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A Vision Transformer Approach to Fundus Image Classification 12th

A Vision Transformer Approach to Fundus Image Classification

引用

12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Leite, Danilo Camara, José Rodrigues, João Cunha, António University of Trás-os-Montes and Alto Douro Vila Real Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal LARSyS & ISE Universidade do Algarve Faro8005-226 Portugal

ISBN: (纸本)9783031606649

Glaucoma is a condition that affects the optic nerve, with loss of retinal nerve fibers, increased excavation of the optic nerve, and a progressive decrease in the visual field. It is the leading cause of irreversible blindness in the world. Manual classification of glaucoma is a complex and time-consuming process that requires assessing a variety of ocular features by experienced clinicians. Automated detection can assist the specialist in early diagnosis and effective treatment of glaucoma and prevent vision loss. This study developed a deep learning model based on vision transformers, called ViT-BRSET, to detect patients with increased excavation of the optic nerve automatically. ViT-BRSET is a neural network architecture that is particularly effective for computer vision tasks. The results of this study were promising, with an accuracy of 0.94, an F1-score of 0.91, and a recall of 0.94. The model was trained on a new dataset called BRSET, which consists of 16,112 fundus images of patients with increased excavation of the optic nerve. The results of this study suggest that ViT-BRSET has the potential to improve early diagnosis through early detection of optic nerve excavation, one of the main signs of glaucomatous disease. ViT-BRSET can be used to mass-screen patients, identifying those who need further examination by a doctor. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Excavation

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Autonomous UAV Landing Approach for Marine Operations

Autonomous UAV Landing Approach for Marine Operations

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作者： André Moura José Antunes João J. Martins André Dias Alfredo Martins José M. Almeida Eduardo Silva Robotics and Autonomous Systems Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal

The use of autonomous vehicles in maritime operations is a technological challenge. In the particular case of autonomous aerial vehicles (UAVs), their application ranges from inspection and surveillance of offshore power plants, and marine life observation, to search and rescue missions. Manually landing UAVs onboard water vessels can be very challenging due to limited space onboard and wave agitation. This paper proposes an autonomous solution for the task of landing commercial multicopter UAVs with onboard cameras on water vessels, based on the detection of a custom landing platform with computer vision techniques. The autonomous landing behavior was tested in real conditions, using a research vessel at sea, where the UAV was able to detect, locate, and safely land on top of the developed landing platform.

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Line Fitting-Based Corner-Like Detector for 2D Laser Scanners Data∗ 10

Line Fitting-Based Corner-Like Detector for 2D Laser Scanner...

引用

10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Sousa, Ricardo B. Sobreira, Heber Miguel Silva, Manuel F. Moreira, Antonio Paulo University of Porto Faculty of Engineering Porto4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal ISEP Polytechnic of Porto R. Dr. António Bernardinode Almeida Porto4249-015 Portugal

ISBN: (纸本)9798350394245

The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates. © 2024 IEEE.

关键词： Feature extraction

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Evaluation of the Economic Feasibility of Price Arbitrage Operations in the Iberian Electricity Market

Evaluation of the Economic Feasibility of Price Arbitrage Op...

引用

International Conference on European Electricity Market, EEM

作者： Francisco Lobo João Tomé Saraiva FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

This paper describes a study developed to analyse the interest in investing in Li-ion batteries to perform price arbitrage in the power system of Portugal. In this context, it was developed a methodology to identify the most suitable hours for charging and discharging the energy, and the new market prices were estimated for these hours. It was concluded that at current investment costs in this storage technology, and current market prices, this investment would not be viable in the lifetime of the batteries despite the recent rise of electricity market prices and also the larger price spread. This spread is now larger given the depression of prices at sunny hours that is getting typical in the Iberian electricity market.

关键词： Lithium-ion batteries Visualization Costs Europe Aging Electricity supply industry Depression

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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Roadmap for Implementing Business Intelligence systems in Higher Education Institutions: Exploratory Work 20

Roadmap for Implementing Business Intelligence Systems in Hi...

引用

20th International Conference on Smart Business technologies, ICSBT 2023

作者： Sequeira, Nuno Reis, Arsénio Branco, Frederico Alves, Paulo School of Science and Technology University of Trás-os-Montes and Alto Douro Quinta Dos Prados Vila Real Portugal Instituto Politécnico de Bragança Bragança Portugal INESC TEC Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9789897586675

Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms. © 2023 ICSBT International Conference on Smart Business technologies. All rights reserved.

