Applied Mechanics and Materials Vol. 913

Abstract: Abstract. Magnetic Resonance Imaging (MRI) is a medical tool that is used to form images of organs, soft tissues, bones and almost all internal body structures. The MRI image acquisition process takes a long time. One of the efforts to shorten the examination acquisition time is using the parallel imaging technique, namely SENSE. However, SENSE has a weakness, namely reducing the Signal Noise to Ratio (SNR). One of the denoising methods that can increase SNR is the Nonlocal mean filter (NLM). Post-image acquisition denoising becomes a cheaper and more effective alternative to use. The aim of this research is to measure the increase of SNR value in MRI SENSE images between before the denoising technique and after the denoising technique. This research is expected to produce a faster scanning time and maintain the quality of the MRI image. This experimental research was carried out by applying the SENSE parallel imaging technique to R-factors 2 and 4. The sequence used is T2WI TSE on axial slice phantom. The T2WI TSE SENSE phantom MRI image was corrected with the NLM denoising technique to produce a better quality image. Each variation is measured image information before and after the denoising technique. Image information is assessed quantitatively by measuring SNR. The results of the parametric test showed that there was an increase in the SNR value after the application of the denoising technique on the Phantom T2WI TSE SENSE MRI image at r-factor 2 and r-factor 4. The different test on the SNR assessment resulted in a p value < 0.001.

Abstract: Covid-19 is a type of pneumonia disease currently affecting people around the world. Covid-19 and tuberculosis are lung diseases that are dangerous and spread quickly to other humans. Chest radiography is one of the main modalities in the management of suspected COVID-19 and tuberculosis patients as it provides radiological information on lung infections that can be used as diagnostic guidelines and patient care. This study aims to determine the differences in optical density and contrast in Covid-19 and pneumonia tuberculosis cases in order to find the specific characteristics of Covid-19 and tuberculosis. This research processed secondary data from Covid-19 and tuberculosis positive patients using image-J software that can be easily obtained and operated by anyone. After processing, the density and contrast were analyzed, particularly for the lungs. Based on image processing results, the average density for Cnovid-19 radiographs is 1,066, while for Tuberculosis radiograph is 1,519. The average contrast values for Covid-19 and tuberculosis radiographs are 0.37 and 1.03. Thus, it can be concluded that the contrast of the Covid-19 radiograph is lower than the tuberculosis radiograph. The difference in optical density on the tuberculosis and Covid-19 cases is 0.5 as the opacity on the chest radiographs of Covid-19 patients is evenly distributed over the entire lung surface.

Abstract: Elderly people need special attention and some of them need to be monitored continuously and in real-time. Fall detection is one of the systems used to monitor the daily life of the elderly. Various devices and methods were developed to monitor the condition of the elderly on daily activity. The system that has been proposed in previous studies uses a number of sensors that are placed on the body. However, this system tends to be high cost, complex installation, and inconvenient to use. Therefore, an alternative system is needed to overcome this problem. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. Testing on larger data sets is needed to verify the proposed method's performance further. The use of cameras can be a solution to monitoring the activities of the elderly. With the image processing method, it is possible to estimate the activities of the elderly. The purpose of this study is to developed a fall detection method using PoseNet with pose calculations based on key joins. This study developed a fall detection method using PoseNet with pose calculations based on key joins. The key-join used is Left and Right Shoulder and is only measured at the y-coordinate. We calculated the difference absolute standard deviation value (DASDV) and average amplitude change (AAC) on the Y-coordinate. From 10 falling and non-falling conditions trials, we obtained 85% and 80% accuracy for AAC and DASDV. The result of this research can be used as a source or comparison for future research.

Abstract: Several image enhancement applications can be used to increase SNR and brightness. However, if the setting of tube current-time is too high, the application can not reduce the radiation dose. So, It is necessary to test tube current-time variations on the SNR value and image quality to determine the effect on body organs and radiation dose. This study aims to determine the influence of tube current-time on SNR and with this research is expected to be an input for radiographers in the field to pay attention to parameter settings for patients. We compared five tube current-time variations on the Antebrachii examination by using Phantom (58 kV, 32 mA, 50 mSec), (58 kV, 100 mA, 50 mSec), (58 kV, 32 mA, 5 mAs), (58 kV , 32 mA, 5 mAs) and (58kV, 100 mA, 5 mAs). Then we made a circle of ROI with a diameter of 0.2 cm² on the Bone, Soft-tissue and Air sections in that four tube current-time variations, each of which has 7 ROI points tocalculate the SNR and the radiation dose is seen from the image. There is a difference in SNR on tube current-time changes and affects the image quality with an Adjusted R square value of 0.003 and 0.865. It shows that there is a change in tube current that affects the Signal to Noise Ratio between organs. Since it is proven that changes in tube current-time affect image quality, further research can formulate an ideal parameter for the examination of each organ.

Abstract: One of the environmental factors that can influence the onset of fatigue is noise exposure. Besides noise has a negative impact on everything from cognitive processes to mental and physical health. Noise's impact can be measured using brain activity and the waves it generates. The main goal of this article is to review how electroencephalography can be used to determine the impact of noise on cognitive performance. The method is based on bibliometric analysis stages, including literature study from the Scopus database and the snowball search method. The findings revealed that of the 30 articles examined, 28 used the experimental design method with continuous noise, and 10 used the time-frequency domain for feature extraction. According to the results of the study, there seems to be an increase in the use of EEG for performance measurement, such as mental fatigue, mental workload, working memory, attention, stress, and the presence of noise factors that influence cognitive performance.