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Background: Nowadays, cardiovascular diseases (CVD) remain the main cause of death worldwide. A heart sound signal or phonocardiogram (PCG) is the most simple, economical and non-invasive tool to detect CVDs. Advances in technology and signal processing allow the design of computer-aided systems for heart illnesses detection from PCG signals. Purpose: The paper proposes a pipeline and benchmark for binary heart sounds classification. The features extraction architecture is focused on the use of Matching Pursuit time-frequency decomposition using Gabor dictionaries and the Linear Predictive Coding method of a residual. We compare seven classifiers with two different approaches: feature averaging and cycle averaging. Methods: We test our proposal on the PhysioNet/CinC challenge 2016 database, which comprises a wide variety of heart sounds recorded from patients with normal and different pathological heart conditions. We conduct a 10-fold stratified cross-validation method to evaluate the performance of different classification algorithms. The feature sets were also tested when using an oversampling method for balancing. Results: The benchmark identified systems showing a satisfying performance in terms of accuracy, sensitivity, and Matthews correlation coefficient. Results can be improved when using feature averaging and an oversampling strategy.

In this article we present an approach that uses sound to communicate geometrical data related to a virtual object. This has been developed in the framework of a multimodal interface for product design. The interface allows a designer to evaluate the quality of a 3-D shape using touch, vision, and sound. Two important considerations addressed in this article are the nature of the data that is sonified and the haptic interaction between the user and the interface, which in fact triggers the sound and influences its characteristics. Based on these considerations, we present a number of sonification strategies that are designed to map the geometrical data of interest into sound. The fundamental frequency of various sounds was used to convey the curve shape or the curvature to the listeners. Two evaluation experiments are described, one involves partipants with a varied background, the other involved the intended users, i.e. participants with a background in industrial design. The results show that independent of the sonification method used and independent of whether the curve shape or the curvature were sonified, the sonification was quite successful. In the first experiment participants had a success rate of about 80% in a multiple choice task, in the second experiment it took the participants on average less than 20 seconds to find the maximum, minimum or inflection points of the curvature of a test curve.

Vehicular ad hoc networks have been identified as a key technology for enabling safety and infotainment applications in the context of smart and connected vehicles. In this sense, diverse approaches of multi-hop broadcast protocols have been proposed to collect and disseminate context information through the network. However, before vehicular ad hoc networks applications fulfill their expected potential to connect smart vehicles, several issues must be addressed. Among these issues, those related to security are of particular importance. In this article, the main security issues of broadcast message dissemination in vehicular ad hoc networks are discussed. Moreover, a review of the most relevant threats and proposed solutions to secure broadcast message dissemination in vehicular ad hoc networks is presented and discussed. As mentioned, security is an important topic which has not been fully addressed in vehicular ad hoc networks; therefore, the aim of this article is to introduce security issues and proposed solutions related to three main security concerns associated with the message dissemination process in vehicular ad hoc networks: network access, data consistency, and broadcast protocols.

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