Prof. Dr. A. S. Paterno

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Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 March 2011 in Sensors

Journal Article

Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented.

1 January 2013

Book Chapter

Artifact Compensation in Bioimpedance Spectroscopy Using Particle Swarm Optimization

A. S. Paterno

L. H. Negri

11 November 2022 in Sensors

Journal Article

Parasitic Effects on Electrical Bioimpedance Systems: Critical Review

David William Cordeiro Marcôndes

A. S. Paterno

Pedro Bertemes-Filho

Parasitic capacitance represents the main error source in measurement systems based on electrical impedance spectroscopy. The capacitive nature of electrodes’ impedance in tetrapolar configuration can give origin to phase errors when electrodes are coupled to parasitic capacitances. Nevertheless, reactive charges in tissue excitation systems are susceptible to instability. Based on such a scenario, mitigating capacitive effects associated with the electrode is a requirement in order to reduce errors in the measurement system. A literature review about the main compensation techniques for parasitic capacitance was carried out. The selected studies were categorized into three groups: (i) compensation in electronic instrumentation; (ii) compensation in measurement processing, and (iii) compensation by negative impedance converters. The three analyzed methods emerged as effective against fixed capacitance. No method seemed capable of mitigating the effects of electrodes’ capacitance, that changes in the frequency spectrum. The analysis has revealed the need for a method to compensate varying capacitances, since electrodes’ impedance is unknown.

1 January 2006

Conference Paper

Measuring Orthodontic Forces with HiBi FBG Sensors

M.S. Milczeswki

Jean Carlos Cardozo Da Silva

I. Abe

José Augusto Rodrigues Simões

A. S. Paterno

Hypolito Kalinowski

HiBi FBG are used to demonstrate a technique to evaluate orthodontic forces. The experiments are performed in an artificial maxilla supporting dental wax and metal teeth. The teeth are instrumented using an orthodontic technique and loads are applied representing auxiliary appliance. The values of the loads (extra-oral) are based on clinical applications. It is demonstrated that the tooth vestibular surface is subject to a force of 0.13 N. The size and sensitivity of the FBG allow for the determination of forces applied orthogonally to the surface of the teeth.

2 September 2024 in Sensors

Journal Article

Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition

Rafael Kingeski

Elisa Henning

A. S. Paterno

Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%.

3 April 2024 in The International Journal of Advanced Manufacturing Technology

Journal Article

GRAAL—modeling, prototyping and assessing a gamified responsible augmented assembly line system

Salvador S. Agati

Marcelo da Silva Hounsell

A. S. Paterno

About Me

Aleksander S. Paterno received his Bachelor's degree in Electronic and Telecommunications Industrial Engineering from the Federal University of Technology–Paraná (1999) and His Master's and Doctoral degrees in Electrical Engineering and Industrial Informatics from the same university in 2003 and 2007, respectively. He is an associate professor in the Department of Electrical Engineering and a member of its graduate program. In administrative roles, he served as the head of the Department of Electrical Engineering during the management period of 2013–2015. Additionally, he was the Dean of Undergraduate Studies at the Center for Technological Sciences during the management periods of 2016–2020 and pro tempore during 2020–2021. His research focuses on optoelectronic instrumentation systems and their applications, digital signal processing with applications in biomedical engineering, and instrumentation, as well as telecommunications and higher education pedagogy. He is dedicated to the development of fiber optic sensing systems, starting from the design and simulation of sensors to their fabrication in optical fibers. This involves the development of electronic sensors with industrial applications that can be integrated into industrial automation and biomedical engineering environments.

Prof. Dr. A. S. Paterno

Prof. Dr. A. S. Paterno

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About Me