Abstract
Any form of eating disorder is detrimental for health. Having an eating disorder increases the risks for chronic diseases and general morbidity, leading to several health problems such as obesity, hypertension and cardio-vascular diseases. The risk is greater for elderly people, as ageing submits the body to several functional changes that affect health and nutrition conditions. Automatic monitoring systems can help to prevent these risks by supporting people to maintain appropriate eating behaviours. Ageing services based on ICT assistive services are increasing as a result of the awareness of the growing socio-economic relevance of this issue, especially when we consider the rural and very sparsely-populated areas. In order to assess these requirements, systems should be automatic, non-intrusive and low cost. This paper presents an evaluation test of the Microsoft Kinect sensor for monitoring older people's meal intake, with the aim of contributing to the development of an automatic diet monitoring system. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Abstract
Water and energy are essential for human existence, and its rational use should be encouraged. According to the literature review, water consumption directly affects energy consumption and are inseparably linked resources. The energy to water part of the water/energy nexus, increasingly highlighted as an important issue for future planning and strategic policy considerations. Joint consideration of both water/energy domains can identify new options for increasing overall resource use efficiency. This work is part of the project ENERWAT that has as goal to measure in situ the water/energy consumption related with water supply end use in rural and urban dwellings in order to validate the data collected by survey. A methodology for low cost measure and store water/energy consumes was developed. Water, Gas and electricity data was stored in image format. In this paper, a CNN architecture was applied and trained to read water/energy. The models suited their proposed. The achieved accuracy for test set was: water - dozen: 0.98, unit: 0.92; gas: dozen: 0.94, unit: 0.99; and electricity - dozen: 0.99, units 0.99. The more challenge digit was water unit digit due to partial occlusion. It is presented a day of readings and discussed some events. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
In developed societies populations are aging. Facing the global slump states are reducing expenses bringing crisis to health care systems. Solutions to decrease costs are needed. Within ICT, smartphones' features can help provide personalised health and care services that meet individual needs. There is a huge rise of applications that effectively help people but they act independently, each one for a certain purpose. In this paper we propose the HelpWave system, a cloud-centred architecture information system that integrates data from the users' smartphones APPs. Conceived as a social care network its aim is to reinforce connection between caregivers and carereceiver as for instance, older people. (C) 2015 Published by Elsevier B.V.

Abstract
The wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30–120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data. © 2018 Association for Computing Machinery.

Abstract
Vineyard parcels delimitation is a preliminary but important task to support zoning activities, which can be burdensome and time-consuming when manually performed. In spite of being desirable to overcome such issue, the implementation of a semi-/fully automatic delimitation approach can meet serious development challenges when dealing with vineyards like the ones that prevail in Douro Region (north-east of Portugal), mainly due to the great diversity of parcel/row formats and several factors that can hamper detection as, for example, interrupted rows and inter-row vegetation. Thereby, with the aim of addressing vineyard parcels detection and delimitation in Douro Region, a preliminary method based on segmentation and morphological operations upon high-resolution aerial imagery is proposed. This method was tested in a data set collected from vineyards located at the University of Trás-os-Montes and Alto Douro(Vila Real, Portugal). The presence of some of the previously mentioned challenging conditions - namely interrupted rows and inter-row grassing - in a few parcels contributed to lower the overall detection accuracy, pointing out the need for future improvements. Notwithstanding, encouraging preliminary results were achieved. © 2018 Association for Computing Machinery.