In the last years, architectural practice has been confronted with a paradigm shift towards the application of digital methods in design activities. In this regard, it is a pedagogic challenge to provide a suitable co...In the last years, architectural practice has been confronted with a paradigm shift towards the application of digital methods in design activities. In this regard, it is a pedagogic challenge to provide a suitable computational background for architectural students, to improve their ability to apply algorithmic-parametric logic, as well as fabrication and prototyping resources to design problem solving. This challenge is even stronger when considering less favored social and technological contexts, such as in Brazil, for example. In this scenario, this article presents and discusses the procedures and the results from a didactic experience carried out in a design computing-oriented discipline, inserted in the curriculum of a Brazilian architecture course. Hence, this paper shares some design computing teaching experiences and presents some results on computational methods and creative approaches, with a view to contribute to a better understanding about the relations between logical thinking, mathematics and architectural design processes.展开更多
In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon or...In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.展开更多
文摘In the last years, architectural practice has been confronted with a paradigm shift towards the application of digital methods in design activities. In this regard, it is a pedagogic challenge to provide a suitable computational background for architectural students, to improve their ability to apply algorithmic-parametric logic, as well as fabrication and prototyping resources to design problem solving. This challenge is even stronger when considering less favored social and technological contexts, such as in Brazil, for example. In this scenario, this article presents and discusses the procedures and the results from a didactic experience carried out in a design computing-oriented discipline, inserted in the curriculum of a Brazilian architecture course. Hence, this paper shares some design computing teaching experiences and presents some results on computational methods and creative approaches, with a view to contribute to a better understanding about the relations between logical thinking, mathematics and architectural design processes.
文摘In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.
