A new mobile robot control approach via fusion of control signals.

This paper proposes an alternative approach to address the problem of coordinating behaviors in mobile robot navigation: fusion of control signals. Such approach is based on a set of two decentralized information filters, which accomplish the data fusion involved. Besides these two fusion engines, control architectures designed according to this approach also embed a set of different controllers that generate reference signals for the robot linear and angular speeds. Such signals are delivered to the two decentralized information filters, which estimate suitable overall reference signals for the robot linear and angular speeds, respectively. Thus, the background for designing such control architectures is provided by the nonlinear systems theory, which makes this approach different from any other yet proposed. This background also allows checking control architectures designed according to the proposed approach for stability. Such analysis is carried out in the paper, and shows that the robot always reaches its final destination, in spite of either obstacles along its path or the environment layout. As an example, a control architecture is designed to guide a mobile robot in an experiment, whose results allows checking the good performance of the control architecture and validating the design approach proposed as well.     

A methodology to design the link cost functions for impairment aware routing algorithms in optical networks

We propose a methodology to design the link cost function and, consequently, a systematic form to design a RWA algorithm. We call this methodology link cost function design (LCFD) and it consists of four steps: The choice of the link cost function input variables, the expansion of the cost function in terms of a series, the selection of an overall network performance indicator as the optimization target, and finally, the execution of an optimization process to find the series coefficients that optimize the network performance indicator based on off-line network simulations. The optimization process is performed by a computational intelligence technique, the particle swarm optimization. The proposed methodology (LCFD) is used to design an adaptive IA-RWA algorithm, which we call Power Series Routing (PSR). The effectiveness of both methodology and IA-RWA algorithm is investigated. The PSR is compared with other algorithms found in the literature by means of computational simulations and our proposal presented lower blocking probabilities with shorter computation time. Furthermore, we investigate the sensitivity and the ability of the proposed PSR to adapt itself to topological changes in the network due to both link/node addition/failure. We also investigate the behavior of the PSR in a scenario where the traffic load distribution is randomly chosen (non-uniform traffic), and we compared it to other three routing algorithms.     

Local and global approaches for the adaptive control of a cascade of amplifiers

In this work we present and analyze three approaches for the adaptive control of the operating point of a cascade of erbium-doped fiber amplifiers (EDFAs), aimed at optical networks performance enhancement. The first approach is called Annealing Search Heuristic with Backpropagation and flexible output (AsHB flex) and uses machine learning concepts to update the amplifier gains through an iterative process. The second one (Exhaustive Method) uses an exhaustive search to evaluate all possible solutions for the problem and obtain the optimum solution. The last one (MaxGain) is a heuristic method that uses previous knowledge about the problem to obtain the solutions. The amplifier characteristics and specifications were obtained experimentally through measurements of gain, noise figure, gain ripple and power consumption on commercially available EDFAs. We performed comparisons among these approaches and others found in the literature, and the results show that the three proposals outperformed the previous ones in terms of noise figure, gain ripple and BER. For example, in a link with four amplifiers the Exhaustive Method achieved a reduction in the cascade noise figure from 10.05 to 5.18 dB, a reduction in the gain ripple from 24.08 to 18.56 dB and a reduction in the BER in almost two orders of magnitude, when compared with the traditional approach, which defines the gain to compensate the loss of the previous link. However, the computation time of Exhaustive Method becomes prohibitive as the number of amplifiers in the link increases. Both MaxGain and AsHBflex obtained similar solutions, close to the optimum operation point in a reasonable time.     

Adaptive motion control law of a robotic wheelchair

In the present work, a dynamic model of a robotic wheelchair is developed considering a lateral deviation of the center of mass. The Lyapunov and input/output stability theories are used to design a novel tracking and positioning adaptive control for the robotic wheelchair. Properties of the dynamic model with respect to its matrices and parameters are shown. A filter is used to obtain a closed loop equation that allows designing the adaptive control law. Then, a projection algorithm is used to improve the adaptive control in the sense of avoiding parameter drift. Experimental results show good performance of the adaptive control.     

Impact of amplifier noise figure modeling in simulations of impairment-aware all-optical networks

In this article, we analyze and quantify the impact of the use of different amplifier noise figure models in impairment-aware optical network simulations. We compare network simulations that use three different models, two of them already known and largely employed in the literature, and a third one that we propose here, which is more appropriate to use for gain-clamped amplifiers. We present simulation results for the network blocking probability for each model, for different amplifier output saturation powers. And we also present simulation results for the distribution of optical powers at the input of each amplifier in the network. From our results we can conclude in which cases one can use a simpler model and in which other cases it is worth using a more elaborate model. We show that if the simplest and most common model is used for any network simulation, the obtained blocking probability is overestimated.     

Defining amplifier’s gain to maximize the transmission rate in optical systems using evolutionary algorithms and swarm intelligence

Optical networks are currently the only technology capable of providing extremely high data transmission rates. Because of this, systems must be increasingly efficient and immune to failures. One way to improve network efficiency is to use dynamic approaches like Adaptive Control of Operating Point, which consists of autonomously choosing the best operating point for optical amplifiers on the link, thus providing the best configuration concerning Quality of transmission. Unlike the previous works that focused on optimizing Optical Signal-To-Noise Ratio, our proposal and analysis are focused on maximizing the transmission rate. In this paper, we compare the results obtained by five different and widely used evolutionary and swarm-based algorithms in the search for maximizing the transmission rate in optical links. We have observed that the differential evolution provided the best results in the analyzed scenarios.

     

OSNR model to consider physical layer impairments in transparent optical networks

We propose a model that considers several physical impairments in all-optical networks based on optical signal-to-noise degradation. Our model considers the gain saturation effect and amplified spontaneous emission depletion in optical amplifiers, coherent crosstalk in optical switches, and four-wave mixing in transmission fibers. We apply our model to investigate the impact of different physical impairments on the performance of all-optical networks. The simulation results show the impact of each impairment on network performance in terms of blocking probability as a function of device parameters. We also apply the model as a metric for impairment-constraint routing in all-optical networks. We show that our proposed routing and wavelength assignment algorithm outperforms two common approaches.

TDFA/Raman hybrid amplifiers covering the entire S-band pumped by a single laser

Hybrid amplifier configurations are reported cascading one Raman and one or two Tm/sup 3+/-doped fiber stages, using a single pump at 1426 nm, allowing seamless amplification of the entire S-band (1460-1530 nm). Different configurations were studied, with gain over the entire band width and peak gain up to 24 dB. Noise figure as low as 7 dB was achieved.     

Novel strategies for sparse regenerator placement in translucent optical networks

In this paper, we propose two strategies for sparse regenerator placement (RP) in translucent optical networks, named most used regenerator placement (MU-RP), and most simultaneous used regenerator placement (MSU-RP). Our proposals are compared to well known RP algorithms presented in literature for two different network topologies for different network loads, distribution of load along the networks and number of translucent nodes. MSU-RP presented remarkable results and outperformed all previous approaches in all cases, while MSU-RP obtained a slightly superior or similar performance when compared to previous approaches presented in the literature.     

Characterization of efficient dual-wavelength (1050 + 800 nm) pumping scheme for thulium-doped fiber amplifiers

We report on the characterization of a recently introduced dual-wavelength pumping scheme for thulium-doped fiber amplifiers using 800 and 1050 nm. Using a counterpropagating pump configuration, 180 mW of total pump power yielded 27-dB small-signal gain and less than 5-dB noise figure. Furthermore, using optical frequency domain reflectometry, the distributed gain in this configuration was evaluated, allowing for optimization of the doped fiber length.