Marcelo A. C. Fernandes

8-VSB channel coding analysis for DTV broadcast

This article analyzes the channel coding stage in 8-VSB (8-level vestigial sideband) transmission for DTV (digital television) broadcast, proposed by ATSC (Advanced Television Systems Committee). We present BER (bit error rate) results for a codec simulator developed by the authors for the purpose of assessing the robustness of the ATSC 8-VSB coding stage in presence of an additive Gaussian noise channel and of impulsive channel noise surges. We discuss implementation aspects for the developed simulator.

Performance of an adaptive space-time processing receiver for the user terminal of 3G WCDMA systems under COST 259 channel models

This paper presents an adaptive multi-target space-time architecture for downlink receivers in the user terminal of WCDMA systems under soft handoff. The receiver consists of M antennas and K array processors, each one followed by a chip-rate equalizer and a despreader. The resulting signals from these K branches are combined in an adaptive ratio combiner which is updated by the common channel information in UTRA FDD systems. When a large number of antennas is used each array processor is able to collect a given target ray and filter out all the others. On the other hand, when only a small number of antennas is used, due to cost or physical limitation, the chip-rate equalizers try to eliminate the residual interchip interference that strongly affects receiver performance. In both cases an efficient coherent receiver is obtained that exploits the space and time diversity in the incoming signal at the user terminal. All the signal structure of the FDD WCDMA downlink signal is exploited for improved performance. Simulation results using COST-259 channel models for vehicle speed of 50 km/h are compared with results obtained for a difficult hypothetical fixed channel condition.

Dynamic planning navigation strategy for mobile terrestrial robots

This paper proposes a new dynamic planning navigation strategy for use with mobile terrestrial robots. The strategy was applied to situations in which the environment and obstacles were unknown. After each displacement event, the robot replanned its route using a control algorithm that minimized the distance to the target and maximized the distance between the obstacles. Using a spatial localization sensor and a set of distance sensors, the proposed navigation strategy was able to dynamically plan optimum routes that were free of collisions. Simulations performed using different types of environment demonstrated that the technique offers a high degree of flexibility and robustness, and validated its potential use in real applications involving mobile terrestrial robots.

Problem-based learning applied to the artificial intelligence course

The purpose of this article is to present a problem‐based learning (PBL) proposal for use in the Artificial Intelligence course. The proposal includes problems associated with the area of mobile robotics, in which the student develops artificial intelligence solutions to optimize the movement of a robot in an unknown environment, avoiding obstacles. The proposed method was applied during three semesters (2012.1, 2013.1, and 2013.2), in the Applied Artificial Intelligence course of the Computational Engineering programme at the Federal University of Rio Grande do Norte. The results obtained in opinion surveys with the students during the three semesters are presented with the aim of validating the PBL proposal. The responses obtained in the opinion surveys show that the proposed method was well received, given that more than 80% of the responses were positive. Details of the implementation of the proposal are provided in order to assist others involved in the teaching of similar academic programme.

Platform for Real-Time Simulation of Dynamic Systems and Hardware-in-the-Loop for Control Algorithms

The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS), could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embedded control algorithms. In the academic sphere, it could be used to support research into new embedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerning on-board control systems.

Chip-level adaptive equalization supervised by a time-multiplexed common pilot sequence in CDMA systems

The objective of this paper is to propose a reception technique for improving the performance of CDMA systems relative to conventional direct channel reception. The proposed receiver employs a linear adaptive equalizer trained by a time-multiplexed common pilot sequence. The equalizer, which works in fully supervised mode guided by this signal, provides substantial benefits compared to a conventional RAKE-finger receiver. The functional details and performance results of the reception scheme are described. The receiver can be applied to any transmission technique using CDMA, including broadband WCDMA and other systems.

Fuzzy controller applied to electric vehicles with continuously variable transmission

This paper proposes an energy efficiency control strategy for electric vehicles (EV) equipped with continuously variable transmission (CVT). The strategy is based on a fuzzy controller that acts directly on the ratio of the CVT system, and uses the maximum efficiency motor rotation speed as a reference. Different to earlier proposals, this project involved the development of a more precise system that optimizes the maximum efficiency function of the PMDC motor for use as a reference for the controller. A full characterization of the system is provided, together with the results of simulation modeling employing different types of routes taken by the vehicle.

Beamforming and power control in sensor arrays using reinforcement learning.

The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.

Neural equalization applied to systems with bidimensional digital modulation

A new equalization model for digital communication systems is proposed, based on a multi-layer perceptron (MLP) artificial neural network with a backpropagation algorithm. Unlike earlier techniques, the proposed model, called the bidimensional neural equalizer, is composed of two independent MLP networks that operate in parallel for each dimension of the digital modulation scheme. A heuristic method to combine the errors of the two MLP networks is also proposed, with the aim of reducing the convergence time. Simulations performed for linear and nonlinear channels demonstrated that the new model could improve performance in terms of the bit error rate and the convergence time, compared to existing models.

Efficient 3D Objects Recognition Using Multifoveated Point Clouds

Technological innovations in the hardware of RGB-D sensors have allowed the acquisition of 3D point clouds in real time. Consequently, various applications have arisen related to the 3D world, which are receiving increasing attention from researchers. Nevertheless, one of the main problems that remains is the demand for computationally intensive processing that required optimized approaches to deal with 3D vision modeling, especially when it is necessary to perform tasks in real time. A previously proposed multi-resolution 3D model known as foveated point clouds can be a possible solution to this problem. Nevertheless, this is a model limited to a single foveated structure with context dependent mobility. In this work, we propose a new solution for data reduction and feature detection using multifoveation in the point cloud. Nonetheless, the application of several foveated structures results in a considerable increase of processing since there are intersections between regions of distinct structures, which are processed multiple times. Towards solving this problem, the current proposal brings an approach that avoids the processing of redundant regions, which results in even more reduced processing time. Such approach can be used to identify objects in 3D point clouds, one of the key tasks for real-time applications as robotics vision, with efficient synchronization allowing the validation of the model and verification of its applicability in the context of computer vision. Experimental results demonstrate a performance gain of at least 27.21% in processing time while retaining the main features of the original, and maintaining the recognition quality rate in comparison with state-of-the-art 3D object recognition methods.