The purpose of this research is to determine the adaptive statistical iterative reconstruction (ASIR) level that enables optimal image quality and dose reduction in the chest computed tomography (CT) protocol with ASIR. A chest phantom with 0-50 % ASIR levels was scanned and then noise power spectrum (NPS), signal and noise and the degree of distortion of peak signal-to-noise ratio (PSNR) and the root-mean-square error (RMSE) were measured. In addition, the objectivity of the experiment was measured using the American College of Radiology (ACR) phantom. Moreover, on a qualitative basis, five lesions' resolution, latitude and distortion degree of chest phantom and their compiled statistics were evaluated. The NPS value decreased as the frequency increased. The lowest noise and deviation were at the 20 % ASIR level, mean 126.15 ± 22.21. As a result of the degree of distortion, signal-to-noise ratio and PSNR at 20 % ASIR level were at the highest value as 31.0 and 41.52. However, maximum absolute error and RMSE showed the lowest deviation value as 11.2 and 16. In the ACR phantom study, all ASIR levels were within acceptable allowance of guidelines. The 20 % ASIR level performed best in qualitative evaluation at five lesions of chest phantom as resolution score 4.3, latitude 3.47 and the degree of distortion 4.25. The 20 % ASIR level was proved to be the best in all experiments, noise, distortion evaluation using ImageJ and qualitative evaluation of five lesions of a chest phantom. Therefore, optimal images as well as reduce radiation dose would be acquired when 20 % ASIR level in thoracic CT is applied.
In recent years, the connected car market has been expanding. Various car manufacturers are trying to provide Internet of things (IoT) services by collecting and analysing sensing data from cars. However, there is not a well-defined standardized IoT platform to handle the big data for the various car OEM companies or service providers. To resolve this issue, we propose a globally standardized IoT platform for connected cars based on Global Standard 1 (GS1). We extended and remodelled Electronic Product Code global (EPCglobal), one of GS1 standards, and developed a new IoT platform framework called open-language for IoT (Oliot). Then, based on the framework, we modelled car events and developed some hardware and software modules to capture, store, and share the event data. We also implemented demonstration services using the shared data for verification. This research can provide a new ecosystem to the connected car industries and service providers to enable standardized handling and processing of big data. As a result, it will be much easier to create and provide a greater variety of services and combinations of services.
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