Daniel Castro Silva

Performance analysis in soccer: a Cartesian coordinates based approach using RoboCup data

In soccer, like in business, results are often the best indicator of a team’s performance in a certain competition but insufficient to a coach to asses his team performance. As a consequence, measurement tools play an important role in this particular field. In this research work, a performance tool for soccer, based only in Cartesian coordinates is presented. Capable of calculating final game statistics, suisber of shots, the calculus methodology analyzes the game in a sequential manner, starting with the identification of the kick event (the basis for detecting all events), which is related with a positive variation in the ball’s velocity vector. The achieved results were quite satisfactory, mainly due to the number of successfully detected events in the validation process (based on manual annotation). For the majority of the statistics, these values are above 92% and only in the case of shots do these values drop to numbers between 74 and 85%. In the future, this methodology could be improved, especially regarding the shot statistics, integrated with a real-time localization system, or expanded for other collective sports games, such as hockey or basketball.

Computation Sharing in Distributed Robotic Systems: A Case Study on SLAM

Aiming at increasing team efficiency, mobile robots may act as a node of a Robotic Cluster to assist their teammates in computationally demanding tasks. Having this in mind, we propose two distributed architectures for the Simultaneous Localization And Mapping (SLAM) problem, our main case study. The analysis focuses especially on the efficiency gain that can be obtained. It is shown that the proposed architectures enable us to raise the workload up to values that would not be possible in a single robot solution, thus gaining in localization precision and map accuracy. Furthermore, we assess the impact of network bandwidth. All the results are extracted from frequently used SLAM datasets available in the robotics community and a real world testbed is described to show the potential of using the proposed philosophy.

An Inverted Ant Colony Optimization approach to traffic

With an ever increasing number of vehicles traveling the roads, traffic problems such as congestions and increased travel times became a hot topic in the research community, and several approaches have been proposed to improve the performance of the traffic networks.This paper introduces the Inverted Ant Colony Optimization (IACO) algorithm, a variation of the classic Ant Colony algorithm that inverts its logic by converting the attraction of ants towards pheromones into a repulsion effect. IACO is then used in a decentralized traffic management system, where drivers become ants that deposit pheromones on the followed paths; they are then repelled by the pheromone scent, thus avoiding congested roads, and distributing the traffic through the network.Using SUMO (Simulation of Urban MObility), several experiments were conducted to compare the effects of using IACO with a shortest time algorithm in artificial and real world scenarios - using the map of a real city, and corresponding traffic data.The effect of the behavior caused by this algorithm is a decrease in traffic density in widely used roads, leading to improvements on the traffic network at a local and global level, decreasing trip time for drivers that adhere to the suggestions made by IACO as well as for those who do not. Considering different degrees of adhesion to the algorithm, IACO has significant advantages over the shortest time algorithm, improving overall network performance by decreasing trip times for both IACO-compliant vehicles (up to 84%) and remaining vehicles (up to 71%). Thus, it benefits individual drivers, promoting the adoption of IACO, and also the global road network. Furthermore, fuel consumption and CO2 emissions from both vehicle types decrease significantly when using IACO (up to 49%).

Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review

Background: Recurrence is an important cornerstone in breast cancer behavior, intrinsically related to mortality. In spite of its relevance, it is rarely recorded in the majority of breast cancer datasets, which makes research in its prediction more difficult. Objectives: To evaluate the performance of machine learning techniques applied to the prediction of breast cancer recurrence. Material and Methods: Revision of published works that used machine learning techniques in local and open source databases between 1997 and 2014. Results: The revision showed that it is difficult to obtain a representative dataset for breast cancer recurrence and there is no consensus on the best set of predictors for this disease. High accuracy results are often achieved, yet compromising sensitivity. The missing data and class imbalance problems are rarely addressed and most often the chosen performance metrics are inappropriate for the context. Discussion and Conclusions: Although different techniques have been used, prediction of breast cancer recurrence is still an open problem. The combination of different machine learning techniques, along with the definition of standard predictors for breast cancer recurrence seem to be the main future directions to obtain better results.

