THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING
Year:2003 | Volume:1 | Issue:4 (b)|Papers Count:5

In this paper we will show that it is both possible and easy to use a standard HDL language like Verilog HDL, along with PLI to model asynchronous circuits at all levels of abstraction, including the behavioral level (CSP level). Our method allows CSP (Communicating Sequential Processes) codes to be simulated on ordinary Verilog simulators. The suggested algorithm is easy to implement (less than 100 lines of code in our case), thus other asynchronous designers not only can implement their own codes, but also they can further improve and optimize the method. In this way, one can customize the way that CSP features are added to Verilog HDL. Furthermore, it does not need any preprocessing or extra tool. The designer can write his code in Verilog language from the very beginning steps of the design, while channel communications become like atomic actions, and tine-grained concurrency within processes is available. We believe that this method has the potential to override methods that involve CSP-like languages and new simulators for asynchronous circuits which are described by CSP-like languages, and enables designers to exchange their codes easily.  

This paper presents a fault tolerant routing algorithm for the star graph. The suggested algorithm is based on the concept of unsafety vectors, originally proposed for hypercubes. In this algorithm, each node starts by computing a first level unsafety set, composed of the set of unreachable neighbors. It then performs some exchanges with its neighbors to determine the unsafety nodes. After that, all nodes have the addresses of all faulty nodes. Based on the information gathered in each node, fault tolerant routing between a source node and a destination node is realized. We conducted a performance comparison between the star and hypercube graph using the unsafety vectors routing algorithm under different working conditions. The results obtained through simulation experiments reveal that the hypercube is of superior performance compared to the star graph in the presence of low fault rates. But its performance under high fault rates falls short of that of the star graph.  

In this paper, we present an active expert system (OA V-VVT expert) to verify and validate the knowledge base (KB). Our work is based on new approach to representation and reasoning techniques, Ex-OA V KB, based on Object-Attribute- Value (OA V) knowledge representation. All common issues of verification and validation are considered in OAV-VVT expert. OAV-VVT expert can be either used during the building of knowledge base or refinement of existing KB. OA V-VVT expert is an expert system with reasoning and explanation mechanisms. It is application and knowledge representation independent. OA V-VVT expert improves the performance of V&V phase in building the knowledge base by checking unreferenced attribute values and illegal attribute values, during transforming the existing KB to Ex- OA V KB. All necessary algorithms to transform different knowledge representation techniques have been presented in this paper to justify that the Ex-OA V KB is feasible.  

The conventional view on the problem of robustness in speech recognition is that performance degradation in ASR systems is due to mismatch between training and test conditions. If problem of robustness in ASR systems is considered as a mismatch between the training and testing conditions the solution would be to find a way to reduce it. Common approaches are: Data-Driven methods such as speech signal enhancement and using robust features and model-based methods that alter or adapt model of speech signal. In this paper, we study a model of environment and obtain a relation between noisy and clean speech features based on this model. We propose two techniques for mapping noisy features to clean features in cepstrum domain. We implement the proposed methods and some of precedent data-driven methods such as: spectral subtraction, cepstral mean normalization, cepstral mean and variance mean normalization and SNR-dependent cepstral normalization .We show that proposed methods outperform precedent methods and are effective for robust speech recognition in noisy environments.  

In this paper, we introduce open cellular learning automata and then study its convergence behavior. It is shown that for a class of rules called commutative rules, the open cellular learning automata in stationary external environments converges to a stable and compatible configuration. The numerical results also confirm the theory.