20836735
Kernel smoothing of periodograms under Kullback-Leibler discrepancy
Kernel smoothing on the periodogram is a popular nonparametric method for spectral density estimation. Most important in the implementation of this method is the choice of the bandwidth, or span, for smoothing. One idealized way of choosing the bandwidth is to choose it as the one that minimizes the Kullback-Leibler (KL) discrepancy between the smoothed estimate and the true spectrum. However, this method fails in practice, as the KL discrepancy is an unknown quantity. This paper introduces an estimator for this discrepancy, so that the bandwidth that minimizes the unknown discrepancy can be empirically approximated via the minimization of it. It is shown that this discrepancy estimator is consistent. Numerical results also suggest that this empirical choice of bandwidth often outperforms some other commonly used bandwidth choices. The same idea is also applied to choose the bandwidth for log-periodogram smoothing.
14075649
Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms
This article proposes a new nonparametric procedure for estimating log spectra. This procedure consists of three major components: (1) a novel statistical model for modelling the unknown target log spectrum, (2) an AIC-based model selection criterion for choosing a 'best' fitting model, and (3) a genetic algorithm for effectively searching the 'best' fitting model. Numerical experiments are conducted to evaluate and compare the practical performance of the proposed procedure with some other common log spectral estimation procedures appearing in the literature. These other procedures include wavelet techniques, kernel smoothing and regression spline fitting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures.
41225454
Segmenting images corrupted by correlated noise
Image segmentation is fundamental to many image analysis problems. It aims to partition a digital image into a set of non-overlapping homogeneous regions. This paper describes a new segmentation procedure which is designed to segment images corrupted by correlated noise. This procedure is based on Rissanen's (1989) minimum description length principle.
51888770
Statistical performance guarantees in large-scale cross-path packet switch
Recently, a quasi-static routing scheme called path switching was proposed for large-scale packet switch systems. The path-switched Clos network is called a cross-path switch. The cross-path switch is capable of supporting multirate and multicast traffic. In this paper, we develop a general framework for it to handle multimedia traffic. We study the statistical behavior of the cross-path switch using exponential bounded burstiness (EBB) processes as source session traffic models. With characterizing the service guarantees by service curves, we derive a set of statistical bounds on the delay, backlog and departure processes at the switch on a per-session basis. These bounds are necessary for providing per-session statistical quality of service (QoS) guarantees in the cross-path switch and achieving high system utilization.
767826
The Spectral Independent Components of Natural Scenes
We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.
28008024
Multiresolution Remeshing Using Weighted Centroidal Voronoi Diagram
We present a novel method for multiresolution remeshing of irregular mesh. First, the original mesh (two-manifold any genus) is decomposed into several patches, each patch is homeomorphic to a 2D triangle. The goal of this decomposition process is that the decomposed patches are size-equally. First, a mesh is manually cut into a few disk-like patches. With the help of weighted centroidal Voronoi diagram (WCVD), each patch is then automatically partitioned into more triangular patches with nearly equal size. Recursively subdividing these triangular patches, we finally get a semi-regular mesh.
323922
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels
Decoding is a strategy that allows us to assess the amount of information neurons can provide about certain aspects of the visual scene. In this study, we develop a method based on Bayesian sequential updating and the particle filtering algorithm to decode the activity of V1 neurons in awake monkeys. A distinction in our method is the use of Volterra kernels to filter the particles, which live in a high dimensional space. This parametric Bayesian decoding scheme is compared to the optimal linear decoder and is shown to work consistently better than the linear optimal decoder. Interestingly, our results suggest that for decoding in real time, spike trains of as few as 10 independent but similar neurons would be sufficient for decoding a critical scene variable in a particular class of visual stimuli. The reconstructed variable can predict the neural activity about as well as the actual signal with respect to the Volterra kernels.
9084974
Isolated word recognition using modular recurrent neural networks
Abstract This paper describes a novel method of using recurrent neural networks (RNN) for isolated word recognition. Each word in the target vocabulary is modeled by a fully connected recurrent network. To recognize an input utterance, the best matching word is determined based on its temporal output response. The system is trained in two stages. First, the RNN speech models (RSM) are trained independently to capture the essential static and temporal characteristics of individual words. This is performed by using an iterative re-segmentation training algorithm which gives the optimal phonetic segmentation automatically for each training utterance. The second-stage involves mutually discriminative training among the RSMs, aiming at minimizing the probability of misclassification. A series of simulation experiments have been performed to demonstrate the effectiveness of the proposed recognition method. For the recognition of (A) 20 English words, (B) 11 Cantonese digits and (C) 58 Cantonese CV syllables, the top-1 accuracy are 91.9, 93.6 and 87.1%, respectively.