7981159
Extraction of temporally correlated sources with its application to non-invasive fetal electrocardiogram extraction
This letter addresses the problem of fetal electrocardiogram (FECG) extraction. A class of source extraction algorithms is proposed and it uses a combination of two different approaches: Barros's source extraction approach and Hyvarinen's fixed-point approach. The proposed algorithms can rapidly extract the desired FECG with little noise, and can work well in some poor situations. The validity and performance of the algorithms are confirmed by extensive computer simulations and experiments on real-world data.
35739145
A Flexible Algorithm for Extracting Periodic Signals
In this paper, we propose a flexible two-stage algorithm for extracting desired periodic signals. In the first stage, if the period and phase information of the desired signal is available (or can be estimated), a minimum mean square error approach is used to coarsely recover the desired source signal. If only the period information is available (or can be estimated), a robust correlation based method is proposed to achieve the same goal. The second stage uses a higher-order statistics based Newton-like algorithm, derived from a constrained maximum likelihood criteria, to process the extracted noisy signal as cleanly as possible. A parameterized nonlinearity is adopted in this stage, adapted according to the estimated statistics of the desired signal. Compared with many existing extraction algorithms, the proposed algorithm has better performance, which is confirmed by simulations.
17900866
Robust extraction of specific signals with temporal structure
There is a trend to develop blind or semi-blind source extraction algorithms based on second-order statistics, due to its low computation load and fast processing speed. An important and primary work is done by Barros and Cichocki, who propose an extraction algorithm based on a time delay. The algorithm is simple and fast, but its performance is not satisfying. The paper extends their work and proposes a robust algorithm based on eigenvalue decomposition of several delayed covariance matrices. It is faster and has better performance, which is confirmed by theoretical analysis and computer simulations.
14479142
WDM optical interconnects with recirculating buffering and limited range wavelength conversion
All-optical communication, in particular, wavelength-division-multiplexing (WDM) technique, has been proposed as a promising candidate to meet the ever-increasing demands on bandwidth from emerging bandwidth-intensive computing/networking applications. However, with current technology, the cost of optical communication, especially the cost of optical buffering and wavelength conversion, remains a major concern for such applications. In this paper, we study WDM optical interconnects that utilize low cost recirculating buffering and limited range wavelength conversion. We first consider the packet scheduling problem in this type of interconnect, and formalize the problem of maximizing throughput and minimizing packet delay as a matching problem in a bipartite graph. We give an optimal parallel algorithm for this problem that runs in O(Bk/sup 2/) time, compared to O((N+B)/sup 3/k/sup 3/) time if directly applied to existing matching algorithms for general bipartite graphs, where N is the number of input/output fibers of the interconnect, B is the number of fiber delay lines, and k is the number of wavelengths. We also consider efficient switching fabric designs for this type of interconnect. We distinguish between the switching fabric connecting the input fibers to the output fibers and the switching fabric connecting the input fibers to the delay lines and show that by adopting the idea of concentration, the cost of the latter can be reduced significantly in terms of the number of crosspoints.
2916019
Pseudogene.org: a comprehensive database and comparison platform for pseudogene annotation
The Pseudogene.org knowledgebase serves as a comprehensive repository for pseudogene annotation. The definition of a pseudogene varies within the literature, resulting in significantly different approaches to the problem of identification. Consequently, it is difficult to maintain a consistent collection of pseudogenes in detail necessary for their effective use. Our database is designed to address this issue. It integrates a variety of heterogeneous resources and supports a subset structure that highlights specific groups of pseudogenes that are of interest to the research community. Tools are provided for the comparison of sets and the creation of layered set unions, enabling researchers to derive a current ‘consensus’ set of pseudogenes. Additional features include versatile search, the capacity for robust interaction with other databases, the ability to reconstruct older versions of the database (accounting for changing genome builds) and an underlying object-oriented interface designed for researchers with a minimal knowledge of programming. At the present time, the database contains more than 100 000 pseudogenes spanning 64 prokaryote and 11 eukaryote genomes, including a collection of human annotations compiled from 16 sources.
8318108
Weakly-supervised relation classification for information extraction
This paper approaches the relation classification problem in information extraction framework with bootstrapping on top of Support Vector Machines. A new bootstrapping algorithm is proposed and empirically evaluated on the ACE corpus. We show that the supervised SVM classifier using various lexical and syntactic features can achieve promising classification accuracy. More importantly, the proposed <i>BootProject</i> algorithm based on random feature projection can significantly reduce the need for labeled training data with only limited sacrifice of performance.
6686875
Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron
The authors investigate the use of two types of features extracted from face images for recognizing facial expressions. The first type is the geometric positions of a set of fiducial points on a face. The second type is a set of multi-scale and multi-orientation Gabor wavelet coefficients extracted from the face image at the fiducial points. They can be used either independently or jointly. The architecture developed is based on a two-layer perceptron. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, they have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of feature expressions.
6801587
Performance analysis of k-fold multicast networks
Multicast involves transmitting information from a single source to multiple destinations, and is an important operation in high-performance networks. A k-fold multicast network was recently proposed as a cost-effective solution to providing better quality-of-service functions in supporting real-world multicast applications. To give a quantitative basis for network designers to determine the suitable value of system parameter k under different traffic loads, in this paper, we propose an analytical model for the performance of k-fold multicast networks under Poisson traffic. We first give the stationary distribution of network states, and then derive the throughput and blocking probability of the network. We also conduct extensive simulations to validate the analytical model, and the results show that the analytical model is very accurate under the assumptions made. The analytical and simulation results reveal that by increasing the fold of the network, network throughput increases very fast when the fanouts of multicast connections are relatively small, compared with the network size.