A new family of IIR adaptive notch filters is presented. The family is based on a second-order factorization of the all-pass transfer function that forms the multiple notch filter. These realizations represent an exte...
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A new family of IIR adaptive notch filters is presented. The family is based on a second-order factorization of the all-pass transfer function that forms the multiple notch filter. These realizations represent an extension of a previous ad-hoc scheme for adaptive notch filtering that avoids a high-order polynomial root finding in order to obtain the unknown frequencies of interest. An interesting aspect related to these novel algorithms is the fact that they introduce a different compromise between bias and SNR if compared with previous realizations available in the literature. Specifically, lower bias than in other approaches for low SNR can be achieved using the new realizations. This property is particularly attractive for multiple sinusoids estimation and tracking. In addition to the algorithm presentation, a discussion of the different properties and characteristics (stationary points, convergence) is included. Also, computer simulations are presented to illustrate the expected performance of the proposed adaptive filters.
Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such ...
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ISBN:
(纸本)0780387422
Orthogonal subspace projection (OSP) has received considerable interest in hyperspectral image classification. It performs well when the complete knowledge of image endmembers is available. In reality, obtaining such prior knowledge is very difficult, if not impossible. This work investigates a variant of OSP, called uniform projection (UP) and presents a UP-based unsupervised algorithm for hyperspectral image classification. It first implements a uniform target detector (UTD) to find a set of interfering signatures, I, then another uniform I-annihilated detector (UIAD) to find desired target signatures. The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification. In this case, OSP can be used for this purpose. In order to demonstrate the utility of the proposed unsupervised classification technique for hyperspectral imagery, a series of computer simulations and experiments are conducted for analysis.
The interaction of simultaneously co-allocated jobs can often create contention in the network infrastructure of a dedicated computational grid. This contention can lead to degraded job run-time performance. We presen...
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The interaction of simultaneously co-allocated jobs can often create contention in the network infrastructure of a dedicated computational grid. This contention can lead to degraded job run-time performance. We present several bandwidth-aware co-allocating meta-schedulers. These schedulers take into account inter-cluster network utilization as a means by which to mitigate this impact. We make use of a bandwidth-centric parallel job communication model that captures the time-varying utilization of shared inter-cluster network resources. By doing so, we are able to evaluate the performance of grid scheduling algorithms that focus not only on node resource allocation, but also on shared inter-cluster network bandwidth.
In this paper, we present the development of a digital image retrieval technique using autocorrelogram and wavelet based texture feature. Autocorrelogram is a color feature that is able to incorporate the spatial corr...
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In this paper, we present the development of a digital image retrieval technique using autocorrelogram and wavelet based texture feature. Autocorrelogram is a color feature that is able to incorporate the spatial correlation of alike color and is tolerant to large changes in appearance, color, contrast, and brightness. A wavelet transform is used to analyze the textures that are characterized by their energy distribution in each sub-band. We propose an integration of the autocorrelogram and wavelet based texture to improve the performance of the image retrieval system. Experimental results suggest that the proposed integration offers much better performance than using the features separately.
Detecting action potentials has an important role in analyzing extracellular neuronal recordings. Current algorithms require subjective tuning by a user in the form of user-specified parameters. This work describes a ...
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Detecting action potentials has an important role in analyzing extracellular neuronal recordings. Current algorithms require subjective tuning by a user in the form of user-specified parameters. This work describes a fully automatic template-matching spike detection algorithm that does not require any tuning. This algorithm is robust to noise and performs better than an optimum threshold detection algorithm.
Tremor detection in extracellular neuronal recordings has required spike detection because tremors in extracellular neuronal recordings are caused by the fluctuation of action potential's firing rate. This work de...
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Tremor detection in extracellular neuronal recordings has required spike detection because tremors in extracellular neuronal recordings are caused by the fluctuation of action potential's firing rate. This work describes an automatic tremor detection algorithm of extracellular neuronal recordings without using a spike detector. The new algorithm is robust to noise and its performance is not affected by various morphologies of action potentials.
In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally construct...
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In this paper, we develop and evaluate distributed implementations of source localization estimators from energy-based measurements obtained via an ad-hoc network of acoustic sensors. The distributed locally constructed algorithms that we present produce at each node a sequence of estimates approximating a desired source localization algorithm. As our investigation reveals, the localization performance of these distributed algorithms depends on the type of desired localization algorithm, the network topology and the number of communication and fusion steps employed in these approximations.
The study of the effects of scanning on texture features is of great interest to computer-based screening systems. A mathematical model is developed for understanding how the original image gets distorted due to the c...
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The study of the effects of scanning on texture features is of great interest to computer-based screening systems. A mathematical model is developed for understanding how the original image gets distorted due to the contrast variability and geometric distortion inherent in the scanning process. Both quantitative and qualitative results (for sixty common texture features) are given.
Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications,...
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ISBN:
(纸本)0780385543
Spectral measures have been used in material identification and discrimination. They are effective if the spectral signatures are calibrated and not contaminated. However, it may not be true in many real applications, specifically, for mixed pixels and subpixel targets. This paper investigates the issue of discrimination and identification for subpixel targets and further develops sample spectral covariance/correlation matrix-based hyperspectral measures to account for spectral variability within subpixel targets. Two types of measures are of interest and studied, Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures. In order to substantiate the proposed measures, a real data-based comparative analysis is conducted and compared to two spectral similarity measures, spectral angle mapper (SAM) and spectral information divergence (SID) for performance evaluation. The experiments show that both Mahalanobis distance-based hyperspectral measures and matched filter-based hyperspectral measures work very effectively and outperformed the SAM and the SID in discrimination and identification for subpixel targets.
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