Proposes two multichannel time slot sorters which sort time division multiplexed (TDM) optical inputs arranged as N frames with N time slots per frame using O(N log/sup 2/ N) optical switch elements and optical delay ...
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Proposes two multichannel time slot sorters which sort time division multiplexed (TDM) optical inputs arranged as N frames with N time slots per frame using O(N log/sup 2/ N) optical switch elements and optical delay lines connected in a feed forward fashion. Two space-time variants of the spatial odd-even merge algorithm [Batcher, 1968] are used to design the sorters. The proposed sorters use fewer switches than previously proposed sorters using switches with feedback line delays and other sorters using switches with feedforward line delays. One of the sorters uses O(N log N) fewer switches as compared to the ones in Jordan et al. (1992) and Lee et al. (1993).
Considers time division multiplexed (TDM) optical switching networks built from (2/spl times/2) LiNbO/sub 3/ switches and feed forward fiber delays, useful for TDM systems in telecommunications and pipelined systems s...
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Considers time division multiplexed (TDM) optical switching networks built from (2/spl times/2) LiNbO/sub 3/ switches and feed forward fiber delays, useful for TDM systems in telecommunications and pipelined systems such as time multiplexed optical multiprocessors. The researchers at the University of Strathclyde and the British Telecommunication proposed [Hunter and Smith, 1992] optical TDM switching networks which are mathematically equivalent to the Benes network [Benes, 1964] thus requiring theoretically a minimal number of switches. The networks were constructed recursively and justified mathematically, but without a control algorithm. The present authors provide a derivation of an optical TDM switching network equivalent to the networks of Hunter and Smith which yields an explicit control algorithm.
The properties of Artificial Neural Networks(ANNs) like massive parallelism, generalization make them amenable for application in various diagnostic/time-critical problem domains including alarm processing in power sy...
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In this paper, we develop an image compression algorithm based on an information-theoretic analysis of transform coding in the end-to-end imaging process. Our analysis accounts for the radiance-field statistics, the i...
In this paper, we develop an image compression algorithm based on an information-theoretic analysis of transform coding in the end-to-end imaging process. Our analysis accounts for the radiance-field statistics, the image-gathering system design, and transform coding and compression in determining the information density of the transmitted image. This approach allows the specification of imaging systems that implement transform-coding image compression at arbitrary bit rates with maximum information density. Ultimately, the fidelity of an optimally restored image is limited by the information capacity of the imaging system. Our results indicate that with informationally optimized transform coding, even images compressed to low bit rates can be restored with relatively high fidelity.
Researchers have shown that the neural network models can be used to find solutions for many optimization problems. Most of these models are energy based models and there is no guarantee the network converges to a glo...
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An integrated model for real time alarm processing in a real world terminal power station is applied. The integrated model is a combination of a generic neuro-expert system model, object model, and UNIX operating syst...
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An integrated model for real time alarm processing in a real world terminal power station is applied. The integrated model is a combination of a generic neuro-expert system model, object model, and UNIX operating system process (UOSP) model. It is shown how the massive parallelism and fast execution features of ANNs help to cope with real-time system constraints like data variability and fast response time. For further enhancing reliability, a practical use of competing expert system-artificial neural networks (ES-ANN) objects is proposed.< >
Neural network (or parallel distributed processing) models have been shown to have some potential for solving optimisation problems. Most formulations result in NP-complete problems and solutions rely on energy based ...
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Neural network (or parallel distributed processing) models have been shown to have some potential for solving optimisation problems. Most formulations result in NP-complete problems and solutions rely on energy based models, so there is no guarantee that the network converges to a global optimal solution. In this paper, we propose a non-energy based neural shortest path network based on the principle of dynamic programming and least take all network. No problem of local minima exists and it guarantees to reach the optimal solution. The network can work purely in an asynchronous mode which greatly increases the computation speed.
High performance computing and networking are becoming the backbone of the scientific and information infrastructure, incorporating emerging technologies into productive applications at an accelerating pace. These tec...
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High performance computing and networking are becoming the backbone of the scientific and information infrastructure, incorporating emerging technologies into productive applications at an accelerating pace. These technologies are important both for the national security and as the basis of the future economic competitiveness. The Federal High Performance computing and Communications Program provides an innovative and coordinated research agenda for the US in these areas. The question of greatest interest to the III/V community is, "What role will emerge for compound semiconductors as computing reinvents itself?". The authors present some insights, and a sample of the GaAs-related research funded by the Advanced Research Projects Agency. Results of these efforts will significantly contribute to the future role these semiconductors will play in mainstream computing.< >
Most neural network models are energy based models and there is no guarantee that the network converges to a global optimal solution. A new neural shortest path network model is proposed in which no special convergenc...
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Most neural network models are energy based models and there is no guarantee that the network converges to a global optimal solution. A new neural shortest path network model is proposed in which no special convergence procedure needs to be performed. The network can work in a purely asynchronous mode, and is guaranteed to reach the global optimal solution.< >
Investigations conducted in three separate countries by four research groups on the use and effectiveness of CASE within industry are reported. Comparisons are drawn between the three sets of research findings, and si...
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Investigations conducted in three separate countries by four research groups on the use and effectiveness of CASE within industry are reported. Comparisons are drawn between the three sets of research findings, and similarities and differences in the impact of CASE within the different countries are identified. The report combines the experience of the authors, and provides a wider insight into the use and effectiveness of CASE tools on a worldwide scale.
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