10392733
Pre-routing estimation of shielding for RLC signal integrity
The formula-based K/sub eff/ model is a figure of merit for inductive coupling, and has been used to solve the simultaneous shield insertion and net ordering (SINO) and simultaneous signal and power routing (SPR) problems. The authors first show that the K/sub eff/ model has a high fidelity compared to the SPICE-computed noise under an accurate RLC circuit model. We then develop simple yet accurate formulae to estimate numbers of shields needed by optimal SINO solutions under the K/sub eff/ model. Extensive experiments show that our pre-routing estimation has errors less than 10% compared to solutions given by detailed SINO algorithms. These formulae can be used effectively as a pre-routing congestion estimation for layout planning and synthesis.
61488328
Interval-valued reduced order statistical interconnect modeling
We show how recent advances in the handling of correlated interval representations of range uncertainty can be used to predict the impact of statistical manufacturing variations on linear interconnect. We represent correlated statistical variations in RLC parameters as sets of correlated intervals, and show how classical model order reduction methods - AWE and PRIMA - can be re-targeted to compute interval-valued, rather than scalar-valued reductions. By applying a statistical interpretation and sampling to the resulting compact interval-valued model, we can efficiently estimate the impact of variations on the original circuit. Results show the technique can predict mean delay with errors between 5-10%, for correlated RLC parameter variations up to 35%.
18482054
A multi-agent system for online course content management
This paper presents a multiagent system to assist a teacher managing his/her course contents placed on Web servers. In this system there is a set of agents and every agent may work independently from or collaboratively with others. Once generated, an agent can reside in a teacher's daily working computer (called administration host) or a proxy host, and can move between the two hosts. Each agent is devoted to one piece of job and all of them, as a whole, coordinately conduct a sequence of management work during the entire process of teaching a course. A teacher may administrate agents via a specific system shell on his/her administration host, or a usual Web browser on another computer/PDA/mobile phone. The system has been carefully modularized, and thus a new type of agent, if necessary, can be relatively easily developed and quickly incorporated into the system to further enhance or extend its management capability.
14453892
A One-Bit-Matching Learning Algorithm for Independent Component Analysis
Independent component analysis (ICA) has many practical applications in the fields of signal and image processing and several ICA learning algorithms have been constructed via the selection of model probability density functions. However, there is still a lack of deep mathematical theory to validate these ICA algorithms, especially for the general case that super- and sub-Gaussian sources coexist. In this paper, according to the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, we propose a one-bit-matching ICA learning algorithm on the Stiefel manifold. It is shown by the simulated and audio experiments that our proposed learning algorithm works efficiently on the ICA problem with both super- and sub-Gaussian sources and outperforms the extended Infomax and Fast-ICA algorithms.
14385647
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.
18913141
Smart hyperspaces and project Ubikids
A smart space is an electronics-enhanced physical environment that can sense the existence of users and other entities, and provide them the right services, in the right way, at the right time. Such senses and services are based on collaborative work of many interconnected devices and computers, which may be small enough to be embedded to everyday objects in the space. So far, many creative researches have been conducted to develop and experiment a variety of smart spaces. The next research issue to emerge, we believe, is to interconnect and integrate these isolated smart spaces together into a higher level space known as a hyperspace. In this paper, after briefly reviewing the smart space concepts and related research, we describe what a smart hyperspace is all about and what related challenging issues exist. We then explain our vision and motivations towards building a specific smart hyperspace, called Ubikids, to assist parents and provide services to make them more convenient, prompt, reliable, precise, and can remotely take care of their kids. Finally, we show the fundamental approaches, conceptual designs, crucial problems and so on, regarding the project Ubikids.
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.