Nowadays, unmanned aerial vehicles/drones are involved in a continuously growing number of security incidents. Therefore, the research interest in drone versus human movement detection and characterization is justified by the fact that such devices represent a potential threat for indoor/office intrusion, while normally, a human presence is allowed after passing several security points. Our paper comparatively characterizes the movement of a drone and a human in an indoor environment. The movement map was obtained using advanced signal processing methods such as wavelet transform and the phase diagram concept, and applied to the signal acquired from UWB sensors.

Limited information is available on the design of combined innovation and entrepreneurship training courses, and with even less available on delivering such courses to multidisciplinary teams. We designed an extracurricular project-based training course in innovation and entrepreneurship and delivered it to transdisciplinary teams of students who were then asked to build an autonomous mobile system for healthcare applications and to create a business model to implement it. The course was created through a backward educational design and design thinking to assist teams of students in transforming an idea into a technical solution and developing it into a business model through a project-based learning experience. A transdisciplinary group of 31 students, mostly in bachelor’s degree programs, worked either all together, in plenary sessions, in subject-specific groups, or in teams of 5–6 members, under the guidance of their trainers during a ten-day summer school. We used questionnaires to evaluate the changes in student perceptions and a satisfaction survey to evaluate the students’ experience. The qualitative observations together with the results of the quantitative instruments revealed positive changes in student perceptions towards innovation and entrepreneurship. Additionally, the satisfaction survey offered positive feedback and some recommendations for further improvement. The implementation of the course showed that planning is important, but flexibility is essential, that covering fewer topics might lead to deeper understanding, and that managing expectations and practicing empathy are crucial.

In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

The present paper introduces a new computing paradigm based on the idea of string folding. Comparisons between the computational power of the proposed model with the classical families of languages from the Chomsky hierarchy are studied. Some preliminary results are reported and some conjectures are discussed. In this respect, the proposed model is promising not only because of the expected theoretical results, but also because of the possible indirect applications in various fields (as for instance, mathematical linguistics, DNA computing, computing using light, and so on).