Groupme: city data and multi-preference activity allocation

Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

Confronto tra Bluetooth classic e Bluetooth low energy nelle comunicazioni tra sensori inerziali e dispositivi mobili in applicazioni biomeccaniche in ambito medico e sportivo

In questo elaborato sono stati confrontati i moduli bluetooth WT11, BLE113 , BT121 rispetto alle loro caratteristiche di banda, consumo, range e utilizzabilita in un contesto applicativo stringente come quello degli utilizzi biomeccanici. Si sono prima elencati i settori di riferimento, per poi descrivere il contesto applicativo in ambito medico e sportivo. Il confronto finale ha tenuto conto delle modalita di comunicazione bluetooth classic e bluetooth low energy, cercando di motivare quale modulo risulti migliore per questo particolare e innovativo contesto.