Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.

Comparison of different solutions for the hybridization of a motorcycle engine for Formula Student applications and evaluation of the effectiveness on the simulated lap time

Following the latest environmental concerns, the importance of minimising the detrimental effect of emissions of terrestrial vehicles has become a major goal for the whole automotive field. The key to achieve an emission-free long term future is the electrification of vehicle fleets; this huge step cannot be taken without intermediate technologies. In this context, hybrid vehicles are fundamental to reach this goal. Specifically, mild hybrid vehicles represent a trade-off between cost and emissions that could act now as a bridge towards electrification. Like the industry, also student engineering competitions are likely to take the same route: Combustion vehicles may well turn into hybrid vehicles. For this reason, a preliminary design overview is necessary to pinpoint the key performance indicators for the prototypes of the future.