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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, NoĆ©lia; 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 60ā100%, whereas a non-optimized approach provides 40ā65%, while minimizing the total number of numerology indexes in operation.
- Deep PC-MAC: a deep reinforcement learning pointer-critic media access protocolPublication . Al-Tam, Faroq; Mazayev, Andriy; Correia, NoƩlia; 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.
- Radio Resource Scheduling with Deep Pointer Networks and Reinforcement LearningPublication . Al-Tam, Faroq; Mazayev, Andriy; Correia, NoƩlia; 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, NoƩlia; 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 DomĆnguez, JosĆ© Manuel; Al-Tam, Faroq; Mateo Sanguino, TomĆ”s de J.; Correia, NoĆ©liaImproving 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.
- Flow setup aware controller placement in distributed software-defined networkingPublication . Correia, NoƩlia; Al-Tam, FaroqIn distributed software-defined networking, switches can be assigned to a single master controller and one or more slave controllers, for resilience. However, only master controllers are allowed to install flow rules. Also, controllers install flow rules just at switches under their domain. This means that a particular flow may trigger the flow setup procedure multiple times, if the traversed switches are under different controller domains. This results into extra network load and instability. Here, in this article, the controller placement is planned having into account flow setup efficiency and possible future migrations from master to slave controllers. Results show that such flow setup and migration awareness results into long-term quality solutions for controller placement.
- A2PC: augmented advantage pointer-critic model for low latency on mobile IoT with edge computingPublication . Carvalho, Rodrigo; Al-Tam, Faroq; Correia, NoƩliaAs a growing trend, edge computing infrastructures are starting to be integrated with Internet of Things (IoT) systems to facilitate time-critical applications. These systems often require the processing of data with limited usefulness in time, so the edge becomes vital in the development of such reactive IoT applications with real-time requirements. Although different architectural designs will always have advantages and disadvantages, mobile gateways appear to be particularly relevant in enabling this integration with the edge, particularly in the context of wide area networks with occasional data generation. In these scenarios, mobility planning is necessary, as aspects of the technology need to be aligned with the temporal needs of an application. The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. This article presents a novel scalable DRL model that incorporates a pointer-network (Ptr-Net) and an actor-critic algorithm to handle complex action spaces. The model synchronously determines the gateway location and visit time. Ultimately, the gateways are able to attain high-quality trajectory planning with reduced latency.