Fatigue is a condition experienced by a person that causes a decrease in a person's vitality and productivity. Fatigue can be characterized by slowed reaction time and fatigue. People’s condition is a significant factor in driving safety. Based on this increase in the number of accidents according to the Central Statistics Agency (BPS), experts conducted research on detecting fatigue that often occurs. In this study, a system that can detect fatigue is developed using parameters obtained from physiological indicators such as heart signals by using the Low Frequency/High Frequency ratio parameter, muscle signals using the average frequency domain of the muscle signal and oxygen saturation. The detection tool in this study uses the ECG Click Module, EMG Click Module, and Oximeter Click which will be connected to the ARM microcontroller, namely STM32F407ZG. The parameters that have been obtained are processed using the Fuzzy Logic method to determine the level of fatigue. Based on the tests results carried out on three subjects, parameter values were obtained where in the subject the three parameters entered into fuzzy logic, it was found that the three subjects were detected in a fairly tired state. The aggregated output that found from subject A was 0.6303, the aggregated output of subject B was 0.77948, and the aggregated output of subject C was 0.79188. Furthermore for future research development, the signal processing can be done more complex, besides that signal processing and fuzzy logic processing can be embedded so the process runs in realtime.
Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.