361698
The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
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.
6071992
The asymptotic memory capacity of the generalized Hopfield network
This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that (n-1) and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows that the generalized Hopfield network can store the larger number of sample patterns than Hopfield network.
6431369
A Neural Network Approach to Real-Time Pattern Recognition
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.
370694
Further Results on the Asymptotic Memory Capacity of the Generalized Hopfield Network
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.
1465121
MFCC and SVM Based Recognition of Chinese Vowels
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.
26305122
The Stability of the Generalized Hopfield Networks in Randomly Asynchronous Mode
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. Copyright 1997 Elsevier Science Ltd.
18106069
TOMSCOP - a synchronous P2P collaboration platform over JXTA
We focus on design and implementation of a synchronous P2P (peer-to-peer) collaboration platform called TOMSCOP (Technology Of Multiuser Synchronous Collaboration Platform). Based on the elementary peer group services offered by the JXTA general framework, TOMSCOP provides the extra four types of services: synchronous message transportation, peer room administration, peer communication support and application space management. By using the four services, different kinds of shared applications for various specific purposes can be relatively easily developed and associated collaborative cyber spaces or communities can be quickly built across the JXTA virtual network overlaid on top of the existing physical networks.
22474771
Smart u-Things and Ubiquitous Intelligence
Smart u-things are real things with attached, embedded or blended computers, networks, and/or some other devices such as sensors, actors, e-tags and so on, and they can sense, compute, communicate and take some adaptive actions/reactions/proactions according to their goals, situated contexts, users’ needs, etc. It is envisioned that smart u-thing will be everywhere eventually towards ubiquitous intelligence and smart world. One of the profound implications of such ubiquitous smart u-things is that various kinds and levels of intelligence will exist pervasively in real everyday objects, environments, systems and even ourselves, and possibly be extended from man-made to natural things. The ubicomp/percomp can be regarded, in a sense, as the computing of all these smart/intelligent u-things, which are the basic elements and components of the smart world. After clarifying the essential features and three categories of smart u-things, i.e., smart object, smart space and smart system, the talk is devoted to discuss possible challenges in smart u-things’ research in terms of real world complexity. The main intentions are to examine the possible hard issues to suggest some potential research lines; and to let researchers in this field coolhead and being aware of the hardness of these challenges in making real things truly smart.
22258067
A principled approach for formative Web learning assessment and adaptive tutoring
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.
2850355
An Optimal Node Selection Scheme in Multi-hop Wireless Network
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.
16215317
Large-scale placement by grid-warping
Grid-warping is a new placement algorithm based on a strikingly simple idea: rather than move the gates to optimize their location, we elastically deform a model of the 2-D chip surface on which the gates have been roughly placed, "stretching" it until the gates arrange themselves to our liking. Put simply: we move the grid, not the gates. Deforming the elastic grid is a surprisingly simple, low-dimensional nonlinear optimization, and augments a traditional quadratic formulation. A preliminary implementation, WARP1, is already competitive with most recently published placers, e.g., placements that average 4% better wirelength, 40% faster than GORDIAN-L-DOMINO.