Unidade de Investigação - INESCC-DL – Instituto de Engenharia de Sistemas e Computadores de Coimbra [delegação Politécnico de Leiria]
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- 2ARTs: A Platform for Exercise Prescriptions in Cardiac Recovery PatientsPublication . Pereira, Andreia; Martinho, Ricardo; Pinto, Rui; Rijo, Rui; Grilo, CarlosDue to limited access, increasing costs and an ageing population, the global healthcare system faces significant coverage problems that call for innovative approaches. Health professionals are actively seeking alternative methods to provide care to an increasingly needy population, without increasing human effort and associated costs. eHealth platforms, which use technology to provide patient care, are emerging as transformative solutions for addressing these problems. This study is centered on the demand for a Decision Support System (DSS) in cardiology to enable doctors to prescribe individualized care inside Cardiac Rehabilitation Programmes (CRPs). The 2ARTs project’s main objective is to include a cardiac rehabilitation platform with a DSS within the hospital infrastructure. This DSS uses models to classify patients into different groups, delivering crucial information to assist with decisions regarding treatment. Regarding the DSS, Principal Component Analysis (PCA) emerged as a standout technique for dimensionality reduction, due to its interoperability with clustering algorithms and superior evaluation metrics. The most appropriate clustering technique was determined to be the K-means algorithm, which was supported by the experts analysis. In accordance with the goals of the 2ARTs project, this integration of PCA and K-means provides meaningful insights that improve reasoned decision-making.
- Accuracy versus complexity of MARG-based filters for remote control pointing devicesPublication . Rasteiro, Miguel ; Costelha, Hugo; Conde Bento, Luis; Assunção, PedroAlthough most current pointing devices rely on relative rotation increments, absolute orientation allows for a more intuitive interaction. However, this is difficult to implement in low-energy consumption devices since accurate fusion filters are computationally intensive. This work presents a comparative study of low-complexity filters and state-of-the-art orientation tracking systems, enabling to access complexity versus portability. A relevant set of different MARG units currently available on the market were studied under systematic tests and human subjective user analysis. Experimental results show that it is possible to obtain similar accuracy using low-complexity filters to the ones observed with state-of-the-art orientation tracking systems. © 2015 IEEE.
- Analysis of Power Quality Disturbances in Industry in the Centre Region of PortugalPublication . Moreira, Licinio; Leitão, Sérgio; Vale, Zita; Galvão, João; Franco Marques, Pedro JoséPower quality issues have taken a more prominent role in power systems over the last years. These issues are of major concern for energy customers, primarily for customers with a widespread use of electronic devices in their manufacturing processes. Even though the quality of service is increasing, customers are becoming more demanding of the energy provider. This research aims to provide some industrial managers the technical support in deciding of investments in the mitigation of power quality disturbances, such as the use of less sensitive devices or the use of interface devices (UPS, DVR...) In order to recommend an appropriate solution, the problem is characterized. The technical and economic influences of the PQ disturbances in the manufacturing processes are assessed resorting to power quality audits in the customer facilities. This research covered a significant number of facilities in several industrial activities.
- Applying deep learning to real-time UAV-based forest monitoring: Leveraging multi-sensor imagery for improved resultsPublication . Marques, Tomás; Carreira, Samuel; Miragaia, Rolando; Ramos, João; Pereira, AntónioRising global fire incidents necessitate effective solutions, with forest surveillance emerging as a crucial strategy. This paper proposes a complete solution using technology that integrates visible and infrared spectrum images through Unmanned Aerial Vehicles (UAVs) for enhanced detection of people and vehicles in forest environments. Unlike existing computer vision models relying on single-sensor imagery, this approach overcomes limitations posed by limited spectrum coverage, particularly addressing challenges in low-light conditions, fog, or smoke. The developed 4-channel model uses both types of images to take advantage of the strengths of each one simultaneously. This article presents the development and implementation of a solution for forest monitoring ranging from the transmission of images captured by a UAV to their analysis with an object detection model without human intervention. This model consists of a new version of the YOLOv5 (You Only Look Once) architecture. After the model analyzes the images, the results can be observed on a web platform on any device, anywhere in the world. For the model training, a dataset with thermal and visible images from the aerial perspective was captured with a UAV. From the development of this proposal, a new 4- channel model was created, presenting a substantial increase in precision and mAP (Mean Average Precision) metrics compared to traditional SOTA (state-of-the-art) models that only make use of red, green, and blue (RGB) images. Allied with the increase in precision, we confirmed the hypothesis that our model would perform better in conditions unfavorable to RGB images, identifying objects in situations with low light and reduced visibility with partial occlusions. With the model’s training using our dataset, we observed a significant increase in the model’s performance for images in the aerial perspective. This study introduces a modular system architecture featuring key modules: multisensor image capture, transmission, processing, analysis, and results presentation. Powered by an innovative object detection deep-learning model, these components collaborate to enable real-time, efficient, and distributed forest monitoring across diverse environments.
- Assessing the influence of uncertainty in land cover mapping and digital elevation models on flood risk mappingPublication . Gonçalves, Luísa M.S.; Fonte, Cidália C.; Gomes, RicardoThis paper proposes an approach to assess the influence of the uncertainty present in the parameters dependent on the land cover and elevation data over the peak flow values and the subsequent delineation of flooded areas. The proposed approach was applied to produce vulnerability and risk maps that integrate uncertainty for the urban area of Leiria, Portugal. A SPOT-4 satellite image and DEMs of the region were used. The peak flow was computed using the Soil Conservation Service method and HECHMS, HEC-RAS, Matlab and ArcGIS software programs were used. The analysis of the results obtained for the presented case study enables the identification of the order of magnitude of uncertainty associated to the watershed peak flow value and the identification of the areas which are more susceptible to flood risk to be identified.
