Federal Fluminense University
Rio de Janeiro | Brazil
Main Specialties: Biotechnology, Clinical & Laboratory Immunology, Cytopathology, Hematology, Nuclear Medicine, Nuclear Radiology, Orthopaedic Sports Medicine, Other, Public Health, Statistics
Additional Specialties: Electrical and Computer Engineering
B.Sc. degree from Federal University of Rio de Janeiro (UFRJ) in Electrical and Computer Engineering (ECE); M.Sc. from the Instituto Tecnológico de Aeronáutica (ITA), Sao Jose dos Campos; M.Sc. degree in ECE at Northwestern University, Evanston, Illinois (IL), USA; and Ph.D. in ECE from the Illinois Institute of Technology (IIT), Chicago, IL, USA. Taught at: DeVry University; DePaul University; Universidade Estadual do Norte Fluminense (UENF), Campos de Goytacazes, Rio de Janeiro (RJ), Brazil; and for the Universidade Estadual da Zona Oeste (UEZO), Rio de Janeiro, RJ, Brazil. Visiting professor at the Polytechnic Institute of Rio de Janeiro (IPRJ)/State University of Rio de Janeiro (UERJ) in Nova Friburgo, RJ. Currently working at Universidade Federal Fluminense (UFF), Niterói, RJ for the Department of Telecommunications. Research interests include signal/image/video processing, inverse problems, computational & mathematical modeling, stochastic models, multimedia, electronic instrumentation, machine learning and remote sensing. Reviewer for the following journals/magazines: IMAVIS (Elsevier); Pattern Recognition (Elsevier); COMPELECENG (Elsevier); IET Image Processing; EURASIP Journal on Advances in Signal Processing (JASP) (Springer); International Journal on Computational Science & Applications (IJCSA); International Journal of Electrical and Computer Engineering (IJECE); Oriental Journal of Computer Science and Technology (OJCST); International Journal of Ambient Computing and Intelligence (IJACI); Journal of Microwaves, Optoelectronics and Electromagnetic Applications (JMOE); and International Journal of Image Processing (IJIP). Engaged in topics such as technology transfer, STEM education, environmental issues and digital inclusion. Member of IEEE, ACM, and IASTED. Editor of IJACI, EURASIP JASP, IJCSA, IJIP and OJCST.
Primary Affiliation: Federal Fluminense University - Rio de Janeiro , Brazil
1PubMed Central Citations
Download Table of Contents here: https://www.researchgate.net/publication/338007871_Computer_Vision_and_Data_Storage_in_UAVsVolume 1 concentrates on UAS control and performance methodologies including Computer Vision andData Storage, Integrated Optical Flow for Detection and Avoidance Systems, Navigation and Intelligence, Modeling and Simulation, Multisensor Data Fusion, Vision in Micro-Aerial Vehicles (MAVs), Computer Vision in UAV using ROS, Security Aspects of UAV and Robot Operating System, Vision inIndoor and Outdoor Drones, Sensors and Computer Vision, and Small UAVP for Persistent Surveillance.
Estrela, V.V.; Saotome, O.; Loschi, H.J.; Hemanth, J.; Farfan, W.S.; Aroma, J.; Saravanan, C.; Grata, E.G.H. Emergency Response Cyber-Physical Framework for Landslide Avoidance with Sustainable Electronics †. Technologies 2018, 6, 42.
An Emergency Response (ER) Cyber-Physical System (CPS) to avoid landslides and survey areas located on or near slopes is introduced that handles two problems: electronic waste disposal, and environmental disasters. Uncomplicated detection circuits using salvaged components can pinpoint floods in impoverished regions. CPSs simplify hazard prediction and mitigation in disaster supervision. Nonetheless, few green practices and efforts have been accomplished in this regard. Recent technical advances help landslides studies and the evaluation of suitable risk alleviation measures. This work addresses in situ meters, and cameras to observe ground movements more accurately. The ER-CPS identifies and can help mitigate landslides using techniques based on motion detection that can productively predict and monitor the zone conditions to classify it, and the landslide-related data can be transmitted to inspecting stations to lessen the erosion/sedimentation likelihood while increasing security.
Curr Med Imaging 2020 Jan 28. Epub 2020 Jan 28.
LCMAT, UENF, Campos de Goytacazes, RJ, Brazil.
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Curr Med Imaging Rev 2020 Jan 28. Epub 2020 Jan 28.
