Engenharia Elétrica

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3D simulation of Triple-Gate MOSFETs
(2010-05-19) CONDE, J.; CERDEIRA, A.; Marcelo Antonio Pavanello; KILCHYTSKA, V.; FLANDRE, D.
In this paper we present a new approach of analyzing 3D structure for Triple-Gate MOSFETs with three different mesh regions, one at the top and two in the sidewalls of the fin, which allows the consideration of different carrier mobility at each region due to the crystalline orientation and technological processing. A procedure for the extraction of the mobility parameters in each region is developed. Validation of the proposed structure was made for a FinFET arrays with fixed channel length and different fin widths, obtaining a very good coincidence between experimental and simulated characteristics. © 2010 IEEE.
3D simulation of triple-gate MOSFETs with different mobility regions
(2011-07-05) CONDE, J.; CERDEIRA, A.; Marcelo Antonio Pavanello; KILCHYTSKA, V.; FLANDRE, D.
In this paper we present a new approach for analyzing 3D structure triple-gate MOSFETs using three different regions, one at the top and two in the sidewalls of the fin, which allows for considering different carrier mobilities in each region due to crystalline orientation and technological processing. A procedure for the extraction of the mobility parameters in each region is developed. Robustness of the proposed structure is validated by experimental data obtained on FinFETs. A very good agreement is obtained between experimental and simulated characteristics. © 2011 Elsevier B.V. All rights reserved.
3D triple-gate simulation considering the crystallographic orientations
(2008-09-04) CONDE, J. E.; CERDEIRA, A.; Marcelo Antonio Pavanello
Current in FinFET's transistors flows along two different crystallographic orientations, since, typically the FIN top region orientation is <100>, while the sidewalls have <110> orientation. In this paper we present how to represent the mesh structure for these devices, in order to simulate in 3-D, considering different mobility values for each orientation. Results of 3-D simulation considering also the effect of the series resistance are shown and compared with experimental results. © The Electrochemical Society.
A charge-based continuous model for small-geometry graded-channel SOI MOSFET's
(2005-09-07) Michelly De Souza; Marcelo Antonio Pavanello
In this work a continuous model for analog simulation of short-channel Graded-Channel (GC) Silicon-On-Insulator (SOI) nMOSFET is presented. Effects of channel length modulation and velocity saturation have been included in the model formulation, which is based on the series combination of two conventional SOI nMOSFETs, each one representing one of the regions of GC SOI MOSFET channel and its characteristics. Experimental results and numerical bidimensional simulations are used to validate the model with excellent agreement in both cases.
A charge-based continuous model for submicron graded-channel nMOSFET for analog circuit simulation
(2005) De Souza M.; Pavanello M.A.; Iniguez B.; Flandre D.
In this work a continuous analytical model for analog simulation of submicron asymmetrically doped silicon-on-insulator (SOI) nMOSFET using the graded-channel (GC) architecture, valid from weak to strong inversion regimes, is proposed. Analytical models accounting for mobility degradation due to the vertical field, channel length modulation, drain induced barrier lowering and velocity saturation effects have been included in the model formulation. Also the action of parasitic bipolar transistor intrinsic to the SOI MOSFET has been considered. The proposed model considers the highly doped part of the GC transistor acting as a 'main' transistor, whose drain voltage is modulated by the remaining part of the channel. Experimental results and two-dimensional simulated data were used to test the model, by comparing the drain current and some important characteristics for analog circuit design, such as the transconductance over the drain current ratio and output conductance, achieving a good agreement in both cases. © 2005 Elsevier Ltd. All rights reserved.
A compact model and an extraction method for the FinFET spreading resistance
(2011-09-02) Marcelo Parada; MALHEIRO, C. T.; AGOPIAN, P. G. D.; Renato Giacomini
This work presents a study of the FinFET series resistance focused on the spreading component. A new simple analytical expression is proposed to easily estimate and model this parasitic parameter. The extraction method departs from the drain current versus gate voltage curves of several channel and source/drain lengths. The resistance values extracted from simulated devices are compared to the outputs of the analytic model and a very good agreement is achieved. The proposed model showed accurate estimative for a wider range of devices then previously published models. © The Electrochemical Society.
A continuous authentication system based on user behavior analysis
(2010-02-15) BROSSO, I.; LA NEVE, A.; BRESSAN, G.; RUGGIERO, W. V.
