Vision-based Object’s Shape Determination for Robot Alignment

This study provides vision-based system solutions for a peg-in-hole problem faced by a fork lift like robot used to transport copper wire spools from a rack, in which the spools are arranged side by side to a specified place. The copper wire spool (a cylindrical object on which the copper wire is wound and have a rim at each end) is held by 3 cylindrical shafts; one of the shafts is inserted through the center hole of the spool and another two shafts is held at the bottom of the spool. The aim of the development of vision-based system is to enable the robot to pick up the spool autonomously. To enable the center cylindrical shaft to be inserted nicely through the center hole of the spool, the center point of the spool must be on the center line of the camera Field of View (FOV). The problem to be solved in this study is how to determine that the center point is overlapped with the center line of the camera FOV. Firstly, a circle with the same radius of the spool’s rim was created at the center of the camera frame on screen, and then the spool’s front rim was tracked until it is overlapped with the circle on the screen image to ensure it is on the line of the camera FOV. However, the scope of this paper is limited to copper wire spool detection, and the confirmation of the front rim overlapping conditions is based on real time video processing. The proposed system uses Circular Hough Transform (CHT), binarization, morphology and edge detection of the sampled images from real-time video recording. A Logitech Webcam C270, which has an autofocus camera and HD view with lower price is used. By integrating the Logitech webcam for windows with MATLAB R2016a, all computations, programming and processing of this project are done using the MATLAB. Several experiments had been carried out and from the result obtained, the system is able to track the spool and determine the correct position of the robot to pick up the spool

Experimental evaluation of the effect of ozone treatment on the oxidation and removal of dry soot deposits of the exhaust gas recirculation system

The integration of alternative energy sources as a replacement for fossil fuels across various industrial sectors, including power generation, emergency systems, or marine applications, is uncertain. As a result, the utilization of traditional fuels is not anticipated to be fully phased out in the near future. To address this, new technologies, such as those that employ oxidising atmospheres, have been explored as a means to enhance the pollution control capabilities of existing technologies, as the Exhaust Gas Recirculation (EGR) system. In this regard, the present study has assessed the efficacy of ozone atmosphere exposure in mitigating the formation of undesired fouling deposits within the system, with the aim of facilitating more efficient operation of EGR devices and extending their service life. To this end, dry soot samples have been exposed to various ozone atmospheres at different temperatures and ozone concentrations through the utilization of an experimental test bench. The oxidation potential of these atmospheres has been evaluated through the analysis of the deposit mass loss. Likewise, confocal microscopy techniques have been employed to obtain the 3D topography of the fouling samples before and after the ozone treatment, allowing the assessment of the deposit thickness reduction, as well as the surface roughness variation. Additionally, thermogravimetric analysis has been conducted to examine the effects of the oxidation processes on fouling samples composition. The findings of this study have revealed that ozone atmospheres have been effective in reducing deposit mass at ozone treatment temperatures above 100 °C. The reduction in mass has reached 78.5% and 91.8% with treatment temperature of 140 °C with ozone concentrations of 30 gO3/m³ and 50 gO3/m³, respectively. It has also been established that treatment conditions with ozone concentrations of 30 gO3/m³ and 50 gO3/m³ are effective in reducing the thickness of deposits even at intermediate treatment temperatures, resulting in a thickness reduction of 78.6% and 81.1% at 80 °C, respectively. Additionally, it has been observed that the ozone exposure leads to the increase in the proportion of volatile material within the deposiUniversidade de Vigo/CISUGAgencia Estatal de Investigación | Ref. PDC2021-121778-10

Sistem Diagnosis Otomatis Identifikasi Penyakit Jantung Coroner Menggunakan Ektraksi Ciri GLCM dan Klasifikasi SVM

Jantung coroner merupakan salah satu penyakit jantung yang sangat banyak menyerang manusia. Penyebab jantung coroner adalah terjadinya penumpukan lemak dan kolestrol pada dinding pembuluh darah. Penyebab utama dari jantung coroner karena gaya hidup manusia yang kurang sehat. Pola makan tidak yang buruk, obesitas dan kurang berolahraga menjadi penyebab utama munculnya penyakit jantung coroner. Penyempitan pembuluh darah sangat berkaitan erat dengan aliran darah yang terjadi ke mata khususnya bagian iris. Sehingga diperlukannya suatu cara dengan metode yang lebih efesien dan murah untuk mengetahui dan mengidentifikasi penyakit jantung sejak diri dengan menggunakan iridologi. Sistem yang dirancang dengan menggunakan Circle Hough Tranform (CHT) sebagai deteksi bagian iris secara otomatis, Gray Level Co- occurrence Matrix (GLCM) sebagai ekstraksi ciri dan Support Vector Machine (SVM) sebagai klasifikasi. Pengujian telah dilakukan terhadap 40 data citra yang memperoleh tingkat keberhasilan identifikasi sebesar 87.5%

A Circle Hough Transform Implementation Using High-Level Synthesis

Circle Hough Transform (CHT) has found applications in biometrics, robotics, and imageanalysis. In this work, the focus is the development of a Field Programmable Gate Array (FPGA) based accelerator that performs a series of procedures and results in circle detection. The design is performed using Vivado High-Level Synthesis (HLS) tools and targeted for a Zynq UltraScale+ ZCU106. The implementation includes the following procedures: Gaussian filter, Sobel edge operator, thresholding, and finally the CHT algorithm. The performance is evaluated based on the execution time as compared to the software (Python code) execution and the analysis tools provided by Vivado HLS tool. The accuracy of detection is evaluated due to the approximation done for the sake of faster execution. The CHT requires a large amount of memory for its implementation, and thus the overall resource utilization is to be optimized. In this work we evaluate both the speed (time) and the number of logical blocks and memory components required for implementation. The core of the work is the efficient implementation of the Circle Hough Transform using High-Level Synthesis

Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking

The first step in monitoring an observer’s eye gaze is identifying and locating the image of their pupils in video recordings of their eyes. Current systems work under a range of conditions, but fail in bright sunlight and rapidly varying illumination. A computer vision system was developed to assist with the recognition of the pupil in every frame of a video, in spite of the presence of strong first-surface reflections off of the cornea. A modified Hough Circle detector was developed that incorporates knowledge that the pupil is darker than the surrounding iris of the eye, and is able to detect imperfect circles, partial circles, and ellipses. As part of processing the image is modified to compensate for the distortion of the pupil caused by the out-of-plane rotation of the eye. A sophisticated noise cleaning technique was developed to mitigate first surface reflections, enhance edge contrast, and reduce image flare. Semi-supervised human input and validation is used to train the algorithm. The final results are comparable to those achieved using a human analyst, but require only a tenth of the human interaction