Digital chest radiography offers many advantages over filmbased radiography, such as immediate image display, no film processing and room storage, wider dynamic range and lower radiation dose. In general, a raw X-ray image acquired directly from a digital flat detector contains poor quality of image, which may not be suitable for diagnosis and treatment planning. Therefore, a pre-processing technique is usually required to enhance image quality. This paper presents an improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement. The proposed technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images. The proposed approach was tested using a radiographic survey phantom and a radiographic chest phantom and compared with conventional enhancement methods, such as histogram equalization, unsharp masking, CLAHE. The results show that the proposed N-CLAHE method yields great improvement on the pre-processing correction for digital chest radiography.

User experience (UX) evaluation investigates how people feel about using products or services and is considered an important factor in the design process. However, there is no comprehensive UX evaluation method for time-continuous situations during the use of products or services. Because user experience changes over time, it is difficult to discern the relationship between momentary UX and episodic or cumulative UX, which is related to final user satisfaction. This research aimed to predict final user satisfaction by using momentary UX data and machine learning techniques. The participants were 50 and 25 university students who were asked to evaluate a service (Experiment I) or a product (Experiment II), respectively, during usage by answering a satisfaction survey. Responses were used to draw a customized UX curve. Participants were also asked to complete a final satisfaction questionnaire about the product or service. Momentary UX data and participant satisfaction scores were used to build machine learning models, and the experimental results were compared with those obtained using seven built machine learning models. This study shows that participants’ momentary UX can be understood using a support vector machine (SVM) with a polynomial kernel and that momentary UX can be used to make more accurate predictions about final user satisfaction regarding product and service usage.

In the current design processes, it has been often needed to use a level of final user satisfaction to evaluate products or services. Evaluation of the final user satisfaction on products and services has been considered an interesting challenge because it is difficult to measure the final user satisfaction according to products and services. Several papers and articles regarding the measurement of UX (user experience) as the satisfaction have been published. However, in the most approaches, UX was measured by questionnaire or survey collection method, which may lead to bias and a lack of exact feeling data of the target users. On the other hand, soft biometric data such as gender, age and facial expression can be used as the essential data for the user satisfaction analysis. In this research, we assume that the facial expression is essential in physical expressions and can be used as the accurate satisfaction data. It may be possible to capture the user’s facial expression during the particular use of products or services without users’ consciousness. However, in general cases, it is difficult to get the final user satisfaction.This study aimed to propose a framework to classify the final user satisfaction of products or services by the facial expression recognition and machine learning. The proposed framework consists of the three main steps. First, the data of facial expression, gender, age and the final user satisfaction are experimentally collected. Second, classification models are built by machine learning algorithms using the data. Finally, the model evaluation is employed to verify the accuracy of the model. After making the classification model, it is possible to classify the final user satisfaction only from the data of facial expression, gender and age.

Unsupervised classification is a popular tool for unlabeled datasets in data mining and exploratory data analysis, such as K-means and Fuzzy C-mean. Although these unsupervised techniques have demonstrated substantial success for satellite imagery, they have some limitations. The initialization number of clusters in K-means clustering application is often needed in advance as an input parameter to the algorithm. Our previous paper regarding the initialization number of clusters in K-means clustering application with a co-occurrence matrix technique has been published. Although our previous approach regarding the number of cluster was discovered, but it was limited to count a number of peaks in occurrence matrix as the number of clusters by manual counting. The best of our previous approach need to automatically find and count a number of peaks in occurrence matrix. In this research, we assume that the satellite imagery is given and we have no knowledge beforehand for segmentation. Hence, this paper presents a simple, parameter-free K-means method for K-means in satellite imagery clustering application to determine the initialization number of clusters with image processing algorithms based on the co-occurrence matrix technique. A maxima technique is proposed for automatic counting a number of peaks in occurrence matrix as the number of clusters. The parameter-free method was tested with hyperspectral imagery and multispectral imagery. The results from the tests confirm the effectiveness of the proposed method in K-means method and compared with isodata algorithm.