- Autonomous Wireless Sensor with a Low Cost TEG for Application in Automobile VehiclesPublication . Costa, A.; Costa, D.; Morgado, J.; Santos, Helder; Ferreira, Carlos Daniel HenriquesThe present work consists in the development of an autonomous, low cost, reliable, energy scavenger sensor for automotive applications. Thermoelectric generators typically exhibit low efficiency but high reliability, making them suitable for autonomous, low average energy consumption, applications. A prototype sensor was developed for mounting in the engine exhaust pipe using a step-up voltage converter, a microcontroller, temperature and pressure sensing elements, conditioning electronics and a wireless transceiver, all powered by a low cost TEG (Peltier module TEC1-12706), through the scavenging of exhaust gases thermal energy. During the tests the prototype was able to sustain a regular signal transmission throughout the engine operation. The sensor was installed directly at the measuring point eliminating wired cables to hot and vibrating parts, thus, simplifying the installation of components and improving the reliability of the vehicle systems.
- Development of CART model for prediction of tuberculosis treatment loss to follow up in the state of São Paulo, Brazil: A case–control studyPublication . Yamaguti, Verena Hokino; Alves, Domingos; Rijo, Rui, Rui Pedro Charters Lopes; Miyoshi, Newton Shydeo Brandão; Ruffino-Netto, AntônioBackground: Tuberculosis is the leading cause of infectious disease-related death, surpassing even the immunodeficiency virus. Treatment loss to follow up and irregular medication use contribute to persistent morbidity and mortality. This increases bacillus drug resistance and has a negative impact on disease control. Objective: This study aims to develop a computational model that predicts the loss to follow up treatment in tuberculosis patients, thereby increasing treatment adherence and cure, reducing efforts regarding treatment relapses and decreasing disease spread. Methods: This is a case-controlled study. Included in the data set were 103,846 tuberculosis cases from the state of São Paulo. They were collected using the TBWEB, an information system used as a tuberculosis treatment monitor, containing samples from 2006 to 2016. This set was later resampled into 6 segments with a 1-1 ratio. This ratio was used to avoid any bias during the model construction. Results: The Classification and Regression Trees were used as the prediction model. Training and test sets accounted for 70% in the former and 30% in the latter of the tuberculosis cases. The model displayed an accuracy of 0.76, F-measure of 0.77, sensitivity of 0.80 and specificity of 0.71. The model emphasizes the relationship between several variables that had been identified in previous studies as related to patient cure or loss to follow up treatment in tuberculosis patients. Conclusion: It was possible to construct a predictive model for loss to follow up treatment in tuberculosis patients using Classification and Regression Trees. Although the fact that the ideal predictive ability was not achieved, it seems reasonable to propose the use of Classification and Regression Trees models to predict likelihood of treatment follow up to support healthcare professionals in minimising the loss to follow up.
- Energy audit as an input for energy management and energy efficiency improvement in a gypsum manufacturing plantPublication . Bernardo, Hermano; Oliveira, Filipe Tadeu; Serrano, LuisThis paper aims at presenting the main results of an energy audit performed to a gypsum production plant, in Portugal, which due to the amount of energy consumed must comply with the Portuguese program SGCIE (Intensive Energy Consumption Management System). The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy consuming facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. An energy audit was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns, sector/equipment load profiles and thermal equipment performance. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. A number of tangible targets and measures were devised and set to be implemented in the next few years. Results show that there is a considerable potential for reduction in the energy consumption and greenhouse gases emissions of gypsum manufacturing plants. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.
- Energy management in municipal solid waste treatment: A case study of a mechanical biological treatment facilityPublication . Bernardo, Hermano; Oliveira, Filipe Tadeu; Quintal, EdgarOver the last few years, mechanical biological treatment systems for municipal solid waste have been introduced in many European countries. In most cases, this was driven by the European Union Landfill Directive, which requires the diversion of biodegradable municipal waste from landfill to alternative processes. Although this type of treatment allows energy recovery from municipal solid waste, the process of mechanical biological treatment appears to be an intensive energy consumer, due to high demand of electricity consumed by process equipment. This paper presents the main results of an energy audit performed to a Mechanical Biological Treatment facility in Portugal, which due to the amount of energy consumed must comply with the Portuguese Program called Intensive Energy Consumption Management System – SGCIE. The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. To implement actions that improve energy efficiency, it is necessary for the facilities operation to be associated with an effective energy management methodology, as well as an efficient facilities management procedure. The implementation of any energy management system should start with an energy audit, which was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns and sector/equipment load profiles. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. Results shown that there is a considerable potential for reducing energy consumption and greenhouse gases emissions on Mechanical Biological Treatment units. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.
- High Sensitivity Micro-machined Piezoresistive Strain SensorPublication . Caseiro, D.; Santos, S.; Ferreira, Carlos Daniel Henriques; Neves, CarlosThis paper presents a micro-machined piezoresistive sensor capable of measuring very small strains. The sensor design, based on piezoresistive sensing technology, was optimized by the numerical method using Finite Element Method (FEM) to enhance sensibility. The high sensibility is achieved through a reduction of section and through the action of the bending moment. As a result, a sensor with a sensitivity of 569.4608 μV/V/με, which can be fabricated by the SensoNor MultiMEMS process, is proposed. Furthermore, practical essays with macro prototypes confirmed and validated the numerical analysis. Such a sensor can be a direct replacement for the strain gauges and its very high sensitivity opens the door to many other applications, that otherwise would not be possible.
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