João Crisóstomo Weyl Albuquerque Costa

Spectrum allocation policy modeling for elastic optical networks

Today, optical transmission technologies are able to support 400Gbps over a single optical channel. However, this capacity cannot tit in the current fixed frequency grid optical spectrum. On the other hand, high rate optical channels have to co-exist with different ranges of line rates in order to serve heterogeneous bandwidth requests from variety of internet applications. Todays fixed rate and rigid frequency grid optical transmission systems cause over provisioning, where usually more spectral resources are provided than necessary. Recently, the concept of elastic optical networks has been proposed in order to reduce this waste of resources. In networks with such feature enabled, modulation parameters and central frequencies are not fixed and the resources can be allocated with a fine granularity, in contrast to the traditional WDM networks. This flexibility makes it possible to adapt to the granularity of the requested bandwidth without over provisioning. However, this heterogeneous bandwidth allocation may on the other hand result in fragmentation of spectral resources under dynamic traffic. In this study we quantify the fragmentation in elastic optical networks and calculate the blocking probability (BP) together with fragmentation on an elastic optical channel. In this regard, an analytical model based on a Markov Chain is developed under dynamic and flexible bandwidth traffic scenario. By using this analytical model different spectrum allocation policies are compared. A novel spectrum allocation policy is proposed which has lower BP and fragmentation ratio compared to the existing strategies.

Strategies for improving the modeling and interpretability of Bayesian networks

One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.

Performance evaluation based on system modeling using Statecharts extensions

Abstract This paper presents two extensions for Statecharts: the Stochastic Statecharts, which use the original statecharts notation with a minor modification in the formal semantics and the Queuing Statecharts, which do not follow the pure Statecharts notation, but a join between Statecharts and queuing network representations. Some basic elements of Statecharts are redefined such as events and conditions, besides some concepts referring to the dynamic system behavior. The specification approaches show the basic behavior of a generic queuing system by means of templates and standard events. It is presented the PerformCharts, a new simulation environment based on Statecharts specification, which allows model solution using either Markov chains or the Network Simulator (NS).

Technical Communication: Performance analysis of multi-service wireless network: An approach integrating CAC, scheduling, and buffer management

Traffic management (TM) mechanisms such as Call Admission Control (CAC), Scheduling, and Buffer Management (BM) play a key role in the design of multi-service wireless network by providing service differentiation from diverse applications and assigning the network resources (radio channels or buffer) according to the quality of service (QoS) requirements of each service class. We propose in this work two new models that integrate CAC, Scheduling, and BM in the design of multi-service wireless network. By presenting their Markovian models and their performance metrics, we investigate their respective design tradeoffs and compare their performance.

A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges

This paper proposes a novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.

A global expectation-maximization based on memetic swarm optimization for structural damage detection

During the service life of engineering structures, structural management systems attempt to manage all the information derived from regular inspections, evaluations and maintenance activities. However, the structural management systems still rely deeply on qualitative and visual inspections, which may impact the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of collapses. Meanwhile, structural health monitoring arises as an effective discipline to aid the structural management, providing more reliable and quantitative information; herein, the machine learning algorithms have been implemented to expose structural anomalies from monitoring data. In particular, the Gaussian mixture models, supported by the expectation-maximization (EM) algorithm for parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a structure when influenced by several sources of operational and environmental variations. Unfortunately, the optimal parameters determined by the EM algorithm are heavily dependent on the choice of the initial parameters. Therefore, this paper proposes a memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance. The superiority of the GEM-PSO approach over the state-of-the-art ones is attested on damage detection strategies implemented through the Mahalanobis and Euclidean distances, which permit one to track the outlier formation in relation to the main clusters, using real-world data sets from the Z-24 Bridge (Switzerland) and Tamar Bridge (United Kingdom).

A semi-Markov decision process-based joint call admission control for inter-RAT cell re-selection in next generation wireless networks

In the next generation wireless networks (NGWNs), where different radio access technologies (RAT) will coexist and work in collaboration to provide ubiquitous access, a mechanism called Joint Call Admission Control (JCAC) will play an important role by deciding whether or not an incoming service request will be accepted according to an admission constraint as well as determining in which RAT (among the available) it will be connected. In this paper, we propose an optimal JCAC for inter-RAT cell re-selection problem also referred to as initial RAT selection in co-located wireless networks, which supports both real-time services and non-real-time services. To properly meet the JCAC goals, we propose a cost function that weigh two criteria: the blocking cost function, which takes into account the priority of each service class in each RAT, and the alternative acceptance cost, which reflects the multiplicity of RATs working in a collaborative fashion, mandatory in NGWN. We use the framework of Semi-Markov Decision Process (SMDP) to formulate the optimization problem and the value iteration algorithm to compute the optimal policy. Our model still takes into consideration the ratio between the radius of the co-located RATs and shows how it may impact on optimal initial RAT selection. Numerical results, supported by an analysis of the structure of the optimal policy, show that the proposed optimal JCAC selects for real-time service class the biggest RAT and for non-real-time service class the smallest one. This optimal JCAC policy is ratified by the current trend in the design of NGWN and also follows the 3rd Generation Partnership Project (3GPP) expectations.

Transfer Function Estimation of Telephone Lines from Input Impedance Measurements

The ability of a specific telephone line to support a certain digital subscriber line (DSL) service is determined by its downstream and upstream data rates, which are mainly dependent on the lines transfer function. In this way, methods for transfer function estimation play an important role on proper DSL deployment. Most of the existing methods derive the transfer function via line topology identification (LTI) processes. This paper proposes a method which directly estimates the transfer function of telephone lines without any previous LTI process. The results obtained from both simulations and experimental procedure using twisted-pair cables indicate that the proposed method achieves accurate estimations even for lines with bridged-taps.

Numerical simulations and experimental results of a hybrid EDFA-Raman amplifier

In this work numerical simulated and experimental results are obtained for a hybrid EDFA-Raman amplifier. Rigorous numerical models of both EDFA and Raman amplifiers are implemented and validated with experimental measurements. Results of ripple smaller than 1 dB are obtained for the hybrid amplifier gain, in 40 nm of bandwidth. The numerical model permits to achieve optimal design and to predict flat amplifier gain using genetic algorithm.

A New Parallel Approach for 3D Ray-Tracing Techniques in the Radio Propagation Prediction

A new computational parallel model based on 3D ray-tracing for radio propagation prediction is presented. This approach considers that the main tasks in a 3D ray-tracing technique can be evaluated in an independent and/or parallel way. The workload distribution among the participant nodes of the parallel architecture (cluster of PCs) is performed through a random assignment of the initial rays and the field points for them. Simulations are realized in order to validate and evaluate the performance of the proposed model. The presented results show that the scalability of the model is obtained naturally due to independence of the involved processes. The efficiency of the model presents behavior above the ideal for cases with ostensible processing of rays. These characteristics favor to the increase of the prediction precision through the increase of the density of launched rays and the possibility of incorporation of new propagation mechanisms.