Development program for heat balance analysis fuel to steam efficiency boiler and data wireless transfer

This research aim to improve a combustion system of boiler within increase combustion efficiency and use all out of the energy.This research aim to improve a combustion system of boiler within increase combustion efficiency and use all out of the energy. The large boilers were used in the industrial factories which consume a lot of energy for production. By oil and gas fuel will be increasing costs everyday cause many factories interested in energy saving with any method technical engineering, specifically for production costs and environment effect decreasing. This researching was installed and program was invented in the industrial factory. This industry factory consumed cogeneration energy for fabric dying. The efficiency before installing the software is measured about 65.85 - 71.98% which heat in exhaust gas about 20% of overall energy is filled in the system. After installing heat loss in the system has been fallen until remain about 5 - 12% and efficiency of heat in system has been reached a peak of 80 - 85%

Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images

History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to detect tumors in pneumonia, lungs, tuberculosis, emphysema, or other pleura (the membrane covering the lungs) diseases. Disadvantages of CT imaging system are: inferior soft tissue contrast compared to MRI as it is X-ray-based Radiation exposure. Lung CT image segmentation is a necessary initial step for lung image analysis. The main challenges of segmentation algorithms exaggerated due to intensity in-homogeneity, presence of artifacts, and closeness in the gray level of different soft tissue. The goal of this paper is to design and evaluate an automatic tool for automatic COVID-19 Lung Infection segmentation and measurement using chest CT images. The extensive computer simulations show better efficiency and flexibility of this end-to-end learning approach on CT image segmentation with image enhancement comparing to the state of the art segmentation approaches, namely GraphCut, Medical Image Segmentation (MIS), and Watershed. Experiments performed on COVID-CT-Dataset containing (275) CT scans that are positive for COVID-19 and new data acquired from the EL-BAYANE center for Radiology and Medical Imaging. The means of statistical measures obtained using the accuracy, sensitivity, F-measure, precision, MCC, Dice, Jacquard, and specificity are 0.98, 0.73, 0.71, 0.73, 0.71, 0.71, 0.57, 0.99 respectively; which is better than methods mentioned above. The achieved results prove that the proposed approach is more robust, accurate, and straightforward

การปรับปรุงคุณภาพภาดฃพโดยวิธีการปรับเท่าฮีสดตแกรมออกเป็นสองส่วนผ่านทางเวฟเล็ต

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2557The image enhancement process of a digital image is a part of digital signal processing which it plays an important role in some industrial segments, for instance, object classification or bio-identification, satellite exploration or aerial images, robot vision, cancer treatment, etc. These image applications are necessary to consume many resources in physical hardware and computer programming including the intelligent algorithm development of complexly advanced processing in order to enhance digital images – as well as be easy to analyse by humans. This research is aimed to present on Contrast Enhancement using Bi-Histogram Equalization via Two Dimensional Discrete Wavelet Transform (BHE2DWT) in order to compose the coefficient wavelet from other signals. At the end of the process, information on the approximation band from DWT will be divided into two sub-histograms, and then histograms will be independently equalized. After that resulting histograms will be decomposed by IDWT. Resulting images will be not only more displaying in high contrast than their original images, but also preserve the absolute mean brightness error (AMBE) to close their original images.Rajamangala University of Technology Phra Nakho

การปรับปรุงคุณภาพของภาพโดยยังคงรักษาค่าเฉลี่ยความสว่างของภาพโดยใช้การถ่วงน้ำหนักฮีตโตแกรมกระจายตามพื้นที่

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2556Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers, DSP boards and computers have been developed traditional algorithms to improve the quality of the resulting image. This paper aims to present the contrast enhancement using weighted bi-histogram equalisation. In additional, this technique must use two weighted factors which is calculated by the histogram distribution ratio. Likewise, an original image will be equalised by the modification of the probability density function of the gray levels. As the experimental results, the contrasts of resulting images are improved for robot vision, implement, and human perception including absolute mean brightness error (AMBE) limitation.Rajamangala University of Technology Phr

Image Segmentation Using Fuzzy C-Mean Via Image Filter

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2558The segmentation of interesting objects within the image is an important process to provide information for other procedures. The major problem of image segmentation is the number of objects suitable for classifying objects. This research aims to present on Image Segmentation using Fuzzy C- Mean Via Image Filter. The objects will be split up using their object intensities. The proposed method can classify several objects at the same process. Therefore, it saves time to process less than other existing methods. The proposed method is consisted of three steps: noise reduction, histogram smoothing and object classification. The results of the proposed method found that objects are automatically classified by Fuzzy C-Mean. Moreover, the algorithm that successfully calculates the proper number of segmentationsRajamangala University of Technology Phra Nakho

การลดสัญญาณรบกวนแบบสุ่มของภาพโดยยังคงรักษาขอบวัตถุในภาพ

รายงานวิจัย -- มหาวิทยาลัยเทคโนโลยีราชมงคลพระนคร, 2558This report aims to present the random noise elimination of image based on edge preservation. This solution is a pixel processing using 29 templates. Each template consists of the considered pixel and its neighbor pixels. To eliminate noises, the smoothing process replaces the new pixel intensity into the same considered processing pixel by the mean intensity of neighbor pixels. The condition of the template selection is based on the minimum variance of templates. As the experimental results, this solution brings the peak signal-to-noise ratio (PSNR) higher than Gaussian filter, Tomita’s marks, Fukuda’s marks and Nagao’s marks and also gains contrast difference.Rajamangala University of Technology Phra Nakho

COVI³D: Automatic COVID-19 CT Image-Based Classification and Visualization Platform Utilizing Virtual and Augmented Reality Technologies

Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information