LCMAT, UENF, Campos de Goytacazes, RJ. Brazil.
Download full-text PDF
V. V. Estrela, A. C. B. Monteiro, R. P. França, Y. Iano, A. KHELASSI, and N. Razmjooy, “Health 4.0: Applications, Management, Technologies and Review”, Medical Technologies Journal, vol. 2, no. 4, pp. 262-276, Jan. 2019.
Medical Technologies Journal
The Industry 4.0 Standard (I4S) employs technologies for automation and data exchange through cloud computing, Big Data (BD), Internet of Things (IoT), forms of wireless Internet, 5G technologies, cryptography, the use of semantic database (DB) design, Augmented Reality (AR) and Content-Based Image Retrieval (CBIR). Its healthcare extension is the so-called Health 4.0. This study informs about Health 4.0 and its potential to extend, virtualize and enable new healthcare-related processes (e.g., home care, finitude medicine, and personalized/remotely triggered pharmaceutical treatments) and transform them into services. In the future, these services will be able to virtualize multiple levels of care, connect devices and move to Personalized Medicine (PM). The Health 4.0 Cyber-Physical System (HCPS) contains several types of computers, communications, storage, interfaces, biosensors, and bioactuators. The HCPS paradigm permits observing processes from the real world, as well as monitoring patients before, during and after surgical procedures using biosensors. Besides, HCPSs contain bioactuators that accomplish the intended interventions along with other novel strategies to deploy PM. A biosensor detects some critical outer and inner patient conditions and sends these signals to a Decision-Making Unit (DMU). Mobile devices and wearables are present examples of gadgets containing biosensors. Once the DMU receives signals, they can be compared to the patient’s medical history and, depending on the protocols, a set of measures to handle a given situation will follow. The part responsible for the implementation of the automated mitigation actions are the bioactuators, which can vary from a buzzer to the remote-controlled release of some elements in a capsule inside the patient’s body. Decentralizing health services is a challenge for the creation of health-related applications. Together, CBIR systems can enable access to information from multimedia and multimodality images, which can aid in patient diagnosis and medical decision-making. Currently, the National Health Service addresses the application of communication tools to patients and medical teams to intensify the transfer of treatments from the hospital to the home, without disruption in outpatient services. HCPS technologies share tools with remote servers, allowing data embedding and BD analysis and permit easy integration of healthcare professionals expertise with intelligent devices. However, it is undeniable the need for improvements, multidisciplinary discussions, strong laws/protocols, inventories about the impact of novel techniques on patients/caregivers as well as rigorous tests of accuracy until reaching the level of automating any medical care technological initiative.
Razmjooy, Navid, Vania Vieira Estrela and Hermes Jose Loschi. "A Survey of Potatoes Image Segmentation Based on Machine Vision." Applications of Image Processing and Soft Computing Systems in Agriculture. IGI Global, 2019. 1-38. Web. 1 Mar. 2019. doi:10.4018/978-1-5225-8027-0.ch001
Applications of Image Processing and Soft Computing Systems in Agriculture
The quality control of the agricultural products, which in many cases is through intuitive observation of the visible features of the product, plays a key role in the survival of the agricultural industry. For a long time, the qualitative categorization of these products has been performed by trained people who search products for the specific characteristics. On the other hand, hard and repetitive working can cause people to make some mistakes in computing the quality control errors. Hence, by entering the machine vision systems into this subject, they turned into a reliable, low-cost and real-time technology. Despite the existence of machine vision systems in this process, there are still major challenges in categorizing agricultural products in terms of quality, size, shape, and examination of defects. Potato is one of the most important agricultural products that is produced and has a high application. Unfortunately, it suffers from various types of diseases and defects. Hence, its quality control has a particular importance.
Hemanth, D.J., Vieira Estrela, V. Deep Learning for Image Processing Applications, vol. 31, Advances in Parallel Computing, 2017
Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques.
Encyclopedia of E-Health and Telemedicine
IST 2014 - 2014 IEEE International Conference on Imaging Systems and Techniques, Proceedings
Int J Comput Appl 2012 Aug;51(19):17-24
Instituto Federal de Educacao, Ciencia e Tecnologia do Sudeste de Minas Gerais (IF Sudeste MG), Av. Dr. José Sebastião da Paixão, s/n°, Lindo Vale CEP: 36180-000, Rio Pomba, MG, Brazil.
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