Browsing by Keyword "Software"
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Item 2nd International Workshop on Knowledge-Oriented Product Line Engineering(2011) Hamza, Haitham S.; Martinez, Jabier; Rummler, Andreas; SWTSoftware Product Line Engineering (PLE) exploits systematic reuse by identifying and methodically reusing software artifacts to develop different but related software systems. Developing Product Lines requires analysis skills to identify, model, and encode domain and product knowledge into artifacts that can be systematically reused across the development life-cycle. As such, Knowledge plays a paramount role in the success of the various activities of PLE. The objective of KOPLE is to bring together SPL researchers and practitioners from academia and industry to investigate the role of Knowledge in PLE. Knowledge is usually encapsulated in PL architectures in a tacit or implicit way, and this may appear to be sufficient for industry to implement successful product lines. Nevertheless, KOPLE also aims to become a discussion forum about techniques and methods to convert from tacit to explicit Knowledge in PLE and to process and use this Knowledge for optimizing and innovating PLE processes.Item 3D Active Surfaces for Liver Segmentation in Multisequence MRI Images(2016-08-01) Bereciartua, Arantza; Picon, Artzai; Galdran, Adrian; Iriondo, Pedro M.; COMPUTER_VISION; Tecnalia Research & InnovationBiopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59.Item 5G MEC deployments: Low-latency services context synchronization in mobility(VDE Verlag GmbH, 2022) Regueiro, Cristina; Gutierrez-Agüero, Iván; Lage, Oscar; Zhang, Yu-Dong; CIBERSEC&DLT5G Multi Access Edge Computing (MEC) is a rising technology aimed to reduce latency by bringing services close to the users. However, current MEC based services coverage is geographically limited and will often require of MEC data centers synchronization to avoid operation handovers when moving to a different coverage area; this is especially relevant in mobility. This paper focuses on this situation, identifying the benefits and limitations of the current MEC-based deployments to maintain the service continuity. Additionally, it proposes a theoretical method for data synchronization in future 5G MEC-based deployments to allow applications context reallocation between several MEC data centers achieving to properly offer different services in mobility. This new method is generic and can be applied to any sector (industry, health, automation, etc.).Item Above 40g acceleration for pick-and-place with a new 2-dof PKM(2009) Pierrot, F.; Baradat, C.; Nabat, V.; Company, O.; Krut, S.; Gouttefarde, M.; Tecnalia Research & InnovationThis paper introduces a new two-degree-offreedom parallel manipulator producing two translations in the vertical plane. One drawback of existing robots built to realize those dof is their lack of rigidity along the transversal axis, another one being their limited ability to provide very high acceleration. Indeed, these architectures cannot be lightweight and stiff at the same time. The proposed architecture is a spatial mechanism which guarantees a good stiffness along the transversal axis. This parallel architecture is composed by two actuated kinematic chains, and two passive chains built in the transversal plane. The key feature of this robot comes from the passive chains which are coupled for creating a kinematic constraint: the platform stays in one plane. A stiffness analysis shows that the robot can be lighter and stiffer than a classical 2 dof mechanism. A prototype of this robot is presented and preliminary tests show that accelerations above 40 g can beachieved while keeping a low trackingerror.Item Acapulco: An extensible tool for identifying optimal and consistent feature model configurations(Association for Computing Machinery, Inc, 2022-09-12) Martinez, Jabier; Strüber, Daniel; Horcas, Jose Miguel; Burdusel, Alexandru; Zschaler, Steffen; Felfernig, Alexander; Fuentes, Lidia; Cleland-Huang, Jane; Assuncao, Wesley K.G.; Assuncao, Wesley K.G.; Quinton, Clement; Guo, Jianmei; Schmid, Klaus; Huchard, Marianne; Ayala, Inmaculada; Rojas, Jose Miguel; Le, Viet-Man; Horcas, Jose Miguel; SWTConfiguring feature-oriented variability-rich systems is complex because of the large number of features and, potentially, the lack of visibility of the implications on quality attributes when selecting certain features. We present Acapulco as an alternative to the existing tools for automating the configuration process with a focus on mono- and multi-criteria optimization. The soundness of the tool has been proven in a previous publication comparing it to SATIBEA and MODAGAME. The main advantage was obtained through consistency-preserving