Titulo Estágio
Predictive Air Quality Monitoring
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
DEI-FCTUC
Enquadramento
This internship will take place in the context of the NanoSen-AQM project. The challenge of the NanoSen-AQM project is to monitor ambient air pollution and inform the public of air quality in real time in a sustainable way. The goal is to develop an electronic system based on low cost and low consumption sensors and validate the system at different locations in the Sudoe territory, based on certified instruments for measuring air pollutants.
The electronic system uses gas sensors based on nanotechnology and microelectronics, computer learning techniques to discriminate and quantify toxic gases in the air, and cloud computing technology for managing and visualizing air quality. Small in size, lightweight and easy to use, the system is easily integrable into stations, mobile units and personal air pollution measurement equipment and thus suitable for use in sensor networks. These provide high spatial and temporal resolution data, which allow the validation of predictive models of air quality.
The main outputs are high performance nanosensors for the detection of toxic gases in the air; multi-sensor systems adaptable to a wide variety of platforms for monitoring air quality; and a cloud computing system to monitor and predict air quality, and inform and raise public awareness about air quality.
The project involves universities, R&D centers, SMEs and public administrations in Spain, France and Portugal. The transnational nature of the partnership allows the value chain to be covered and addresses the transboundary nature of air pollution.
Objetivo
In this internship the student should conceive and develop learning models capable of predicting air quality indicators based on time-series sensor data. This activity includes the following actions:
- Dataset and data representation preparation
- Specification of the learning model
- Specification of the algorithm, fine-tuning of the model, test and validation.
- Deployment and test of the conceived architecture
- Integration of intelligent data processing modules into cloud services and validation of models.
Plano de Trabalhos - Semestre 1
- Analyse the state of the art data and possible learning architectures (2 months);
- Define the learning architecture of the system (1 months);
- Start implementing the learning system (1 month)
- Write intermediate report (1 month);
Plano de Trabalhos - Semestre 2
- Implement the learning system (2 months);
- Test and evaluate performance (2 months);
- Write final report (1 month);
Condições
This work will be carried out in the Laboratory of Neural Networks (LARN) of CISUC, where there will be a regular supervision and feedback on the behalf of the supervisor and co-supervisor.
Familiarity with machine learning and data mining algorithms and software tools are essential. Participating students will acquire valuable knowledge and experience with model building and data science by mining massive datasets, which skills are currently in high demand for various technology employers due to the relevance to various applications.
Observações
Grant under Nanosen-AQM Project will be available during the second semester internship depending on the 1st semester candidate evaluation. A 3-month scholarship of 745 euros per month is foreseen for this work, renewable for another 3 months.
Logistics @Laboratory of Neural Networks (LARN)
DEI-FCTUC
Orientador
Bernardete Ribeiro, Alberto Cardoso, Catarina Silva
bribeiro@dei.uc.pt 📩