Auzuir Ripardo de Alexandria

Scopus Publications

Approaches to contactless optical thermometer employing LaNbO4:Er3+ single-crystal fibers
J. P. C. do Nascimento, F. F. do Carmo, F. E. A. Nogueira, M. A. S. da Silva, C. Singh, R. S. da Silva, A. R. de Alexandria, A. S. B. Sombra
Journal of Physics and Chemistry of Solids, 2026
Advances in Urological Surgery: The 3D Modeling Revolution
Auzuir Ripardo de Alexandria, Veronica Penza, Alberto Neri, Leonardo Serra de Mattos
IEEE Access, 2026
Robotic-assisted surgery has transformed modern urological procedures, enhancing precision, reducing surgical trauma, and improving patient recovery. The integration of emerging technologies such as three-dimensional (3D) modeling, virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) is reshaping the landscape of surgical planning and execution. This study aims to explore the impact of 3D modeling applications in robotic or laparoscopic urological surgery, evaluating their role in surgical planning, intraoperative guidance, and training while identifying the main challenges and future prospects. A systematic topic review of scientific articles published between 2019 and 2025 was conducted using databases such as Web of Science, PubMed, and IEEE Xplore. The selection criteria focused on studies integrating 3D modeling, AR, and AI in urological procedures. The analysis examined technological advancements, clinical applications, and the benefits of these innovations in surgical practice. The findings indicate that the use of 3D models in laparoscopic or robotic surgery enhances preoperative planning by providing a more detailed anatomical representation, improves intraoperative precision through augmented visualization, and facilitates surgical training through immersive simulations. Despite these benefits, challenges such as high costs, interoperability limitations, and the need for multicenter clinical validation remain significant barriers to widespread adoption. This study contributes to the field by summarizing the latest advancements in 3D modeling for laparoscopic or robotic surgery, highlighting best practices, identifying gaps in research, and proposing directions for future improvements. Additionally, it underscores the role of these technologies in optimizing patient outcomes and surgical efficiency. The incorporation of 3D modeling into laparoscopic or robotic urological surgery presents promising opportunities for improving surgical precision, reducing complications, and enhancing training methodologies. However, to fully realize this potential, efforts must be made to standardize methodologies, reduce costs, and validate these technologies in broader clinical applications.
Lung and chest wall structures segmentation in CT images
J.H.S. Felix, P.C. Cortez, M.A. Holanda, V.H.C. Albuquerque, D.F. Colaço, A.R. Alexandria
Computational Vision and Medical Image Processing Vipimage 2007, 2026
Computed Tomography (CT) of thorax is nowadays the most accurate image technique for diagnosis of the majority of lung and chest diseases. Despite this fact there are still limitations of CT in diagnosing and specially quantifying lung diseases such as Chronic Obstructive Pulmonary Disease (COPD). The present paper presents a method of automatic classification capable to segment the lungs and the chest wall elements in patients with COPD in supine and prone positions. The technique of binary mathematical morphology was used to segment the lungs and the thoracic cavity using region growing following for negative this image. The lungs, the thoracic cavity and the pulmonary vessels were all successfully segmented with the application of the mathematical morphology and region growing. This method of processing CT images may be a promising tool for qualitative and quantitative studies of chest CT images.
