We develop redundant CORDIC scheme where the scale factor is forced to be constant while computing angles for 2 × 1 plane rotations. Based on the scheme, we present a fixed-point implementation of matrix triangul...
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We develop redundant CORDIC scheme where the scale factor is forced to be constant while computing angles for 2 × 1 plane rotations. Based on the scheme, we present a fixed-point implementation of matrix triangularization by Luk's parallel algorithm, with the following additional features: (1) the final scaling operation is done by shifting, (2) the number of iterations in CORDIC rotation unit is reduced by about 25% by expressing the direction of the rotation in radix-2 and radix-4, and (3) the conventional number representation of rotated output is obtained on-the-fly, not from a carry-propagate adder. The number of hardware modules and the speed are evaluated and compared with the previous CORDIC schemes.
This paper outlines means of using special sets of orthonormally related windows to realize Cohen's class of time-frequency distributions (TFDs). This is accomplished by decomposing the kernel of the distribution ...
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This paper outlines means of using special sets of orthonormally related windows to realize Cohen's class of time-frequency distributions (TFDs). This is accomplished by decomposing the kernel of the distribution in terms of the set of analysis windows to obtain short time Fourier transforms (STFTs). The STFTs obtained using these analysis windows are used to form spectrograms which are then linearly combined with proper weights to form the desired TFD. A set of orthogonal analysis windows which also have the scaling property proves to be very effective, requiring only 1 + log2(N - 1) distinct windows for an overall analysis of N + 1 points, where N = 2n, with n a positive integer. Application of this theory offers very fast computation of TFDs, since very few analysis windows needed and fast, recursive STFT algorithms can be used. Additionally, it is shown that a minimal set of specially derived orthonormal windows can represent most TFDs, including Reduced Interference Distributions (RIDs) with only three distinct windows plus an impulse window. Finally, the Minimal Window RID (MW-RID) which achieves RID properties with only one distinct window and an impulse window is presented.
In this paper, we use the concept of evolutionary spectrum to solve key problems in array processing. We present Cross-power Evolutionary Periodogram for direction finding and blind separation of nonstationary signals...
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In this paper, we use the concept of evolutionary spectrum to solve key problems in array processing. We present Cross-power Evolutionary Periodogram for direction finding and blind separation of nonstationary signals. We model nonstationary signals received by each sensor in the array as a sum of complex sinusoids with time-varying amplitudes. These amplitudes carry information about the direction of arrival which may also be time-varying. We first estimate the time-varying amplitudes, then use the results for the estimation of evolutionary cross-power distributions of the sensor data. Next, using cross-power estimates at time-frequency samples of interest, we estimate the directions of arrival using one of the existing high resolution direction finding methods. If the directions are time-varying, we select time-frequency points around the time of interest. By carrying out the estimation at different times, we obtain the directions as a function of time. If the sources are stationary, then we can use all time-frequency points of interest for the estimation of fixed directions. We also use whitening and subspace methods to find the mixing matrix and separate the signals received by the array. Simulation examples illustrating the performances of the proposed algorithms are presented.
This paper describes a scheduling method for hard real-time Digital signalprocessing (DSP) applications, implemented on a multi-processor. Due to the very high operating frequencies of DSP applications (typically hun...
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ISBN:
(纸本)0819416207
This paper describes a scheduling method for hard real-time Digital signalprocessing (DSP) applications, implemented on a multi-processor. Due to the very high operating frequencies of DSP applications (typically hundreds of kHz) runtime overhead should be kept as small as possible. Because static scheduling introduces very little run-time overhead it is used as much as possible. Dynamic pre-emption of tasks is allowed if and only if it leads to better performance in spite of the extra run-time overhead. We essentially combine static scheduling with dynamic pre-emption using static priorities. Since we are dealing with hard real-time applications we must be able to guarantee at compile-time that all timing requirements will be satisfied at run-time. We will show that our method performs at least as good as any static scheduling method. It also reduces the total amount of dynamic pre-emptions compared with run time methods like deadline monotonic scheduling.
