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        Evaluation of multi‑class support‑vector machines strategies and kernel adjustment levels in hand posture recognition by analyzing sEMG signals acquired from a wearable device

        Thays Falcari,Osamu Saotome,Ricardo Pires,Alexandre Brincalepe Campo 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.2

        One-vs-One (OVO) and One-vs-All (OVA) are decomposition methods for multi-class strategies used to allow binarySupport-Vector Machines (SVM) to transform a given k-class problem into pairwise small problems. In this context, thepresent work proposes the analysis of these two decomposition methods applied to the hand posture recognition problem inwhich the sEMG data of eight participants were collected by means of an 8-channel armband bracelet located on the forearm. Linear, Polynomial and Radial Basis Function kernels functions and its adjustments level were implemented combined tothe strategies OVO and OVA to compare the performance of the SVM when mapping posture data into the classifi cationspaces spanned by the studied kernels. Acquired sEMG signals were segmented considering 0.16 s e 0.32 s time windows. Root Mean Square (RMS) feature was extracted from each time window of each posture and used for SVM training. Thepresent work focused in investigating the relationship between the multi-class strategies combined to kernels adjustmentslevels and SVM classifi cation performance. Promising results were observed using OVA strategy which presents a reducednumber of binary SVM implementation achieved a mean accuracy of 97.63%.

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        Diagnosis of Bowel Endometriosis Using Endoscopic Ultrasound-guided Fine Needle Aspiration

        Ana Catarina Carvalho,Ricardo Cardoso,Francisco Pires,Sofia Ventura,Francisco Portela,Paula Ministro,Américo Silva 대한소화기학회 2023 대한소화기학회지 Vol.81 No.1

        Endometriosis is a relatively common gynecological condition in women of reproductive age. The rectosigmoid region is the most commonly affected segment when the gastrointestinal tract is involved. A differential diagnosis of colorectal neoplasia is difficult because of the similar clinical, endoscopic, and radiology findings. A 42-year-old female presented with abdominal distention and was subsequently diagnosed with a large bowel obstruction in the rectum. A temporary colostomy was performed, and endoscopy revealed a rectal mass obstructing the rectum. The biopsy showed normal mucosa, and it was difficult to exclude rectal malignancies even after the imaging workup. Endoscopic ultrasound demonstrated a hypoechoic lesion below the rectal mucosa, and fine needle aspiration confirmed the diagnosis of bowel endometriosis. Bowel endometriosis is a challenging diagnosis. Endoscopic ultrasound- guided fine-needle aspiration is useful for acquiring adequate samples for histological confirmation and a definitive diagnosis of bowel endometriosis.

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        A Computer Vision System for Pallets Verification in Quality Control

        Marcus Vinicius Barbosa de Morais,Sara Dereste dos Santos,Ricardo Pires 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.7

        Flexible manufacturing systems have gained their place in industries thanks to the possibility of being adapted to a particular task. A robotized palletizing cell is an example of such a system, which is responsible for organizing and stacking loads to be transported. A key part in this process is the pallet and if it presents a problem, the whole cell may stop. Furthermore, it is possible that manipulator robots, transported loads, and any other part of the cell suffer damage. To avoid these problems, the objective of this work is to develop an artificial vision system capable of selecting wood pallets that are in good condition, which means free of defects such as splinters, cracks and broken or out of dimensions elements. The artificial vision system has been developed based on image processing and machine learning techniques and presented better results compared to the past studies. Image processing parameters of Canny and Hough Transform have been adjusted, followed by comparisons among Support-Vector Machines with different kernels and their parameters. An accuracy of 100% in the classification has been obtained based on the Support-Vector Machine with radial basis function kernel.

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