle swarm optimization algorithm in signal detection and blind extraction

Abstract: The particle swarm optimization (PSO) algorithm, which originated as a simulation of a simplified social system, is an evolutionary computation technique. In this paper the binary and real-valued versions of PSO algorithm are exploited in two important signal processing paradigm: multiuser detection (MUD) and blind extraction of sources (BES), respectively. The novel approaches are effective and efficient with parallel processing structure and relatively feasible implementation. Simulation results validate either PSO-MUD or PSO-BES has a significant performance improvement over conventional methods.

An FPGA implementation of parallel ICA for dimensionality reduction in hyperspectral images

Abstract: Independent component analysis (ICA) is a technique that extracts independent source signals by searching for a linear or nonlinear transformation which minimizes the statistical dependence between components. ICA has been used in a variety of signal processing applications including dimensionality reduction in hyperspectral image (HSI) analysis. Due to the computation complexities and convergence rates, ICA is very time-consuming for high volume or dimension data set like hyperspectral images. Hardware implementation provides not only an optimal parallelism environment, but also a potential faster and real-time solution. This work synthesizes a parallel ICA (pICA) algorithm on field programmable gate array (FPGA). In the proposed implementation method, the pICA is partitioned into three temporally independent functional modules, and each of which is synthesized individually with several ICA-related reconfigurable components (RCs) that are developed for reuse and retargeting purpose. All modules are then integrated into a design and development environment for performing many subtasks such as FPGA synthesis, optimization, placement and routing. In a case study, we synthesize the pICA algorithm for hyperspectral image dimensionality reduction on the pilchard reconfigurahle computing platform embedded with Xilinx: VIRTEX V1000E. The FPGA executes at the maximum frequency of 20.161 MHz, and the pilchard board transfers data directly with CPU on the 64-bit memory bus at the maximum frequency of 133MHz. The performance comparisons between the proposed and another two ICA-related FPGA implementations show that the proposed FPGA implementation of pICA has potential in performing complicated algorithms on large volume data sets.

Estimation of multiple orientations in multidimensional signals

Abstract: We present a solution to the general problem of estimating multiple orientations in multidimensional signals. The solution is divided in a linear part that provides the mixed-orientation space (MOS) and a nonlinear part that gives the actual orientation spaces. We show that the angle between two overlaid orientations is an invariant that can be derived from the MOS without solving the nonlinear part and that all other invariants are generated by this angle. Results obtained for synthetic images illustrate that the above invariant is a useful image feature for various applications such as pattern recognition and texture segmentation.

Low-rank decomposition of multi-way arrays: a signal processing perspective

Abstract: In many signal processing applications of linear algebra tools, the signal part of a postulated model lies in a so-called signal sub-space, while the parameters of interest are in one-to-one correspondence with a certain basis of this subspace. The signal sub-space can often be reliably estimated from measured data, but the particular basis of interest cannot be identified without additional problem-specific structure. This is a manifestation of rotational indeterminacy, i.e., non-uniqueness of low-rank matrix decomposition. The situation is very different for three-or higher-way arrays, i.e., arrays indexed by three or more independent variables, for which low-rank decomposition is unique under mild conditions. This has fundamental implications for DSP problems which deal with such data. This paper provides a brief lour of the basic elements of this theory, along with many examples of application in problems of current interest in the signal processing community.

Analysis of reflection of signal transmitted in low-voltage powerline with complex wavelet

Abstract: Recently, the low-voltage powerline communication has become a hot research topic for electrical advancements. However, effective implementation of high-quality signal transmission over this kind of network presents difficulties, because of the unpredictable load effects, noise, high attenuation, reflection, and resonance effects. Furthermore, as the frequency of the reflection signal is the same as that of the sending signal, it is difficult to pick up the useful information from the received signal. This paper aims to find out an effective way to deal with this problem. Since there is phase difference between the reflection signal and the sending signal, the complex wavelet transform is used to get additional information. By use of the additional information, a novel method to obtain the useful signal is proposed. Experiment results show the effectiveness of the proposed method.