This paper presents a continuous authentication system based on user behavior analysis that makes use of environmental context information, users' behavior analysis and Neuro-Fuzzy Logic. This system must be able to acquire information in context, making them into a computational environment. This information is the basis of user behavior. The System, based on the evidences of the behavior, establishes if it can trust the user or not. According to the user behavior, levels of trust are released, to access the application software. Weights are attributed in the fuzzyfication process, according to the rules that were previously established for the parameters which help to establish the evidences of behavioral trust, in its different degrees. The neuro-fuzzy logic allows that the user behavioral database be continuously updated, interacting with the fuzzyfication mechanism, so as to keep trust levels updated according to the user behavior, in a more accurate and faithful way. © 2010 IEEE.
A Convolutional Neural Network-based Mobile Application to Bedside Neonatal Pain Assessment
(2021-10-18) CARLINI, L. P.; FERREIRA, L. A.; COUTRIN, G. A. S.; VAROTO, V. V.; HEIDERICH, T. M.; BALDA R. C. X.; BARROS, M. C. M.; GUINSBURG R.; Carlos E. Thomaz
More than 500 painful interventions are carried out during the hospitalisation of a newborn baby in a neonatal intensive care unit. Since neonates are not able to verbally communicate pain, some studies have been done to identify the presence and intensity of pain by behavioural analysis, mainly by facial expression. These studies allow a better understanding of this painful experience faced by the neonate. In this context, this work proposes and implements a mobile application for smartphones that uses Artificial Intelligence (AI) techniques to automatically identify the facial expression of pain in neonates, presenting feasibility in real clinical situations. Firstly, a Convolutional Neural Network architecture was adapted and trained with face images captured before and after painful clinical procedures carried out routinely. Then, this computational model was optimised to a mobile environment to make it practical for everyday use. Moreover, we used an explainable AI method to identify facial regions that might be relevant to pain assessment. Our results showed that is possible to classify the facial expression of the pain of neonates with high accuracy. Additionally, our methodology presented novel results highlighting as well sound facial regions that agree with pain scales used by neonatologists and with the visual perception of adults when assessing pain in neonates, whether they are health professionals or not.
A framework of intentional characters for simulation of social behavior
(2010-07-12) DA COSTA, L.C.; CLUA, E.W.; GIRALDI, G. A.; BERNARDINI, F. C.; Reinaldo Bianchi; SCHULZE, B.; MONTENEGRO, A. A.
Realism is thriving today in many types of media, particularly in video games, where the polygon count continues growing up. However, this realism pushes up the necessity of a real behavior of the virtual actors, in order to follow the credibility of the characters. We present an approach for crowd simulation that works adaptively to situations that occur in virtual atmosphere. Our approach allows that a realistic character adapts his behavior and actions with the information noticed from the atmosphere which it is close to. For this work, the execution atmosphere will be a simulation of a real catastrophic atmosphere, such as as flooding and collapses.
A fully analytical continuous model for graded-channel SOI MOSFET for analog applications
(2004-09-11) Michelly De Souza; Marcelo Antonio Pavanello; INIGUEZ, B.; FLANDRE, D.
In this work an analytical model of Graded-Channel (GC) Silicon-On-Insulator (SOI) nMOSFETs is proposed for analog applications. The model is based on a series association of two conventional SOI nMOSFETs each representing one part of the GC SOI nMOSFET channel. From this assumption, we propose a current model that considers the GC SOI MOSFET as a conventional SOI transistor, represented by one part of the channel only, in which the drain voltage is modulated by the remaining part. The proposed model has been verified through the comparison between its results and experimental measurements, presenting a good agreement. Some important characteristics for analog circuits, such as transconductance and Early voltage, are compared between the model results and experimental curves.
A hybrid approach to learn, retrieve and reuse qualitative cases
(2017-11-10) HOMEM, T. P. D.; PERICO, D. H.; SANTOS, P. E.; COSTA, A. H. R.; Reinaldo Bianchi; DE MANTARAS, R. L.
© 2017 IEEE.The application of Artificial Intelligence methods is becoming indispensable in several domains, for instance in credit card fraud detection, voice recognition, autonomous cars and robotics. However, some methods fail in performances or solving some problems, and hybrid approaches can outperform the results when compared to traditional ones. In this paper we present a hybrid approach, named qualitative case-based reasoning and learning (QCBRL), that integrates three well-known AI methods: Qualitative Spatial Reasoning, Case-Based Reasoning and Reinforcement Learning. QCBRL system was designed to allow an agent to learn, retrieve and reuse qualitative cases in the robot soccer domain. We applied our method in the Half-Field Offense and we have obtained promising results.
