On the Efficient Speech Feature Extraction Based on Independent Component Analysis
A new efficient code for speech signals is proposed. To represent speech signals with minimum redundancy we use independent component analysis to adapt features (basis vectors) that efficiently encode the speech signals. The learned basis vectors are sparsely distributed and localized in both time and frequency. Time-frequency analysis of basis vectors shows the property similar with the critical bandwidth of human auditory system. Our results suggest that the obtained codes of speech signals are sparse and biologically plausible.

title: A modified infomax algorithm for blind signal separation
abstract: We present a new algorithm to perform blind signal separation (BSS), which takes a trade-off between the ordinary gradient infomax algorithm and the natural gradient infomax algorithm. Analyzing the algorithm, we show that desired equilibrium points are locally stable by choosing appropriate score functions and step sizes. The algorithm provides better performance than the ordinary gradient algorithm, and it is free from approximation error and the small-step-size restriction of the natural gradient algorithm. In simulations on convolved mixtures, the algorithm provides much better performance than the other algorithms while requiring less computation.

title: Out-of-Vocabulary Rejection based on Selective Attention Model
abstract: Based on a psychological selective attention theory a new algorithm is developed to provide reliable out-of-vocabulary (OOV) rejection for speech recognition systems in noisy environments. The developed attention model is based on Broadbent's ‘early filtering’ theory, and the attention adaptation process utilizes a gradient-descent error minimization algorithm with error backpropagation rule. The developed model is applied to isolated-word recognition tasks, and much higher in-vocabulary recognition rates are achieved with the same out-of-vocabulary rejection rates.

title: Blind signal separation and independent component analysis
abstract: Independent component analysis (ICA) is emerging as a new paradigm in signal processing and data analysis. ICA has been proposed as a solution to the blind source separation problem (BSS) in which sensory signals are given as mixtures of unknown source signals. Since neither the sources nor the mixing environment are known a priori there is no general analytical solution to this problem. However, since the last decade researchers have found good approximate solutions based on assumptions about the source statistics. They were able to formulate solutions or algorithms that were derived from well-known cost functions including maximum likelihood estimation, information maximization and higher-order cumulants. The ICA framework touches many theoretical research areas, and therefore receives attention in several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. More recently, there have been numerous applications of ICA in adaptive 0ltering, speech signal processing, biomedical signal processing, computational neuroscience, image coding, text data modeling, and 0nancial data analysis. The standard ICA formulation has, in general, strong assumptions or simpli0cations such as: the number of sensors has to be equal or greater than the number of sources, there is no additive noise signal in the sensors, and the sources are modeled as random variables. However, when applying ICA to real-world problems it becomes evident that those restrictions are cumbersome and although the standard ICA algorithms give good approximate solutions, there is great need in going beyond the standard assumptions and develop new algorithms that can relax some of the assumptions. This special issue on ICA and BSS is a timely publication covering important recent 0ndings in ICA theory as well as new applications of ICA in data analysis. Leading researchers present their work to advance this 0eld beyond traditional borders and show how this 0eld can bridge disciplines and yield new methods that lead to mutual bene0ts and advances in science and engineering. This issue is composed of a review article and two parts: the 0rst part presents new theoretical 0ndings while the second part presents novel applications. Often, there is no such sharp division into these two parts since illustrative simulations support theoretical 0ndings and practical methods include algorithmic modi0cations to design and evaluate the application-speci0c methods. In “Frontiers of research in BSS=ICA”, V. David S: anchez A. formulates some of the basic concepts and problems in the areas of blind signal separation and independent component analysis. He then moves to a concise description of key methods of data preprocessing and algorithms that provide solutions to the instantaneous mixture and the

title: Training Algorithm with Incomplete Data for Feed-Forward Neural Networks
abstract: A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.

title: A filter bank approach to independent component analysis for convolved mixtures
abstract: We present a filter bank approach to perform independent component analysis (ICA) for convolved mixtures. Input signals are split into subband signals and subsampled. A simplified network performs ICA on the subsampled signals, and finally independent components are synthesized. The proposed approach achieves superior performance than the frequency domain approach and faster convergence with less computational complexity than the time domain approach. Furthermore, it requires shorter unmixing filter length and less computational complexity than other filter bank approaches by designing efficient filter banks. Also, a method is proposed to resolve the permutation and scaling problems of the filter bank approach.

