We suggest a new method to design adaptation algorithms that guarantee improved performance and are applicable for a class of plants with nonconvex parameterization. The main idea of the method is, first, to augment t...
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In this paper we introduce a common framework for evaluating the performance of multiple weight, partially adaptive Space-Time adaptive Processing (STAP) algorithms in terms of composite weight vectors. We then evalua...
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In this paper we introduce a common framework for evaluating the performance of multiple weight, partially adaptive Space-Time adaptive Processing (STAP) algorithms in terms of composite weight vectors. We then evaluate the performance of these STAP algorithms using synthetic and Mountain Top (MT) data and address some limitations of high dimensional STAP algorithms in a non-stationary clutter environment. As part of the evaluation, we also familiarize the reader with the MT database and address important issues in processing the data.
The architecture and some basic applications of a single-chip software-programmable digital signal processor are presented. This NMOS 3.5 um silicon gate circuit is well-suited for adaptive algorithms and some input a...
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In this paper, we propose new adaptive connection admission control (CAC) algorithms which calculate cell loss performance of related function blocks in a switch node using only a declared peak cell rate and an estima...
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In this paper, we propose new adaptive connection admission control (CAC) algorithms which calculate cell loss performance of related function blocks in a switch node using only a declared peak cell rate and an estimated mean cell rate to reduce inaccuracy of the declared source parameters. We measure only the mean cell rate of the aggregate cell stream in a link to guarantee accuracy in measurement. Proposed CAC algorithms can allow more relaxed source parameter declarations and thus guarantee efficient bandwidth utilization.
In this paper, we examine a high resolution direction of arrival (DOA) estimation techniques. Among of these algorithms which can yield accurate DOA estimates for multiple narrow band sources are Gabriel's thermal...
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In this paper, we examine a high resolution direction of arrival (DOA) estimation techniques. Among of these algorithms which can yield accurate DOA estimates for multiple narrow band sources are Gabriel's thermal noise, adaptive angular response, and maximum-likelihood method. These algorithms are capable of detecting pulse that exists for short periods of time. Under this environment, the performance of different adaptive superresolution array algorithms that utilize the Howell-Applebaum technique is compared. Two different cases are discussed. Firstly, the adaptive array is subjected to only one signal plus thermal noise. Secondly, the case of the presence of two incident signals plus thermal noise is investigated. It is shown that every algorithm offers its best performance depending upon the incident signal parameters, number of antennas, the range of input signal-to-noise ratio, and the angle of separation between the incoming signals.
In this paper, we present an adaptive algorithm for the estimation of source parameters when a release of pollutant in the atmosphere is observed by a sensor network in complex flow field. Due to the error-based obser...
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In this paper, we present an adaptive algorithm for the estimation of source parameters when a release of pollutant in the atmosphere is observed by a sensor network in complex flow field. Due to the error-based observations, inverse statistical methods have to be used to perform an estimation of the parameters (position of the source, time and mass of the release) of interest. However, given the complexity of the dispersion model, even with a Gaussian assumption on the sensor-based errors, direct inversion cannot be done. In order to have quick results, classical MCMC, while accurate, is too slow. We then demonstrate the accuracy of using adaptive techniques such as the AMIS (Population Monte-Carlo based). We finally compare the results with the classical MCMC estimation in term of accuracy and velocity of implementation.
We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an optimal- target process via a learning procedure. We show, under appropriate conditions, that the adaptive process and...
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ISBN:
(纸本)1595935045
We study a class of adaptive Markov Chain Monte Carlo (MCMC) processes which aim at behaving as an optimal- target process via a learning procedure. We show, under appropriate conditions, that the adaptive process and optimal (nonadaptive) MCMC algorithm share identical asymptotic properties. The special case of adaptive MCMC algorithms governed by stochastic approximation is considered in details and we apply our results to the adaptive Metropolis lgorithm of [1]. We also propose a new class of adaptive MCMC algorithms, called quasi-perfect adaptive MCMC which possesses appealing theoretical and practical properties, as demonstrated through numerical simulations. Copyright 2006 ACM.
The dynamics equation of robot manipulators is non linear and coupled. An inverse dynamic control algorithm that requires a full knowledge of the dynamics of the system is one way to solve the control movement. Adapti...
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The increasing demand for high data rate services necessitates the adoption of very wideband waveforms. In this case, the channel is frequency-selective, that is, a large number of resolvable multipaths are present i...
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The increasing demand for high data rate services necessitates the adoption of very wideband waveforms. In this case, the channel is frequency-selective, that is, a large number of resolvable multipaths are present in this environment and fading is not highly correlated across the band. Orthogonal frequency division multiplexing (OFDM) is well-known to be effective against multipath distortion. It is a multicarrier communication scheme, in which the bandwidth of the channel is divided into subcarriers and data symbols are modulated and transmitted on each subcarrier simultaneously. By inserting guard time that is longer than the delay spread of the channel, an OFDM system is able to mitigate intersymbol interference (ISI). Deploying an adaptive antenna array at the receiver can help separate the desired signal from interfering signals which originate from different spatial locations. This enhancement of signal integrity increases system capacity. In this research, we apply adaptive array algorithms to OFDM systems and study their performance in a multipath environment with the presence of interference. A novel adaptive beamforming algorithm based on the minimum mean-squared error (MMSE) criterion, which is referred to as frequency-domain beamforming, is proposed that exploits the characteristics of OFDM signals. The computational complexity of frequency-domain beamforming is also studied. Simulation results show employing an adaptive antenna array with an OFDM system significantly improves system performance when interference is present. Simulations also show that the computational complexity of the algorithm can be reduced by half without significant performance degradation. adaptive array algorithms based on the maximum signal-to-noise ratio (MSNR) and the maximum signal-to-interference-plus-noise ratio (MSINR) criteria are also applied to adaptive vector OFDM systems (AV-OFDM). Simulation results show that the adaptive algorithm based on the MSNR cri
We have constructed an automated learning apparatus to control quantum systems. By directing intense shaped ultrafast laser pulses into a variety of samples and using a measurement of the system as a feedback signal, ...
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We have constructed an automated learning apparatus to control quantum systems. By directing intense shaped ultrafast laser pulses into a variety of samples and using a measurement of the system as a feedback signal, we are able to reshape the laser pulses to direct the system into a desired state. The feedback signal is the input to an adaptive learning algorithm. This algorithm programs a computer-controlled, acousto-optic modulator pulse shaper. The learning algorithm generates new shaped laser pulses based on the success of previous pulses in achieving a predetermined goal.
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