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Author Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera edit  doi
openurl 
  Title E-Pilots: A System to Predict Hard Landing During the Approach Phase of Commercial Flights Type Journal Article
  Year 2022 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 10 Issue (down) Pages 7489-7503  
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  Abstract More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event.
This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.		  
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  Notes IAM; 600.139; 600.118; 600.145 Approved no  
  Call Number Admin @ si @ GHE2022 Serial 3721  
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Author David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina edit  doi
openurl 
  Title A Survey of FPGA-Based Vision Systems for Autonomous Cars Type Journal Article
  Year 2022 Publication IEEE Access Abbreviated Journal ACESS  
  Volume 10 Issue (down) Pages 132525-132563  
  Keywords Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures  
  Abstract On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.  
  Address 16 December 2022  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Editor  
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  Notes IAM; 600.166 Approved no  
  Call Number Admin @ si @ CNB2022 Serial 3760  
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Author Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez edit  openurl
  Title Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project Type Journal Article
  Year 2023 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS  
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  Notes IAM Approved no  
  Call Number Admin @ si @ TGM2023 Serial 3830  
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