Project Description: In the modern era, artificial intelligence and the Internet of Things have enabled us to develop smart edge devices that can sense, think, and communicate. These devices are based on Von Neumann's architecture which greatly contributed to the advancement of computing technologies. In spite of this, one of the drawbacks of Von Neumann's architecture is that the continuous transfer of data between memory and processors requires a considerable amount of processing time and power, and thereby represents a bottleneck in the process. Therefore, this project has as its objective to develop a novel architecture allowing the development of the new concept of "in-memory sensing" with the capability of sensing, computing, and memorizing data in the edge. As a proof of concept, prostate cancer markers will be selected as the target analyte for the in-memory sensor.
Project Description: Wearable sensors integrated into clothes hold the potential of revolutionizing personalized healthcare and telemedicine, enabling the continuous and noninvasive monitoring of human vital signals, and providing critical information and alerts to patients. These devices require suitable power sources that allow their continuous operation. Emerging wearable triboelectric nanogenerators (TENGs), energy harvesters that convert abundant mechanical energy into electrical sources, are attractive solutions to realize self-power and sustainable sensors. The development of reliable textile-based triboelectric sensors is paramount to addressing the challenge of powering wearable sensors by using a sustainable and renewable energy source. To reach these objectives, there is an urgent need to develop reliable and scalable textile-based TENGs devices that can be fabricated with existing textile technologies to meet the stringent requirements of both device industrialization and truly wearable green power sources and sensors.
Project Description:The use of sensor technology in the agriculture field enables precise data acquisition and analysis, providing insights into plant health, resource dynamics, and environmental conditions. This facilitates data-driven decision-making for enhanced agricultural practices. This project aims to contribute to current research and development activities by implementing a more sustainable and cost-effective sensor development approach. Designing sensor prototypes, developing innovative solutions, and collaborating with interdisciplinary teams to integrate and characterize sensors in real agricultural settings are the primary goals.
Project Description: The current phase of manufacturing processes is characterized by a rapid transition to more agile, resilient, and sustainable smart factories. Greener production with lower energy consumption and reduced waste generation are targeted using smart connected systems continuously monitoring and optimizing plant resources in a context-related connection between factory and environment.
Project Description: Sustainable electronics is a new concept that accounts for the environmental, social, and economic impact of electronic devices through the entire life cycle to reduce natural resources and CO2 footprint. The implementation of such new concept articulates along four main pathways: (i) eco-design that incorporates environmental requirements into the specifications; (ii) research of new sustainable materials; (iii) efficient manufacturing (net zero production); (iv) efficient recycling. Within this context, and for certain applications biodegradable thin-film transistor (TFT) technology has emerged as a viable alternative to the resource-intensive manufacturing by simplifying the processes and to the uncontrolled increase of e-waste by ensuring efficient recycling.
Project Description: iNEST (Interconnected Nord-Est Innovation Ecosystem), financially supported in the frame of PNRR Program, is aimed at extending the beneficial effects of digitalization to the key specialization areas of “Nord-Est” (Friuli-Venezia Giulia, Veneto and Province Autonome di Trento e Bolzano): industrial and manufacturing, agriculture, marine and mountain environment, architecture and construction, tourism, culture, wellness and food are the fields addressed.
Project Description: The research projects under Spoke 04 aim to improve the quality of food and nutrition to meet the needs and expectations of modern consumers. This includes reformulating food, using innovative and sustainable technologies, and designing new foods while considering emerging interindividual features that drive personalized nutrition.
Biosensors and chemical indicators, due to their high sensitivity, specificity, reproducibility and stability, are emerging as promising tools and have the potential to monitor and control the quality and properties of novel foods and ensure the safety of novel food development. The main focus is the implementation of sensors for food quality and nutrition assessment. Specifically, the planned activities are sensor fabrication, development of readout electronics and characterization from a controlled laboratory environment to actual use, and development of a correlation model to extend the use of this technology in food engineering.
Project Description: Sestosenso develops technologies for next generations of collaborative robots capable of self-adapting to different time-varying operational conditions and capable of safe and smooth adaptation from autonomous to interactive when human intervention is required either for collaboration or training/teaching. The project proposes a new sensing technology from the hardware and up to the cognitive perception and control levels, based on networks of embedded proximity and tactile sensors on the robot body, providing a unified proxy-tactile perception of the environment, required to control the robot actions and interactions, safely and autonomously. Within the project, the same technologies are also applied to wearable devices (like exoskeletons) to provide the user with better spatial awareness and to enforce safety in critical human-robot interactive tasks.