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- Hessian-polar context: a descriptor for microfilaria recognitionPublication . Al-Tam, Faroq; dos Anjos, Antonio; Shahbazkia, Hamid R.This paper presents a new effective descriptor for microfilaria. Since microfilaria is a thin elastic object, the proposed descriptor handles it locally. At each medial point of the microfilaria, the local structure of the microfilaria votes for a given shape. Accumulating these votes in the polar domain yields a rich descriptor. Experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.
- Optimization of mixed numerology profiles for 5G wireless communication scenariosPublication . Correia, Noélia; Al-Tam, Faroq; Rodriguez, JonathanThe 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.
- Illumination correction and analysis of two-dimensional microscopy images of Loa loa microfilariaePublication . Al-Tam, Faroq; Shahbazkia, Hamid Reza; Anjos, António dosThis 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.
- On load balancing via switch migration in software-defined networkingPublication . Correia, Noélia; Al-Tam, FaroqSwitch-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.
- Deep PC-MAC: a deep reinforcement learning pointer-critic media access protocolPublication . Al-Tam, Faroq; Mazayev, Andriy; Correia, Noélia; Rodriguez, J.Developing artificial intelligence (AI) solutions for communication problems is one of the hottest topics nowadays. This article presents Deep PC-MAC, a novel deep reinforcement learning (DRL) solution to solve the fair coexistence problem (FCP) between heterogeneous nodes in the unlicensed bands. It is based on a hybrid architecture between pointer networks (Ptr-nets) and advantage actor-critic (A2C), i.e., pointer-critic architecture. The proposed model allows base stations to fairly share unlicensed bands with incumbent nodes. It jointly protects the incumbent nodes from spectrum starvation and improves key-performance indicators (KPIs). Deep PC-MAC is trained from scratch with zero-knowledge about FCP and experimental results demonstrate its efficiency when compared to a baseline method.
- Fractional switch migration in multi-controller software-defined networkingPublication . Al-Tam, Faroq; Correia, NoéliaMapping switches to controllers in multi-controller software-defined networking (SDN) is still a hot research topic. Many factors have to be considered when establishing this mapping. Among them are the load balancing and mapping stability. Load balancing is important to improve resources utilization, and mapping stability reduces the control plane overhead created when exchanging information triggered by new mappings. This article presents a model for dynamic switch-controller mapping to achieve load balancing and minimize the number of new switch-controller assignments. To that end, for load balancing, flows from a switch are allowed to be handled by multiple controllers, and to increase assignment stability, the assignments at time t - 1 are taken into consideration when calculating the assignments at time t. The model is formulated as a convex quadratic programming problem, and the properties and feasibility of this model are mathematically analyzed. In addition, a heuristic algorithm is developed to deal with large-scale networks. The experimental results show the effectiveness of the proposed approach when compared to recent academic work, where the proposed model leads to a slight improvement in the load balancing and increases the stability of the switch-controller assignment by approximate to 91%. (C) 2019 Elsevier B.V. All rights reserved.
- Radio Resource Scheduling with Deep Pointer Networks and Reinforcement LearningPublication . Al-Tam, Faroq; Mazayev, Andriy; Correia, Noélia; Rodriguez, J.This article presents an artificial intelligence (AI) adaptable solution to handle the radio resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio resource management (RRM) and aims to efficiently allocate frequency domain resources to users. The proposed solution is an advantage pointer critic (APC) deep reinforcement learning (DRL) agent. It is built with a deep pointer network architecture and trained by the policy gradient algorithm. The proposed agent is deployed in a system level simulator and the experimental results demonstrate its adaptability to network dynamics and efficiency when compared to baseline algorithms.
- Adaptive spectrum allocation for 5G wireless communication scenariosPublication . Correia, Noélia; Al-Tam, Faroq; Rodriguez, J.5G resources should be properly planned for users to have a good quality of service. This planning includes defining the most suitable numerology indexes and best spectrum allocation considering the requirements of current traffic, which may change over time. Furthermore, when accounting for changes in traffic pattern, any necessary reconfigurations should be minimized. Here in this article, an optimization model is developed for the planning of spectrum allocation to the best mix numerology. The model considers adapting the operating numerology mix according to the current presence (or not) of traffic requirements. The model also works under any wireless communication scenario in 5G, and under any traffic pattern.
- Analysis of machine learning techniques applied to sensory detection of vehicles in intelligent crosswalksPublication . Lozano Domínguez, José Manuel; Al-Tam, Faroq; Mateo Sanguino, Tomás de J.; Correia, NoéliaImproving 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.
- Warping, matching and reporting 2-D electrophoresis protein gel imagesPublication . Al-Tam, Faroq; Shahbazkia, Hamid Reza; Anjos, António dosIn 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.