title: Lambda GLSP setup with QoS requirements in optical Internet
abstract: GMPLS is currently being specified as the control plane for next generation optical networks. While one merit of GMPLS is that network operators are able to control the traffic of Internet Protocol (IP) and thus offer performance superior to that of traditional Internet Gateway Protocols (IGP), it has been a challenge to develop an efficient Traffic Engineering (TE) technique to satisfy the different traffic requirements of end-users in GMPLS domain. Several TE techniques have been thought of by creating a set of Generalized Label Switched Paths (GLSPs) in the optical network. In this paper we present a technique for establishing GLSP in optical Internet, that supports Quality of Service (QoS) considering the traffic flows with delay QoS requirements. This approach accounts for the processing delay at the network layer that may be encountered in optical nodes that terminate lightpaths and perform some types of traffic grooming. Additionally, we extend the QoS routing framework by defining additional extensions that can be used in conjunction with Shared Risk Group (SRG) identifiers to reserve backup paths that do not share network resources with the working paths that they are protecting. We provide some simulation results to demonstrate the effectiveness of our routing algorithms.

title: NodeWiz: peer-to-peer resource discovery for grids
abstract: Efficient resource discovery based on dynamic attributes such as CPU utilization and available bandwidth is a crucial problem in the deployment of computing grids. Existing solutions are either centralized or unable to answer advanced resource queries (e.g., range queries) efficiently. We present the design of NodeWiz, a grid information service (CIS) that allows multi-attribute range queries to be performed efficiently in a distributed manner. This is obtained by aggregating the directory services of individual organizations in a peer-to-peer information service.

title: Embedding Technical Analysis Into Neural Network Based Trading Systems
abstract: We have recently proposed a promising trading system for the S P 500 index which consists of a feature selection component and a simple filter for data preprocessing two specialized neural networks for return prediction and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filter ing and return integration leads to further improvements in the system. The new system uses a well known technical indicator to split the data and an additional indicator for reducing the number of unprofitable trades. Several system combinations are explored and tested over a 5 year trading period. The most promising system yielded an annual rate of return ARR of 15.99% with 54 trades. This compares favorably to the ARR for the buy and hold strategy 11.05% and to the best results obtained using the system with no technical analysis knowl edge embedded 13.35% with 126 trades.

title: Design and implementation of a multi-threaded TMN agent system
abstract: A telecommunications management network adopts the OSI manager-agent model in which an agent system plays a more important role than a manager system. Because the existing agent systems sequentially perform a CMIS operation on selected managed objects, it cannot guarantee quick response time when many managed objects are selected. We propose a multi-threaded process structure for the agent system which performs CMIS operations concurrently on managed objects. We implemented our agent system using OSIMIS-4.O TMN platform in Solaris. We measured response time to a CMIS operation to evaluate the performance of our system. Experimental results show that the performance of our system is not so good when network resources reside within the agent system, but very good when network resources reside outside the agent system.

title: Geometric Snakes for Triangular Meshes
abstract: Feature detection is important in various mesh processing techniques, such as mesh editing, mesh morphing, mesh compression, and mesh signal processing. In spite of much research in computer vision, automatic feature detection even for images still remains a difficult problem. To avoid this difficulty, semi-automatic or interactive techniques for image feature detection have been investigated. In this paper, we propose a geometric snake as an interactive tool for feature detection on a 3D triangular mesh. A geometric snake is an extension of an image snake, which is an active contour model that slithers from its initial position specified by the user to a nearby feature while minimizing an energy functional. To constrain the movement of a geometric snake onto the surface of a mesh, we use the parameterization of the surrounding region of a geometric snake. Although the definition of a feature may vary among applications, we use the normal changes of faces to detect features on a mesh. Experimental results demonstrate that geometric snakes can successfully capture nearby features from user-specified initial positions.