GeoHealth: A Georeferenced System for Health Data Analysis in Primary Care

The Primary Care Information System (SIAB) concentrates basic healthcare information from all different regions of Brazil. The information is collected by primary care teams on a paper-based procedure that degrades the quality of information provided to the healthcare authorities and slows down the process of decision making. To overcome these problems we propose a new data gathering application that uses a mobile device connected to a 3G network and a GPS to be used by the primary care teams for collecting the families' data. A prototype was developed in which a digital version of one SIAB form is made available at the mobile device. The prototype was tested in a basic healthcare unit located in a suburb of Sao Paulo. The results obtained so far have shown that the proposed process is a better alternative for data collecting at primary care, both in terms of data quality and lower deployment time to health care authorities.

Characterization of Stability Region for General Autonomous Nonlinear Dynamical Systems

The existing characterization of stability regions was developed under the assumption that limit sets on the stability boundary are exclusively composed of hyperbolic equilibrium points and closed orbits. The characterizations derived in this technical note are a generalization of existing results in the theory of stability regions. A characterization of the stability boundary of general autonomous nonlinear dynamical systems is developed under the assumption that limit sets on the stability boundary are composed of a countable number of disjoint and indecomposable components, which can be equilibrium points, closed orbits, quasi-periodic solutions and even chaotic invariant sets.

A New Approach for Evaluating the BER of a Multirate, Multiclass OFFH-CDMA System

In this letter, we propose a new approach to evaluate the bit error rate (BER) of a multirate, multiclass optical fast frequency hopping code-division multiple-access (OFFH-CDMA) system. This proposed approach does not require knowledge of the generated users' code sequences, which makes the system analysis straightforward. Furthermore, the presented formalism can also be successfully applied to most multi-weight multi-length family of codes, as long as the corresponding code parameters are employed.

Development of an AMR-ACB Array for Gastrointestinal Motility Studies

The association between anisotropic magnetoresistive (AMR) sensor and AC biosusceptometry (ACB) to evaluate gastrointestinal motility is presented. The AMR-ACB system was successfully characterized in a bench-top study, and in vivo results were compared with those obtained by means of simultaneous manometry. Both AMR-ACB and manometry techniques presented high temporal cross correlation between the two periodicals signals (R = 0.9 +/- 0.1; P < 0.05). The contraction frequencies using AMR-ACB were 73.9 +/- 7.6 mHz and using manometry were 73.8 +/- 7.9 mHz during the baseline (r = 98, p < 0.05). The amplitude of contraction using AMR-ACB was 396 +/- 108 mu T.s and using manometry were 540 +/- 198 mmHg.s during the baseline. The amplitudes of signals for AMR-ACB and manometric recordings were similarly increased to 86.4% and 89.3% by neostigmine, and also decreased to 27.2% and 21.4% by hyoscine butylbromide in all animals, respectively. The AMR-ACB array is nonexpensive, portable, and has high-spatiotemporal resolution to provide helpful information about gastrointestinal tract.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

The Dependence of Retention Time on Gate Length in UTBOX FBRAM With Different Source/Drain Junction Engineering

The floating-body-RAM sense margin and retention-time dependence on the gate length is investigated in UTBOX devices using BJT programming combined with a positive back bias (so-called V th feedback). It is shown that the sense margin and the retention time can be kept constant versus the gate length by using a positive back bias. Nevertheless, below a critical L, there is no room for optimization, and the memory performances suddenly drop. The mechanism behind this degradation is attributed to GIDL current amplification by the lateral bipolar transistor with a narrow base. The gate length can be further scaled using underlap junctions.