How to take advantage of aliasing in bandlimited signals

Abstract: In signal processing systems, aliasing is normally treated as a disturbing signal. That motivates the need for effective analog, optical and digital anti-aliasing filters. However, aliasing also conveys valuable information on the signal above the Nyquist frequency. Hence, an effective processing of the samples, based on a model of the input signal, would virtually allow the sampling frequency to be increased using slower and cheaper converters. We present such an algorithm for bandlimited signals that are sampled below twice the maximum signal frequency. Using a subspace method in the frequency domain, we show that these signals can be reconstructed from multiple sets of samples. The offset between the sets is unknown and can have arbitrary values. This approach can be applied to the creation of super-resolution images from sets of low resolution images. In this application, registration parameters have to be computed from aliased images. We show that parameters and high resolution images can be computed precisely, even when high levels of aliasing are present on the low resolution images.

New class of broadband arrays with frequency invariant beam patterns

Abstract: In this paper, a new class of broadband arrays with frequency-invariant beam patterns is proposed. By suitable substitutions, the beam pattern of a continuous sensor array with continuous temporal processing can be regarded as the Fourier transform of its spatio-temporal distribution. Based on this principle, starting from the desired frequency-invariant beam pattern, and by a series of substitutions, a simple design method is derived. This method can be applied to one-dimensional (1D), 2D, or 3D broadband arrays, either with continuous arrays and signal processing or with discrete arrays and signal processing. A 2D discrete design example is presented.

Signal and image processing in navigational systems

Abstract: Preface About the Author Introduction THEORY OF FLUCTUATING TARGET RETURN SIGNALS IN NAVIGATIONAL SYSTEMS Probability Distribution Density of the Amplitude and Phase of the Target Return Signal Two-Dimensional Probability Distribution Density of the Amplitude and Phase Probability Distribution Density of the Amplitude Probability Distribution Density of the Phase Probability Distribution Density Parameters of the Target Return Signal as a Function of the Distribution Law of the Amplitude and Phase of Elementary Signals Conclusions References Correlation Function of Target Return Signal Fluctuations Target Return Signal Fluctuations The Correlation Function and Power Spectral Density of the Target Return Signal The Correlation Function with the Searching Signal of Arbitrary Shape The Correlation Function under Scanning of the Three-Dimensional (Space) Target The Correlation Function in Angle Scanning of the Two-Dimensional (Surface) Target The Correlation Function under Vertical Scanning of the Two-Dimensional (Surface) Target Conclusions References Fluctuations Under Scanning of the Three-Dimensional (Space) Target with the Moving Radar Slow and Rapid Fluctuations The Doppler Fluctuations of a High-Deflected Radar Antenna The Doppler Fluctuations in the Arbitrarily Deflected Radar Antenna The Total Power Spectral Density with the Pulsed Searching Signal Conclusions References Fluctuations Under Scanning of the Two-Dimensional (Surface) Target by the Moving Radar General Statements The Continuous Searching Nonmodulated Signal The Pulsed Searching Signal with Stationary Radar The Pulsed Searching Signal with the Moving Radar: The Aspect Angle Correlation Function The Pulsed Searching Signal with the Moving Radar: The Azimuth Correlation Function The Pulsed Searching Signal with the Moving Radar: The Total Correlation Function and Power Spectral Density of the Target Return Signal Fluctuations Short-Range Area of the Radar Antenna Vertical Scanning of the Two-Dimensional (Surface) Target Determination of the Power Spectral Density Conclusions References Fluctuations Caused by Radar Antenna Scanning General Statements Line Scanning Conical Scanning Conical Scanning with Simultaneous Rotation of Polarization Plane Conclusions References Fluctuations Caused by the Moving Radar with Simultaneous Radar Antenna Scanning General Statements The Moving Radar with Simultaneous Radar Antenna Line Scanning The Moving Radar with Simultaneous Radar Antenna Conical Scanning Conclusions References Fluctuations Caused by Scatterers Moving Under the Stimulus of the Wind Deterministic Displacements of Scatterers under the Stimulus of the Layered Wind Scatterers Moving Chaotically (Displacement and Rotation) Simultaneous Deterministic and Chaotic Motion of Scatterers Conclusions References Fluctuations Under Scanning of the Two-Dimensional (Surface) Target with the Continuous Frequency-Modulated Signal General Statements The Linear Frequency-Modulated Searching Signal The Asymmetric Saw-Tooth Frequency-Modulated Searching Signal The Symmetric Saw-Tooth Frequency-Modulated Searching Signal The Harmonic Frequency-Modulated Searching Signal Phase Characteristics of the Transformed Target Return Signal under Harmonic Frequency Modulation Conclusions References Fluctuations Under Scanning of the Three-Dimensional (Space) Target by the Continuous Signal with a Frequency that Varies with Time General Statements The Nontransformed Target Return Signal The Transformed Target Return Signal Conclusions References Fluctuations Caused by Variations in Frequency from Pulse to Pulse Three-Dimensional (Space) Target Scanning Two-Dimensional (Surface) Target Scanning Conclusions References GENERALIZED APPROACH TO SPACE-TIME SIGNAL AND IMAGE PROCESSING IN NAVIGATIONAL SYSTEMS Foundations of the Generalized Approach to Signal Processing in Noise Basic Concepts Criticism Initial Premises Likelihood Ratio The Engineering Interpretation Generalized Detector Conclusions References Theory of Space-Time Signal and Image Processing in Navigational Systems Basic Concepts of Navigational System Functioning Basics of the Generalized Approach to Signal and Image Processing in Time Basics of the Generalized Approach to Space-Time Signal and Image Processing Space-Time Signal Processing and Pattern Recognition Based on the Generalized Approach to Signal Processing Peculiarities of Optical Signal Formation Peculiarities of the Formation of the Earth's Surface Radar Image Foundations of Digital Image Processing Conclusions References Implementation Methods of the Generalized Approach to Space-Time Signal and Image Processing in Navigational Systems Synthesis of Quasioptimal Space-Time Signal and Image Processing Algorithms Based on the Generalized Approach to Signal Processing The Quasioptimal Generalized Image Processing Algorithm The Classical Generalized Image Processing Algorithm The Difference Generalized Image Processing Algorithm The Generalized Phase Image Processing Algorithm The Generalized Image Processing Algorithm: Invariant Moments The Generalized Image Processing Algorithm: Amplitude Ranking The Generalized Image Processing Algorithm: Gradient Vector Sums The Generalized Image Processing Algorithm: Bipartite Functions The Hierarchical Generalized Image Processing Algorithm The Generalized Image Processing Algorithm: The Use of the Most Informative Area The Generalized Image Processing Algorithm: Coding of Images The Multichannel Generalized Image Processing Algorithm Conclusions References Object Image Preprocessing Object Image Distortions Geometrical Transformations Detection of Boundary Edges Conclusions References Appendix I: Classification of Stochastic Processes Appendix II: The Power Spectral Density of the Target Return Signal with Arbitrary Velocity Vector Direction of the Moving Radar in Space and with the Presence of Roll and Pitch Angles Notation Index Index

