A filter bank approach to independent component analysis for convolved mixtures
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: 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: A new neuro-fuzzy identification model of nonlinear dynamic systems
abstract: Multilayer neural networks with error back-propagation learning algorithms have the capability of learning an arbitrary continuous nonlinear function with examples of input and output sample pairs and a great potential for identifying nonlinear dynamic systems with unknown characteristics. A fuzzy system is composed of fuzzification of input, reasoning (or inference) by fuzzy rules, and defuzzification of fuzzy output. In general, there are some difficulties in finding suitable fuzzification and defuzzification methods and fuzzy rules. Formation of fuzzy rules with complex input-output relationships can be replaced by building neural networks with input and output sample pairs. A neuro-fuzzy identifier is proposed to have a cascaded structure of fuzzification, neural network, and defuzzification and additionally is shown to be able to compensate for fuzzification and defuzzification error of fuzzy logic. Computer simulation shows that neuro-fuzzy identification is very effective in modeling the fuzzy system whose fuzzy rules can not be obtained easily.

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: Building information systems based on neural networks

title: Optimal Transmission Schedules in TWDM Optical Passive Star Networks
abstract: This paper is concerned with optimal transmission schedules in the TWDM (time and wavelength division multiplexed) optical passive star network. Our model of the network consists of a set V of N nodes with N tunable transmitters and N fixed-tuned receivers (where each node is assigned a transmitter-receiver pair), k wavelengths (each wavelength is shared by N k receivers where N k is an integer), and tuning time δ > 0 (in units of time slot, for transmitters to tune from one wavelength to another). We assume that at any given time slot, at most one transmission can be done per wavelength. An optimal transmission schedule is defined to be the one that schedules transmissions such that for each node v in V, the transmitter in v transmits once to every receiver in V — {v} within a repeating cycle of minimum length. We present an optimal transmission schedule for each tuning time, and show that the cycle length of any optimal transmission schedule is max {N(N — 1)/k, kδ + N — 1}.

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: Scale-Space Derived From B-Splines
abstract: This paper proposes a scale-space theory based on B-spline kernels. Our aim is twofold: 1) present a general framework, and show how B-splines provide a flexible tool to design various scale-space representations. In particular, we focus on the design of continuous scale-space and dyadic scale-space frame representations. A general algorithm is presented for fast implementation of continuous scale-space at rational scales. In the dyadic case, efficient frame algorithms are derived using B-spline techniques to analyze the geometry of an image. The relationship between several scale-space approaches is explored. The behavior of edge models, the properties of completeness, causality, and other properties in such a scale-space representation are examined in the framework of B-splines. It is shown that, besides the good properties inherited from the Gaussian kernel, the B-spline derived scale-space exhibits many advantages for modeling visual mechanism including the efficiency, compactness, orientation feature and parallel structure.

title: Mesh parameterization with a virtual boundary
abstract: Parameterization of a 3D triangular mesh is a fundamental problem in various applications of meshes. The convex combination approach is widely used for parameterization because of its good properties, such as fast computation and one-to-one embedding. However, the approach has a drawback: most boundary triangles have high distortion in the embedding compared with interior ones. In this paper, we present an extension of the convex combination approach that resolves the drawback by using a virtual boundary. Since the virtual boundary is fixed onto a given convex polygon instead of the real boundary, the real boundary triangles can better reflect the shape of the corresponding 3D triangles. The proposedapproach obtains a parameterization of a 3D mesh with less d istortion than with the original convex combination approach.

title: Feature-based surface light field morphing
abstract: A surface light field is a function that gives the colors of each object point viewed from different directions. Object representation with a surface light field provides a nice structure for 3D photography. This paper presents a feature-based morphing technique for two objects equipped with surface light fields. The technique consists of geometry morphing and in-between light field mapping. Geometry morphing is accomplished by 3D mesh morphing, where we introduce a vertex merging technique to generate a simpler metamesh. In in-between light field mapping, an in-between object is rendered by extracting necessary fragments from input surface light fields. We also propose an acceleration technique for rendering an in-between object. Experimental results with real and synthetic data show natural and plausible morphing between objects with surface light fields. The proposed morphing technique can be used as an editing tool for 3D photography.