关键词： Decision making

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lucas Ribeiro Hélder P. Oliveira Xiao Hu Tania Pereira INESC TEC INESC TEC - Institute for Systems and Computer Engineering Technology and Science FEUP - Faculty of Engineering University of Porto Porto Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP - Faculty of Science University of Porto Porto Portugal Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Nell Hodgson Woodruff School of Nursing Emory University Atlanta USA INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.

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Smart Contracts for the CloudAnchor Platform 20th

Smart Contracts for the CloudAnchor Platform

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20th International Conference on Practical Applications of Agents and Multi-Agent systems , PAAMS 2022

作者： Vasco, Eduardo Veloso, Bruno Malheiro, Benedita ISEP/IPP – School of Engineering Polytechnic Institute of Porto Porto Portugal UPT – Universidade Portucalense Porto Portugal INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9783031181917

CloudAnchor is a multi-agent brokerage platform for the negotiation of Infrastructure as a Service cloud resources between Small and Medium Sized Enterprises, acting either as providers or consumers. This project entails the research, design, and implementation of a smart contract solution to permanently record and manage contractual and behavioural stakeholder data on a blockchain network. Smart contracts enable safe contract code execution, increasing trust between parties and ensuring the integrity and traceability of the chained contents. The defined smart contracts represent the inter-business trustworthiness and Service Level Agreements established within the platform. CloudAnchor interacts with the blockchain network through a dedicated Application Programming Interface, which coordinates and optimises the submission of transactions. The performed tests indicate the success of this integration: (i) the number and value of negotiated resources remain identical;and (ii) the run-time increases due to the inherent latency of the blockchain operation. Nonetheless, the introduced latency does not affect the brokerage performance, proving to be an appropriate solution for reliable partner selection and contractual enforcement between untrusted parties. This novel approach stores all brokerage strategic knowledge in a distributed, decentralised, and immutable database. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Novel Approach for Offshore Photovoltaic Panels Inspection with VTOL UAV

Novel Approach for Offshore Photovoltaic Panels Inspection w...

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OCEANS

作者： R. Morais João J. Martins P. Lima A. Dias A. Martins J. Almeida E. Silva Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Instituto Superior de Engenharia do Porto (ISEP) Porto Portugal

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Solar energy will contribute to global economic growth, increasing worldwide photovoltaic (PV) solar energy production. More recently, one of the outstanding energy achievements of the last decade has been the development of floating photovoltaic panels. These panels differ from conventional (ter-restrial) panels because they occupy space in a more environmen-tally friendly way, i.e., aquatic areas. In contrast, land areas are saved for other applications, such as construction or agriculture. Developing autonomous inspection systems using unmanned aerial vehicles (UAV s) represents a significant step forward in solar PV technology. Given the frequently remote and difficult-to-access locations, traditional inspection methods are no longer practical or suitable. Responding to these challenges, an in-novative inspection framework was developed to autonomously inspect photovoltaic plants (offshore) with a Vertical Takeoff and Landing (VTOL) UAV. This work explores two different methods of autonomous aerial inspection, each adapted to specific scenarios, thus increasing the adaptability of the inspection process. During the flight, the aerial images are evaluated in real-time for the autonomous detection of the photovoltaic modules and the detection of possible faults. This mechanism is crucial for making decisions and taking immediate corrective action. An offshore simulation environment was developed to validate the implemented system.

关键词： Economics Oceans Solar energy Production Inspection Autonomous aerial vehicles Real-time systems

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Fontes, Miguel Leite, Danilo Dallyson, João Cunha, António UTAD—University of Trás-os-Montes and Alto Douro Vila Real5001-801 Portugal INESC TEC—Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal UFMA—Federal University of Maranhão São Luís Brazil

ISBN: (纸本)9783031606649

Artificial intelligence (AI) is playing a growing role today in several areas, especially in health, where understanding AI models and their predictions is extremely important for health professionals. In this context, Explainable AI (XAI) plays a crucial role in seeking to provide understandable explanations for these models. This article analyzes two different XAI approaches applied to analyzing gastric endoscopy images. The first, more conventional approach uses Grad CAM, while the second, even less explored but with great potential, is based on "similarity-based explanations". This example-based XAI technique aims to provide representative examples to support the decisions of AI models. In this study, we compare these two techniques applied to two different models: one based on the VGG16 architecture and the other based on ResNet50, designed to classify images from the KVASIR-capsule database. The results reveal that Grad-CAM provided intuitive explanations only for the VGG16 model, while the "similarity-based explanations" technique provided consistent explanations for both models. We conclude that exploring other XAI techniques can be a significant asset in improving the understanding of the various AI models. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Endoscopy

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