It is easily demonstrated by simulation that modern digital signal analysis algorithms, such as 'MUSIC'1, have the potential to discriminate (detect two spectrally similar signal components at up to about two ...
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A high accuracy optical linear algebraic processor (OLAP) using the digital multiplication by analog convolution (DMAC) algorithm is described for use in an efficient matrix inverse update algorithm with speed and acc...
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Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is no...
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Despite the enhanced time-frequency analysis (TFA) detailing capability of quadratic TFAs like the Wigner and Cohen representations, their performance with signals of large dynamic range (DNR in excess of 40 dB) is not acceptable due to the inability to totally suppress the cross-term artifacts which typically are much stronger than the weakest signal components that they obscure. AMTI and GMTI radar targets exhibit such high dynamic range when microDoppler is present, with the aspects of interest being the weakest components. This paper presents one of two modifications of linear TFA to provide the enhanced detailing behavior of quadratic TFAs without introducing cross terms, making it possible to see the time-frequency detail of extremely weak signal components. The technique described here is based on subspace-enhanced linear predictive extrapolation of the data within each analysis window to create a longer data sequence for conventional STFT TFA. The other technique, based on formation of a special two-dimensional transformed data matrix analyzed by high-definition two-dimensional spectral analysis methods such as 2-D AR or 2-D minimum variance, is compared to the new technique using actual AMTI and GMTI radar data.
The well-known uncertainty principle is often invoked in signalprocessing. It is also often considered to have the same implications in signal analysis as does the uncertainty principle in quantum mechanics. The unce...
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ISBN:
(纸本)0819406945
The well-known uncertainty principle is often invoked in signalprocessing. It is also often considered to have the same implications in signal analysis as does the uncertainty principle in quantum mechanics. The uncertainty principle is often incorrectly interpreted to mean that one cannot locate the time-frequency coordinates of a signal with arbitrarily good precision, since, in quantum mechanics, one cannot determine the position and momentum of a particle with arbitrarily good precision. Renyi information of the third order is used to provide an information measure on time-frequency distributions. The results suggest that even though this new measure tracks time-bandwidth results for two Gabor log-ons separated in time and/or frequency, the information measure is more general and provides a quantitative assessment of the number of resolvable components in a time frequency representation. As such, the information measure may be useful as a tool in the design and evaluation of time-frequency distributions.
In this paper, the problem of choosing a method for time -frequency signal analysis is discussed. It is shown that a natural approach leads to the introduction of the concepts of the analytic signal and instantaneous ...
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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. advanced three-dimensional (3D) image processing is...
Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs. In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Combining complementary information through registration between pre- and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy. Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signalprocessingalgorithms that take
Establishing measures for local stationarity is an open problem in the field of time-frequency analysis. One promising theoretical measure, known as the spread, provides a means for quantifying potential correlation b...
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Establishing measures for local stationarity is an open problem in the field of time-frequency analysis. One promising theoretical measure, known as the spread, provides a means for quantifying potential correlation between signal elements. In this paper we investigate the issue of generalizing techniques developed by the authors to better estimate the spread of a signal. Existing techniques estimate the spread as the rectangular region of support of the associated expected ambiguity function oriented parallel to the axes. By applying Radon Transform techniques we can produce a parameterized model which describes the orientation of the region of support providing tighter estimates of the signal spread. Examples are provided that illustrate the enhancement of the new method.
This paper addresses time-frequency (TF) analysis from a statistical signalprocessing perspective, with the goal of developing a general statistical methodology for TF analysis. We review earlier work on statistical ...
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This paper addresses time-frequency (TF) analysis from a statistical signalprocessing perspective, with the goal of developing a general statistical methodology for TF analysis. We review earlier work on statistical models for nonstationary stochastic signals, including frequency modulated locally stationary processes, which have covariance functions which yield nonnegative Wigner distributions. For such processes, time-frequency spectra may be defined without invoking `local-' or `quasi-' stationarity. These results are extended to include general time-varying linear systems and their associated time-frequency spectra. Any time-varying linear system driven by white noise has associated with it a nonnegative time-frequency spectrum. The bilinear class of time-frequency distributions are estimators of this time-frequency spectrum, as are adaptive methods such as positive time-frequency distributions and adaptive multitaper spectrograms. An analysis of the statistical properties of these estimators, including moments and distributional properties, is reviewed.
The Fourier-Mellin transform (FMT) of an input function is defined as and is the magnitude squared of the Mellin transform of the magnitude squared of the Fourier transform of the input function [1]. As such the FMT i...
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ISBN:
(纸本)0819404098
The Fourier-Mellin transform (FMT) of an input function is defined as and is the magnitude squared of the Mellin transform of the magnitude squared of the Fourier transform of the input function [1]. As such the FMT is unchanged by translations and dilations of the input function. While the FMT has found applications in optical pattern recognition [3] [5] ship classification by sonar and radar [15] and image processing [10] only cursory attention has been paid to the truncation error incurred by using a finite number of samples of the input function. This paper establishes truncation bounds for computing the FMT for band-limited functions from a finite number of samples of the input function. These bounds naturally suggest an implementation of the FMT by the method of direct expansions [4] [14]. This approach readily generalizes to a direct expansion for the Wigner-Ville distribution [13] and the Q distribution [2]. 1 Principal Notation u(x) fff00 e_2tu(t)dt Fourier transform of u M(u s) fD X_i2r8() Mellin transform of u . FM(u s) M(lI(x)I2 s)________ Fourier-Mellin transform of u Q(U V f002rt U(wft)_V(w/fr) Q distribution of U and V W(U V t w) fe_i2ntY U(w + y/2) V(w y/2) dy Wigner-Ville distribution of U and V
An innovative approach is being used to implement and simulate the infrared (IR) and laser radar signalprocessingalgorithms for the advanced Sensor Technology Program (ASTP) and the Discrimination Interceptor Techno...
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ISBN:
(纸本)0819428221
An innovative approach is being used to implement and simulate the infrared (IR) and laser radar signalprocessingalgorithms for the advanced Sensor Technology Program (ASTP) and the Discrimination Interceptor Technology Program (DITP). Although the algorithms will run on four different computer architectures, they will use the same source code for all implementations. The initial development and testing will occur in Mathcad on a Windows 95/NT personal computer, then move to simulation on a Silicon Graphics (SGI) workstation, then to scaled real-time simulation on a parallel high performance computer (HPC), and finally to the actual flight processor, the miniaturized parallel Wafer Scale signal Processor (WSSP) with a MIMD (multiple-instruction and multiple data) architecture. This flexibility is accomplished with code wrappers that implement interchangeable interface layers for the code modules, one wrapper for Mathcad matrices, one for C++ objects on the workstation, one for message passing with static routing on the HPC, and one for dynamically routed message passing on the WSSP. With this approach, developers can move modules back and forth from the workstation simulation environment to the implementation hardware. This will eliminate the need to maintain different versions of the same algorithm. The signalprocessingalgorithms will be modified to work in a massively parallel architecture, with a message passing interface, which is simulated on the Silicon Graphics workstation, emulated on the HPC, and implemented on the WSSP. This approach will allow for pipeline processing as well as multiple, concurrently running instances of modules. In addition, innovative algorithms will fuse active laser radar detections and passive multicolor IR sensor measurements to improve target state estimation.
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