configuration operators (CPCOs) that guarantee the validity of the configurations during the IBEA genetic algorithm evolution process. We present a new version of Acapulco built on top of FeatureIDE, extensible through the easy integration of objective functions, providing pre-defined reusable objectives, and being able to handle complex feature model constraints.Item Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques(2023-03) Fister, D.; Pérez-Aracil, J.; Peláez-Rodríguez, C.; Del Ser, J.; Salcedo-Sanz, S.; IAIn this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature in the first and second August fortnights, using input data from previous months, at two different locations (Paris, France) and (Córdoba, Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the heatwave of 2003, which affected France and the Iberian Peninsula. Three different computational frameworks for air temperature prediction are proposed: a Convolutional Neural Network (CNN), with video-to-image translation, several ML approaches including Lasso regression, Decision Trees and Random Forest, and finally a CNN with pre-processing step using Recurrence Plots, which convert time series into images. Using these frameworks, a very good prediction skill has been obtained in both Paris and Córdoba regions, showing that the proposed approaches can be an excellent option for seasonal climate prediction problems.Item Acquisition of human traces with Bluetooth technology: Challenges and proposals(2014-01) Cabero, José María; Molina, Virginia; Urteaga, Iñigo; Liberal, Fidel; Martín, José Luis; Tecnalia Research & InnovationThis paper highlights the challenges to be taken into consideration when Bluetooth is used as a radio technology to capture proximity traces between people. Our study analyzes the limitations of Bluetooth-based trace acquisition initiatives carried out until now in terms of granularity and reliability. We then propose an optimal configuration for the acquisition of proximity traces and movement information using a fine-tuned Bluetooth system based on custom hardware. With this system and based on such a configuration, we have carried out an intensive human trace acquisition experiment resulting in a proximity and mobility database of more than 5 million traces with a minimum granularity of 5 s.Item ACSmI: a solution to address the challenges of cloud services federation and monitoring towards the cloud continuum(2023) Alonso, Juncal; Huarte, Maider; Arrieta, Leire Orue Echevarria; HPA; Tecnalia Research & InnovationThe evolution of cloud computing has changed the way in which cloud service providers offer their services and how cloud customers consume them, moving towards the use of multiple cloud services, in what it is called multi-cloud. Multi-cloud is gaining interest by the expansion of IoT, edge and the cloud continuum where distributed cloud federation models are necessary for effective application deployment and operation. This work presents advanced cloud service meta-intermediator (ACSmI), a solution that implements a cloud federation, supporting the seamless brokerage of cloud services. Technical details addressing the discovered shortcomings are presented, including a proof of concept built on JHipster, Java, InfluxDB, Telegraf and Grafana. ACSmI contributes to relevant elements of the European Gaia-X initiative, specifically to the federated catalogue, continuous monitoring, and certification of services. The experiments show that proposed solution effectively saves up to 75% of the DevOps teams’ effort to discover, contract and monitor cloud services.Item An active adaptation strategy for streaming time series classification based on elastic similarity measures(2022-08) Oregi, Izaskun; Pérez, Aritz; Del Ser, Javier; Lozano, Jose A.; Quantum; IAIn streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.Item Adaptive Multifactorial Evolutionary Optimization for Multitask Reinforcement Learning(2022-04-01) Martinez, Aritz D.; Del Ser, Javier; Osaba, Eneko; Herrera, Francisco; IA; QuantumEvolutionary computation has largely exhibited its potential to complement conventional learning algorithms in a variety of machine learning tasks, especially those related to unsupervised (clustering) and supervised learning. It has not been until lately when the computational efficiency of evolutionary solvers has been put in prospective for training reinforcement learning models. However, most studies framed so far within this context have considered environments and tasks conceived in isolation, without any exchange of knowledge among related tasks. In this manuscript we present A-MFEA-RL, an adaptive version of the well-known MFEA algorithm whose search and inheritance operators are tailored for multitask