基于小波变换的ECG信号压缩及其FPGA实现

小波变换在ECG信号处理中的应用得到了很多研究人员的关注。本文研究了5层5/3提升小波变换及其反变换的FPGA实现,并将其应用于ECG信号的压缩,在均方误差可控的范围内获得了较大的压缩比,并利用设计的硬核实现了信号的重建。     

Managing computer files via artificial intelligence approaches

Agent-oriented computing has been regarded as a very promising methodology to developing intelligent software systems. Intelligent agent technology has, thus, been successfully applied in many industrial and commercial areas. Cased based reasoning (CBR) is an effective and efficient analogical reasoning method for solving problems using the knowledge of past experiences, which are stored in a knowledge base as cases. CBR has been extensively employed to tackle such problems as design, planning, classification, and advising in many different application fields. On the other hand, as various files are created on computers, how to efficiently manage computer files becomes a significant issue. So far, there are a number of file management systems available. However, none of them can deal with these crucial problems of file management: Which files should be deleted after their use? Which files should be temporarily kept or permanently preserved? To the best of our knowledge, these problems have not yet been investigated in the open literature. To bridge this gap, in this paper we explore the value of the above artificial intelligence approaches in managing computer files. We develop an intelligent agent based personal file management system, where CBR is employed to guide users to managing their files. Through extensive practical experiments, we validate the effectiveness and efficiency of the developed system.     

Performance modeling,real-time dispatching and simulation of wafer fabrication systems using timed extended object-oriented Petri nets

Semiconductor wafer fabrication system (SWFS) is the most complex and capital-intensive phase in the entire semiconductor manufacturing cycle. With characteristics of re-entrant processing routes, equipment uncertainty, product diversity and improving technologies, great challenges are presented in SWFS’s modeling, scheduling and simulation. To implement efficient production control, this paper provides a timed extended object-oriented Petri nets (TEOPNs) approach to performance modeling, real-timed dispatching and simulation of SWFSs. The TEOPNs models are constructed in a hierarchy to accord with the real-world SWFS’s organization, and a new type of signal place is added into the TEOPNs to respond the dynamic states of all processing facilities. A novel autonomy and coordination-based real-time dispatching mechanism (A&C-RDM) is developed in this paper, which executes under the support of the TEOPNs-based hybrid real-time dispatching control system (HRDCS). Owning to the ability of gathering dynamic real-time information of all production facilities and WIP products, the HRDCS can make adaptive dispatching decisions according to the local and global real-time processing status. Two sets of key elements of real-time dispatching, i.e. the state thresholds and dispatching rules, are defined in the HRDCS so that the A&C-RDM can integrate different types of dispatching rules. A set of simulation experiments prove the efficiency of the proposed modeling and dispatching algorithm. In summary, the proposed TEOPNs, HRDCS and A&C-RDM form the cornerstones of a real-time dispatching simulation prototype of SWFS, and the work described in this paper carries out an advanced integrated “modeling–dispatching–simulation” methodology.     

An e-commerce performance assessment model: Its development and an initial test on e-commerce applications in the retail sector of China

We developed a performance assessment model for e-commerce; it included indicators, indicator weights, and evaluation methods. The model has seven methods of assessing e-commerce performance and uses four criteria to compare and select the appropriate one for a particular situation. This model was tested in the retail sector of China. According to the data collected from 70 Chinese retailers, 16 indicators of e-commerce performance consisted of four constructs: marketing and sales, customer service, supply chain efficiency, and financial performance. The indicators for those constructs provide a comprehensive measurement of performance. Then weights were assigned for each indicator using a majority aggregation method. Comparison of the results from seven evaluation methods showed that discordance analysis and simple additive weighting were the best evaluation methods for the enterprises we had sampled.     

Grid resource management policies for load-balancing and energy-saving by vacation queuing theory

The resource management is the central component of grid system. The analysis of the workload log file of LCG including the job arrival and the resource utilization daily cycle shows that the idle sites in the Grid are the source of load imbalance and energy waste. Here we focus on these two issues: balancing the workload by transferring jobs to idle sites at prime time to minimize the response time and maximize the resource utilization; power management by switch the idle sites to sleeping mode at non-prime time to minimize the energy consume. We form the M/G/1 queue model with server vacations, startup and closedown to analysis the performance metrics to instruct the design of load-balancing and energy-saving policies. We provide our Adaptive Receiver Initiated (ARI) load-balancing strategy and power-management policy for energy-saving. The simulation experiments prove the accuracy of our analysis and the comparisons results indicate our policies are largely suitable for large-scale heterogeneous grid environment.     

A routing-table-based adaptive and minimal routing scheme on network-on-chip architectures

In this paper, we present a routing algorithm that combines the shortest path routing and adaptive routing schemes for NoCs. In specific, routing follows the shortest path to ensure low latency and low energy consumption. This routing scheme requires routing information be stored in a series of routing tables created at the routers along the routing path from the source to the destination. To reduce the exploration space and timing cost for selecting the routing path, a routing list and routing table for each node are created off-line. Routing table is updated on-line to reflect the dynamic change of the network status to avoid network congestion. To alleviate the high hardware implementation cost associated with the routing tables, a method to help reduce the size of the routing tables is also introduced. Compared to the existing routing algorithms, the experimental results have confirmed that the proposed algorithm has better performance in terms of routing latency and power consumption.     

Design and implementation of an agent-based collaborative product design system

The evolution of computer science and technology has brought new opportunities for multidisciplinary designers and engineers to collaborate with each other in a concurrent and coordinated manner. The development of computational agents with unified data structures and software protocols contributes to the establishment of a new way of working in collaborative design, which is increasingly becoming an international practice. In this paper, based on the analysis of the dynamic nature of collaborative design process, a new framework for collaborative design is described. This framework adopts an agent-based approach and relocates designers, managers, systems, and the supporting agents in a unified knowledge representation scheme for product design. In order to model the constantly evolving design process and the rationales resulted from design collaboration, a Collaborative Product Data Model (CPDM) and a constraint-based Collaborative Design Process Model (CDPM) are proposed to facilitate the management and coordination of the collaborative design process as well as design knowledge management. A prototype system of the proposed framework is implemented and its feasibility is evaluated using a real design scenario whose objective is designing a set of dining table and chairs.     

Complete discriminant evaluation and feature extraction in kernel space for face recognition

This work proposes a method to decompose the kernel within-class eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unreliable subspace due to limited number of training samples. A weighting function is proposed to circumvent undue scaling of eigenvectors corresponding to the unreliable small and zero eigenvalues. Eigenfeatures are then extracted by the discriminant evaluation in the whole kernel space. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results on FERET, ORL and GT databases show that our approach consistently outperforms other kernel based face recognition methods.

Towards capacity and profit optimization of video-on-demand services in a peer-assisted IPTV platform

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has three major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud” model and a “physical” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology. Second, we propose an algorithm called Zebra that pre-stripes content across multiple peers during idle hours to speed up P2P content delivery in an IPTV environment with limited upload bandwidth. We also perform simulations to measure Zebra’s effectiveness at reducing load on the content server during peak hours. Third, we provide a cost-benefit analysis of P2P video content delivery, focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model, while the conventional no-P2P model generates more profits when the request rate is low. The flat-reward model generally falls in between the usage-based model and the built-in model in terms of profitability except for low request rates. We also find that built-in and flat-reward models are more profitable than the usage-based model for a wide range of subscriber community sizes. Funding for J. Rahe’s research has been provided by AT&T Labs, the State of California under the MICR Oprogram, and by the Toshiba Corporation. Zhen Xiao is partially supported by China MOST project (2006BAH02A10).     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.