A Chaotic Approach of Differential Evolution Optimization Applied to Loudspeaker Design Problem

Over the past few years, the field of global optimization has been very active, producing different kinds of deterministic and stochastic algorithms for optimization in the continuous domain. These days, the use of evolutionary algorithms (EAs) to solve optimization problems is a common practice due to their competitive performance on complex search spaces. EAs are well known for their ability to deal with nonlinear and complex optimization problems. Differential evolution (DE) algorithms are a family of evolutionary optimization techniques that use a rather greedy and less stochastic approach to problem solving, when compared to classical evolutionary algorithms. The main idea is to construct, at each generation, for each element of the population a mutant vector, which is constructed through a specific mutation operation based on adding differences between randomly selected elements of the population to another element. Due to its simple implementation, minimum mathematical processing and good optimization capability, DE has attracted attention. This paper proposes a new approach to solve electromagnetic design problems that combines the DE algorithm with a generator of chaos sequences. This approach is tested on the design of a loudspeaker model with 17 degrees of freedom, for showing its applicability to electromagnetic problems. The results show that the DE algorithm with chaotic sequences presents better, or at least similar, results when compared to the standard DE algorithm and other evolutionary algorithms available in the literature.

Improving the Robustness of Distributed Failure Detectors in Adverse Conditions

Failure detection is at the core of most fault tolerance strategies, but it often depends on reliable communication. We present new algorithms for failure detectors which are appropriate as components of a fault tolerance system that can be deployed in situations of adverse network conditions (such as loosely connected and administered computing grids). It packs redundancy into heartbeat messages, thereby improving on the robustness of the traditional protocols. Results from experimental tests conducted in a simulated environment with adverse network conditions show significant improvement over existing solutions.

Dependence of Generation-Recombination Noise With Gate Voltage in FD SOI MOSFETs

A model for computing the generation-recombination noise due to traps within the semiconductor film of fully depleted silicon-on-insulator MOSFET transistors is presented. Dependence of the corner frequency of the Lorentzian spectra on the gate voltage is addressed in this paper, which is different to the constant behavior expected for bulk transistors. The shift in the corner frequency makes the characterization process easier. It helps to identify the energy position, capture cross sections, and densities of the traps. This characterization task is carried out considering noise measurements of two different candidate structures for single-transistor dynamic random access memory devices.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning to Optimization of Broad-Band Reflector Antennas Satellite

This paper aims to provide an improved NSGA-II (Non-Dominated Sorting Genetic Algorithm-version II) which incorporates a parameter-free self-tuning approach by reinforcement learning technique, called Non-Dominated Sorting Genetic Algorithm Based on Reinforcement Learning (NSGA-RL). The proposed method is particularly compared with the classical NSGA-II when applied to a satellite coverage problem. Furthermore, not only the optimization results are compared with results obtained by other multiobjective optimization methods, but also guarantee the advantage of no time-spending and complex parameter tuning.

Influence of 60-MeV Proton-Irradiation on Standard and Strained n- and p-Channel MuGFETs

In this work the proton irradiation influence on Multiple Gate MOSFETs (MuGFETs) performance is investigated. This analysis was performed through basic and analog parameters considering four different splits (unstrained, uniaxial, biaxial, uniaxial+biaxial). Although the influence of radiation is more pronounced for p-channel devices, in pMuGFETs devices, the radiation promotes a higher immunity to the back interface conduction resulting in the analog performance improvement. On the other hand, the proton irradiation results in a degradation of the post-irradiated n-channel transistors behavior. The unit gain frequency showed to be strongly dependent on stress efficiency and the radiation results in an increase of the unit gain frequency for splits with high stress effectiveness for both cases p- and nMuGFETs.

Bat-Inspired Optimization Approach for the Brushless DC Wheel Motor Problem

This paper presents a metaheuristic algorithm inspired in evolutionary computation and swarm intelligence concepts and fundamentals of echolocation of micro bats. The aim is to optimize the mono and multiobjective optimization problems related to the brushless DC wheel motor problems, which has 5 design parameters and 6 constraints for the mono-objective problem and 2 objectives, 5 design parameters, and 5 constraints for multiobjective version. Furthermore, results are compared with other optimization approaches proposed in the recent literature, showing the feasibility of this newly introduced technique to high nonlinear problems in electromagnetics.