Propose a new definition for the time-dependent spectrum of a non-stationary process. The authors show that this spectrum can be considered as the dual of Priestley's (1981) evolutionary spectrum. They demonstrate...
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Propose a new definition for the time-dependent spectrum of a non-stationary process. The authors show that this spectrum can be considered as the dual of Priestley's (1981) evolutionary spectrum. They demonstrate how this dual spectrum successfully characterizes signals whose components are highly localized in time. They give an estimator of this dual spectrum. Finally, they present examples which demonstrate that the new spectral definition is more suitable than current approaches for many signals. They show that the value of the method depends on the signal under consideration. They also illustrate that if the signal content is not known, then combining estimates of the evolutionary spectrum and its dual yields good results.< >
Presents an improvement to the evolutionary periodogram, an estimator of the time-dependent spectrum of a non-stationary signal. In this improvement the authors allow this estimator to be used in an iterative fashion....
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Presents an improvement to the evolutionary periodogram, an estimator of the time-dependent spectrum of a non-stationary signal. In this improvement the authors allow this estimator to be used in an iterative fashion. At each iteration, the old spectral estimate is used to update the set of expansion functions used to describe the new time-dependent estimate. This update allows for the estimation of complicated spectra without having to increase the expansion order, as would be the case in the non-iterative (EP). The advantages of iterative time-frequency estimation are discussed and examples are presented to demonstrate these advantages.< >
The main crux of motion error extraction lies on how to reduce the effects of terrain back scattering on the accuracy of extraction. A new solution which can significantly reduce these effects is presented in this pap...
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The main crux of motion error extraction lies on how to reduce the effects of terrain back scattering on the accuracy of extraction. A new solution which can significantly reduce these effects is presented in this paper. The phase term contained in SAR raw data consists of two parts: the phase caused by the vehicle's motion and the ground reflectivity function respectively. Since the former depends heavily on SAR geometry, the least variance estimation can be used to obtain, with high accuracy, the vehicle's motion error according to the pre-determined geometry relationship. computer simulations show that this method is highly accurate and it decreases significantly the dependence of the accuracy on the terrain mapped.< >
We propose an adaptive procedure to model non-stationary signals using autoregressive systems with time-varying parameters. A non-stationary signal that is representable by a time-varying autoregressive system has par...
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We propose an adaptive procedure to model non-stationary signals using autoregressive systems with time-varying parameters. A non-stationary signal that is representable by a time-varying autoregressive system has parameters which are expandable in terms of a set of basis functions. The parameters can be found by posing a minimum least-squares modeling problem and solving a large set of normal equations. The costly calculations involved in this problem make an adaptive solution quite desirable. Using the parameter expansions, we convert the modeling into a linear prediction problem and solve it adaptively for a given set of basis functions. We apply our procedure in the modeling of a segment of speech and in the estimation of the evolutionary spectrum of a non-stationary signal.< >
Effective motion compensation is the key for achieving high quality SAR images. A frequency domain method which is based on an analysis of the azimuth spectrum of SAR raw data to extract aircraft's motion-error, h...
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Effective motion compensation is the key for achieving high quality SAR images. A frequency domain method which is based on an analysis of the azimuth spectrum of SAR raw data to extract aircraft's motion-error, has the advantage of having the ability to operate without accelerometer and INS. However, an antenna with a wide azimuth beam is required. Furthermore,its accuracy depends heavily on the contrast of the ground reflectivity function. A new method based on the time domain approach is presented. Compared with the existing method, the new approach not only has all the mentioned features, but also the advantages of low computation requirement as well as the wide-band motion-error extraction capability. Furthermore, it relaxes some requirements on azimuth antenna beamwidth as well as the ground reflectivity function contrast.< >
The problem of scheduling interference-free transmissions with maximum throughput in a multi-hop radio network is NP-complete. The computational complexity becomes intractable as the network size increases. In this pa...
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The problem of scheduling interference-free transmissions with maximum throughput in a multi-hop radio network is NP-complete. The computational complexity becomes intractable as the network size increases. In this paper, the scheduling is formulated as a combinatorial optimization problem. An efficient neural network approach, namely, mean field annealing, is applied to obtain optimal transmission schedules. Numerical examples show that this method is capable of finding an interference-free schedule with (almost) optimal throughput.< >
We present a method to compute depth from the amount of defocus in two images obtained from the same view-point but with different camera parameter settings. The change in defocus (blur) between the two images is prop...
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We present a method to compute depth from the amount of defocus in two images obtained from the same view-point but with different camera parameter settings. The change in defocus (blur) between the two images is proportional to the depth in the scene. We introduce a novel method to estimate the blur using a multiresolution local frequency representation of the input image pair. A confidence measure is used to discriminate between high error and low error blur estimates. Quantitative experimental results are shown for both real and synthetic images.
作者：
A. OrichiD.B. KochCommunications
Information and Signal Processing Group Department of Electrical and Computer Engineering University of Tennessee Knoxville TN USA
Wireless indoor networks which can provide terminal portability to any node in the network, promise to reduce significantly the expense and troubles of wiring and rewiring associated with wired networks. Since wireles...
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Wireless indoor networks which can provide terminal portability to any node in the network, promise to reduce significantly the expense and troubles of wiring and rewiring associated with wired networks. Since wireless systems operate on bandlimited channels and have to adjust to the constant fluctuations of the indoor environment (i.e., multipath and intermittent fading), any viable wireless technology should be able to accommodate those constraints. A very attractive wireless technology for indoor networks is spread spectrum because of its ability to mitigate multipath fading and incorporate multiple access for bandwidth efficient sharing. A simulated model for a noncoherent FH/BFSK spread spectrum multiple access indoor network in a factory environment is developed. The system is evaluated based on the bit error rate (BER) performance for various chip rates, data rates, and number of hopping frequencies for a given signal to noise plus interference density ratio and the results are compared to theoretical upper and lower bounds.< >
A new radial Butterworth kernel (RBK) with two adjustable parameters for the Cohen's (1966) class of distributions (CCD) is presented in the ambiguity function domain. It is particularly suitable for signals with ...
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A new radial Butterworth kernel (RBK) with two adjustable parameters for the Cohen's (1966) class of distributions (CCD) is presented in the ambiguity function domain. It is particularly suitable for signals with a high degree of of nonstationarity and, by adjusting these parameters, the resulting time-frequency distribution can effectively suppress the crossterms while retaining the autoterms. Numerical experiments using the RBK are compared with other famous distributions. Finally, the RBK is generalized such that a wide variety of shapes can be obtained in the ambiguity function domain.< >
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