José Manuel Torres

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Associate Professor

Universidade Fernando Pessoa
Country flagPortugal

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About

José Manuel Torres is an Associate Professor at the University Fernando Pessoa, Portugal. His research areas include artificial intelligence in medical imaging, edge computing, and blockchain technology, as evidenced by his recent articles on glaucoma diagnosis and sleep monitoring. He is also involved in projects related to object and event detection in sports applications and secure electronic voting solutions.

Recent Grants

Grant: Close

Automating City Accessibility Mapping using AI

Open Date: 2023-09-01

Close Date:

Grant: Close

Artificial Intelligence for Disaster Risk Reduction (AI4DRR)

Open Date: 2022-01-01

Close Date:

Grant: Close

Avaliação de Risco Sísmico através de Inteligência Artificial e Vibrações

Open Date: 2021-03-01

Close Date: 2023-03-01

Grant: Close

Laboratório Associado de Sistemas Inteligentes

Open Date: 2021-01-01

Close Date: 2025-12-31

Grant: Close

Intelligent Systems Associate Laboratory

Open Date: 2021-01-01

Close Date: 2025-12-31

Articles (7)

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system’s success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system’s maintenance intervals.

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2024

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2024

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2024

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2024

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2023

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2023

Year:

2022

Collaborators (4)

Pedro Sobral

-

PORTUGAL
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Ivo Pereira

Assistant Professor

Universidade Fernando Pessoa

PORTUGAL
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Rui Silva Moreira

Professor Associado

Universidade Fernando Pessoa

PORTUGAL
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Christophe Soares

Professor Associado

Universidade Fernando Pessoa

PORTUGAL
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