VHDL-AMS is the Analog and Mixed-Signal Extensions to VHDL. The paper gives a brief overview of the added features to VHDL. A mixed-signal simulator has been developed based on VHDL-AMS. A new synchronization algorithm is adopted in the simulator. Using the new algorithm the analog kernel does not need to synchronize the digital kernel at each digital event time point. The efficiency of the new synchronization algorithm is tested by examples. Simulation results show the newly developed algorithm can speed up the simulation.

This paper develops an effective randomized on-demand QoS routing algorithm on networks with inaccurate link-state information. Several new techniques are proposed in the algorithm. First, the maximum safety rate and the minimum delay for each node in the network are pre-computed, which simplify the network complexity and provide the routing process with useful information. The routing process is dynamically directed by the safety rate and delay of the partial routing path developed so far and by the maximum safety rate and the minimum delay of the next node. Randomness is used at the link level and depends dynamically on the routing configuration. This provides great flexibility for the routing process, prevents the routing process from overusing certain fixed routing paths, and adequately balances the safety rate and delay of the routing path. A network testing environment has been established and five parameters are introduced to measure the performance of QoS routing algorithms. Experimental results demonstrate that in terms of the proposed parameters, the algorithm outperforms existing QoS algorithms appearing in the literature.

Message sequence chart (MSC) is a graphical and textual language for the description and specification of the interactions between system components. MSC specifications allow convenient expression of multiple scenarios, and offer an intuitive and visual way of describing design requirements. Like any other aspect of the specification and design process, MSCs are amenable to errors, and their analysis is important. In this paper, the verification problem of MSC specification for timing inconsistency is studied, which means that no execution scenario described by an MSC specification is timing consistent. An algorithm is developed to check MSC specifications for timing inconsistency.

After introducing the overview of our requirements description model HRFSM, the paper presents a dynamic software execution model (DERTS) of embedded real-time software, which can integrate control flow, data flow and time. Based on DERTS, a checking method is also presented. It consists of three kinds of checking and can check the consistency and completeness of the requirement specifications of embedded real-time software. Besides providing information helpful to improve the efficiency of analyzing and checking specifications, the checking method is flexible, and easy to understand and to use for the analyst.

Constraint-based partial evaluation (CBPE) is a program optimization technique based on partial evaluation (PE) and constraint solving. Conventional PE only utilizes given parameter values to specialize programs. However, CBPE makes use of not only given values but also the following information: (a) the relationship between input parameters and program variables; (b) logical structure of a program to be evaluated. In this paper, a formal description of CBPE method for imperative languages is presented, and some related problems are discussed.

Testing equivalence on pi processes has been studied in literature. The equivalence is not closed under the input prefix operator and is therefore not a congruence relation. This note takes a look at testing congruence on finite pi processes. A complete equational system is given for the congruence relation.

Classification is an important technique in data mining. The decision trees built by most of the existing classification algorithms commonly feature over-branching, which will lead to poor efficiency in the subsequent classification period. In this paper, we present a new value-oriented classification method, which aims at building accurately proper-sized decision trees while reducing over-branching as much as possible, based on the concepts of frequent-pattern-node and exceptive-child-node. The experiments show that while using relevant analysis as pre-processing, our classification method, without loss of accuracy, can eliminate the over-branching greatly in decision trees more effectively and efficiently than other algorithms do.

A system of linear equations is in general solved to approximate discretely the illumination function in radiosity computation. To improve the radiosity solution, a method that performs shooting and gathering in an interleaving manner is proposed in the paper. Besides, a criterion has been set up and tested for choosing object elements used in the gathering operation, and a criterion is established to quantify the solution errors by taking into account more reasonably of the human perception of the radiosity solution. Experimental results show that the method proposed has nice performance in improving the radiosity solution.

In this paper, an effective and robust active speech detection method is proposed based on the 1/f process technique for signals under non-stationary noisy environments. The Gaussian 1/f process, a mathematical model for statistically self-similar random processes based on fractals, is selected to model both the speech and the background noise. An optimal Bayesian two-class classifier is developed to discriminate them by their 1/f wavelet coefficients with Karhunen-Loeve-type properties. Multiple templates are trained for the speech signal, and the parameters of the background noise can be dynamically adapted in runtime to model the variation of both the speech and the noise. In our experiments, a 10-minute long speech with different types of noises ranging from 20dB to 5dB is tested using this new detection method. A high performance with over 90% detection accuracy is achieved when average SNR is about 10dB.

This paper describes the implementation of a sizable subset of OpenMP on networks of workstations (NOWs) and the source-to-source OpenMP compiler (AutoPar) is used for the JIAJIA home-based shared virtual memory system (SVM). The paper suggests some simple modifications and extensions to the OpenMP standard for the difference between SVM and SMP (symmetric multi processor), at which the OpenMP specification is aimed. The OpenMP translator is based on an automatic parallelization compiler, so it is possible to check the correctness of the semantics of OpenMP programs which is not required in an OpenMP-compliant implementation. AutoPar is measured for five applications including both programs from NAS Parallel Benchmarks and real applications on a cluster of eight Pentium II PCs connected by a 100Mbps switched Ethernet. The evaluation shows that the parallelization by annotating OpenMP directives is simple and the performance of generated JIAJIA code is still acceptable on NOWs.

This paper presents a concurrent object-oriented modeling language based on Petri nets: OMNets, which helps greatly to avoid the inheritance anomaly problem appeared in concurrent OO languages. OMNets separates the functional part and the synchronization part of objects and uses Petri nets to describe the synchronization part. Both parts are reusable through inheritance.

Recent extensive measurements of real-life traffic demonstrate that the probability density function of the traffic is non-Gaussian. If a traffic model does not capture this characteristics, any analytical or simulation results will not be accurate. In this work, we study the impact of non-Gaussian traffic on network performance, and present an approach that can accurately model the marginal distribution of real-life traffic. Both the long- and short-range autocorrelations are also accounted. We show that the removal of non-Gaussian components of the process does not change its correlation structure, and we validate our promising procedure by simulations.

This paper presents a new method that eliminates noise in Web page classification. It first describes the presentation of a Web page based on HTML tags. Then through a novel distance formula, it eliminates the noise in similarity measure. After carefully analyzing Web pages, we design an algorithm that can distinguish related hyperlinks from noisy ones. We can utilize non-noisy hyperlinks to improve the performance of Web page classification (the CAWN algorithm). For any page, we can classify it through the text and category of neighbor pages related to the page. The experimental results show that our approach improved classification accuracy.