EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

In this article we propose an efficient and accurate method for fault location in underground distribution systems by means of an Optimum-Path Forest (OPF) classifier. We applied the time domains reflectometry method for signal acquisition, which was further analyzed by OPF and several other well-known pattern recognition techniques. The results indicated that OPF and support vector machines outperformed artificial neural networks and a Bayesian classifier, but OPF was much more efficient than all classifiers for training, and the second fastest for classification.

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.

Behavior of triple-gate Bulk FinFETs with and without DTMOS operation

In this paper, the combination of the Dynamic Threshold (DT) voltage technique with a non-planar structure is experimentally studied in triple-gate FinFETs. The drain current, transconductance, resistance, threshold voltage, subthreshold swing and Drain Induced Barrier Lowering (DIBL) will be analyzed in the DT mode and the standard biasing configuration. Moreover, for the first time, the important figures of merit for the analog performance such as transconductance-over-drain current, output conductance. Early voltage and intrinsic voltage gain will be studied experimentally and through three-dimensional (3-D) numerical simulations for different channel doping concentrations in triple-gate DTMOS FinFETs. The results indicate that the DTMOS FinFETs always yield superior characteristic; and larger transistor efficiency. In addition, DTMOS devices with a high channel doping concentration exhibit much better analog performance compared to the normal operation mode, which is desirable for high performance low-power/low-voltage applications. (C) 2011 Elsevier Ltd. All rights reserved.

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.

Analysis of temperature variation influence on the analog performance of 45 degrees rotated triple-gate nMuGFETs

This work presents the analog performance of n-type triple-gate MuGFETs with high-k dielectrics and TiN gate material fabricated in 45 degrees rotated SOI substrates comparing their performance with standard MuGFETs fabricated without substrate rotation. Different fin widths are studied for temperatures ranging from 250 K up to 400 K. The results of transconductance, output conductance, transconductance over drain current ratio, intrinsic voltage gain and unit-gain frequency are studied. It is observed that the substrate rotation improves the carrier mobility of narrow MuGFETs at any temperature because of the changing in the conduction plane at the sidewalls from (1 1 0) to (1 0 0). For lower temperatures, the improvement of the carrier mobility of rotated MuGFETs is more noticeable as well as the rate of mobility improvement with the temperature decrease is larger. The output conductance is weakly affected by the substrate rotation. Although this improvement in the transconductance of rotated MuGFETs is negligibly transferred to the intrinsic voltage gain, the unity-gain frequency of rotated device is improved due to the larger carrier mobility in the entire range of temperatures studied. (C) 2011 Elsevier Ltd. All rights reserved.

Temperature impact on the tunnel fet off-state current components

In this work, the temperature impact on the off-state current components is analyzed through numerical simulation and experimentally. First of all, the band-to-band tunneling is studied by varying the underlap in the channel/drain junction, leading to an analysis of the different off-state current components. For pTFET devices, the best behavior for off-state current was obtained for higher values of underlap (reduced BTBT) and at low temperatures (reduced SRH and TAT). At high temperature, an unexpected off-state current occurred due to the thermal leakage current through the drain/channel junction. Besides, these devices presented a good performance when considering the drain current as a function of the drain voltage, making them suitable for analog applications. (C) 2012 Elsevier Ltd. All rights reserved.

GIDL behavior of p- and n-MuGFET devices with different TiN metal gate thickness and high-k gate dielectrics

This work studies the gate-induced drain leakage (GIDL) in p- and n-MuGFET structures with different TiN metal gate thickness and high-k gate dielectrics. As a result of this analysis, it was observed that a thinner TiN metal gate showed a larger GIDL due to the different gate oxide thickness and a reduced metal gate work function. In addition, replacing SiON by a high-k dielectric (HfSiON) results for nMuGFETs in a decrease of the GIDL On the other hand, the impact of the gate dielectric on the GIDL for p-channel MuGFETs is marginal. The effect of the channel width was also studied, whereby narrow fin devices exhibit a reduced GIDL current in spite of the larger vertical electric field expected for these devices. Finally, comparing the effect of the channel type, an enhanced GIDL current for pMuGFET devices was observed. (C) 2011 Elsevier Ltd. All rights reserved.

Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation

In this paper we discuss the problem of how to discriminate moments of interest on videos or live broadcast shows. The primary contribution is a system which allows users to personalize their programs with previously created media stickers-pieces of content that may be temporarily attached to the original video. We present the system's architecture and implementation, which offer users operators to transparently annotate videos while watching them. We offered a soccer fan the opportunity to add stickers to the video while watching a live match: the user reported both enjoying and being comfortable using the stickers during the match-relevant results even though the experience was not fully representative.

EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

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.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

A regional multirnodulus algorithm for blind equalization of QAM signals: Introduction and steady-state analysis

It is well known that constant-modulus-based algorithms present a large mean-square error for high-order quadrature amplitude modulation (QAM) signals, which may damage the switching to decision-directed-based algorithms. In this paper, we introduce a regional multimodulus algorithm for blind equalization of QAM signals that performs similar to the supervised normalized least-mean-squares (NLMS) algorithm, independently of the QAM order. We find a theoretical relation between the coefficient vector of the proposed algorithm and the Wiener solution and also provide theoretical models for the steady-state excess mean-square error in a nonstationary environment. The proposed algorithm in conjunction with strategies to speed up its convergence and to avoid divergence can bypass the switching mechanism between the blind mode and the decision-directed mode. (c) 2012 Elsevier B.V. All rights reserved.