reinforcement learning environments. Specifically, our approach includes crossover and inheritance mechanisms for refining the exchange of genetic material, which rely on the multilayered structure of modern deep-learning-based reinforcement learning models. In order to assess the performance of the proposed approach, we design an extensive experimental setup comprising multiple reinforcement learning environments of varying levels of complexity, over which the performance of A-MFEA-RL is compared to that furnished by alternative nonevolutionary multitask reinforcement learning approaches. As concluded from the discussion of the obtained results, A-MFEA-RL not only achieves competitive success rates over the simultaneously addressed tasks, but also fosters the exchange of knowledge among tasks that could be intuitively expected to keep a degree of synergistic relationship.Item Adopting SOA tutorial(2008) Santos, Igor; Bastida, Leire; Schuster, Stefan; ADV_INTER_PLAT; Digital BaseThis half-day tutorial provides an unbiased, comprehensive and vendor independent overview of the potential benefits and risks implied when embarking on a SOA strategy. Course participants will gain knowledge and a clear understanding of the concept of Service-oriented Architectures (SOA), covering the entire SOA lifecycle, from conception through planning, development and deployment to maintenance.Item Affect-aware behaviour modelling and control inside an intelligent environment(2010-10) Leon, Enrique; Clarke, Graham; Callaghan, Victor; Doctor, Faiyaz; Tecnalia Research & InnovationThe evidence suggests that human actions are supported by emotional elements that complement logic inference in our decision-making processes. In this paper an exploratory study is presented providing initial evidence of the positive effects of emotional information on the ability of intelligent agents to create better models of user actions inside smart-homes. Preliminary results suggest that an agent incorporating valence-based emotional data into its input array can model user behaviour in a more accurate way than agents using no emotion-based data or raw data based on physiological changes.Item Affective brain-computer interfaces: Psychophysiological markers of emotion in healthy persons and in persons with amyotrophic lateral sclerosis(2009) Nijboer, Femke; Carmien, Stefan P.; Leon, Enrique; Morin, Fabrice O.; Koene, Randal A.; Hoffmann, Ulrich; Tecnalia Research & InnovationAffective Brain-Computer Interfaces (BCI) are systems that measure signals from the peripheral and central nervous system, extract features related to affective states of the user, and use these features to adapt human-computer interaction (HCI). Affective BCIs provide new perspectives on the applicability of BCIs. Affective BCIs may serve as assessment tools and adaptive systems for HCI for the general population and may prove to be especially interesting for people with severe motor impairment. In this context, affective BCIs will enable simultaneous expression of affect and content, thus providing more quality of life for the patient and the caregiver. In the present paper, we will present psychophysiological markers for affective BCIs, and discuss their usability in the day to day life of patients with amyotrophic lateral sclerosis (ALS).Item Aggregate Farming in the Cloud: The AFarCloud ECSEL project: The AFarCloud ECSEL project(2020-10) Castillejo, Pedro; Johansen, Gorm; Cürüklü, Baran; Bilbao-Arechabala, Sonia; Fresco, Roberto; Martinez-Rodriguez, Belen; Pomante, Luigi; Rusu, Cristina; Martínez-Ortega, José-Fernán; Centofanti, Carlo; Hakojärvi, Mikko; Santic, Marco; Häggman, Johanna; Tecnalia Research & Innovation; BIGDATAFarming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.Item An agile manufacturing system for large workspace applications(2016-07-01) Yang, Hai; Baradat, Cédric; Krut, Sébastien; Pierrot, François; MEDIOS DE PRODUCCIÓN Y ROBOTICA; Tecnalia Research & InnovationREMORA aims at offering an agile robotic solution for manufacturing tasks done on very large parts (e.g., very long and slender parts found in aeronautic industries). For such tasks, classical machine tools are designed at several tens of meters. Both their construction and operation require huge infrastructure supports. REMORA is a novel lightweight concept and flexible robotic solution that combines the ability of walking and manufacturing. The robot is a mobile manufacturing system which can effectuate operations with good payload capacity and good precisions for large workspace applications. This new concept combines parallel kinematics to ensure high stiffness but low inertia and mobile robotics to operate in very large workspaces. This results in a machining center of new