Real-time emotion recognition based on facial expressions using Artificial Intelligence techniques: A review and future directions
Cheng Qian, Joao Alexandre Lobo Marques, Auzuir Ripardo de Alexandria
Multidisciplinary Reviews, 2025
In recent years, the real-time facial expression recognition system based on artificial intelligence technology has garnered significant attention from academia and industry. This paper presents a systematic literature review and bibliometric analysis to examine the latest publications in this field, summarizing the development and research significance of facial expression recognition technology and emphasizing its vital role in human-computer interaction and affective computing. The study used PRISMA to review 386 articles published from January 2019 to December 2023 in Web of Science, Scopus, IEEE Xplore, and ACM Digital Library. It encompasses covering various research methodologies, datasets, and application areas, as well as artificial intelligence technology, algorithms, and models. This review highlights advancements in Facial Expression Recognition, particularly the predominant use of databases such as FER2013 and CK+ while identifying Convolutional Neural Networks as the primary technique for real-time emotion classification. A quantitative analysis of research trends over the past five years indicates a shift toward keywords like transfer learning and applications in domains such as healthcare and the Internet of Things. Contemporary deep learning models, including CNNs, ResNet, and VGG, demonstrate impressive accuracy in classifying seven basic emotions, facilitating real-time applications across multiple fields. However, challenges such as overfitting, sensitivity to environmental factors, and the necessity for high-performance computing resources impede the broader deployment of these systems. These findings underscore the urgent need for further research to address these limitations and enhance the ethical application of FER technologies. Finally, based on the review and analysis results, this paper outlines future research directions for this technology, including multimodal information fusion, computational modelling, personalized emotion recognition, and interdisciplinary cooperation, thereby providing valuable references and inspiration for future works.
Digital Watermarking in Cloud Environments For Copyright Protection
Digital Watermarking in Cloud Environments for Copyright Protection, 2025
Preface
Digital Watermarking in Cloud Environments for Copyright Protection, 2025
AI-Driven Solutions for Solar Energy Efficiency, Irradiance Modeling, and PV Forecasting
AI Driven Solutions for Solar Energy Efficiency Irradiance Modeling and Pv Forecasting, 2025
Inverse Design of Plasmonic Nanostructures Using Machine Learning for Optimized Prediction of Physical Parameters
Luana S. P. Maia, Darlan A. Barroso, Aêdo B. Silveira, Waleska F. Oliveira, André Galembeck, Carlos Alexandre R. Fernandes, Dayse G. C. Bandeira, Benoit Cluzel, Auzuir R. Alexandria, Glendo F. Guimarães
Photonics, 2025
Plasmonic nanostructures have been widely studied for their unique optical properties, which are useful in sensing, photonics, and energy. However, the efficient design of these structures, considering the complex relationship between geometry, material, and optical response, remains a challenge. In this study, we propose a machine learning-based approach to address the inverse design problem in nanostructures, using data generated by numerical simulations via the Finite Element Method (FEM). We used a dataset of over 140,000 entries to train the regression models CatBoost, Random Forest, and Extra Trees, capable of predicting physical parameters, such as the radius of the nanocylinder, based on the simulated optical response. The CatBoost model achieved the best performance, with a Mean Absolute Error below 0.3 nm on unseen data. In parallel, we applied a direct design approach to experimental data of metallic nanoparticles, focusing on the optical absorption prediction from particle size. In this case, Random Forest presented the best performance, with a lower risk of overfitting. The results indicate that machine learning models are promising tools for optimizing the design and characterization of plasmonic nanostructures, thus reducing the need for costly experimental techniques.
Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions †
Cheng Qian, João Alexandre Lobo Marques, Auzuir Ripardo de Alexandria, Simon James Fong
Sensors, 2025
Facial expression recognition (FER) is essential for discerning human emotions and is applied extensively in big data analytics, healthcare, security, and user experience enhancement. This study presents a comprehensive evaluation of ten state-of-the-art deep learning models—VGG16, VGG19, ResNet50, ResNet101, DenseNet, GoogLeNet V1, MobileNet V1, EfficientNet V2, ShuffleNet V2, and RepVGG—on the task of facial expression recognition using the FER2013 dataset. Key performance metrics, including test accuracy, training time, and weight file size, were analyzed to assess the learning efficiency, generalization capabilities, and architectural innovations of each model. EfficientNet V2 and ResNet50 emerged as top performers, achieving high accuracy and stable convergence using compound scaling and residual connections, enabling them to capture complex emotional features with minimal overfitting. DenseNet, GoogLeNet V1, and RepVGG also demonstrated strong performance, leveraging dense connectivity, inception modules, and re-parameterization techniques, though they exhibited slower initial convergence. In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. The results highlight the critical trade-offs between computational efficiency and predictive accuracy, emphasizing the importance of selecting appropriate architecture based on application-specific requirements. This research contributes to ongoing advancements in deep learning, particularly in domains such as facial expression recognition, where capturing subtle and complex patterns is essential for high-performance outcomes.
