Category:Hardware ideas: Difference between revisions

Many people have suggested modifications to some part of the OLPC's hardware. Or they have suggested that some item or other should be added to the OLPC.

These pages contain various ideas for improvements or changes to the various hardware elements of the OLPC. This category could also point to pages about entirely new hardware accessories such as a USB-powered smoke signal generator, or an audio-controlled bullock cart.

You will find a link to the category page at the bottom of every page in this category.

Are computer-aided engineering tools such as modeling, simulation, visualization, optimization, artificial intelligence and advanced design, documentation, manufacturing and information management being used in the hardware design aspects of the OLPC program? The following open ended synthesis technique can be instantiated in software to arrive at optimal designs for the OLPC laptop. This is abstracted from http://www.mae.cornell.edu/ccsl/papers/Biomimetics05_Lipson.pdf. A simple model of evolutionary adaptation There are a variety of computational models of open-ended synthesis loosely inspired by natural evolutionary adaptation. Perhaps the simplest approach uses a direct representation. We start off with a large set of initial candidate designs – this is the initial population. These designs may be random, blank, or may be seeded with some prior knowledge in the form of solutions we think are good starting points. We then begin evolving this population through repeated selection and variation. To perform selection, we first measure the performance of each solution in the population. The performance, termed fitness in evolutionary terminology, captures the merit of the design with respect to some target performance we are seeking as the designers. The fitness metric needs to be solution-neutral, i.e measure the extent to which the target task has been achieved, regardless of how it was achieved. We select better solutions (parents) and use them to create a new generation of solutions (offspring). The offspring are variations of the parents, created through variation operators like mutation and recombination. The process is repeated generation after generation until good solutions are found. In practice, there are many modifications to the simple process described above: We use special representations, clever selection methods and sophisticated variation and evaluation methods, as well as multiple co-evolving populations. Most interestingly, we let the representations and the evaluation methods evolve too, to allow for a more open-ended search.