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Author | Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
Title | Frequency-based Enhancement Network for Efficient Super-Resolution | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 10 | Issue | Pages | 57383-57397 | |
Keywords | Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution | ||||
Abstract | Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet | ||||
Address | 18 May 2022 | ||||
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Publisher | IEEE | Place of Publication | Editor | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ BRF2022a | Serial | 3747 | ||
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Author | Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach | ||||
Title | First in Vivo Computer-Aided Diagnosis of Colorectal Polyps using White Light Endoscopy | Type | Journal Article | ||
Year | 2022 | Publication | Endoscopy | Abbreviated Journal | END |
Volume | 54 | Issue | Pages | ||
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Address | 2022/04/14 | ||||
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Publisher | Georg Thieme Verlag KG | Place of Publication | Editor | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ GTC2022a | Serial | 3746 | ||
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Author | Y. Mori; M.Misawa; Jorge Bernal; M. Bretthauer; S.Kudo; A. Rastogi; Gloria Fernandez Esparrach | ||||
Title | Artificial Intelligence for Disease Diagnosis-the Gold Standard Challenge | Type | Journal Article | ||
Year | 2022 | Publication | Gastrointestinal Endoscopy | Abbreviated Journal | |
Volume | 96 | Issue | 2 | Pages | 370-372 |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ MMB2022 | Serial | 3701 | ||
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Author | Parichehr Behjati Ardakani | ||||
Title | Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution | Type | Book Whole | ||
Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality. | ||||
Address | April, 2022 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca;Pau Rodriguez | ||
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ISSN | ISBN | 978-84-124793-1-7 | Medium | ||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Beh2022 | Serial | 3713 | ||
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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 | ||||
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Notes | IAM; ADAS; 600.139; 600.145; 600.118 | Approved | no | ||
Call Number | Admin @ si @ HYF2022 | Serial | 3720 | ||
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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 |
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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 | ||||
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Notes | IAM; 601.139; 600.145; 600.096 | Approved | no | ||
Call Number | Admin @ si @ SBG2022 | Serial | 3719 | ||
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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 | ||||
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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 | Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate | ||||
Title | Weather Classification by Utilizing Synthetic Data | Type | Journal Article | ||
Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 22 | Issue | 9 | Pages | 3193 |
Keywords | Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems | ||||
Abstract | Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets. | ||||
Address | 21 April 2022 | ||||
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Publisher | MDPI | Place of Publication | Editor | ||
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Notes | IAM; 600.139; 600.159; 600.166; 600.145; | Approved | no | ||
Call Number | Admin @ si @ MKE2022 | Serial | 3761 | ||
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Author | Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier | ||||
Title | Effects of Non-Driving Related Tasks during Self-Driving mode | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 23 | Issue | 2 | Pages | 1391-1399 |
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Abstract | Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study. | ||||
Address | Feb. 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ MHE2022 | Serial | 3468 | ||
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Author | Guillermo Torres; Sonia Baeza; Carles Sanchez; Ignasi Guasch; Antoni Rosell; Debora Gil | ||||
Title | An Intelligent Radiomic Approach for Lung Cancer Screening | Type | Journal Article | ||
Year | 2022 | Publication | Applied Sciences | Abbreviated Journal | APPLSCI |
Volume | 12 | Issue | 3 | Pages | 1568 |
Keywords | Lung cancer; Early diagnosis; Screening; Neural networks; Image embedding; Architecture optimization | ||||
Abstract | The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed
forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection. |
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Address | Jan 2022 | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ TBS2022 | Serial | 3699 | ||
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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 | |
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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 | Miquel Angel Piera; Jose Luis Muñoz; Debora Gil; Gonzalo Martin; Jordi Manzano | ||||
Title | A Socio-Technical Simulation Model for the Design of the Future Single Pilot Cockpit: An Opportunity to Improve Pilot Performance | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 10 | Issue | Pages | 22330-22343 | |
Keywords | Human factors ; Performance evaluation ; Simulation; Sociotechnical systems ; System performance | ||||
Abstract | The future deployment of single pilot operations must be supported by new cockpit computer services. Such services require an adaptive context-aware integration of technical functionalities with the concurrent tasks that a pilot must deal with. Advanced artificial intelligence supporting services and improved communication capabilities are the key enabling technologies that will render future cockpits more integrated with the present digitalized air traffic management system. However, an issue in the integration of such technologies is the lack of socio-technical analysis in the design of these teaming mechanisms. A key factor in determining how and when a service support should be provided is the dynamic evolution of pilot workload. This paper investigates how the socio-technical model-based systems engineering approach paves the way for the design of a digital assistant framework by formalizing this workload. The model was validated in an Airbus A-320 cockpit simulator, and the results confirmed the degraded pilot behavioral model and the performance impact according to different contextual flight deck information. This study contributes to practical knowledge for designing human-machine task-sharing systems. | ||||
Address | Feb 2022 | ||||
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Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ PMG2022 | Serial | 3697 | ||
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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 |
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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. |
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Address | 5 dec 2022 | ||||
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Publisher | Springer | Place of Publication | Editor | ||
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ BGG2022 | Serial | 3759 | ||
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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 | |
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Call Number | Admin @ si @ BSC2022 | Serial | 3832 | ||
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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 |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ RBG2022b | Serial | 3834 | ||
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