Obstructive Sleep Apnea (OSA) is a sleep breathing disorder affecting at least 3-7% of male adults and 2-5% of female adults between 30 and 70 years. It causes recurrent partial or total obstruction episodes at the level of the pharynx which causes cessation of breath during sleep. The number of obstruction episodes per sleep hour, known as Apnea-Hypopnea Index (AHI), along with the degree of the daytime sleepiness, determine the severity of OSA. Usually, OSA is diagnosed at a Sleep Unit in a hospital by the time-consuming polysomnography (PSG) test. Based on the expected impact of anatomical and physiological effects of the altered structure of the upper airway in OSA patients’ voices, the assessment of OSA from speech has been proposed as a simple way to help in the diagnostic process. In this paper, we review previous research to assess OSA from speech and underline the difficulty of a weak connection between OSA and speech. We present results to model OSA using, to the best of our knowledge, for the first time Deep Learning on the largest existing database of OSA voice recordings and speakers’ clinical variables. Using state-of-the-art speaker recognition techniques: acoustic subspace modeling (i-vectors), and deep neural network embeddings (x-vectors), we confirm the weak connection between speech and OSA. We hypothesize that this weak effect is mediated by undesired sources of variability as speakers’ age, body mass index (BMI), or height, and we propose Domain-Adversarial Training (DAT) to remove them. Our results show that, taking BMI as adversarial domain, when classifying voices from OSA extreme cases (AHI ≤ 10 vs AHI ≥ 30) accuracy increases from 69.39% to 76.60%. We hope these results can encourage the use of adversarial-domain neural networks to remove the undesired effects of clinical variables or speaker factors when assessing health disorders from speech.

In this paper, an adaptive image calibration technique suitable for cultural heritage surveillance in controlled environments is proposed. Heritage pieces, as objects important to conserve, are prone to degradation and thus in the need of a constant watch in order to assess the moment where maintenance is required, preferably in a non-invasive way. By means of a crowdsourcing approach, we take as surveillance cues the pictures visitors take of the piece under surveillance to avoid the need of a permanent human effort on monitoring. In this paper we propose a novel image calibration approach destined to monitor the state of a cultural heritage piece taking as information cues photographs of different sources. Our technique aims to surpass the colorimetry difference between pictures taken by different cameras, performing an adaptive linear interpolation having a given display with a set of colours as reference values, thus transforming the initial colour space of any picture to a fixed one. This is performed in the RGB domain. Lastly, the pictures are transformed to the CIELAB space to extract the colour coordinates and measure the overall objective quality of the calibration. Acting like this allows the heritage monitoring of its possible degradation along time in an effective way without incurring in expensive resources.

Colour changes of cultural heritage objects can be related with degradation of materials, thus a proper colour monitoring system can be used to detect conservation problems. With this purpose, a monitoring methodology for cultural heritage preventive conservation based on tailored colour reference charts and image analysis is proposed. Reference colour charts have been designed and tested for use in museums. Charts containing 64 colour patches have been printed using high-stability inks on 4 different substrates: Acid-free paper SkyLight, Acid-free paper covered with a propylene film, FOREX® and GlassPack. The stability has been studied by accelerated ageing in an UV chamber, and the harmlessness of the materials by Oddy Test. The final selection of material, laminated paper, is a balance between the colour change upon ageing and the performance in the Oddy Test. Using this material and the proposed design, colour change of copper and silver coupons has been assessed using images that are adjusted and calibrated by an adaptive calibration framework employing a given set of reference colours which homogenises the visual information in the supplied images. Thus, regardless of the camera of origin, any processed picture will deliver reliable information of the state of the colour in the metal surfaces at the moment it was taken. Results demonstrate the adequacy of the approach and the design for colour calibration, so these charts can be used to monitor colour change of sensitive materials –metal coupons– using photographs. As colour change of reference metals is a consequence of corrosion by environmental factors this may be used as a measure of air quality in museum environments. This methodology can be used to design a low-cost preventive conservation tool, where colour change of metal coupons –or other reference materials– can be followed through image analysis of pictures taken periodically by conservators or visitors, introducing citizen science in the conservation strategy.

Many studies have developed technologies intended to automate the process toward improving sustainability and achieving a circular economy in the construction industry. Despite some research works that have been recently conducted to help construction agents in the management of construction and demolition waste (CDW), developing new CDW monitoring and traceability tools—through a collaborative and integrative approach—is still an urgent challenge. This paper developed a hybrid mobile app for the real-time traceability of construction waste generation and management, where agents involved in CDW management can work together and trace the generation and management of waste. For this, a research method was developed following three stages: (1) planning and design, including identification of the information to be incorporated in the app and the output data; (2) development of the app; and (3) app testing by several construction agents, stressing the benefits and limitations. The developed app allows not only estimating but also tracing the amount of CDW generated in a construction project, ensuring the correct management and control of the waste. In addition, it promotes collaborative work, as the agents involved in CDW management, i.e., construction companies, site managers, truck drivers, waste managers, and management plants, can work together and see in real-time the management of the waste.

In this paper we present a colour calibration framework for cultural heritage preventive conservation. It includes a custom colour chart based on a regular sRGB division and a multi-dimensional calibration approach based on hyper-surfaces that map the original colours to the calibrated space taking information from all the channels. As shown, our work outperforms past proposals. This solution has been designed to be automatic and match the requirements of our cultural heritage surveillance framework needs, such as non-invasiveness and easiness to deploy. Its performance has been compared to publicly available calibration proposals and charts, and their results judged basing on the context and restrictions of the intended goal, such as illumination. Conclusions on the need for adapting technological solutions to the context of the goal, its cases of use when valid and self-designed methods are drawn. This work is part of the MIPAC-CM project.

Monitoring environmental factors such as pollutants, relative humidity, light, etc. is needed to ensure a proper preventive conservation of cultural heritage. Since existing systems are expensive and difficult to maintain, we propose as a sustainable alternative the use crowdsourced photographs taken by visitors using their cell phones. These images of a tailor made panel, including sensitive materials and a calibration color chart, are calibrated and colorimetric information extracted from them. The aim of this paper is assessing the robustness of the color chart and the calibration method used, and evaluating how the ageing of the materials of the chart can affect the accuracy of the calibration process. A choice is made between four candidate materials and a set of industrial inks. Several tests have been performed and mathematical metrics extracted, so the most suitable material is adequately selected. It has been checked how the image calibration process, employed for homogenizing information from the crowdsourced pictures, performs with real-life materials and colors to assess the possible degradation that may happen. This work is a part of a bigger project with the aim of building a crowdsourcing-based monitoring system for preventive conservation of cultural heritage.