Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor

The current trend of applying transfer learning from convolutional neural networks (CNNs) trained on large datasets can be an overkill when the target application is a custom and delimited problem, with enough data to train a network from scratch. On the other hand, the training of custom and lighter CNNs requires expertise, in the from-scratch case, and or high-end resources, as in the case of hardware-aware neural architecture search (HW NAS), limiting access to the technology by non-habitual NN developers. For this reason, we present ColabNAS, an affordable HW NAS technique for producing lightweight task-specific CNNs. Its novel derivative-free search strategy, inspired by Occam's razor, allows to obtain state-of-the-art results on the Visual Wake Word dataset, a standard TinyML benchmark, in just 3.1 GPU hours using free online GPU services such as Google Colaboratory and Kaggle Kernel

Human recognition for resource constrained mobile robot applied to Covid-19 Disinfection

The global COVID-19 pandemic has stimulated the production of disinfection robots by institutions and companies. The concept of automated disinfection without involving human operators looks interesting in the eyes of the hospital management, and not only. It can save lives by avoiding the cleaning staff working in highly infected environments. At the same time, it can reduce costs by diminishing staff. The most commonly adopted robots, like the one from the UVD company, use UV-C light to disinfect surfaces. UV-C radiations alter DNA and RNA so that organisms cannot replicate. Others use also vapor and fogging systems that spray chemical disinfectants, such as ozone. However, UV-C lamps strongly limit human-machine cooperation. Direct exposure to UV-C radiation to the skin has to be avoided for health reasons. Fortunately, the outstanding results of machine learning offer new possibilities for robotics automation. It can be used to deeply understand the outside world and take actions accordingly, shutting down the lamps whenever a human is detected. So that human-machine cooperation is enabled