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Level-crossing ADCs reduce the size of data streams in wearable devices. However, in the context of electrocardiogram (ECG) signals, such an event-driven data source results in a variable length two-dimensional (time-amplitude tuples) data vector for each ECG beat. It is difficult to apply many standard signal processing techniques to this data making classifiers more complex. In this paper we resolve these difficulties by mapping the variable length 2D vectors to a fixed length feature vector comprising the first 81 coefficients of a Chebyshev polynomial expansion of the ECG beat. We show that beat reconstruction based on these 81 coefficients results in an average RMS difference to the original beat of only ≈ 3.08%. Using these coefficients as the feature set input to a simple three-layered ANN binary (Normal / Abnormal) ECG classifier and we demonstrate 98.15% average accuracy and 96.07% average sensitivity. Using the same simple ANN structure we also constructed a 4-class ANN structure which achieved 98.80% average accuracy and 91.5% average sensitivity. Both these networks have only 20k parameters and outperform the state-of-the-art classifiers, enabling low-power edge computing.

International audience; To detect cardiovascular diseases (CVD), electrocardiogram (ECG) of a patient must be recorded and analyzed for a long period. For an effective diagnosis, the ECG recording system must automatically adapt to new patients. This paper presents a low-complexity artificial neural network that exclusively uses the consecutive slopes of ECG signal as inputs. These features are extracted using a level-crossing ADC and a simple TDC-based event-driven processing chain. The proposed clockless system can detect arrhythmias in ECG with 98.4% accuracy and reduce the ANN hardware complexity by more than half compared to recent literature. It is perfectly adapted to integrated wearable monitoring systems and shows good adaptability to new patients. Keywords-Artificial neural network (ANN), electrocardiogram (ECG), cardiac arrhythmia classification (CAC), event-driven, time-to-digital converter (TDC), levelcrossing ADC

International audience; The paper is based on idea realized under Erasmus+ Strategic Partnership Key Action 2 and focused on educational activities for attracting technical education to young people. The project based on exchange of good practices involves seven partners from three countries (Poland, Estonia and France). Three of them represents sector of higher education and four are schools located in the neighbour of the technical university. Unfortunately, in recent years number of candidates entering to technical universities (particularly in Europe) decreases. Partners exchange ideas how to increase number of students who starts education in technical area. Another project goal is to examine possibilities how the university can contribute in increasing quality of technical education at schools. Main project ideas and results of project activities are reported in the paper.

International audience; This paper demonstrates a novel motion control approach for biomimetic underwater vehicles with pitching fins. Even though these vehicles are highly maneuverable, the actua-tion of their different degrees of freedom (DOFs) is strongly coupled. To address this problem, we propose to use smooth DOF prioritization depending on which maneuver the vehicle is about to do. DOF prioritization has allowed us to develop a modular, easily applicable and extendable motion control architecture for U-CAT vehicle, which is meant for archaeological shipwreck penetration. We demonstrate the benefits of this architecture by developing an remotely operated vehicle autopilot for depth and yaw using a nonlinear state feedback controller. We also show the extensibility of the approach by controlling 3 DOFs of a fully autonomous U-CAT. The real-time experimental results show high position tracking precision (depth RMS error: 1.9 cm; yaw RMS error: 2.5 •) Comparative experiments justify the use of DOF prioritization.