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For autonomous vehicles to be fully aware of its environment, it needs to collect data consistently from other vehicles and Road Side Units (RSU) in the surroundings. This heavy exchange increases latency and cybersecurity threats. This paper introduces Multi-Access Edge Computing (MEC), a 5G specification, as a promising solution to this significant issue. It proposes the adoption of a MEC platform, at the autonomous car premises capable of providing the environment required for running vehicular MEC applications, therefore reducing latency and decentralizing data treatment. For that purpose, the standard established by the European Telecommunications Standards Institute (ETSI) for the MEC framework is adopted. This work introduces few open-source solutions while analyzing how strongly it respects the ETSI standard, and it supports mobility in a Cooperative, Connected, and Automated Mobility (CCAM) context. It also recommends possible MEC security solutions., Comment: VIRTUAL ITS EUROPEAN CONGRESS, Nov 2020, Lisbon, Portugal

This paper describes the LIA speaker recognition system developed for the Speaker Recognition Evaluation (SRE) campaign. Eight sub-systems are developed, all based on a state-of-the-art approach: i-vector/PLDA which represents the mainstream technique in text-independent speaker recognition. These sub-systems differ: on the acoustic feature extraction front-end (MFCC, PLP), at the i-vector extraction stage (UBM, DNN or two-feats posteriors) and finally on the data-shifting (IDVC, mean-shifting). The submitted system is a fusion at the score-level of these eight sub-systems.

In this paper, a new efficient feature extraction method based on the adaptive threshold of wavelet package coefficients is presented. This paper especially deals with the assessment of autonomic nervous system using the background variation of the signal Heart Rate Variability HRV extracted from the wavelet package coefficients. The application of a wavelet package transform allows us to obtain a time-frequency representation of the signal, which provides better insight in the frequency distribution of the signal with time. A 6 level decomposition of HRV was achieved with db4 as mother wavelet, and the above two bands LF and HF were combined in 12 specialized frequencies sub-bands obtained in wavelet package transform. Features extracted from these coefficients can efficiently represent the characteristics of the original signal. ANOVA statistical test is used for the evaluation of proposed algorithm.