Synthesis algorithms, future

In the following section, we present the relevant parts of Mechatronic UML and give an overview of our synthesis approach. For the formalization of the approach, we give fundamental definitions for the input behavioral specifications in Section 3. In Section 4, we present the concept of composition rules which formalize interdependent concerns. These composition rules are applied within the automatic composition of protocol behavior, as defined by the synthesis algorithm in Section 5. As the effect of the application of a set of composition rules cannot be anticipated, the result of the synthesis can violate properties of the protocol behavior. Therefore, we present the check for role conformance in Section 6. Related work is discussed in Section 7 and at last we conclude with a summary and future work in Section 8. 

Monte Carlo sampling, 26 999, 1001—1004 in control systems, 26 1046 future trends in, 26 1047-1048 HSGA algorithm and, 26 1032 in process scheduling, 26 1042-1043 in process synthesis and design, 26 1041 quasi-Monte Carlo sampling and, 26 1011-1016 for risk analysis, 26 1045 in supply chain management, 26 1043-1044... 

Since computational power has increased enormously in personal computers, and since there is a strong need for maximal compression of multimedia, especially over the internet, it seems reasonable to expect future growth in the development of model-based sound synthesis. Model-based image generation and algorithmic sound synthesis are already under consideration for the MPEG-4 compression standard. 

Physics-based synthesis can provide extremely high quality and expressivity in a very compact algorithm. Such computational models can provide extremely low bit rates at very high quality levels for certain sounds. In addition to data compression applications, such models can also provide a foundation for the future evolution of musical instruments, moving it from the real world of wood and metal into the virtual world where formerly impossible modifications are easily tried out. 

Also, the fundamental problem related to the synthesis of selective and technologically compatible membranes should be deeply considered. The contemporary solution of all these problems will probably determine success at the industrial level. Also of extreme importance is the study of linearization procedures and the development of algorithms for the deconvolution of multiple information at the outputs of multifunctional ACSs, whose future looks bright indeed. 

Optimal process synthesis Two problems (a) chemical process optimization for maximization of net present value (NPV) while minimizing uncertainty in the future demand of two products, and (b) utility system optimization for minimization of both total annual cost and CO2 emission. Multi-Criteria Branch and Bound (MCBB) Algorithm The existing MCBB algorithm was modified to increase speed, reliability and suitahility for a wide range of applications. Mavrotas and Diakoulaki (2005)... 

The other major problem with deductive synthesis is related to the formality of the specifications. Where do the formal specifications come from If deductive synthesizers guarantee total correctness of the resulting algorithms with respect to their specifications, what guarantee do we have that these specifications correctly capture our intentions These questions are often either dismissed as irrelevant or considered as intractable issues that are left for future research. But what use are these synthesizers if we don t even know whether they solve our problems or not See [Le Charlier 85] and [Flener and Popelmsky 94] for a detailed discussion of this controversial topic. 

The first building block is the actual specification formalism used to elaborate the input to algorithm synthesis. An arbitrary choice has been made here to investigate synthesis from incomplete specifications. Starting from the pros and cons of specifications by examples, and of specifications by axioms, as outlined in Part I, we define, in Section 6.1, a specification approach that is based on examples and properties. Then, in Section 6.2, we illustrate this approach on a few sample problems. Future work and related work are discussed in Section 6.3 and Section 6.4, respectively, before drawing some conclusions in Section 6.5. 

While the research projects presented here combine to demonstrate Algorithmic and Register-Transfer Level Synthesis, they are far from the final solution. Rather they each have served to demonstrate basic concepts and approaches to the steps in synthesis, and together have shown two methods of integrating the steps into a synthesis system. In this Chapter, we make observations based on the results of the synthesis programs presented in the previous chapters and suggest future work in the area. 

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