14663639
An Effective Method to Improve Convergence for Sequential Blind Source Separation
Based on conventional natural gradient algorithm (NGA) and equivariant adaptive separation via independence algorithm (EASI), a novel sign algorithm for on-line blind separation of independent sources is presented. A sign operator for the adaptation of the separation model is obtained from the derivation of a generalized dynamic separation model. A variable step-size sign algorithm rooted in NGA is also derived to better match the dynamics of the input signals and unmixing matrix. The proposed algorithms are appealing in practice due to their computational simplicity. Experimental results verify the superior convergence performance over conventional NGA and EASI algorithm in both stationary and non-stationary environments.
9409183
A Novel Hybrid Approach to the Permutation Problem of Frequency Domain Blind Source Separation
We explore the permutation problem of frequency domain blind source separation (BSS). Based on performance analysis of three approaches: exploiting spectral continuity, exploiting time envelope structure and beamforming alignment; we present a new hybrid method which incorporates a psychoacoustic filtering process for the misaligned permutations unable to be delt with by these approaches. We use a sub- space based method (MUSIC) rather than conventional beamforming for the accurate estimation of the direction of arrivals (DOAs) of the source components, and a frequency dependent distance for the correla- tion of time envelopes. The proposed methods are compared with other approaches by signal to interference ratio (SIR) evaluation, and the new hybrid approach is shown to have the best performance.
7681564
Localization of P300 Sources in Schizophrenia Patients Using Constrained BSS
A robust constrained blind source separation (CBSS) algorithm has been proposed for separation and localization of the P300 sources in schizophrenia patients. The algorithm is an extension of the Infomax algorithm, based on minimization of mutual information, for which a reference P300 signal is used as a constraint. The reference signal forces the unmixing matrix to separate the sources of both auditory and visual P300 resulted from the corresponding stimulations. The constrained problem is then converted to an unconstrained problem by means of a set of nonlinear penalty functions. This leads to the modification of the overall cost function, based on the natural gradient algorithm (NGA). The P300 sources are then localized based on electrode – source correlations.
1884379
Penalty Function Approach for Constrained Convolutive Blind Source Separation
A new approach for convolutive blind source separation(BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the convolutive BSS into a joint diagonalization problem with unconstrained optimization. Theoretical analyses together with numerical evaluations reveal that the proposed method not only improves the separation performance by significantly reducing the effect of large errors within the elements of covariance matrices at low frequency bins and removes the degenerate solution induced by a null unmixing matrix, but also provides an unified framework to constrained BSS.
385307
STING: A Statistical Information Grid Approach to Spatial Data Mining
Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exist in spatial databases, is a challenging task due to the huge amounts of spatial data and to the new conceptual nature of the problems which must account for spatial distance. Clustering and region oriented queries are common problems in this domain. Several approaches have been presented in recent years, all of which require at least one scan of all individual objects (points). Consequently, the computational complexity is at least linearly proportional to the number of objects to answer each query. In this paper, we propose a hierarchical statistical information grid based approach for spatial data mining to reduce the cost further. The idea is to capture statistical information associated with spatial cells in such a manner that whole classes of queries and clustering problems can be answered without recourse to the individual objects. In theory, and confirmed by empirical studies, this approach outperforms the best previous method by at least an order of magnitude, especially when the data set is very large.
12623712
A transparent rate adaptation algorithm for streaming video over the Internet
The lack of end-to-end quality of service support in the current Internet has caused significant difficulties to ensuring playback continuity in video streaming applications. This study addresses this challenge by investigating a new adaptation algorithm to adjust the bit-rate of video data in response to the network bandwidth available to improve playback continuity. Unlike previous works, the proposed algorithm is transparent to the video client, requires no parameter tuning, and yet can outperform existing algorithms. This paper presents this algorithm, evaluates and compares its performance with the best algorithm currently available using extensive trace-driven simulations.
11138955
Computing an Almost Minimum Set of Spanning Line Segments of a Polyhedron
A set of spanning line segments (SLS) is a subset of the edges of a finite polyhedron in E3 such that an arbitrary plane intersects the polyhedron if and only if the plane intersects at least one of the line segments of the SLS. In this paper an algorithm is presented for computing an almost minimum set of spanning line segments for an arbitrary polyhedron . When the number of extreme vertices of is odd, the computed SLS is minimum; when the number of extreme vertices of is even, the size of the computed SLS is at most the minimum size plus one. The algorithm has linear-time complexity for a convex polyhedron, hence is optimal in this case; its time complexity is Θ(m log m) for an arbitrary polyhedron, where m is the number of vertices of the polyhedron.
15982467
On intrinsic representations of 3D polygons for shape blending
Abstract An intrinsic representation of a geometric object comprises a set of parameters that are invariant under Euclidean transformations and determine the shape and size of the object uniquely. Unlike the case of a two-dimensional polygon which has essentially only one intrinsic representation, a three-dimensional (3D) polygon allows several variations. We study three different intrinsic representations of a 3D polygon, focusing on properties regarding their applications in shape blending. We show that only one of these representations, which is a natural extension to the intrinsic functions of a 3D differentiable curve, is suitable for shape blending, while the others lead to discontinuous shape transformation. Detailed discussions about the evaluation criteria, numerical sensitivity, and an inherent difficulty in shape blending of 3D polygons are also presented.
33629250
Robust Visual Tracking Via Incremental Maximum Margin Criterion
Robust visual object tracking is one of the key problems in computer vision. Subspace based tracking method is a promising approach in handling appearance variability. Linear Discriminant Analysis(LDA) has been applied to this problem, but LDA is not a stable algorithm especially for visual tracking. Maximum Margin Criterion(MMC) is a recently proposed discriminant criterion. Its promising specialities make it a better choice for the tracking problem. In this paper, we present a novel subspace tracking algorithm based on MMC. We also proposed an incremental version of the corresponding algorithm so that the tracker can update in realtime. Experiments show our tracking algorithm is able to track objects well under large lighting, pose and expression variation.