X-ray luminescence is produced when contrast agents absorb energy from X-ray photons and release a portion of that energy by emitting photons in the visible and near-infrared range. X-ray luminescence computed tomography (XLCT) was introduced in the past decade as a hybrid molecular imaging modality combining the merits of both X-ray imaging (high spatial resolution) and optical imaging (high sensitivity to tracer nanophosphors).
Phase-retrieval techniques aim to recover the original signal from just the modulus of its Fourier transform, which is usually much easier to measure than its phase, but the standard iterative techniques tend to fail if only part of the modulus information is available. We show that a neural network can be trained to perform phase retrieval using only incomplete information, and we discuss advantages and limitations of this approach.
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis. This project compares the performance of four neural network architectures in segmenting retinal images, using a combined dataset from different databases, namely the UNet, DR-VNet, UNet-ResNet and UNet-VGG.
We use neural network algorithms for finding compression methods of images in the framework of iterated function systems which is a collection of the transformations of the interval $(0, 1)$ satisfying suitable properties.
To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and more intelligent approaches.

 
 
 
 Research aim : This study aims to test the Quality of Moodle Borneo E-Learning (BEL) which includes Service Quality, Student Quality and Lecturer Quality on Perceived Satisfaction, Perceived Usefulness and Benefits based on the E-learning System Success Evaluating Model for active BEL users
 Design/Methode/Approach : This research uses a quantitative approach with non-probability sampling method and the technique used is Quota Sampling. To determine the number of samples, the hair formula was used which determined that the sample consisted of 280 University of Borneo students who had used BEL. The data analysis method used is SEM (Structural Equation Modeling) with the help of the SmartPLS program.
 Research Finding : The results showed that the variables of Service Quality, Student Quality and Lecturer Quality had a positive and significant effect on satisfaction and usability. And the Satisfaction and Usefulness Variables also have a positive and significant effect on benefits.
 Theoretical contribution/Originality : It is hoped that this research can provide insight and knowledge as well as provide information to researchers and academics regarding the analysis of the quality of BEL application service quality using the EESS model.
 Practitionel/Policy implication : The results of this study are used as input for the Borneo Tarakan University's LP3M, so that in the future it can improve the quality and quality of BEL so that in the future students will be more comfortable doing online learning with BEL.
 Research limitation : In this study it only focuses on evaluating the quality of using the BEL application but only looks at it from the perspective of students at the University of Borneo Tarakan. And also focuses on the EESS conceptual model which includes only social factors, namely Service Quality, Learner Quality, Instructor Quality.
 
 
 
 
 
 sitive and significant effect on benefits
Yukito Onodera, Daisuke Hisano, Kazuki Maruta
et al.
This paper experimentally demonstrates 512 color shift keying (CSK) signal transmission for optical camera communication (OCC). We achieved error-free operation with a CMOS image sensor module and a multi-label classification neural network-based equalizer.
This paper compiles measured complex dielectric constants of benign and hazardous materials used for developing algorithms for personnel scanners at airports at 30 GHz. The materials are grouped into broad classifications of potential threat by mapping the materials to the complex dielectric constant plane.
We collected 32 public datasets, of which 28 for medical imaging and 4 for natural images, to conduct study. The images of these datasets are captured by different cameras, thus vary from each other in modality, frame size and capacity. For data accessibility, we also provide the websites of most datasets and hope this will help the readers reach the datasets.
The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for real-time segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected.
Images can be represented by mathematical function using wavelets. Wavelet can be manipulated (shrink/expand) by applying some values to its function. It helps to localize the signals. Application of wavelet in images processing has larger scope as proved. Image compression is one of the dimension. There are various wavelet image compression techniques. This research paper focused on comparison of only two techniques i.e. STW and SPIHT for color JPEG images.
Cells segmentation shows rapid growth in biology. Indeed, using the classical segmentation methods only is not enough to segment this type of images. In this manuscript, we will present a new method of ribosomes segmentation. A pre-treatment phase will precedes the segmentation process and after that a post-processing will proceed.
We work on the breast imaging malignancy segmentation task while focusing on the training process instead of network complexity. We designed a training process based on a modified U-Net, increasing the overall segmentation performances by using both, benign and malignant data for training. Our approach makes use of only a small amount of annotated data and relies on transfer learning from a self-supervised reconstruction task, and favors explainability.
This paper introduces MuRAD (Musculoskeletal Radiograph Abnormality Detection tool), a tool that can help radiologists automate the detection of abnormalities in musculoskeletal radiographs (bone X-rays). MuRAD utilizes a Convolutional Neural Network (CNN) that can accurately predict whether a bone X-ray is abnormal, and leverages Class Activation Map (CAM) to localize the abnormality in the image. MuRAD achieves an F1 score of 0.822 and a Cohen's kappa of 0.699, which is comparable to the performance of expert radiologists.
This work focuses on the automatic quantification of the breast density from digital mammography imaging. Using only categorical image-wise labels we train a model capable of predicting continuous density percentage as well as providing a pixel wise support frit for the dense region. In particular we propose a weakly supervised loss linking the density percentage to the mask size.