Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris-Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

GreenVMAS: Virtual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power Plants.

The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user's energy bill is greatly reduced with the implemented system.

Detection of Cattle Using Drones and Convolutional Neural Networks.

Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.

Agreement Technologies for Energy Optimization at Home.

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments.

Rapid advances in technology make it necessary to prepare our society in every aspect. Some of the most significant technological developments of the last decade are the UAVs (Unnamed Aerial Vehicles) or drones. UAVs provide a wide range of new possibilities and have become a tool that we now use on a daily basis. However, if their use is not controlled, it could entail several risks, which make it necessary to legislate and monitor UAV flights to ensure, inter alia, the security and privacy of all citizens. As a result of this problem, several laws have been passed which seek to regulate their use; however, no proposals have been made with regards to the control of airspace from a technological point of view. This is exactly what we propose in this article: a platform with different modes designed to control UAVs and monitor their status. The features of the proposed platform provide multiple advantages that make the use of UAVs more secure, such as prohibiting UAVs’ access to restricted areas or avoiding collisions between vehicles. The platform has been successfully tested in Salamanca, Spain.

Taxi services and the carsharing alternative: a case study of valencia city.

The public's awareness of pollution in cities is growing. The decrease of carbon dioxide emissions from the use of fossil-fuel-powered cars stands out among the different viable alternatives. To this purpose, more sustainable options, such as carsharing fleets, could be used to replace private automobiles and other services such as taxis. This type of vehicle, which is usually electric, is becoming more common in cities, providing a green mobility option. In this research, we use multi-agent simulations to examine the efficiency of the current taxi fleet in Valencia. After that, we evaluate various carsharing fleet arrangements. Our findings demonstrate the possibility for a mix of the two types of fleets to meet present demand while also improving the city's sustainability.

A device and an app for the diagnosis and self-management of tinnitus.

Tinnitus is an annoying ringing in the ears, in varying shades and intensities. Tinnitus can affect a person's overall health and social well-being (e.g., sleep problems, trouble concentrating, anxiety, depression and inability to work). The diagnostic procedure of tinnitus usually consists of three steps: an audiological examination, psychoacoustic measurement, and a disability evaluation. All steps are performed by physicians, who use specialised hardware/software and administer questionnaires. This paper presents a system, to be used by patients, for the diagnosis and self-management of tinnitus. The system is made up of an app and a device. The app is responsible for executing - through the device - a part of the required audiological and psychoacoustic examinations, as well as administering questionnaires that evaluate disability. The paper reviews the quality of the automated audiometric reporting and the user experience provided by the app. Descriptive and inferential statistics were used to support the findings. The results show that automated reporting is comparable with that of physicians and that user experience was improved by re-designing and re-developing the acufenometry of the app. As for the user experience, two experts in Human-Computer Interaction evaluated the first version of the app: their agreement was good (Cohen's K = 0.639) and the average rating of the app was 1.43/2. Also patients evaluated the app in its initial version: the satisfactory tasks (audiometry and questionnaires) were rated as 4.31/5 and 4.65/5. The unsatisfactory task (acufenometry) was improved and the average rating increased from 2.86/5 to 3.96/5 (p = 0.0005). Finally, the general usability of the app was increased from the initial value of 73.6/100 to 85.4/100 (p = 0.0003). The strengths of the project are twofold. Firstly, the automated reporting feature, which - to the best of our knowledge - is the first attempt in this area. Secondly, the overall app usability, which was evaluated and improved during its development. In summary, the conclusion drawn from the conducted project is that the system works as expected, and despite some weaknesses, also the replication of the device would not be expensive, and it can be used in different scenarios.

Automatic image analyser to assess retinal vessel calibre (ALTAIR). A new tool to evaluate the thickness, area and length of the vessels of the retina.

BACKGROUND AND OBJECTIVES: The examination of the fundus allows to evaluate retinal the microcirculation in vivo. We assess the reliability and validity of ALTAIR software, and to evaluate its clinical relevance by the association of thickness, area and length of the retinal vessels with other measures of vascular structure and function, target organ damage and cardiovascular risk. METHODS: Cross-sectional study involving a total of 250 subjects aged 62 ±â€¯9 years, 51 % males. In a random subsample of 60 subjects (118 retinographies), we estimated the intraobserver, interobserver and interdevice intraclass correlation coefficients (ICC) of the measurements of retinal vascular thickness, area and length in 3 concentric circles. Concurrent validity was assessed with all 250 subjects (495 retinographies), analysing the relationship to age, blood pressure, target organ damage, vascular structure and function, and cardiovascular risk. RESULTS: Of the sample, 69 % were diagnosed with hypertension and 17 % with diabetes. Intraobserver ICC ranged from 0.640 for venous length to 0.906 for arterial area. Interobserver ICC ranged from 0.809 for arterial length to 0.916 for venous area, and interdevice ICC for arteriovenous ratio (AVR) was 0.887, thickness of arteries 0.590 and vein thickness 0.677. We found a moderate correlation between retinal vascular parameters and vascular structure and function, and target organ damage. In multiple linear regression analysis, the association with blood pressure, albumin/creatinine ratio, carotid intima-media thickness and cardiovascular risk is maintained. CONCLUSION: The ALTAIR tool has been useful for analysing the thickness, area and length of retinal vessels, with adequate reliability and a concomitant association of retinal vessel measurements with other cardiovascular parameters and cardiovascular risk. Therefore, in addition to thickness, the area and length of retinal vessels could also play a role in the prediction of cardiovascular risk.

Social computing for image matching.

One of the main technological trends in the last five years is mass data analysis. This trend is due in part to the emergence of concepts such as social networks, which generate a large volume of data that can provide added value through their analysis. This article is focused on a business and employment-oriented social network. More specifically, it focuses on the analysis of information provided by different users in image form. The images are analyzed to detect whether other existing users have posted or talked about the same image, even if the image has undergone some type of modification such as watermarks or color filters. This makes it possible to establish new connections among unknown users by detecting what they are posting or whether they are talking about the same images. The proposed solution consists of an image matching algorithm, which is based on the rapid calculation and comparison of hashes. However, there is a computationally expensive aspect in charge of revoking possible image transformations. As a result, the image matching process is supported by a distributed forecasting system that enables or disables nodes to serve all the possible requests. The proposed system has shown promising results for matching modified images, especially when compared with other existing systems.