Signal processing techniques for damage detection with piezoelectric wafer active sensors and embedded ultrasonic structural radar

Abstract: Embedded ultrasonic structural radar (EUSR) algorithm is developed for using piezoelectric wafer active sensor (PWAS) array to detect defects within a large area of a thin-plate specimen. Signal processing techniques are used to extract the time of flight of the wave packages, and thereby to determine the location of the defects with the EUSR algorithm. In our research, the transient tone-burst wave propagation signals are generated and collected by the embedded PWAS. Then, with signal processing, the frequency contents of the signals and the time of flight of individual frequencies are determined. This paper starts with an introduction of embedded ultrasonic structural radar algorithm. Then we will describe the signal processing methods used to extract the time of flight of the wave packages. The signal processing methods being used include the wavelet denoising, the cross correlation, and Hilbert transform. Though hardware device can provide averaging function to eliminate the noise coming from the signal collection process, wavelet denoising is included to ensure better signal quality for the application in real severe environment. For better recognition of time of flight, cross correlation method is used. Hilbert transform is applied to the signals after cross correlation in order to extract the envelope of the signals. Signal processing and EUSR are both implemented by developing a graphical user-friendly interface program in LabView. We conclude with a description of our vision for applying EUSR signal analysis to structural health monitoring and embedded nondestructive evaluation. To this end, we envisage an automatic damage detection application utilizing embedded PWAS, EUSR, and advanced signal processing.