Nature-inspired methods for hardware and software system design and information processing

硬件和软件系统设计和信息处理的自然启发方法

• 批准号: 156900-2013
• 负责人: Boukadoum, AMounir
• 金额: $ 1.75万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569512
• 关键词: Nature; inspired; methods; hardware; software

My research investigates the use of knowledge engineering, heuristics and nature-inspired computation for system design and analysis. Its basic postulate is that most system design, modeling, and analysis problems can be represented as classification or optimization problems for which a natural equivalent exist and have been solved over the course of evolution, or for which innovative search algorithms can be developed using heuristic search and/or data mining. In doing so, I seek alternative approaches to existing formal techniques when they are inapplicable or too slow. In the past few years, my research included the development and application of intelligent algorithms for data analysis and system design using neural networks, particle swarm optimization and data mining. This led to several original contributions at the algorithmic and applicative levels, namely in instrumentation, embedded software design, computer aided design tools, and medical image processing, In the next five years, I intend to move on to the area of design automation by investigating the development of a reuse-based framework for electronic circuit design that either recalls complete stored designs or finds and integrates fragments from different example designs in the database. I will validate my findings automated RF and microwave circuit design. I expect my research not only to advance the state of the art in intelligent system design and signal processing, and on developing efficient tools for the computer aided design of hardware and software systems, but also to contribute significant advances to rapid system prototyping, hence helping to shorten the design cycle of complex designs and reduce development cost.

我的研究探讨了知识工程，数学和自然启发的计算系统设计和分析的使用。它的基本假设是，大多数系统设计，建模和分析问题可以表示为分类或优化问题，其中存在自然等价物并在进化过程中得到解决，或者可以使用启发式搜索和/或数据挖掘开发创新的搜索算法。在这样做的时候，我寻求替代方法，现有的正式技术时，他们是不适用的或太慢。 在过去的几年里，我的研究包括使用神经网络，粒子群优化和数据挖掘进行数据分析和系统设计的智能算法的开发和应用。这导致了在算法和应用层面上的一些原创性贡献，即在仪器仪表，嵌入式软件设计，计算机辅助设计工具和医学图像处理方面， 在接下来的五年里，我打算通过研究电子电路设计的基于重用的框架的开发来转移到设计自动化领域，该框架可以调用完整的存储设计，也可以在数据库中找到并集成来自不同示例设计的片段。我将验证我的研究结果自动射频和微波电路设计。 我希望我的研究不仅能推动智能系统设计和信号处理的最新发展，并为硬件和软件系统的计算机辅助设计开发有效的工具，而且还能为快速系统原型设计做出重大贡献，从而有助于缩短复杂设计的设计周期，降低开发成本。