Development of a didactic platform for acquisition and processing of biomedical signals for the practice in biomedical engineering

Scolaro, Geovani Rodrigo; Azevedo, Fernando Mendes de; Rathke, Juliano Elesbão; Possa, Philipp; Andrighetto, Eduardo; Adur, Robson; Neto, José Marino

doi:10.1109/icemi.2009.5274004

Cited by 3 publications

(1 citation statement)

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“…Lee et al report the use of EEG signals in a first year electronic engineering laboratory as examples of real, noisy, low amplitude signals with which to explore operational amplifiers, analog filters and sensor interface circuitry. Scolaro et al describe the design of an in‐house 6‐channel, ARM‐based acquisition system designed for electronic engineering students performing biomedical engineering laboratory exercises with wireless electrodes for EEG, ECG, EMG, and EOG (electrooculography: eye movement signals). The aim of the exercise was to familiarize students with aspects of signal acquisition, characteristics and filtering.…”

Section: Introductionmentioning

confidence: 99%

Final‐year projects using open source OpenEEG

Collins

Woolley

Rawson³

et al. 2015

Comp Applic In Engineering

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-This paper describes undergraduate electroencephalography projects using the OpenEEG platform. The projects applied a broad range of curriculum components including computer interfacing, signal processing and software development. Project applications were wide-ranging and provided opportunities to explore research areas such as Brain-Computer Interfacing , affective computing and assistive technology.

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Section: Introductionmentioning

confidence: 99%

Final‐year projects using open source OpenEEG

Collins

Woolley

Rawson³

et al. 2015

Comp Applic In Engineering

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Thermosolar plant with didactic potential for study in modeling, simulation and control applications

2019

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Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection

Wang

Hao

et al. 2022

Processes

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Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant.

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Development of a didactic platform for acquisition and processing of biomedical signals for the practice in biomedical engineering

Cited by 3 publications

References 0 publications

Final‐year projects using open source OpenEEG

Final‐year projects using open source OpenEEG

Thermosolar plant with didactic potential for study in modeling, simulation and control applications

Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection

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