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About

About

My academic path started with a graduation in Physics Engineering (IST/Lisbon), followed by an MSc in Electrotechnical Eng. and Computers (IST/Lisbon), and a PhD in Physics /FCUP/UPorto). Along the way, I participated in different projects that had, as a common feature,  the development of new solutions or approaches in  Applied Optics  (LIDAR measuring systems, biosensors, fiber optic sensing, high resolution optical imaging, optical coherence tomography). As a lecturer at UPorto, I have been mostly envolved in the Physics Eng. and Medical Physics MSc programs. At the Center of Applied Photonics (INESC TEC) I have found the place/team to  develop my research in applied optics, looking for solutions for academic challenges, but most importantly for the challenges we receive from industrial partners, seeking for new measuring approaches using light as a probing agent. 

Interest
Topics
Details

Details

  • Nationality

    Portugal
  • Centre

    Applied Photonics
  • Contacts

    +351220402301
    carla.c.rosa@inesctec.pt
005
Publications

2022

Unravelling an optical extreme learning machine

Authors
Silva, D; Silva, NA; Ferreira, TD; Rosa, CC; Guerreiro, A;

Publication
EPJ Web of Conferences

Abstract
Extreme learning machines (ELMs) are a versatile machine learning technique that can be seamlessly implemented with optical systems. In short, they can be described as a network of hidden neurons with random fixed weights and biases, that generate a complex behaviour in response to an input. Yet, despite the success of the physical implementations of ELMs, there is still a lack of fundamental understanding about their optical implementations. This work makes use of an optical complex media to implement an ELM and introduce an ab-initio theoretical framework to support the experimental implementation. We validate the proposed framework, in particular, by exploring the correlation between the rank of the outputs, H, and its generalization capability, thus shedding new light into the inner workings of optical ELMs and opening paths towards future technological implementations of similar principles.

2022

Reservoir computing with nonlinear optical media

Authors
Ferreira, TD; Silva, NA; Silva, D; Rosa, CC; Guerreiro, A;

Publication
Journal of Physics: Conference Series

Abstract
Reservoir computing is a versatile approach for implementing physically Recurrent Neural networks which take advantage of a reservoir, consisting of a set of interconnected neurons with temporal dynamics, whose weights and biases are fixed and do not need to be optimized. Instead, the training takes place only at the output layer towards a specific task. One important requirement for these systems to work is nonlinearity, which in optical setups is usually obtained via the saturation of the detection device. In this work, we explore a distinct approach using a photorefractive crystal as the source of the nonlinearity in the reservoir. Furthermore, by leveraging on the time response of the photorefractive media, one can also have the temporal interaction required for such architecture. If we space out in time the propagation of different states, the temporal interaction is lost, and the system can work as an extreme learning machine. This corresponds to a physical implementation of a Feed-Forward Neural Network with a single hidden layer and fixed random weights and biases. Some preliminary results are presented and discussed. © Published under licence by IOP Publishing Ltd.

2021

Particle Classification through the Analysis of the Forward Scattered Signal in Optical Tweezers

Authors
Carvalho, IA; Silva, NA; Rosa, CC; Coelho, LCC; Jorge, PAS;

Publication
SENSORS

Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.

2019

Optical fiber-based sensing method for nanoparticle detection through supervised back-scattering analysis: a potential contributor for biomedicine

Authors
Paiva, JS; Jorge, PAS; Ribeiro, RSR; Sampaio, P; Rosa, CC; Cunha, JPS;

Publication
INTERNATIONAL JOURNAL OF NANOMEDICINE

Abstract

2019

Optical fiber-based sensing method for nanoparticles detection through back-scattering signal analysis

Authors
Paiva, JS; Ribeiro, RSR; Jorge, PAS; Rosa, CC; Sampaio, P; Cunha, JPS;

Publication
OPTICAL FIBERS AND SENSORS FOR MEDICAL DIAGNOSTICS AND TREATMENT APPLICATIONS XIX

Abstract
In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identification/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of "naturally" generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to find a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm-far below the diffraction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identification through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fiber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus "blank solutions" (only distilled water) through Multivariate Statistics and Artificial Intelligence (AI)-based techniques. While the state-of-The-Art methods do not offer affordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Supervised
thesis

2021

Resistividade elétrica para a avaliação ambiental do subsolo da pedreira de Alijó, Barroso

Author
Maria Cristina Gomes Marques

Institution
UP-FCUP

2019

Fabrication of Optofluidic Systems by Femtosecond Laser Micromachining

Author
João Miguel Mendes da Silva Maia

Institution
UP-FCUP

2018

Desenvolvimento de Modelo de Gestão de Inventários

Author
Ana Catarina Monteiro Mendes

Institution
UP-FEUP

2018

Lensless diffraction limited imaging with optical fibers

Author
Rui Luís Vieira Oliveira

Institution
UP-FCUP

2018

Toward a 3D Planning Approach for Breast Conserving Surgery

Author
Hooshiar Zolfagharnasab

Institution
UP-FEUP