MAIA: an event-based modular architecture for intelligent agents

Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic. behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform.

An automatic critical care urine meter

Nowadays patients admitted to critical care units have most of their physiological parameters measured automatically by sophisticated commercial monitoring devices. More often than not, these devices supervise whether the values of the parameters they measure lie within a pre-established range, and issue warning of deviations from this range by triggering alarms. The automation of measuring and supervising tasks not only discharges the healthcare staff of a considerable workload but also avoids human errors in these repetitive and monotonous tasks. Arguably, the most relevant physiological parameter that is still measured and supervised manually by critical care unit staff is urine output (UO). In this paper we present a patent-pending device that provides continuous and accurate measurements of patient’s UO. The device uses capacitive sensors to take continuous measurements of the height of the column of liquid accumulated in two chambers that make up a plastic container. The first chamber, where the urine inputs, has a small volume. Once it has been filled it overflows into a second bigger chamber. The first chamber provides accurate UO measures of patients whose UO has to be closely supervised, while the second one avoids the need for frequent interventions by the nursing staff to empty the container

Meter data management for smart monitoring power networks

The electrical power distribution and commercialization scenario is evolving worldwide, and electricity companies, faced with the challenge of new information requirements, are demanding IT solutions to deal with the smart monitoring of power networks. Two main challenges arise from data management and smart monitoring of power networks: real-time data acquisition and big data processing over short time periods. We present a solution in the form of a system architecture that conveys real time issues and has the capacity for big data management.

Impact of automatic meetering infrastructure on multiutility business models

In the last decade energy utility sector has undergone major changes in terms of liberalization, increased competition, efforts in improving energy efficiency, and in new technological solution such as smart meter and grid operations. There are new information technology solutions (e.g. Advanced Metering Infrastructure /AMI ) on the horizon that will not only introduce new technical and organizational concepts, but have a very strong potential to radically change modus operandi of utility companies. Coordinated, multi-utility programs can help accelerate the development and market success of new high-efficiency technologies. These programs provide opportunities for researchers to develop new high-efficiency equipment for manufacturers to sell this new equipment with utility help, for utilities to increase the amount of energy they save from incentive programs, and for consumers to benefit from lower utility bills and a cleaner environment (as energy is reduced, pollutants produced at power plants decline).

Smart meter privacy by suprression of low power frequency components

This paper focuses on the problems associated with privacy protection in smart grid. We will give an overview of a possible realization of a privacy-preserving approach that encompasses privacy-utility tradeoff into a single model. This approach proposes suppression of low power frequency components as a solution to reduce the amount of information leakage from smart meter readings. We will consider the applicability of the procedure to hide the appliance usage with respect to the type of home devices.

Model-Driven Methodology for Rapid Deployment of Smart Spaces based on Resource-Oriented Architectures

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym.

Smart Spaces and Smart Objects interoperability Architecture (S3OiA)

The presented work aims to contribute towards the standardization and the interoperability off the Future Internet through an open and scalable architecture design. We present S³OiA as a syntactic/semantic Service-Oriented Architecture that allows the integration of any type of object or device, not mattering their nature, on the Internet of Things. Moreover, the architecture makes possible the use of underlying heterogeneous resources as a substrate for the automatic composition of complex applications through a semantic Triple Space paradigm. Created applications are dynamic and adaptive since they are able to evolve depending on the context where they are executed. The validation scenario of this architecture encompasses areas which are prone to involve human beings in order to promote personal autonomy, such as home-care automation environments and Ambient Assisted Living.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Refined facial disparity maps for automatic creation of 3D avatars

We propose a new method to automatically refine a facial disparity map obtained with standard cameras and under conventional illumination conditions by using a smart combination of traditional computer vision and 3D graphics techniques. Our system inputs two stereo images acquired with standard (calibrated) cameras and uses dense disparity estimation strategies to obtain a coarse initial disparity map, and SIFT to detect and match several feature points in the subjects face. We then use these points as anchors to modify the disparity in the facial area by building a Delaunay triangulation of their convex hull and interpolating their disparity values inside each triangle. We thus obtain a refined disparity map providing a much more accurate representation of the the subjects facial features. This refined facial disparity map may be easily transformed, through the camera calibration parameters, into a depth map to be used, also automatically, to improve the facial mesh of a 3D avatar to match the subjects real human features.