Entropy Penalized Automated Model Selection On Gaussian Mixture
Gaussian mixture modeling is a powerful approach for data analysis and the determination of the number of Gaussians, or clusters, is actually the problem of Gaussian mixture model selection which has been investigated from several respects. This paper proposes a new kind of automated model selection algorithm for Gaussian mixture modeling via an entropy penalized maximum-likelihood estimation. It is demonstrated by the experiments that the proposed algorithm can make model selection automatically during the parameter estimation, with the mixing proportions of the extra Gaussians attenuating to zero. As compared with the BYY automated model selection algorithms, it converges more stably and accurately as the number of samples becomes large.

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: 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: 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: A Real Trading Model based Price Negotiation Agents
abstract: Sim proposed a market-driven negotiation agent model that makes adjustable amounts of concession by reacting to different market situations and trading constraints, and it was improved with an enhanced market-driven strategy by taking opponent eagerness into consideration. In both Sim's original model and improved model, however, it was implied that a negotiation agent has same behaviors and actions to all trading partners referring to a same trading issue. It is not quite true in a real world trading negotiation. Based on both models, this paper proposes a real trading model based price negotiation agents that take each trading partner as an individual with different strategies and actions. Moreover, negotiation actions between a negotiation agent and a trading partner are kept in secret and unknown to others

title: Peer Collaboration Modes and Private Room Services in TOMSCOP
abstract: This paper presents the implementation of peer group management services in a pure P2P groupware framework called TOMSCOP (technology of multiuser synchronous collaboration platform). To support a variety of computer-based collaborative activities and styles, a user in a group can be assigned a role or identity such as a discusser peer, a chair peer, a presenter peer, a player peer, or an observer peer-Based on the above identities and their control service, we developed three collaboration modes, i.e., discussion mode, presentation mode and rotation mode, for smoothly manipulating shared applications in their different usages. Moreover we designed and implemented the public and private room services for users to create suitable collaboration rooms that match theirs privacy requirements. These modes and services not only provide more functions to TOMSCOP users, but also support software developers for easily and flexibly making P2P shared applications with different purposes

title: Towards a Natural Internet-Based Collaborative Environment with Support of Object Physical and Social Characteristics
abstract: Objects in this article refer to sharable applications, such as a whiteboard and a video player, used by multi-users who are in different sites and have computers connected to networks. The objects are important elements in our Internet-based desktop collaborative system, called virtual collaboration room (VCR). We argue that a natural collaborative environment should be developed in a framework of using both a room metaphor and an object metaphor, i.e., emulating the fundamental characteristics of real rooms and real objects, respectively. This article gives the first systematic specifications of object physical and social characteristics, and discusses how to exploit and implement the object characteristics in VCR. A preliminary prototype of platform independent real-time audio/video communications among multiple users is also described. It can be used together with VCR.

title: A distributed Web document database and its supporting environment
abstract: We propose a new Web documentation database as a supporting environment of the Multimedia Micro-University project. The design of this database facilitates a Web documentation development paradigm that we have proposed earlier. From a script description to its implementation as well as testing records, the database and its interface allow the user to design Web documents as virtual courses to be used in a Web-savvy virtual library. The database supports object reuse and sharing, as well as referential integrity and concurrence. In order to allow real-time course demonstration, we also propose a simple course distribution mechanism, which allows the pre-broadcast of course materials. The system is implemented as a three-tier architecture which runs under MS Windows and other platforms.

title: An Optimal Node Selection Scheme in Multi-hop Wireless Network
abstract: In this paper, a cooperative wireless multi-hop network is considered, cooperative diversity can be realized by allowing nodes close proximity to cooperate in transmission. In order to group receiver nodes into a cluster, it is necessary to select appropriate nodes. We introduce an optimal node selection algorithm based on MIMO antenna selection, this algorithm, combining with cooperative transmission strategy, gives an optimal node selection algorithm using convex optimization theory. Theoretical analysis and simulation indicate that this scheme can give good performance of maximizing the channel capacity through optimal node selection.