Tactile balls, an interactive device facilitating the development of the sense of touch

Celem artykułu jest zaprezentowanie projektu interaktywnego urządzenia wspomagającego rozwój zmysłu dotyku, Kulek-Fakturek. Urządzenie składa się z zestawu półkul z parami o identycznych fakturach. Zadaniem osoby ćwiczącej jest znalezienie i połączenie tych par poprzez badanie/ocenianie dotykiem faktury powierzchni. Poprawne wykonanie zadania zostaje każdorazowo nagrodzone zestawem sygnałów gratyfikujących. Artykuł przedstawia proces projektowania faktury i wytwarzania urządzenia oraz waliduje wartość dydaktyczną łamigłówki w oparciu o opinię specjalistek z dziedziny terapii specjalnej.The aim of the paper is to present a project of a puzzle facilitating the development of the sense of touch, Tactile Balls. The puzzle consists of a set of hemispheres, with pairwise identical textures. The task is to find and connect these pairs by studying their surfaces with the sense of touch. Making a valid connection is gratified with a set of stimuli. The paper shows the process of designing and manufacturing the device and evaluates its educational value based on the special therapy specialists’ opinion

Benchmarking Deep Learning for On-Board Space Applications

Benchmarking deep learning algorithms before deploying them in hardware-constrained execution environments, such as imaging satellites, is pivotal in real-life applications. Although a thorough and consistent benchmarking procedure can allow us to estimate the expected operational abilities of the underlying deep model, this topic remains under-researched. This paper tackles this issue and presents an end-to-end benchmarking approach for quantifying the abilities of deep learning algorithms in virtually any kind of on-board space applications. The experimental validation, performed over several state-of-the-art deep models and benchmark datasets, showed that different deep learning techniques may be effectively benchmarked using the standardized approach, which delivers quantifiable performance measures and is highly configurable. We believe that such benchmarking is crucial in delivering ready-to-use on-board artificial intelligence in emerging space applications and should become a standard tool in the deployment chain