A Lightweight Deep Learning Model for Accurate Plant Disease Detection in Real Applications
Lucas José Lemos Braz, Jermana Lopes de Moraes, Auzuir Ripardo de Alexandria
1st IEEE Latin American Conference on Internet of Things Lciot 2025 Proceedings, 2025
Plant diseases pose a significant threat to global agricultural productivity, especially in resource-limited regions where access to expert diagnostics is scarce. This paper presents USENet, a lightweight deep learning model for accurate and efficient plant disease detection using leaf images. USENet leverages MobileNetV3 inverted residual blocks and Spatial Pyramid Pooling (SPP) to minimize model size and memory footprint while maintaining high accuracy. The model is trained and evaluated using the PlantVillage dataset. USENet achieves a precision of 0.9926, recall of 0.9935, and an F1-score of 0.9930, while having only 41,446 parameters. The model is just 0.722 MB in size, making it suitable for edge deployment. The lightweight nature of USENet makes it ideal for deployment on low-cost microprocessors and mobile devices, providing a practical solution for plant disease detection in remote agricultural areas.
Rough Set Theory Applied to Feature Selection
James George Vasconcelos Alves Júnior, Nicolas Fonteles Leite, Josias Batista Guimarães, João Alexandre Lobo Marques, Sergio Silva Ribeiro, Auzuir Ripardo de Alexandria
Lecture Notes in Computer Science, 2025
Proposal of simultaneous localization and mapping for mobile robots indoor environments using Petri nets and computer vision
Francisco A. X. Mota, Josias G. Batista, Auzuir R. Alexandria
International Journal of Advanced Manufacturing Technology, 2024
Intelligent Classifier for Identifying and Managing Sheep and Goat Faces Using Deep Learning
Chandra Shekhar Yadav, Antonio Augusto Teixeira Peixoto, Luis Alberto Linhares Rufino, Aedo Braga Silveira, Auzuir Ripardo de Alexandria
Agriengineering, 2024
Emotion Detection from EEG Signals Using Machine Deep Learning Models
João Vitor Marques Rabelo Fernandes, Auzuir Ripardo de Alexandria, João Alexandre Lobo Marques, Débora Ferreira de Assis, Pedro Crosara Motta, Bruno Riccelli dos Santos Silva
Bioengineering, 2024
Editorial
Conexoes Ciencia E Tecnologia, 2024
Broadband Beamforming via Frequency Invariance Transformation and PARAFAC Decomposition for Jamming Mitigation in V2X Scenarios
Daniel V. de Lima, João Paulo J. da Costa, Antonio Arlis S. da Silva, Giovanni A. Santos, José Alfredo R. Vargas, Auzuir R. de Alexandria
IEEE Vehicular Technology Conference, 2024
Low Complexity Broadband Array Processing in Dynamic Scenarios with Jamming
Daniel V. De Lima, João Paulo J. Da Costa, Ricardo K. Miranda, Antonio Arlis S. Da Silva, Giovanni A. Santos, José Alfredo R. Vargas, Auzuir R. De Alexandria
IEEE Vehicular Technology Conference, 2024
Framework for time-varying DoA estimation and beamforming against radio jamming in V2X applications
Daniel V. De Lima, Antonio S. Da Silva, João Paulo J. Da Costa, Giovanni A. Santos, Kira Kastell, Auzuir R. De Alexandria, Melissa B. Da Conceição
International Conference on Transparent Optical Networks, 2024
Optimization of Energy Storage Systems with Renewable Energy Generation and Consumption Data