A kernel maximum uncertainty discriminant analysis and its application to face recognition
(2009-02-05) Carlos E. Thomaz; GIRALDI, G. A.
In this paper, we extend the Maximum uncertainty Linear Discriminant Analysis (MLDA), proposed recently for limited sample size problems, to its kernel version. The new Kernel Maximum uncertainty Discriminant Analysis (KMDA) is a two-stage method composed of Kernel Principal Component Analysis (KPCA) followed by the standard MLDA. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other existing kernel discriminant methods, such as Generalized Discriminant Analysis (GDA) and Regularized Kernel Discriminant Analysis (RKDA). The classification results indicate that KMDA performs as well as GDA and RKDA, with the advantage of being a straightforward stabilization approach for the within-class scatter matrix that uses higher-order features for further classification improvements.
A low cost retrofitting technique for mechanical presses
(2015-09-28) MORTAIO, L. A.; RIASCOS, L. A. M.
© 2015 IEEE.The mechanical press is a machine which applies forming force on materials, mainly on metals. A die is placed into the press to mold the material during the stamping process. The eccentric mechanical presses with driving crank has a simple operation, but the slide upstroke and down stroke speeds are fixes and determined by the impact reduced speed on the material (the blank). However, these machines can be retrofitted attaching a servo-drive and PID controls to improve productivity. The objective of this work is to present a hybrid drive approach for controlling and improving the press speed and the stroke rate for eccentric presses, but keeping a die impact reduced speed on the metal sheet. The cost for transforming into a hybrid servo-press is around 20% of a direct drive servo-press.
A maximum uncertainty LDA-based approach for limited sample size problems — with application to face recognition
(2006-01-01) Carlos E. Thomaz; Kitani E.C.; Gillies D.F.
© 2007, The Brazilian Computer Society.A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and instability of the within-class scatter matrix. In practice, particularly in image recognition applications such as face recognition, there are often a large number of pixels or pre-processed features available, but the total number of training patterns is limited and commonly less than the dimension of the feature space. In this study, a new LDA-based method is proposed. It is based on a straightforward stabilisation approach for the within-class scatter matrix. In order to evaluate its effectiveness, experiments on face recognition using the well-known ORL and FERET face databases were carried out and compared with other LDA-based methods. The classification results indicate that our method improves the LDA classification performance when the within-class scatter matrix is not only singular but also poorly estimated, with or without a Principal Component Analysis intermediate step and using less linear discriminant features. Since statistical discrimination methods are suitable not only for classification but also for characterisation of differences between groups of patterns, further experiments were carried out in order to extend the new LDA-based method to visually analyse the most discriminating hyper-plane separating two populations. The additional results based on frontal face images indicate that the new LDA-based mapping provides an intuitive interpretation of the two-group classification tasks performed, highlighting the group differences captured by the multivariate statistical approach proposed.
A method for the online construction of the set of states of a Markov decision process using answer set programming
(2018-06-28) FERREIRA, L. A.; Reinaldo Bianchi; SANTOS, P. E.; DE MANTARAS, R. L.
© 2018, Springer International Publishing AG, part of Springer Nature.Non-stationary domains, that change in unpredicted ways, are a challenge for agents searching for optimal policies in sequential decision-making problems. This paper presents a combination of Markov Decision Processes (MDP) with Answer Set Programming (ASP), named Online ASP for MDP (oASP(MDP)), which is a method capable of constructing the set of domain states while the agent interacts with a changing environment. oASP(MDP) updates previously obtained policies, learnt by means of Reinforcement Learning (RL), using rules that represent the domain changes observed by the agent. These rules represent a set of domain constraints that are processed as ASP programs reducing the search space. Results show that oASP(MDP) is capable of finding solutions for problems in non-stationary domains without interfering with the action-value function approximation process.
A multi-linear discriminant analysis of 2D frontal face images
(2009-10-11) Carlos E. Thomaz; DO AMARAL, V.; GIRALDI, G. A.; KITANI, E. C.; SATO, J. R.; GILLES, D. F.
We have designed and implemented a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The method is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D face data set of well framed images, we determine a most characteristic direction of change by organizing the data according to the features of interest. Our goal here is to use all the facial image features simultaneously rather than separate models for texture and shape information. Our experiments show that the method does produce plausible unseen views for gender, facial expression and ageing changes. We believe that this method could be widely applied for normalization in face recognition and in identifying subjects after a lapse of time. © 2009 IEEE.
