Daniel Costa

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Assistant Professor

University of Porto

Portugal

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About

Daniel Costa is an Assistant Professor at the University of Porto, located in Portugal. His research areas encompass emergency management in smart cities, spatiotemporal vulnerability assessment of urban areas, and the application of TinyML frameworks. He has published extensively on topics such as AI-based object detection, energy management in smart grids, and histological image segmentation.

Recent Grants

Grant: Close

Conecta.ai: projeto e desenvolvimento integrado de Veículos como Plataforma na instrumentação de cidades inteligentes e sustentáveis

Open Date: 2023-01-01

Close Date: 2025-12-01

Grant: Close

Creating safer and more resilient cities through the detection and management of urban emergencies based on optimized sensor networks

Open Date: 2022-01-01

Close Date:

Grant: Close

Positioning of Emergency Vehicles Through Localization and Classification of Critical Events in Smart Cities

Open Date: 2019-08-01

Close Date: 2020-07-01

Grant: Close

Positioning of Emergency Vehicles Through Localization and Classification of Critical Events in Smart Cities

Open Date: 2019-08-01

Close Date: 2020-07-01

Grant: Close

Dynamic Reconfiguration of Multi-sensor Devices in Monitoring Applications

Open Date: 2019-03-01

Close Date: 2022-02-01

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Collaborators (2)

Francisco Vasques

Associate Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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Paulo Portugal

Associate Professor

Faculty of Engineering of the University of Porto

PORTUGAL

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Daniel Costa

Research Interests

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About

Recent Grants

Articles (30)

Emergency response in smart 15-minute cities: the space-time compression

On the spatiotemporal knowledge-driven vulnerability assessment of urban areas: A clustering-based approach

TensorFlores: An enhanced Python-based TinyML framework

TEDA-forecasting: an unsupervised tinyML incremental learning approach for outlier processing and forecasting

Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review

Emergency management in the age of smart cities: toward safer urban futures

Integrating Textual Queries with AI-Based Object Detection: A Compositional Prompt-Guided Approach

Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning

The Road to Intelligent Cities

On the Use of a Bike-Sharing System in Extreme Weather Events: The Case of Porto Alegre, Rio Grande do Sul, Brazil

A method to promote safe cycling powered by large language models and AI agents

Intelligent Edge-powered Data Reduction: A Systematic Literature Review

A data-driven clustering approach for assessing spatiotemporal vulnerability to urban emergencies

A geospatial multi-domain flood prediction tool exploiting open datasets

Enhancing Cycling Safety in Smart Cities: A Data-Driven Embedded Risk Alert System

Internet of Intelligent Things: A convergence of embedded systems, edge computing and machine learning

Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures

CheckFoV: A Java tool to compute and draw Field of View, visual overlapping and targets coverage

Flood-Resilient Smart Cities: A Data-Driven Risk Assessment Approach Based on Geographical Risks and Emergency Response Infrastructure

A geospatial dataset of urban infrastructure for emergency response in Portugal

On the positioning of emergencies detection units based on geospatial data of urban response centres

A composite indicator of liveability based on sociodemographic and Uber quality service dimensions: A data-driven approach

CityZones: A geospatial multi-tier software tool to compute urban risk zones

Symmetry in Scientific Collaboration Networks: A Study Using Temporal Graph Data Science and Scientometrics

A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks

A Survey of Emergencies Management Systems in Smart Cities

On the Use of eXplainable Artificial Intelligence to Evaluate School Dropout

A Computer-Assisted Approach to Assess the Precision of the Reciprocating Angles and the Rotation Speeds of Endodontic Motors

A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions

Collaborators (2)

Francisco Vasques

Paulo Portugal

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