Edmilson Moreira Lima Filho, Aêdo Braga Silveira, Alexandre Marques Ferreira, João Alexandre Lobo Marques, Josias Guimarães Batista, Glendo De Freitas Guimarães, Auzuir Ripardo De Alexandria, Joel José Puga Coelho Rodrigues
IEEE Student Conference on Electric Machines and Systems Scems, 2024
Editorial
Revista Conexões
Conexoes Ciencia E Tecnologia, 2023
Design of Optical Logic Gates Using Mach-Zehnder Interferometers and Machine Learning
C. Alexandre R. Fernandes, Manoel E. N. de Oliveira, Danilo S. Rocha, Auzuir R. de Alexandria, Glendo F. Guimaraes
Journal of Lightwave Technology, 2022
A Novel Virtual Nasal Endoscopy System based on Computed Tomography Scans
Fábio de O. Sousa, Daniel S. da Silva, Tarique da S. Cavalcante, Edson C. Neto, Victor José T. Gondim, Ingrid C. Nogueira, Auzuir Ripardo de Alexandria, Victor Hugo C. de Albuquerque
Virtual Reality and Intelligent Hardware, 2022
Optical Current and Magnetic Field Sensor Using Multimodal Interference in Fiber Optics With Carbon Steel
Luana Samara Paulino Maia, Marcus Aurelio Ribeiro Miranda, Ieda Maria Xavier de Souza, Beatriz Cristina Pereira de Assis, Danilo Sousa Rocha, Jose Marcos Sasaki, Auzuir Ripardo de Alexandria, Glendo de Freitas Guimaraes
IEEE Sensors Journal, 2022
Artificial Intelligence applied to the classification of retinal diseases in Optical Coherence Tomography images
Beatriz Silva Brasil, Auzuir Ripardo de Alexandria, Glendo de Freitas Guimaraes
2022 5th International Conference on Vocational Education and Electrical Engineering the Future of Electrical Engineering Informatics and Educational Technology Through the Freedom of Study in the Post Pandemic Era Icvee 2022 Proceeding, 2022
New trends on computer vision applied to mobile robot localization
Antonio Savio Silva Oliveira, Marcello Carvalho dos Reis, Francisco Alan Xavier da Mota, Maria Elisa Marciano Martinez, Auzuir Ripardo Alexandria
Internet of Things and Cyber Physical Systems, 2022
Development of a computer vision approach as a useful tool to assist producers in harvesting yellow melon in northeastern Brazil
Renê Ripardo Calixto, Luis Gonzaga Pinheiro Neto, Tarique da Silveira Cavalcante, Francisca Gleiciane Nascimento Lopes, Auzuir Ripardo de Alexandria, Ebenezer de Oliveira Silva
Computers and Electronics in Agriculture, 2022
Numerical investigation of the optical eﬃciency of a parabolic trough collector at diﬀerent concentration ratios
Abdel-Farid Mamadou Idrissou, Francisco Frederico dos Santos Matos, Auzuir Ripardo de Alexandria
Energy Sources Part A Recovery Utilization and Environmental Effects, 2021
Development of evanescent field optical fiber sensor for pH measurement using sol-gel technology
Tiago B. Marinho, Victor G. M. Almeida, Waleska F. De Oliveira, Joao I. S. Miranda, Marcos E. R. Da Silva, Auzuir R. De Alexandria, Glendo De F. Guimaraes
2021 SBMO IEEE MTT S International Microwave and Optoelectronics Conference Imoc 2021, 2021
Automatic Segmentation of Macular Holes in Optical Coherence Tomography Images
Odilon L. C. Mendes, Daniel R. Lucena, Abrahao R. Lucena, Tarique S. Cavalcante, Victor Hugo C. De Albuquerque, Meteb Altaf, Mohammad Mehedi Hassan, Auzuir R. Alexandria
IEEE Access, 2021
Automated classification of dynamic renal scintigraphy exams to determine the stage of chronic kidney disease: An investigation
