Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation

Atrial Fibrillation (AF) is the most common supraventricular tachyarrhythmia. Recently, it has been suggested that AF is partially organized on its onset and termination, thus being more suitable for antiarrhythmia and to avoid unnecessary therapy. Although several invasive and non-invasive AF organization estimators have been proposed, the organization time course in the first and last minutes of AF has not been quantified yet. The aim of this work is to study non-invasively the organization variation within the first and last minutes of paroxysmal AF. The organization was evaluated making use of sample entropy, which can robustly estimate electrical atrial activity organization from surface ECG recordings. This work proves an organization decrease in the first minutes of AF onset and an increase within the last minute before spontaneous AF termination. These results are in agreement with the conclusions reported by other authors who made use of invasive recordings.     

Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.

Genetic algorithms to solve the power system restoration planning problem

This study reports the use of a Genetic Algorithm (GA) to solve the Power System Restoration Planning Problem (PSRP). The solution to the PSRP is described by a series of operations or a plan to be used by the Power System operator immediately on the occurrence of a blackout in the electrical power supply. Our GA uses new initialization and crossover operators based on the electrical power network, which are able to generate and maintain the plans feasible along GA runs. This releases the Power Flow program, which represents the most computer demanding component, from computing the fitness function of unfeasible individuals. The method was designed for large transmission systems and results for three different electrical power networks are shown: IEEE 14-Bus, IEEE 30-Bus, and a large realistic system.     

A formal analysis of database replication protocols with SI replicas and crash failures

This paper provides a formal specification and proof of correctness of a basic Generalized Snapshot Isolation certification-based data replication protocol for database middleware architectures. It has been modeled using a state transition system, as well as the main system components, allowing a perfect match with the usual deployment in a middleware system. The proof encompasses both safety and liveness properties, as it is commonly done for a distributed algorithm. Furthermore, a crash failure model has been assumed for the correctness proof, although recovery analysis is not the aim of this paper. This allows an easy extension toward a crash-recovery model support in future works. The liveness proof focuses in the uniform commit: if a site has committed a transaction, the rest of sites will either commit it or it would have crashed.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Interactive smart battery storage for a PV and wind hybrid energy management control based on conservative power theory

This paper presents interactive smart battery-based storage (BBS) for wind generator (WG) and photovoltaic (PV) systems. The BBS is composed of an asymmetric cascaded H-bridge multilevel inverter (ACMI) with staircase modulation. The structure is parallel to the WG and PV systems, allowing the ACMI to have a reduction in power losses compared to the usual solution for storage connected at the DC-link of the converter for WG or PV systems. Moreover, the BBS is embedded with a decision algorithm running real-time energy costs, plus a battery state-of-charge manager and power quality capabilities, making the described system in this paper very interactive, smart and multifunctional. The paper describes how BBS interacts with the WG and PV and how its performance is improved. Experimental results are presented showing the efficacy of this BBS for renewable energy applications.     

Using new scheduling heuristics based on resource consumption information for increasing throughput on rule‐based spam filtering systems

The large increase of spam deliveries since the first half of 2013 entailed hard to solve troubles in spam filters. In order to adequately fight spam, the throughput of spam filtering platforms should be necessarily increased. In this context, and taking into consideration the widespread utilization of rule‐based filtering frameworks in the spam filtering domain, this work proposes three novel scheduling strategies for optimizing the time needed to classify new incoming e‐mails through an intelligent management of computational resources depending on the Central Processing Unit (CPU) usage and Input/Output (I/O) delays. In order to demonstrate the suitability of our approaches, we include in our experiments a comparative study in contrast to other successful heuristics previously published in the scientific literature. Results achieved demonstrated that one of our alternative heuristics allows time savings of up to 10% in message filtering, while keeping the same classification accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

Multilayer Cognitive Maps in the Resolution of Problems using the FCM Designer Tool

Cognitive maps are a tool to represent knowledge from a qualitative perspective, allowing to create models of complex systems where an exact mathematical model cannot be used because of the complexity of the system. In the literature, several tools have been proposed to develop cognitive maps and fuzzy cognitive maps (FCMs); one of them is FCM Designer. This paper designs and implements an extension to the FCM Designer tool that allows creating multilayer FCM. With this extension, it is possible to have several FCMs for the same problem, where each one expresses a different level of knowledge of the system under study, but interlinked. Thus, one can have a first level of detailed abstraction of the system with specific information and then more general levels. In addition, we can have different levels where the variables of one level depend on those of another level. That is, the multilayer approach enriches the modeled systems with flow of information between layers, to derive information about the concepts involved in layers from the concepts in other layers. In our multilayer approach, the relationship between the cognitive maps in different layers can be carried out in various ways: with fuzzy rules, connections with weights and with mathematical equations. This work presents the design and the implementation of the extension of the FCM Designer tool, and several test cases in different domains: a FCM to analyze emergent properties of Wikipedia a FCM for medical analysis for diagnosis, and another like recommender system.     

Optimal Multi-Criteria Waypoint Selection for Autonomous Vehicle Navigation in Structured Environment

This paper deals with autonomous navigation of unmanned ground vehicles (UGV). The UGV has to reach its assigned final configuration in a structured environments (e.g. a warehouse or an urban environment), and to avoid colliding neither with the route boundaries nor any obstructing obstacles. In this paper, vehicle planning/set-points definition is addressed. A new efficient and flexible methodology for vehicle navigation throughout optimal and discrete selected waypoints is proposed. Combining multi-criteria optimization and expanding tree allows safe, smooth and fast vehicle overall navigation. This navigation through way-points permits to avoid any path/trajectory planning which could be time consuming and complex, mainly in cluttered and dynamic environment. To evaluate the flexibility and the efficiency of the proposed methodology based on expanding tree (taking into account the vehicle model and uncertainties), an important part of this paper is dedicated to give an accurate comparison with another proposed optimization based on the commonly used grid map. A set of simulations, comparison with other methods and experiments, using an urban electric vehicle, are presented and demonstrate the reliability of our proposals.