Nonlinear range curvature effect and linear range walk effect appearing in collected SAR data array cause SAR image quality to be distorted. Therefore, two kinds of range cell migration (RCM) compensation processing a...
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Nonlinear range curvature effect and linear range walk effect appearing in collected SAR data array cause SAR image quality to be distorted. Therefore, two kinds of range cell migration (RCM) compensation processing are required in addition to the two dimensional pulse compression processes. In this paper, special purpose hardware which compensates for these linear and nonlinear cell migration effects are implemented in architecture level and logic level by using the compensation algorithm used in digital SAR processing. Pre-layout simulations using CAD tools are also carried out to verify the normal operation of the designed hardware. SAR parameters used during compensation processing are the parameters for spaceborne SEASAT SAR launched in 1978. Data block to be processed is composed of 4096 pulses/spl times/4096 samples.
A new switched capacitor (SC) circuit for image processing is presented. Optimized area occupancy and power consumption allow the integration of the proposed circuit into each pixel, in order to implement high speed p...
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A new switched capacitor (SC) circuit for image processing is presented. Optimized area occupancy and power consumption allow the integration of the proposed circuit into each pixel, in order to implement high speed pixel-parallel processing. The operation performed by the circuit consists of the accumulation of the absolute value of the difference between two voltage inputs, which is the base of a large set of image processingalgorithms. Two circuit configurations have been developed and compared in this paper: a basic implementation and a second improved solution with higher sensitivity and linearity over the full output range. The improved pixel consists of 10 transistors and 3 capacitances and provides an output voltage range of 1.8 V with 3.3 V power supply. A test chip was fabricated in a 0.35 /spl mu/m double-poly triple-metal CMOS process. The resulting 35 /spl mu/m square pixel has a power consumption of 3 /spl mu/W @ 3.3 V. The single absolute difference and accumulation is executed in 2 /spl mu/s, which turns into a computing figure of 1.2 GOPS/mm/sup 2/ and 1.3 TOPS/W.
The proper acquisition and exploitation of carrying signals in an intelligent network ensure performances optimization. Especially, if the used method seeks to use existing resources and improve their uses in order to...
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ISBN:
(纸本)9781467385275
The proper acquisition and exploitation of carrying signals in an intelligent network ensure performances optimization. Especially, if the used method seeks to use existing resources and improve their uses in order to make it more effective. However, based on this concept, the design of a flexible, dynamic, and resistant network can be easily reached. This paper deals with the issue of the good exploiting and processing of signals in intelligent networks. For this, we propose an approach of an autonomous and efficient method inspired from the nervous system that enables getting signals, process it, look for failure in case of problem, find solutions for it or signalize it if it is hard to solve. Finally, we describe how to implement it to be usable in wider applications.
In this paper, the authors present interactive, graphical software for introducing speech coding in undergraduate and graduate courses on digital signalprocessing (DSP) and speech processing. The software facilitates...
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In this paper, the authors present interactive, graphical software for introducing speech coding in undergraduate and graduate courses on digital signalprocessing (DSP) and speech processing. The software facilitates students in understanding and experimentation for practical applications of signalprocessing concepts introduced in DSP courses. It consists of two complementary components. The first is a MATLAB-based graphical simulation of a state-of-the-art speech coder. This component of the software is designed to complement the theoretical aspects of speech coding with practical exposure to the algorithms using hands-on simulations. The second component is an application that provides a framework for evaluation of different speech coding algorithms for a variety of applications. With this tool, students can easily categorize the performance of different speech coding algorithms through objective measurements as well as subjective listening tests. These tools could be used in entry-level graduate courses in DSP and speech coding.
The employment of the generalized algorithm creates a possibility to increase the noise immunity for complex detection systems of more than twice in comparison with using the optimal detection algorithms of signals wi...
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The employment of the generalized algorithm creates a possibility to increase the noise immunity for complex detection systems of more than twice in comparison with using the optimal detection algorithms of signals with a priori known and unknown amplitude-phase structure. A comparative analysis of the detection characteristics of the optimal and generalized detectors is carried out. Investigation avenues to stabilization of the detection characteristics under employment of the generalized detector independent of the signal random parameters based on the tracking systems are proposed.
Direction of Arrival (DOA) estimation based on array signalprocessing is the main content of spatial spectrum estimation. Multiple signal Classification (MUSIC) is the most classical super-resolution spatial spectrum...
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ISBN:
(纸本)9781467347129
Direction of Arrival (DOA) estimation based on array signalprocessing is the main content of spatial spectrum estimation. Multiple signal Classification (MUSIC) is the most classical super-resolution spatial spectrum estimation method. Under ideal condition, the algorithm can precisely estimate the DOA of uncorrelated signals. However, the performance of MUSIC algorithm will degrade seriously or even fail in the coherent source signal estimation. In the condition that many artificial signals have cyclostationary characteristics which use the target signal information, circular cross correlation MUSIC algorithm can further improve the quality of signalprocessing and have better noise suppressing property and resolution. But it is restricted to the cyclic correlation signal resolution. Therefore, this paper proposes an improved circular cross correlation MUSIC algorithm. Simulation results show that the performance of the improved circular cross correlation MUSIC algorithm is superior to the conventional MUSIC algorithm and circular cross correlation MUSIC algorithm in noise suppressing and signal selectivity.
The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast ...
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The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms.
In this paper, we describe an algorithm for finding the exact, nonlinear, maximum likelihood (ML) estimators for the parameters of an autoregressive time series. We demonstrate that the ML normal equations can be writ...
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In this paper, we describe an algorithm for finding the exact, nonlinear, maximum likelihood (ML) estimators for the parameters of an autoregressive time series. We demonstrate that the ML normal equations can be written as an interdependent set of cubic and quadratic equations in the AR polynomial coefficients. We present an algorithm that algebraically solves this set of nonlinear equations for low-order problems. For high-order problems, we describe iterative algorithms for obtaining a ML solution.
In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence of high-dimensional or non-Euclidean observations. We study a non-parametric framework that utili...
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In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence of high-dimensional or non-Euclidean observations. We study a non-parametric framework that utilizes nearest neighbor information among the observations to detect changes in an online setting. It can be applied to data in arbitrary dimension and non-Euclidean data as long as a similarity measure on the sample space can be defined. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test while keeping the false discovery rate controlled at a fixed level. Analytic formulas approximating the average run lengths of the new approaches are derived to make them fast applicable to modern datasets. Simulation studies are provided to support theoretical results. The proposed approach is illustrated with an analysis of the NYC taxi dataset.
First, we discuss how the wavelet transform can be used to perform spatially-varying filtering of an image, suppressing noise locally in smooth regions of the image, and we discuss detection of such regions in a noise...
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First, we discuss how the wavelet transform can be used to perform spatially-varying filtering of an image, suppressing noise locally in smooth regions of the image, and we discuss detection of such regions in a noise-corrupted image. Second, we show how to compute the minimum mean-square estimate of an image given: 1) noisy projections of the image;2) statistics of additive noise in the projections;and 3) constraints on wavelet coefficients of the image. Examples illustrate the resulting procedure.
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