Auzuir Ripardo de Alexandria, Matheus Cruz Ferreira, Elene Firmeza Ohata, Tarique Da Silveira Cavalcante, Francisco Alan Xavier Da Mota, Ingrid Correia Nogueira, Victor Hugo Costa Albuquerque, Victor Jose Timbo Gondim, Edson Cavalcanti Neto
Icracos 2021 2021 3rd International Conference on Research and Academic Community Services Sustainable Innovation in Research and Community Services for Better Quality of Life Towards Society 5, 2021
Automatic quantification of spheroidal graphite nodules using computer vision techniques
Renato F. Pereira, Valberto E. R. da Silva Filho, Lorena B. Moura, N. Arun Kumar, Auzuir R. de Alexandria, Victor Hugo C. de Albuquerque
Journal of Supercomputing, 2020
Stratification of cardiopathies using photoplethysmographic signals
Jermana Lopes de Moraes, Thiago Lucas de Oliveira, Matheus Xavier Rocha, Glauber Gean Vasconcelos, Auzuir Ripardo de Alexandria
Informatics in Medicine Unlocked, 2020
Development of a robotic airboat for online water quality monitoring in lakes
Marcelo Melo, Francisco Mota, Victor Albuquerque, Auzuir Alexandria
Robotics, 2019
Analysis of man-machine interfaces in upper-limb prosthesis: A review
José Ribeiro, Francisco Mota, Tarique Cavalcante, Ingrid Nogueira, Victor Gondim, Victor Albuquerque, Auzuir Alexandria
Robotics, 2019
Localization and navigation for autonomous mobile robots using petri nets in indoor environments
Francisco A. X. Da Mota, Matheus Xavier Rocha, Joel J. P. C. Rodrigues, Victor Hugo C. De Albuquerque, Auzuir Ripardo De Alexandria
IEEE Access, 2018
Advances in photopletysmography signal analysis for biomedical applications
Jermana L. Moraes, Matheus X. Rocha, Glauber G. Vasconcelos, José E. Vasconcelos Filho, Victor Hugo C. De Albuquerque, Auzuir R. Alexandria
Sensors Switzerland, 2018
Modeling, control and simulation of a quadrotor for attitude stabilization
Bárbara B. Carlos, Antonio É. R. M. de Oliveira, Auzuir R. de Alexandria, Rejane C. Sá, Antonio W. O. Rodrigues
Communications in Computer and Information Science, 2017
Global location of mobile robots using Artificial Neural Networks in omnidirectional images
Jessyca Almeida Bessa, Darlan Almeida Barroso, Ajalmar Rego da Rocha Neto, Auzuir Ripardo de Alexandria
IEEE Latin America Transactions, 2015
Thermographic Image Processing Application in Solar Followers
Jonas Platini Reges, Francisco Diego Lima Moreira, Luiz Daniel Santos Bezerra, Auzuir Ripardo de Alexandria, Pedro Pedrosa Reboucas Filho
IEEE Latin America Transactions, 2015
Radial snakes: Comparison of segmentation methods in synthetic noisy images
Jéssyca Almeida Bessa, Paulo César Cortez, John Hebert da Silva Félix, Ajalmar Rêgo da Rocha Neto, Auzuir Ripardo de Alexandria
Expert Systems with Applications, 2015
An OCR System for Numerals Applied to Energy Meters
Auzuir Ripardo de Alexandria, Paulo Cesar Cortez, John Hebert da Silva Felix, Tiberio Menezes de Oliveira, Anaxagoras Maia Girao, Joao Batista Bezerra Frota, Jessyca Almeida
IEEE Latin America Transactions, 2014
Sensor kinect in a telepresence application
A. Poesch, M. Kaestner & E. Reithmeier
Computational Vision and Medical Image Processing IV Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing Vipimage 2013, 2014
PSnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images
Auzuir Ripardo de Alexandria, Paulo César Cortez, Jessyca Almeida Bessa, John Hebert da Silva Félix, José Sebastião de Abreu, Victor Hugo C. de Albuquerque
