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School of Physical and Chemical Sciences

Dr Fabrício de Oliveira Ourique

Fabrício

Lecturer in Digital Signal Processing

Email: f.ourique@qmul.ac.uk
Room Number: G. O. Jones Building, Room 410

Profile

Fabrício de Oliveira Ourique is an experienced academic and researcher in Digital Signal Processing (DSP) with a Ph.D. from the University of New Mexico, USA. He currently serves as a lecturer at Queen Mary University of London, where he teaches DSP modules in the Information and Computational Science and Digital Media Technology programmes. Over the course of his career, Fabrício has held several academic positions, including Associate Professor at the Federal University of Santa Catarina, Brazil, where he led research initiatives and supervised numerous Master’s and Ph.D. students. His work spans various areas, including signal acquisition systems, machine learning, and IoT applications

Teaching

QHE5107 Fundamentals of DSP

Research

Research Interests:

Fabrício de Oliveira Ourique’s research initiatives span a diverse range of fields, including Digital Signal Processing (DSP), machine learning, and IoT applications. His work focuses on applying advanced signal processing techniques to real-world problems, such as image watermarking, time series forecasting, and energy monitoring. He has led various research projects, including the development of embedded systems for smart meters, wearable devices for cardiac signal acquisition, and techniques for semi-automatic music transcription.

 

Publications

T. Crosby, D. Gartner, E. Aspland, F. O. Ourique, P. Harper, E. F. Arruda, and T. England. Resource optimization for cancer pathways with aggregate diagnostic demand: a perishable Inventory approach. IMA Journal of Management Mathematics, dpaa01, 2021

F. Mateus, F. O. Ourique, A. S. Morales, and N. M. da Silva. Implementation of Time series forecasting model to estimate excess deaths in Brazil in 2020. Journal of Health Informatics, 16(1), Jan. 2024.

E.F. Arruda, D.F. Marcelo, and F.O. Ourique. A multi-cluster time aggregation approach for Markov chains. Automatica, 99:382–389, 2019.

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