Extensões na arquitetura SDN para o provisionamento de QoS através do monitoramento e uso de múltiplos caminhos

The substantial increase in the number of applications offered through the computer networks, as well as in the volume of traffic forwarded through the network, have hampered to assure adequate service level to users. The Quality of Service (QoS) offer, honoring specified parameters in Service Level Agreements (SLA), established between the service providers and their clients, composes a traditional and extensive computer networks’ research area. Several schemes proposals for the provision of QoS were presented in the last three decades, but the acting scope of these proposals is always limited due to some factors, including the limited development of the network hardware and software, generally belonging to a single manufacturer. The advent of Software Defined Networking (SDN), along with the maturation of its main materialization, the OpenFlow protocol, allowed the decoupling between network hardware and software, through an architecture which provides a control plane and a data plane. This eases the computer networks scenario, allowing that new abstractions are applied in the hardware composing the data plane, through the development of new software pieces which are executed in the control plane. This dissertation investigates the QoS offer through the use and extension of the SDN architecture. Based on the proposal of two new modules, one to perform the data plane monitoring, SDNMon, and the second, MP-ROUTING, developed to determine the use of multiple paths in the forwarding of data referring to a flow, we demonstrated in this work that some QoS metrics specified in the SLAs, such as bandwidth, can be honored. Both modules were implemented and evaluated through a prototype. The evaluation results referring to several aspects of both proposed modules are presented in this dissertation, showing the obtained accuracy of the monitoring module SDNMon and the QoS gains due to the utilization of multiple paths defined by the MP-Routing, when forwarding data flow through the SDN.

Caminhos da professoralidade no Curso de Dança da UFU: impasses e desafios na construção da identidade profissional

Teacher identity is a subject of study and discussion in the academic world whichhas become an object of attention of researchaddressing teaching and teacher formation. Life history, initial and continuing formation, the meaning of teaching to the teacher, and also pedagogical practice are all contributing factors to teachers’ professional identity. The present study is a proposal developed in the research field of Educational Knowledge and Practice, and its main focus lies in university teaching. Higher education teaching in the context of a dance course, and the issues and challenges of constructing teachers’ professional identity are presented. Thus, my main questions were: what is the teaching path followed by newly hired dance teachers in the Federal University of Uberlândia? How is teaching identity developed in these new teachers’ professional socialization process? What kind of educational knowledge is (re)produced and mobilized by teachers when they join university teaching? In order to answer these questions, my objectives are: to analyze the teaching path of the newly hired dance teachers of the Federal University of Uberlândia; to investigate how their teaching identity is built within their professional socialization process; and identify the kinds of educational knowledge they (re)produce and mobilize as soon as they become university teachers. The present research comprises a qualitative data analysis from previous studies on the subject, having as starting point relevant bibliographic research, followed by an identification questionnaire and an interview conducted with the newly hired dance teachers. The construction of teaching identity is related to objective and subjective conditions involving a teaching job and how the teacher perceives this identity as constantly evolving. Hence I understand the importance of personal and institutional incentives to prepare studies which raise or problematize issues specific to this area, contributing to extend the debate over higher education professionals’ formation, in particular that of dance course teachers on national scope.

Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.

Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.