New Methodology to Evaluate and Optimize Indoor Ventilation Based on Rapid Response Sensors

The recent pandemic increased attention to the need for appropriated ventilation and good air quality as efficient measures to achieve safe and healthy indoor air. This work provides a novel methodology for continuously evaluating ventilation in public areas using modern rapid response sensors (RRS). This methodology innovatively assesses the ventilation of a space by combining a quantitative estimation of the real air exchange in the space-obtained from CO2 experimental RRS measurements and the characteristics of and activity in the space-and indoor and outdoor RRS measurements of other pollutants, with healthy recommendations from different organisations. The methodology allows space managers to easily evaluate, in a continuous form, the appropriateness of their ventilation strategy, thanks to modern RRS measurements and direct calculations (implemented here in a web app), even in situations of full activity. The methodology improves on the existing standards, which imply the release of tracer gases and expert intervention, and could also be used to set a control system that measures continuously and adapts the ventilation to changes in indoor occupancy and activity, guaranteeing safe and healthy air in an energy-efficient way. Sample public concurrence spaces with different conditions are used to illustrate the methodology.

Tools for evaluation and prediction of industrial noise sources. Application to a wastewater treatment plant

In recent years, acoustic pollution caused by noise has considerably increased in many countries. Particularly in Spain, the noisiest country in Europe. It is sometimes difficult to predict the noise levels that a new installation or an expansion of industrial equipment will cause in the surroundings. This work introduces a new methodology for the prediction, evaluation, and analysis of industrial noise sources, as well as a novel tool for predicting and categorizing outdoor noise from its measurement at their sources. A Wastewater Treatment Plant (WWTP) has been used to demonstrate the applicability and validity of this methodology. The continuous level of acoustic pressure equivalent has been measured in different points of the plant using an integrating sound level meter. From these values, noise maps have been built to obtain detailed information of the industrial noise generated in the installation. Also, the typical frequency patterns of each type of source have been used for the calculation of source noise apportionments. To achieve this objective, several noise sources have been selected to provide information about their contribution to the industrial noise in the WWTP surrounding area. Finally, predictions have been validated using actual measurements. This methodology is a useful tool to predict personal exposure to noise and the impact on the environment. This information can be used, in particular, to propose mitigation actions.

S3: An AI-Enabled User Continuous Authentication for Smartphones Based on Sensors, Statistics and Speaker Information

Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community.

Java simulations of embedded control systems

This paper introduces a new Open Source Java library suited for the simulation of embedded control systems. The library is based on the ideas and architecture of TrueTime, a toolbox of Matlab devoted to this topic, and allows Java programmers to simulate the performance of control processes which run in a real time environment. Such simulations can improve considerably the learning and design of multitasking real-time systems. The choice of Java increases considerably the usability of our library, because many educators program already in this language. But also because the library can be easily used by Easy Java Simulations (EJS), a popular modeling and authoring tool that is increasingly used in the field of Control Education. EJS allows instructors, students, and researchers with less programming capabilities to create advanced interactive simulations in Java. The paper describes the ideas, implementation, and sample use of the new library both for pure Java programmers and for EJS users. The JTT library and some examples are online available on http://lab.dia.uned.es/jtt.