generation: (1) agile manufacturing system for large workspace applications, (2) heavy load and good precisions, (3) 5-axis machining and 5-axis locomotion/clamping, (4) self-reconfigurable for specific tasks (workspace and force), and (5) flexible and multifunctional.Item AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus(2022-02-24) Zhou, Xi; Ye, Qinghao; Yang, Xiaolin; Chen, Jiakun; Ma, Haiqin; Xia, Jun; Del Ser, Javier; Yang, Guang; IABased on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient’s ventricles.Item Am IE towards ambient intelligence for the ageing citizens(2009) Kantorovitch, Julia; Kaartinen, Jouni; Abril, Luis Collantes; De las Heras Martín, Ricardo; Cantera, José Alberto Martínez; Criel, Johan; Gielen, Marcel; Medical TechnologiesThis research presents a system, currently under development, which aims at providing an intelligent ambient able to improve the quality of life and delivering customized support to elderly people in need of assistance, according to their own specific situation, and in a non-intrusive and respectful way.Item Analysing encryption mechanisms and functional safety in a ROS-based architecture(2020-02-01) Larrucea, Xabier; González-Nalda, Pablo; Etxeberria-Agiriano, Ismael; Otero, Mari Carmen; Tecnalia Research & InnovationRobot Operating System (ROS) is a middleware for connecting different components of robots. However, its use is becoming more popular in other domains such as in the automotive sector where initial prototypes have been customized and deployed in cars for demonstrating different functional purposes. Nevertheless, ROS has not been yet tested enough to be used in secure and safety environments. For example, in order to strengthen our ROS architecture, we have encrypted the messages within it. Therefore, this paper analyses the impact of Advanced Encryption Standard (AES) encryption mechanism and the functional safety of our prototype. In this sense, we are considering encrypting messages and we assess the timing constraints, as suggested by the ISO 26262, required for assuring a secure communication between components.Item AnHitz, development and integration of language, speech and visual technologies for Basque(2008) Arrieta, Kutz; Leturia, Igor; Iturraspe, Urtza; De Ilarraza, Arantza Diaz; Sarasola, Kepa; Hernáez, Inma; Navas, Eva; HPAAnHitz is a project promoted by the Basque Government to develop language technologies for the Basque language. The participants in AnHitz are research groups with very different backgrounds: text processing, speech processing and multimedia. The project aims to further develop existing language, speech and visual technologies for Basque: up to now its fruit is a set of 7 different language resources, 9 NLP tools, and 5 applications.. But also, in the last year of this project we are integrating, for the first time, such resources and tools (both existing and generated in the project) into a content management application for Basque with a natural language communication interface. This application consists of a Question Answering and a Cross Lingual Information Retrieval system on the area of Science and Technology. The interaction between the system and the user will be in Basque (the results of the CLIR module that are not in Basque will be translated through Machine Translation) using Speech Synthesis, Automatic Speech Recognition and a Visual Interface. The various resources, technologies and tools that we are developing are already in a very advanced stage, and the implementation of the content management application to integrate them all is in work and is due to be completed by October 2008.Item The AQUAS ECSEL Project Aggregated Quality Assurance for Systems: Co-Engineering Inside and Across the Product Life Cycle: Co-Engineering Inside and Across the Product Life Cycle(2019-09) Pomante, Luigi; Muttillo, Vittoriano; Křena, Bohuslav; Vojnar, Tomáš; Veljković, Filip; Magnin, Pacôme; Matschnig, Martin; Fischer, Bernhard; Martinez, Jabier; Gruber, Thomas; SWTThere is an ever-increasing complexity of the systems we engineer in modern society, which includes facing the convergence of the embedded world and the open world. This complexity creates increasing difficulty with providing assurance for factors including safety, security and performance. In such a context, the AQUAS project investigates the challenges arising from e.g., the inter-dependence of safety, security and performance of systems and aims at efficient solutions for the entire product life-cycle. The project builds on knowledge of partners gained in current or former EU projects and will demonstrate the newly developed methods and techniques for co-engineering across use cases spanning Aerospace, Medicine, Transport and Industrial Control.