Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms
Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropriately. In this paper, we utilize a new kind of Bayesian Ying-Yang (BYY) automated model selection (AMS) learning algorithm to select the appropriate number and initial locations of the hidden units or Gaussians automatically for an input data set. It is demonstrated well by the experiments that this BYY-AMS training method is quite efficient and considerably outperforms the typical existing training methods on the training of RBF networks for both clustering analysis and nonlinear time series prediction.

title: MFCC and SVM Based Recognition of Chinese Vowels
abstract: The recognition of vowels in Chinese speech is very important for Chinese speech recognition and understanding. However, it is rather difficult and there has been no efficient method to solve it yet. In this paper, we propose a new approach to the recognition of Chinese vowels via the support vector machine (SVM) with the Mel-Frequency Cepstral Coefficients (MFCCs) as the vowel’s features. It is shown by the experiments that this method can reach a high recognition accuracy on the given vowels database and outperform the SVM with the Linear Prediction Coding Cepstral (LPCC) coefficients as the vowel’s features.

title: The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode
abstract: We present a study on the stability of the generalized Hopfield network in randomly asynchronous mode. First, the stability is investigated from the state space of the network. We introduce a concept of hole and define two kinds of stability in randomly asynchronous mode. By mathematical inductive method, we have proved that a generalized Hopfield network with non negative weights is strictly stable, that is, the network evolves to a simple hole-stable state with probability one when it starts at any initial state. For the general case, we made the simulation experiments on the networks with the number of neurons from 5 to 10. The empirical results have shown that almost any generalized Hopfield network with simple holes is strictly stable.

title: A Neural Network Approach to Real-Time Pattern Recognition
abstract: This paper presents a new neural network approach to real-time pattern recognition on a given set of binary (or bipolar) sample patterns. The perceptive neuron of a binary pattern is defined and constructed as a binary neuron with a neighborhood perceptive field. Letting its hidden units be the respective perceptive neurons of the patterns, a three-layer forward neural network is constructed to recognize these patterns with minimum error probability in a noisy environment. The theoretical and simulation analyses show that the network is effective for pattern recognition and can be under strict real-time constraints.

title: The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
abstract: The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.

title: Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
abstract: This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n− 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n− 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.

title: Probabilistic interval-valued computation: toward a practical surrogate for statistics inside CAD tools
abstract: Interval methods offer a general, fine-grain strategy for modeling correlated range uncertainties in numerical algorithms. We present a new, improved interval algebra that extends the classical affine form to a more rigorous statistical foundation. Range uncertainties now take the form of confidence intervals. In place of pessimistic interval bounds, we minimize the probability of numerical "escape"; this can tighten interval bounds by 10times, while yielding 10-100times speedups over Monte Carlo. The formulation relies on three critical ideas: liberating the affine model from the assumption of symmetric intervals; a unifying optimization formulation; and a concrete probabilistic model. We refer to these as probabilistic intervals, for brevity. Our goal is to understand where we might use these as a surrogate for expensive, explicit statistical computations. Results from sparse matrices and graph delay algorithms demonstrate the utility of the approach, and the remaining challenges

title: A principled approach for formative Web learning assessment and adaptive tutoring
abstract: Web based distance learning is a trend of instruction delivery. One of the most difficult challenges of such a learning mechanism is the assessment of students' learning criteria. It is hard to judge the behavior of a student since the instructor is separated spatially and temporally from the students. However, it is possible to rely on some Web based tools to keep track of a student's course attendance, as well as the navigation behavior of that student. In addition, the navigation behavior of an individual can be compared to those of others. Analysis can be conducted, and an interactive tutorial can be generated to assist the student with a poor score. The paper proposes such a mechanism, as well as its supporting system run on Windows browsers.

title: Simulation Study of AODV Performance over IEEE 802.15.4 MAC in WSN with Mobile Sinks
abstract: Mobility has been incorporated into wireless sensor networks in recent years in order to improve energy efficiency and network coverage. In particular, at the cost of increased message delivery delay, the energy conservation can be achieved by limiting the hop count between the communicating sensor-sink pair. In this paper we allow multihop routing between one sensor and mobile sinks, and consider the delay*energy metric, which is a compound metric to evaluate the performance of diverse data collection schemes. Through extensive simulation experiments of AODV performance over IEEE 802.15.4 MAC with clustered wireless sensor network setting, we investigate the fundamental problems of AODV without information of 802.15.4 MAC, and analyze the influence of incorporating multiple sinks.

title: A P2P groupware system with decentralized topology for supporting synchronous collaborations
abstract: Network based groupware systems are for supporting collaborations among a group of people who are engaged in a common task or goal using computers connected by a variety of networks including the Internet. A synchronous groupware system supports group members' collaborative activities at the same time. Due to the almost all of current synchronous collaborative systems have been implemented either using a centralized topology or a hybrid topology, the research presented in this paper has been devoted to investigation, design and implementation of a peer-to-peer (P2P) groupware system, called DSC, using a decentralized topology. As it does not use any server at all, peers in a group need to coordinately manage their group by themselves, and each peer has to fully handle the correct message passing by itself. The DSC system is implemented using the JXTA technology and platform that enable peers to find each other, form groups and exchange messages across firewalls and NATs. It currently offers three shared objects of Web browser, file viewer and drawing pad as well as a text chat tool. The synchronous controls of a shared space, the objects, telepointer and so on are also provided. Its evaluations with a practical test environment are given in detail.

title: Localized Low-Power Topology Control Algorithms in IEEE 802.15.4-Based Sensor Networks
abstract: Sensor networks have emerged as a promising technology with various applications, and power consumption is one of the key issues. Since each full function device can act as a coordinator or a device in IEEE 802.15.4 standard, 802.15.4-based sensor networks have various possible network topologies. In this paper, we try to construct network topologies with small number of coordinators while still maintaining network connectivity. By reducing the number of coordinators, the average duty cycle is reduced and the battery life is prolonged. Three topology control algorithms are proposed in this paper. Self-pruning is the simplest one with O(l) running time. Ordinal pruning significantly improves self-pruning in terms of power saving with O(n) running time. Layered pruning is a tradeoff between the first two pruning algorithms with O(radicn) running time and a little higher power consumption than ordinal pruning. Furthermore, all three algorithms are independent of the physical radio propagation characteristics