A Multivariate Correlation Assessment of Chess Proficiency Using Brain Signals
(2019-09-05) SILVA, L.; Carlos E. Thomaz THOMAZ, C. E.
© 2019 IEEE.Chess game has attracted the interest of many academic works with several experiments carried out to address the differences in brain activation on proficients and non-proficients chess players. However, none of these works takes into account explicitly the cognitive patterns of the chess players to rank and classify them. In our work, we aim to present a cognitive model, using EEG and multivariate statistical methods, to assess chess volunteers and compare their performance to the traditional metric based on accuracy and response time. In total, 32 volunteers have participated in this study based on visual stimuli computationally generated. Our main results show that it is important not only to top rank the volunteers with high accuracy and low response time, but also understand how the main brain processes occur to a chess expert to achieve such top performance.
A multivariate statistical analysis of muscular biopotencial for human arm movement characterization
(2009-01-14) SILVA, G. A. DA; Castro, M.C.F.; Carlos E. Thomaz
Pattern recognition of electromyographic signals consists of a hard task due to the high dimensionality of the data and noise presence on the acquired signals. This work intends to study the data set as a multivariate pattern recognition problem by applying linear transformations to reduce the data dimensionality. Five volunteers contributed in a previous experiment that acquired the myoelectrical signals using surface electrodes. Attempts to analyse the groups of acquired data by means of descriptive statistics have shown to be inconclusive. This works shows that the use of multivariate statistical techniques such as Principal Components Analysis (PCA) and Maximum uncertainty Linear Discriminant Analysis (MLDA) to characterize the: acquired set of signals through low dimensional scatter plots provides a new understanding of the data spread, making easier its analysis. Considering the arm horizontal movement and the acquired set of data used in this research, a multivariate linear separation between the patterns of interest quantified by the distance of Bhattacharyya suggests that it's possible not only to characterize the angular joint position, but also to confirm that different movements recruit similar amounts of energy to be executed.
A multivariate statistical analysis of the developing human brain in preterm infants
(2007) Thomaz C.E.; Boardman J.P.; Counsell S.; Hill D.L.G.; Hajnal J.V.; Edwards A.D.; Rutherford M.A.; Gillies D.F.; Rueckert D.
Preterm delivery accounts for 5% of all deliveries and its consequences contribute to significant individual, medical, and social problems. The neuroanatomical substrates of these disorders are not known, but are essential for understanding mechanisms of causation, and developing strategies for intervention. In the recent years, multivariate pattern recognition methods that analyse all voxels simultaneously have been proposed to characterise the neuroanatomical differences between a reference group of magnetic resonance (MR) images and the population under investigation. Most of these techniques have overcome the difficulty of dealing with the inherent high dimensionality of 3D MR brain image data by using pre-processed segmented images or a small number of specific features. However, an intuitive way of mapping the classification results back into the original image domain for further interpretation remains challenging. In this paper, we propose the idea of using Principal Components Analysis (PCA) plus the maximum uncertainty Linear Discriminant Analysis (MLDA) approach to classify and analyse MR brain images that have been aligned with either affine or non-rigid registration techniques. This approach avoids the computation costs intrinsic to commonly used covariance-based optimisation processes for solving small sample size problems, resulting in a simple and efficient implementation for the maximisation and interpretation of the Fisher's classification results. In order to demonstrate the effectiveness of the approach, we have used a neonatal MR brain data set that contains images of 93 preterm infants at term equivalent age and 20 term controls. Our results indicate that the two-stage linear framework makes clear the statistical differences between the control and preterm samples, showing a classification accuracy of 95.0% and 97.8% for the controls and preterms samples, respectively, using the leave-one-out method. Moreover, it provides a simple and intuitive method of visually analysing the differences between preterm infants at term equivalent age and the control group, such as differences in cerebrospinal fluid spaces, structure of the corpus callosum, and subtle differences in myelination. © 2006 Elsevier B.V. All rights reserved.
A new analytic model for double gate FinFETs parasitic resistance
(2012-09-02) PEREIRA, A. S. N.; Renato Giacomini
A new analytic model for Parasitic Resistance of Double Gate FinFETs is proposed in this work. The model was developed considering the current distribution observed in three-dimensional simulations. The contact resistance was modeled using a variable impedance transmission line model, to approximate source and drain geometries to the real shapes of these regions. The model has a closed expression, without adjustment parameters and is very accurate when compared to simulation results and published experimental data. © The Electrochemical Society.

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