Computer Methods and Programs in Biomedicine, 2014
A mechatronic description of an autonomous underwater vehicle for dam inspection
Ítalo Jáder Loiola Batista, Antonio Themoteo Varela, Edicarla Pereira Andrade, José Victor Cavalcante Azevedo, Tiago Lessa Garcia, Daniel Henrique da Silva, Epitácio Kleber Franco Neto, Auzuir Ripardo Alexandria, André Luiz Carneiro Araújo
Robotics Concepts Methodologies Tools and Applications, 2013
Análise comparativa de técnicas de contornos ativos aplicadas na segmentação do ventrículo esquerdo em imagens digitais ecocardiográficas de eixo curto
V. O. Silva, P. C. Cortez, J. A. Bessa, A. R. Alexandria, J. S. Abreu
Ifmbe Proceedings, 2013
Novo método de contornos ativo baseado na transforma de Hilbert radial
J. H. S. Felix, P. C. Cortez, A. R. Alexandria, P. P. Rebouças Filho, T. S. Cavalcante, M. A. Holanda
Ifmbe Proceedings, 2013
Method of automatic initialization of active contours applied to lungs in computed tomography images
J. H. S. Felix, P. C. Cortez, T. S. Cavalcante, A. R. Alexandria, P. P. Rebouças Filho, M. A. Alcantara
Ifmbe Proceedings, 2013
A mechatronic description of an autonomous underwater vehicle for dam inspection
Ítalo Jáder Loiola Batista, Antonio Themoteo Varela, Edicarla Pereira Andrade, José Victor Cavalcante Azevedo, Tiago Lessa Garcia, Daniel Henrique da Silva, Epitácio Kleber Franco Neto, Auzuir Ripardo Alexandria, André Luiz Carneiro Araújo
Mobile Ad Hoc Robots and Wireless Robotic Systems Design and Implementation, 2012
AUTOIN: Method of automatic initialization of active contours applied to lungs in CT images
John Hebert da Silva Felix, Paulo Cesar Cortez, Tarique da Silveira Cavalcante, Auzuir Ripardo Alexandria, Marcelo de Alcantara Holanda
IEEE Latin America Transactions, 2012
pSnakes active contours method applied on short axes echocardiogram images to ejection fraction calculation
Auzuir Ripardo de Alexandria, Paulo César Cortez, John Hebert da Silva Felix, José Sebastião de Abreu
Revista Brasileira De Engenharia Biomedica, 2011
Depth calculation using computer vision and sift
Technological Developments in Networking Education and Automation, 2010
Development, application and evaluation of a computational tool for management high voltage break disconnector based on self-organizing maps and image processing
Daniel Freitas Colaço, Auzuir R. de Alexandria, Paulo César Cortez, João Batista B. Frota, José Nunes de Lima, Victor Hugo C. de Albuquerque
Energy Conversion and Management, 2010
Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images
Victor Hugo C. de Albuquerque, Auzuir Ripardo de Alexandria, Paulo César Cortez, João Manuel R.S. Tavares
NDT and E International, 2009
Identification and quantification of pulmonary emphysema through pseudocolors
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network
Victor Hugo C. de Albuquerque, Paulo C. Cortez, Auzuir R. de Alexandria, João Manuel R.S. Tavares
Nondestructive Testing and Evaluation, 2008
Lung and chest wall structures segmentation in CT images
Proceedings of Vipimage 2007 1st Eccomas Thematic Conference on Computational Vision and Medical Image Processing, 2008

Auzuir Ripardo de Alexandria

EDUCATION

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS