Classificação Automática de Modulação Digital com uso de Correntropia para Ambientes de Rádio Cognitivo

Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)

Controle vetorial para velocidade de um motor de indução trifásico utilizando estimador filtro de Kalman

This paper describes the study, computer simulation and feasibility of implementation of vector control speed of an induction motor using for this purpose the Extended Kalman Filter as an estimator of rotor flux. The motivation for such work is the use of a control system that requires no sensors on the machine shaft, thus providing a considerable cost reduction of drives and their maintenance, increased reliability, robustness and noise immunity as compared to control systems with conventional sensors

Propagação de danos em sistemas cooperativos: propriedades termodinâmicas

High-precision calculations of the correlation functions and order parameters were performed in order to investigate the critical properties of several two-dimensional ferro- magnetic systems: (i) the q-state Potts model; (ii) the Ashkin-Teller isotropic model; (iii) the spin-1 Ising model. We deduced exact relations connecting specific damages (the difference between two microscopic configurations of a model) and the above mentioned thermodynamic quanti- ties which permit its numerical calculation, by computer simulation and using any ergodic dynamics. The results obtained (critical temperature and exponents) reproduced all the known values, with an agreement up to several significant figures; of particular relevance were the estimates along the Baxter critical line (Ashkin-Teller model) where the exponents have a continuous variation. We also showed that this approach is less sensitive to the finite-size effects than the standard Monte-Carlo method. This analysis shows that the present approach produces equal or more accurate results, as compared to the usual Monte Carlo simulation, and can be useful to investigate these models in circumstances for which their behavior is not yet fully understood

Effect of target bias on magnetic field enhanced plasma immersion ion implantation

Recent studies have demonstrated that sheath dynamics in plasma immersion ion implantation (PIII) is significantly affected by an external magnetic field, especially in the case when the magnetic field is parallel to the workpiece surface or intersects it at small angles. In this work we report the results from two-dimensional, particle-in-cell (PIC) computer simulations of magnetic field enhanced plasma immersion implantation system at different bias voltages. The simulations begin with initial low-density nitrogen plasma, which extends with uniform density through a grounded cylindrical chamber. Negative bias voltage is applied to a cylindrical target located on the axis of the vacuum chamber. An axial magnetic field is created by a solenoid installed inside the target holder. A set of simulations at a fixed magnetic field of 0.0025 T at the target surface is performed. Secondary electron emission from the target subjected to ion bombardment is also included. It is found that the plasma density around the cylindrical target increases because of intense background gas ionization by the electrons drifting in the crossed E x B fields. Suppression of the sheath expansion and increase of the implantation current density in front of the high-density plasma region are observed. The effect of target bias on the sheath dynamics and implantation current of the magnetic field enhanced PIII is discussed. (C) 2007 Elsevier B.V. All rights reserved.

Effect of electron magnetic trapping in a plasma immersion ion implantation system

In this work we describe a two-dimensional computer simulation of magnetic field enhanced plasma immersion implantation system. Negative bias voltage of 10.0 kV is applied to a cylindrical target located on the axis of a grounded vacuum chamber filled with uniform nitrogen plasma. A pair of external coils creates a static magnetic field with main vector component along the axial direction. Thus, a system of crossed ExB field is generated inside the vessel forcing plasma electrons to rotate in azimuthal direction. In addition, the axial variation of the magnetic field intensity produces magnetic mirror effect that enables axial particle confinement. It is found that high-density plasma regions are formed around the target due to intense background gas ionization by the trapped electrons. Effect of the magnetic field on the sheath dynamics and the implantation current density of the PIII system is investigated. By changing the magnetic field axial profile (varying coils separation) an enhancement of about 30% of the retained dose can be achieved. The results of the simulation show that the magnetic mirror configuration brings additional benefits to the PIII process, permitting more precise control of the implanted dose.

The Impact on Geographic Location Accuracy due to Different Satellite Orbit Ephemerides

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Simulações de Monte Carlo para os modelos Ising e Blume-Capel em redes complexa

We studied the Ising model ferromagnetic as spin-1/2 and the Blume-Capel model as spin-1, > 0 on small world network, using computer simulation through the Metropolis algorithm. We calculated macroscopic quantities of the system, such as internal energy, magnetization, specific heat, magnetic susceptibility and Binder cumulant. We found for the Ising model the same result obtained by Koreans H. Hong, Beom Jun Kim and M. Y. Choi [6] and critical behavior similar Blume-Capel model

ZERO SOUND-VELOCITY IN PI, RHO-MESON GASES

Sharp transitions are perhaps absent in QCD, so that one looks for physical quantities which may reflect the phase change. One such quantity is the sound velocity which was shown in lattice theory to become zero at the transition point for pure glue. We show that even in a simple bag model the sound velocity goes to zero at temperature T = T(v) not-equal 0 and that the numerical value of this T(v) depends on the nature of the meson. The average thermal energy of mesons goes linearly with T near T(v), with much smaller slope for the pion. The T(v) - s can be connected with the Boltzmann temperatures obtained from transverse momentum spectrum of these mesons in heavy-ion collision at mid-rapidity. It would be interesting to check the presence of different T(v) - s in present day finite T lattice theory.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

Static converter: didactic support software

Teaching a course of special electric loads in a continuing education program to power engineers is a difficult task because they are not familiarized with switching topology circuits. Normally, in a typical program, many hours are dedicated to explain the thyristors switching sequence and to draw the converter currents and terminal voltages waveforms for different operative conditions. This work presents teaching support software in order to optimize the time spent in this task and, mainly to benefit the assimilation of the proposed subjects, studying the static converter under different non-ideal operative conditions.

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Another prefiltering algorithm for adaptive IIR filtering

An algorithm for adaptive IIR filtering that uses prefiltering structure in direct form is presented. This structure has an estimation error that is a linear function of the coefficients. This property greatly simplifies the derivation of gradient-based algorithms. Computer simulations show that the proposed structure improves convergence speed.

Asymmetrical magnetization in three-phase core type transformers

The performance of the three-phase core type transformers, under AC/DC double excitation is discussed in this work. It is presented a mathematical model that considers the mutual coupling between phases and the magnetic nonlinearity. The validity of the proposed model is verified by means of the experimental and simulated results.

Análise do escoamento de HFC-134a em tubos capilares usando o modelo de dois fluidos

This work presents a numerical model to simulate refrigerant flow through capillary tubes, commonly used as expansion devices in refrigeration systems. The capillary tube is considered straight and horizontal. The flow is taken as one-dimensional and adiabatic. Steady state and thermodynamic equilibrium conditions are assumed. The two-fluid model, involving four conservation equations and considering the hidrodynamic nonequilibrium between the liquid and vapor phases is applied to the flow region. The pressure profiles and the mass flow rates given by the model are compared with experimental data.

Uma aplicacao da celula de comutacao ZVS-PWM-GEPAE ao conversor DC-AC

This work presents the design and procedure of a DC-to-AC converter using a ZVS Commutation Cell developed by Barbi and Martins (1991) and applied to the family of DC-to-DC PWM converters. Firstly, we show the cell applied to buck converter. The stages of operation and the main current and voltage equations of the resonant devices are presented. Next, we adapt the converter to the regenerative operation mode. Hence, the full bridge converter at low frequency operation is conected on the DC-to-DC stage (at high frequency) output ends (Seixas, 1993). Commutation of zero voltage for all switches, PWM at constant frequency and neither overvoltage nor additional current stress are observed by digital simulation. The design example and experimental results obtained by prototype rated at 275 V, 1 kW and 40 kHz are also presented.