|
Records |
Links |
|
Author |
Oriol Ramos Terrades; Albert Berenguel; Debora Gil |
|
|
Title |
A Flexible Outlier Detector Based on a Topology Given by Graph Communities |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Big Data Research |
Abbreviated Journal |
BDR |
|
|
Volume |
29 |
Issue |
|
Pages |
100332 |
|
|
Keywords |
Classification algorithms; Detection algorithms; Description of feature space local structure; Graph communities; Machine learning algorithms; Outlier detectors |
|
|
Abstract |
Outlier detection is essential for optimal performance of machine learning methods and statistical predictive models. Their detection is especially determinant in small sample size unbalanced problems, since in such settings outliers become highly influential and significantly bias models. This particular experimental settings are usual in medical applications, like diagnosis of rare pathologies, outcome of experimental personalized treatments or pandemic emergencies. In contrast to population-based methods, neighborhood based local approaches compute an outlier score from the neighbors of each sample, are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. A main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters, like the number of neighbors.
This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world and synthetic data sets show that our approach outperforms, both, local and global strategies in multi and single view settings. |
|
|
Address |
August 28, 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
DAG; IAM; 600.140; 600.121; 600.139; 600.145; 600.159 |
Approved |
no |
|
|
Call Number |
Admin @ si @ RBG2022a |
Serial |
3718 |
|
Permanent link to this record |
|
|
|
|
Author |
Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; B. Cardenas; G. Fonseka; E. Anton; Alvaro Pascual; Richard Frodsham; Zaida Sarrate |
|
|
Title |
Time to match; when do homologous chromosomes become closer? |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Chromosoma |
Abbreviated Journal |
CHRO |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
In most eukaryotes, pairing of homologous chromosomes is an essential feature of meiosis that ensures homologous recombination and segregation. However, when the pairing process begins, it is still under investigation. Contrasting data exists in Mus musculus, since both leptotene DSB-dependent and preleptotene DSB-independent mechanisms have been described. To unravel this contention, we examined homologous pairing in pre-meiotic and meiotic Mus musculus cells using a threedimensional fuorescence in situ hybridization-based protocol, which enables the analysis of the entire karyotype using DNA painting probes. Our data establishes in an unambiguously manner that 73.83% of homologous chromosomes are already paired at premeiotic stages (spermatogonia-early preleptotene spermatocytes). The percentage of paired homologous chromosomes increases to 84.60% at mid-preleptotene-zygotene stage, reaching 100% at pachytene stage. Importantly, our results demonstrate a high percentage of homologous pairing observed before the onset of meiosis; this pairing does not occur randomly, as the percentage was higher than that observed in somatic cells (19.47%) and between nonhomologous chromosomes (41.1%). Finally, we have also observed that premeiotic homologous pairing is asynchronous and independent of the chromosome size, GC content, or presence of NOR regions. |
|
|
Address |
August, 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 601.139; 600.145; 600.096 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SBG2022 |
Serial |
3719 |
|
Permanent link to this record |
|
|
|
|
Author |
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Miquel Angel Piera; Debora Gil |
|
|
Title |
Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Applied Sciences |
Abbreviated Journal |
APPLSCI |
|
|
Volume |
12 |
Issue |
5 |
Pages |
2298 |
|
|
Keywords |
Cognitive states; Mental workload; EEG analysis; Neural networks; Multimodal data fusion |
|
|
Abstract |
The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment. |
|
|
Address |
February 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; ADAS; 600.139; 600.145; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ HYF2022 |
Serial |
3720 |
|
Permanent link to this record |
|
|
|
|
Author |
Debora Gil; Aura Hernandez-Sabate; Julien Enconniere; Saryani Asmayawati; Pau Folch; Juan Borrego-Carazo; Miquel Angel Piera |
|
|
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 |
|
Pages |
7489-7503 |
|
|
Keywords |
|
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 600.139; 600.118; 600.145 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GHE2022 |
Serial |
3721 |
|
Permanent link to this record |
|
|
|
|
Author |
Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil |
|
|
Title |
EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment |
Type |
Journal Article |
|
Year |
2024 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
|
Volume |
24 |
Issue |
4 |
Pages |
1174 |
|
|
Keywords |
|
|
|
Abstract |
High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ HYF2024 |
Serial |
4019 |
|
Permanent link to this record |
|
|
|
|
Author |
Sonia Baeza; Debora Gil; I.Garcia Olive; M.Salcedo; J.Deportos; Carles Sanchez; Guillermo Torres; G.Moragas; Antoni Rosell |
|
|
Title |
A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients |
Type |
Journal Article |
|
Year |
2022 |
Publication |
EJNMMI Physics |
Abbreviated Journal |
EJNMMI-PHYS |
|
|
Volume |
9 |
Issue |
1, Article 84 |
Pages |
1-17 |
|
|
Keywords |
|
|
|
Abstract |
Background: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classifcation neural network that optimizes a weighted crossentropy loss trained to discriminate between three diferent types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using diferent confguration of parameters were tested.
Results: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining diferent types of image patterns with PE presented a sensitivity, specifcity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting
pneumonia presented a sensitivity, specifcity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.
Conclusion: This radiomic diagnostic system was able to identify the diferent lung imaging patterns and is a frst step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. |
|
|
Address |
5 dec 2022 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGG2022 |
Serial |
3759 |
|
Permanent link to this record |
|
|
|
|
Author |
David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina |
|
|
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 |
|
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 |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM; 600.166 |
Approved |
no |
|
|
Call Number |
Admin @ si @ CNB2022 |
Serial |
3760 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
|
|
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 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ TGM2023 |
Serial |
3830 |
|
Permanent link to this record |
|
|
|
|
Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
|
|
Title |
A benchmark for the evaluation of computational methods for bronchoscopic navigation |
Type |
Journal Article |
|
Year |
2022 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
|
|
Volume |
17 |
Issue |
1 |
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ BSC2022 |
Serial |
3832 |
|
Permanent link to this record |
|
|
|
|
Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
|
|
Title |
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results |
Type |
Journal Article |
|
Year |
2022 |
Publication |
Journal of Thoracic Oncology |
Abbreviated Journal |
JTO |
|
|
Volume |
17 |
Issue |
9 |
Pages |
S182 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ RBG2022b |
Serial |
3834 |
|
Permanent link to this record |