Overall Survival Prediction for Women Breast Cancer Using Ensemble Methods and Incomplete Clinical Data

Breast Cancer is the most common type of cancer in women worldwide. In spite of this fact, there are insufficient studies that, using data mining techniques, are capable of helping medical doctors in their daily practice.

Antimycobacterial activity of rhodamine 3,4-HPO iron chelators against Mycobacterium avium: analysis of the contribution of functional groups and of chelator's combination with ethambutol

Rhodamine-labelled 3-hydroxy-4-pyridinone (3,4-HPO) chelators exhibit antimycobacterial activity, related but not limited to their iron binding capacity. We previously found that bacterial growth inhibition observed for chelators with ethyl substituents on the amino groups of the xanthene ring of rhodamine and a thiourea linkage between rhodamine and the chelating unit (MRH7 and MRB7) was different from that of compounds with methyl substituents and an amide linkage (MRH8 and MRB8). In this work we evaluated the antimycobacterial activity of two new chelators (MRH10 and MRB9) expressly designed to allow: (a) the direct comparison of the influence of the functional groups per se and (b) identification of the finest combination to achieve a higher biological activity. The activity of the chelators was assessed, as previously, by measuring their effect against M. avium. In this study we also report the antimycobacterial effect of MRH7, which proved to be the best performer of all four chelators, in combination with ethambutol, which is one of the antibiotics currently in use to treat mycobacterial infections. The results are indicative that a combination of 3,4-HPO iron chelators with an antibiotic is a promising strategy to fight M. avium infections. The current results are relevant for the choice of the best chelator in our set of compounds and also for the design of novel molecular architectures to target cellular membranes.

Using Kalman Filters to Reduce Noise from RFID Location System

Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes—linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11–13% of improvement).

Biometric Emotion Assessment and Feedback in an Immersive Digital Environment

Affective computing has increased its significance both in terms of academic and industry attention and investment. Alongside, immersive digital environments have settled as a reliable domain, with progressively inexpensive hardware solutions. Having this in mind, the authors envisioned the automatic real-time user emotion extraction through biometric readings in an immersive digital environment. In the running example, the environment consisted in an aeronautical simulation, and biometric readings were based mainly on galvanic skin response, respiration rate and amplitude, and phalanx temperature. The assessed emotional states were also used to modify some simulation context variables, such as flight path, weather conditions and maneuver smoothness level. The results were consistent with the emotional states as stated by the users, achieving a success rate of 77%, considering single emotions and 86% considering a quadrant-based analysis.

Improving a simulated soccer team's performance through a Memory-Based Collaborative Filtering approach

Collaborative filtering techniques have been used for some years, almost exclusively in Internet environments, helping users find items they are expected to like by using the users past purchases to provide such recommendations. With this concept in mind, this research uses a collaborative filtering technique to automatically improve the performance of a simulated soccer team. Many studies have attempted to address this problem over the last years but none has shown meaningful improvements in the performance of the soccer team. Using a collaborative filtering technique based on nearest neighbors and the FC Portugal team as the test subject (in the context of the RoboCup 2D Simulation League), several simulations were run for matches against different teams with much better, better and worse performance than FC Portugal. The strategy used by FC Portugal was to combine 8 set-plays and 2 team formations. The simulation results revealed an improvement in performance between 32% and 384%. In the future, there are plans to expand this approach to other contexts, such as the 3D Simulation League.

Using Multivariate Adaptive Regression Splines in the Construction of Simulated Soccer Team's Behavior Models

Abstract In soccer, like in other collective sports, although players try to hide their strategy, it is always possible, with a careful analysis, to detect it and to construct a model that characterizes their behavior throughout the game phases. These findings are extremely relevant for a soccer coach, in order not only to evaluate the performance of his athletes, but also for the construction of the opponent team model for the next match. During a soccer match, due to the presence of a complex set of intercorrelated variables, the detection of a small set of factors that directly influence the final result becomes almost an impossible task for a human being. In consequence of that, a huge number of software packages for analysis capable of calculating a vast set of game statistics appeared over the years. However, all of them need a soccer expert in order to interpret the produced data and select which are the most relevant variables. Having as a base a set of statistics extracted from the RoboCup 2D Sim...