Wireless biomedical sensors should dramatically reduce the costs and risks associated with personal health care, while being more and more exploited by telemedicine and efficient e-health systems. However, because of the large power consumption of continuous wireless transmission, the battery life of the sensors is reduced for long-term use. Sub-nyquist continuous-time discrete-amplitude (CTDA) sampling approaches using level-crossing analog-to-digital converters (ADCs) have been developed to reduce the sampling rate and energy consumption of the sensors. However, traditional machine learning techniques and architectures are not compatible with the non-uniform sampled data obtained from level-crossing ADCs. This project aims to develop analog algorithms, circuits and systems for the implementation of machine learning techniques in CTDA sampled data in wireless biomedical sensors. This “near-sensor computing” approach, will help reduce the wireless transmission rate and therefore the power consumption of the sensor. The output rate of the CTDA is directly proportional to the activity of the analog signal at the input of the sensor. Therefore, artificial intelligence hardware that processes CTDA data should consume significantly less energy. For demonstration purposes, a prototype biomedical sensor for the detection and classification of sleep apnea will be developed using integrated circuit prototypes and a commercially available analog front-end interface. The sensor will acquire electrocardiogram and bioimpedance signals from the subject and will use data fusion techniques and machine learning techniques to achieve high accuracy.

Biocontrol products, plant protection products based of living organisms or natural substances, constitute a pathway promising to reduce the use of plant protection products. However, there are still few solutions for diseases field crops. The maturation project proposes to develop new products of biocontrol based on lipopeptides from bacterial cultures Pseudomonas to control the main fungal disease of wheat: the Septoria tritici blotch. It is based on the results of the LIPOCONTROLE project ECOPHYTO PSPE 2 - 2014: “Contributing to the growth of biocontrol” that provide the experimental proof of design (TRL 3) that the Pseudomonas culture extracts containing lipopeptides reduce effectively the disease. The objective of the project is to demonstrate at a field level that lipopeptides extracts from culture of Pseudomonas bacteria constitutes a functional biocontrol solution against Septoria tritici blotch (TRL 5). For achieve this goal, the work program is to design and develop the lipopeptide extract production process up to the pilot scale, to assess the level of toxicity, ecotoxicity and stability of extracts efficiency, to define their use conditions in the greenhouse, to test their effectiveness in the field and to define the strategy for the best dissemination of the solution to wheat producers.