On load balancing via switch migration in software-defined networking

Switch-controller assignment is an essential task in multi-controller software-defined networking. Static assignments are not practical because network dynamics are complex and difficult to predetermine. Since network load varies both in space and time, the mapping of switches to controllers should be adaptive to sudden changes in the network. To that end, switch migration plays an important role in maintaining dynamic switch-controller mapping. Migrating switches from overloaded to underloaded controllers brings flexibility and adaptability to the network but, at the same time, deciding which switches should be migrated to which controllers, while maintaining a balanced load in the network, is a challenging task. This work presents a heuristic approach with solution shaking to solve the switch migration problem. Shift and swap moves are incorporated within a search scheme. Every move is evaluated by how much benefititwillgivetoboththeimmigrationandoutmigrationcontrollers.Theexperimentalresultsshowthat theproposedapproachisabletooutweighthestate-of-artapproaches,andimprovetheloadbalancingresults up to≈ 14% in some scenarios when compared to the most recent approach. In addition, the results show that the proposed work is more robust to controller failure than the state-of-art methods.Portuguese Science and Technology Foundation (FCT) - UID/MULTI/00631/2019;info:eu-repo/semantics/publishedVersio

Illumination correction and analysis of two-dimensional microscopy images of Loa loa microfilariae

This thesis addresses the problem of detecting a common parasitic micro laria that causes loaisis, a major disease problem in Central and Western Africa. The dose of medicine to be administered to the patient is proportional to the estimated number of micro lariae in the patient's body. Therefore, proper estimation of the number of micro lariae is the key for conducting the right procedure. The clinical examination is necessary to estimate the micro lariae density in a blood sample drawn from the patient. Thereafter, visual inspection of the sample is performed. The main challenge in this work is, however, the development of an automatic detection system of micro lariae in 2-D images. Such problem is new in the image processing literature, and the development of such system is very important for performing better diagnosis and treatment of this disease and other similar diseases. A comprehensive review of, both generic and thin, object detectors in 2-D images is presented. A very robust method for microscopy image illumination correction is proposed, and a new powerful descriptor, the Hessian-Polar Context (HPC), for micro lariae is also introduced. These are then combined in a micro lariae detection system, where a simple, yet e cient, hypotheses generator is also presented. Additionally, several methods and applications for di erent image modalities are proposed. These involve a method and an application for the analysis of rice panicle in 2-D images. Additionally, an e cient method for artifact suppression in X-ray image is also proposed. The proposed methods are compared to a set of state-of-the-art methods. Experimental results show that the developed methods are great contributions to the microscopy and X-ray imaging elds

Placement of Controllers in Software Defined Networking under Multiple Controller Mapping

This work focuses on the placement of controllers in software-defined networking architectures. A mathematical model is developed to place controllers under multi- controller switch-controller mapping, where a switch can be assigned to multiple controllers. Resiliency, scalability, and inter-plane latency are all modeled in the proposed model. A scalability factor is introduced to increase the load to capacity gap at controllers, preventing controllers to work near their capacity limit. The proposed model is shown to be effective and resilient under different failure scenarios while, at the same time, taking latency and scalability into consideration. Keywords: Controller Placement, Software-defined Networking, Reliability, Scalabilit

Illumination correction and analysis of two-dimensional microscopy images of Loa loa microfilariae

This thesis addresses the problem of detecting a common parasitic micro laria that causes loaisis, a major disease problem in Central and Western Africa. The dose of medicine to be administered to the patient is proportional to the estimated number of micro lariae in the patient's body. Therefore, proper estimation of the number of micro lariae is the key for conducting the right procedure. The clinical examination is necessary to estimate the micro lariae density in a blood sample drawn from the patient. Thereafter, visual inspection of the sample is performed. The main challenge in this work is, however, the development of an automatic detection system of micro lariae in 2-D images. Such problem is new in the image processing literature, and the development of such system is very important for performing better diagnosis and treatment of this disease and other similar diseases. A comprehensive review of, both generic and thin, object detectors in 2-D images is presented. A very robust method for microscopy image illumination correction is proposed, and a new powerful descriptor, the Hessian-Polar Context (HPC), for micro lariae is also introduced. These are then combined in a micro lariae detection system, where a simple, yet e cient, hypotheses generator is also presented. Additionally, several methods and applications for di erent image modalities are proposed. These involve a method and an application for the analysis of rice panicle in 2-D images. Additionally, an e cient method for artifact suppression in X-ray image is also proposed. The proposed methods are compared to a set of state-of-the-art methods. Experimental results show that the developed methods are great contributions to the microscopy and X-ray imaging elds

Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios

The management of 5G resources is a demanding task, requiring proper planning of operating numerology indexes and spectrum allocation according to current traffic needs. In addition, any reconfigurations to adapt to the current traffic pattern should be minimized to reduce signaling overhead. In this article, the pre-planning of numerology profiles is proposed to address this problem, and a mathematical optimization model for their planning is developed. The idea is to explore requirements and impairments usually present in a given wireless communication scenario to build numerology profiles and then adopt one of the profiles according to the current users/traffic pattern. The model allows the optimization of mixed numerologies in future 5G systems under any wireless communication scenario, with specific service requirements and impairments, and under any traffic scenario. Results show that, depending on the granularity of the profiles, the proposed optimization model is able to provide satisfaction levels of 60–100%, whereas a non-optimized approach provides 40–65%, while minimizing the total number of numerology indexes in operation.Competitiveness and Internationalization Operational Programme (COMPETE 2020), the Regional Operational Program of the Algarve (2020), and Fundação para a Ciência e Tecnologia; i-Five: Extensão do acesso de espectro dinâmico para rádio 5G, POCI-01-0145-FEDER-030500. This work is also supported by Fundação para a ciência e Tecnologia within CEOT (Center for Electronic, Optoelectronic and Telecommunications) and the UID/MULTI/00631/2020 projectinfo:eu-repo/semantics/publishedVersio

Microfilariae Classification Using Multiple Classifiers for Color and Shape Features

This paper presents a multi-classifier approach for classifying microfilariae in 2-D images. A shape descriptor based on the quench function is described. This descriptor is represented as a feature vector that encodes the shape information. The color feature vector is calculated as a histogram. Two classifiers were used to train both color and shape feature vectors, one for each vector. The posterior probabilities calculated from the scores of each classifier are then used to calculate the final classification decision. The experimental results show that, although the proposed approach is simple, it is efficient when compared to various approaches.publishersversionPeer reviewe

Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalks

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.Ministry of Economy and Knowledge of the Andalusian Government, Spain 5947info:eu-repo/semantics/publishedVersio

DNAGear: a free software for spa type identification in Staphylococcus aureus

Staphylococcus aureus is both human commensal and an important human pathogen, responsible for community-acquired and nosocomial infections ranging from superficial wound infections to invasive infections, such as osteomyelitis, bacteremia and endocarditis, pneumonia or toxin shock syndrome with a mortality rate up to 40%. S. aureus reveals a high genetic polymorphism and detecting the genotypes is extremely useful to manage and prevent possible outbreaks and to understand the route of infection. One of current and expanded typing method is based on the X region of the spa gene composed of a succession of repeats of 21 to 27 bp. More than 10000 types are known. Extracting the repeats is impossible by hand and needs a dedicated software. Unfortunately the only software on the market is a commercial program from Ridom. Findings This article presents DNAGear, a free and open source software with a user friendly interface written all in Java on top of NetBeans Platform to perform spa typing, detecting new repeats and new spa types and synchronizing automatically the files with the open access database. The installation is easy and the application is platform independent. In fact, the SPA identification is a formal regular expression matching problem and the results are 100% exact. As the program is using Java embedded modules written over string manipulation of well established algorithms, the exactitude of the solution is perfectly established. Conclusions DNAGear is able to identify the types of the S. aureus sequences and detect both new types and repeats. Comparing to manual processing, which is time consuming and error prone, this application saves a lot of time and effort and gives very reliable results. Additionally, the users do not need to prepare the forward-reverse sequences manually, or even by using additional tools. They can simply create them in DNAGear and perform the typing task. In short, researchers who do not have commercial software will benefit a lot from this application.Peer Reviewe

Warping, matching and reporting 2-D electrophoresis protein gel images

In Proteomics, Differential Analysis is the method of studying 2-D Electrophoresis (2-DE) images by finding the differences. This method involves comparing the images’ spots in order to find the missing, unidentified, and/or misplaced proteins. The manual comparison by visual inspection is a labor-intensive and error-prone task. Matching two gels is not an easy task. Biologists have to identify and quantify the spots one-by-one

Warping, matching and reporting 2-D electrophoresis protein gel images

In Proteomics, Differential Analysis is the method of studying 2-D Electrophoresis (2-DE) images by finding the differences. This method involves comparing the images’ spots in order to find the missing, unidentified, and/or misplaced proteins. The manual comparison by visual inspection is a labor-intensive and error-prone task. Matching two gels is not an easy task. Biologists have to identify and quantify the spots one-by-one