The research work reported in this paper is concerned with the use of higher order spectral estimation techniques as a means to deriving the parameters of 2D autoregressive (AR) models. Image analysis is examined from...
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ISBN:
(纸本)0819416207
The research work reported in this paper is concerned with the use of higher order spectral estimation techniques as a means to deriving the parameters of 2D autoregressive (AR) models. Image analysis is examined from a higher order statistical perspective and in the context of noise. The objective is to develop analysis techniques through which robust autoregressive parameter estimation is accomplished. The approach taken involves the use of 2D AR models derive from third order cumulants. The directionality of the cumulant space influences the AR parameter estimation in a decisive manner. The specific application of the developed methods is in mammography, an area in which it is very difficult to discern the appropriate features. The results show significant discriminating gains through such techniques.
We compare the performance of three parallel supercomputers executing a bispectrum estimation code used to remove distortions from astronomical data. We discuss the issues in parallelizing the code on an 8-processor s...
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ISBN:
(纸本)0819406945
We compare the performance of three parallel supercomputers executing a bispectrum estimation code used to remove distortions from astronomical data. We discuss the issues in parallelizing the code on an 8-processor shared-memory CRAY Y-MP and a 1024-processor distributed-memory nCUBE machine. Results show that elapsed times on the nCUBE machine are comparable to those on the CRAY Y-MP. Execution of the nCUBE was more than 40 times faster than that of a single processor CRAY-2 resulting in more than 50 times better cost performance. Cost performance on the nCUBE is more than 25 times better than an 8- processor CRAY Y-MP.
This paper examines the problem of instantaneous frequency (IF) estimation for Frequency Modulated (FM) signals imbedded in white Gaussian noise. It reviews currently available techniques, and in addition, proposes so...
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This paper examines the problem of instantaneous frequency (IF) estimation for Frequency Modulated (FM) signals imbedded in white Gaussian noise. It reviews currently available techniques, and in addition, proposes some new ones, based on a modelling of the signal phase as a polynomial. Both linear least-squares techniques and Maximum Likelihood (ML) techniques are investigated for estimating the polynomial coefficients. It is seen that the linear least squares approach is efficient (i.e. unbiased and meets the Cramer-Rao bounds) for high SNR, while the ML scheme is efficient for a much larger range of SNR. Theoretical lower variance bounds are given for estimating the polynomial coefficients and are compared with the results of simulations. Guidelines are given as to which estimation method should be used for a given signal class and signal to Noise Ratio (SNR) level.
This paper presents a generalized parametric estimator for the directions of arrival (DOA) of wide-band signals. This estimator is derived by extenting the geometrical explanation of the ML estimator of narrow-band si...
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This paper presents a generalized parametric estimator for the directions of arrival (DOA) of wide-band signals. This estimator is derived by extenting the geometrical explanation of the ML estimator of narrow-band signals to the focussed correlation matrix. The consistency of the estimator for estimating DOA has been proved. This estimator can be considered as a coherent signalprocessing method by which the computation complexity can be reduced approximately by the number of the frequency bins. We have also shown that under certain condition the proposed estimator is equivalent to the ML estimator derived by applying the likelihood principle on the Fourier coefficients of each frequency bin. Such an equivalence implies that the MLE has some inherent advantages from the perspective of improving performance and that the focussing techniques are not necessary for the ML estimator.
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of se...
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ISBN:
(纸本)9780819472946
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may, exhibit insufficient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR, techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.
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. As such, the FMT is u...
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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. As such, the FMT is unchanged by translations and dilations of the input function. While the FMT has found applications in optical pattern recognition, ship classification by sonar and radar, and image processing, 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. This approach readily generalizes to a direct expansion for the Wigner-Ville distribution and the Q distribution.
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