Multi-Person Tracking By Multi-Scale Detection in Basketball Scenarios

Tracking data is a powerful tool for basketball teams in order to extract advanced semantic information and statistics that might lead to a performance boost. However, multi-person tracking is a challenging task to solve in single-camera video sequences, given the frequent occlusions and cluttering that occur in a restricted scenario. In this paper, a novel multi-scale detection method is presented, which is later used to extract geometric and content features, resulting in a multi-person video tracking system. Having built a dataset from scratch together with its ground truth (more than 10k bounding boxes), standard metrics are evaluated, obtaining notable results both in terms of detection (F1-score) and tracking (MOTA). The presented system could be used as a source of data gathering in order to extract useful statistics and semantic analyses a posteriori

Forecasting Forest Vulnerability to Drought in Pyrenean Silver Fir Forests Showing Dieback

Forest dieback is manifested as widespread loss of tree vigor, growth decline and high mortality rates. Forest dieback is becoming increasingly frequent and extended, particularly in drought-prone regions. This is the case of the south-western Spanish Pyrenees, where keystone species such as Silver fir reach their xeric and southern distribution limits. While dieback of this species has been widely documented in this area, we still lack methodologies to forecast the vulnerability of these forests in response to increasing drought stress so as to anticipate their potential dieback in the future. Here we study multiple features of Silver fir forests and trees to evaluate whether previous growth rates and their growth trends are valid predictors of forest dieback. Further, we validate our methodology revisiting two Silver fir sites sampled two decades ago. The defoliation degree was strongly related with radial growth, and growth trends differed between moderately to highly defoliated trees and non-defoliated trees. Forests showing dieback, i.e., those in which 25% of the sampled trees showed defoliation > 50%, were located at low elevation and received less rainfall in summer than forests showing no dieback. Trees showing high defoliation presented lower growth rates than non-defoliated trees. Moreover, we ratified that defoliation has increased considerably over the last two decades in one of the two revisited sites, but we were unable to accurately forecast growth trends in both sites, particularly in the site not showing dieback. The retrospective assessment of growth rates and trends offers valuable information on the vulnerability of Silver fir trees to drought. However, we are still far from being able to forecast the vulnerability of Silver fir forests to increasing drought. A systematic monitoring of growth across a wide tree-ring network of sites might provide valuable information to advance in this direction

V-tracer: a Vehicular Trace Generator for Future Predictive Maintenance

In this paper we present V-tracer, a vehicular trace generator aimed at generating realistic data about mobility of vehicles, as well as their daily operation and wear. The objectives of our approach are two: first, gathering real traces obtained by in-vehicle on-board units (OBUs), and second, as the first target is hard to achieve, generating synthetic data. The final goal will be getting all the information that would be very useful to infer and predict vehicle failures. The traces provided by our generator may be used to perform the predictive maintenance of vehicles in the near future

An interference-resilient IIoT solution for measuring the effectiveness of industrial processes

The development and deployment of the so-called Industrial Internet of Things (IIoT) have significantly increased the control and monitoring capabilities of companies, and thus their potential productivity. In this paper, we propose the use of Raspberry Pi devices in industrial environments to mea-sure productivity parameters. Our proposal can economically and efficiently gather data related with the availability and productivity of industrial machinery. However, since low-cost devices are prone to suffer the negative effects of electromagnetic interferences, we additionally propose an alternative to prevent signal alterations caused by them. More specifically, we propose a filtering mechanism called Smart Coded Filter (SCF), which eliminates wrong signals caused by electromagnetic interferences, and, therefore, highly improves the accuracy when estimating the availability metric. Results obtained demonstrate that our low-cost device provided with the SCF completely ignores 100% of wrong availability data, while reducing up to 70% the number of records stored into the database

Acciones de mejora de un Ingeniero de Mantenimiento en una empresa metalmecánica, en los ámbitos de la maquinaria de afilado, la gestión por procesos basado en la norma ISO 9001:2008 y el Lean Manufacturing

[ES] Descripción de las tareas realizadas por mí, divididas en tres partes atendiendo a la evolución de mi puesto de trabajo dentro de la empresa, desde el trabajo con maquinaria de afilado hasta el puesto de ingeniero de calidad[EN] Description of the tasks performed by me, divided in three parts in response of the evolution of my job inside the company, since working with grinding machinery to the quality engineer job.Sanguesa Blasco, M. (2012). Acciones de mejora de un Ingeniero de Mantenimiento en una empresa metalmecánica, en los ámbitos de la maquinaria de afilado, la gestión por procesos basado en la norma ISO 9001:2008 y el Lean Manufacturing. http://hdl.handle.net/10251/27626Archivo delegad

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient (C-d) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead

Improving Roadside Unit deployment in vehicular networks by exploiting genetic algorithms

Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts

Mitigating Electromagnetic Noise When Using Low-Cost Devices in Industry 4.0

Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE

MoBiSea: a binary search algorithm for product clustering in Industry 4.0

Proprietary systems used to modernize Industry 4.0 usually involve high financial costs. Consequently, using low-cost devices with the same functionalities, capable of replacing these proprietary systems but at a lower cost, has become an incipient trend. However, these low-cost devices usually come with electromagnetic interference problems as they are encapsulated in electrical panels, sitting alongside electromechanical devices. In this article, we present Mode Binary Search, an algorithm specifically designed for use in a low-cost automated-industrial-productivity-data-collection system. Specifically, productivity data are obtained from the availability and sealing signals of the thermoplastic sealing machines in production lines belonging to the agri-food industry. Mode Binary Search was designed to cluster sealing signals, thus enabling us to identify which products have been made. Furthermore, the algorithm determines when the manufacturing of each product starts and ends, in other words, the exact moment a product change occurs and all this without the need for operator supervision or intervention. Finally, we compared our algorithm, based on binary search, with three clustering mechanisms: k-means, k-rms and x-means. Out of all the cases we analyzed, the maximum error committed by Mode Binary Search is limited to 2.69%, thereby outperforming all others

On the Study of Vehicle Density in Intelligent Transportation Systems

Vehicular ad hoc networks (VANETs) are wireless communication networks which support cooperative driving among vehicles on the road. The specific characteristics of VANETs favor the development of attractive and challenging services and applications which rely on message exchanging among vehicles. These communication capabilities depend directly on the existence of nearby vehicles able to exchange information. Therefore, higher vehicle densities favor the communication among vehicles. However, vehicular communications are also strongly affected by the topology of the map (i.e., wireless signal could be attenuated due to the distance between the sender and receiver, and obstacles usually block signal transmission). In this paper, we study the influence of the roadmap topology and the number of vehicles when accounting for the vehicular communications capabilities, especially in urban scenarios. Additionally, we consider the use of two parameters: the SJ Ratio (SJR) and the Total Distance (TD), as the topology-related factors that better correlate with communications performance. Finally, we propose the use of a new density metric based on the number of vehicles, the complexity of the roadmap, and its maximum capacity. Hence, researchers will be able to accurately characterize the different urban scenarios and better validate their proposals related to cooperative Intelligent Transportation Systems based on vehicular communications