Augmented and Virtual Reality are proliferating throughout daily life. Everyday utilities, such as Google Maps, deploy Augmented Reality (AR) to improve the user experience and increase its effectiveness. Similarly, Augmented and Virtual Reality have been used in the educational domain, where these applications have been shown to improve the learning curve and retention. In this study, AR is proposed as an innovative method to improve the effectiveness and efficiency of the education of nursing students. We propose to introduce this innovative technology in the education of future nurses. Within this study the researchers use augmented reality (AR) to help nursing students learn about physical assessment techniques for the heart and the lungs. Researchers have demonstrated increased memory retention while using AR [14][15]. In this study, we provide a holographic overlay including the internal organs heart, ribcage, and lungs to increase the understanding of accurate placement of devices required for assessing cardiac and respiratory issues using anatomical landmarks. In addition, the visual aspects are supported with audio sound tracks to enhance learning. The ability to project images accurately placed onto real world physical objects using AR headsets could lead to increased retention and deeper understanding leading to precision performance related to techniques in physical assessment of the patient.

Sports data analysis and visualization are useful for gaining insights into the games. In this paper, we present a new visual analytics technique called Tennis Fingerprinting to analyze tennis players’ tactical patterns and styles of play. Tennis is a complicated game, with a variety of styles, tactics, and strategies. Tennis experts and fans are often interested in discussing and analyzing tennis players’ different styles. In tennis, style is a complicated and often abstract concept that cannot be easily described or analyzed. The proposed visualization method is an attempt to provide a concrete and visual representation of a tennis player’s style. We demonstrate the usefulness of our method by analyzing matches played by Roger Federer and Rafael Nadal at Wimbledon, Roland Garros, and Australian Open. Although we focus on tennis data analysis and visualization in this paper, this idea can be extended to the analysis of other competitive sports, including E-sports.

Expert advice and conventional wisdom say that important information within a statistical graph should be more salient than the other components. If readers are able to find relevant information quickly, in theory, they should perform better on corresponding response tasks. To our knowledge, this premise has not been thoroughly tested. We designed two types of salient cues to draw attention to task-relevant information within statistical graphs. One type primarily relied on text labels and the other on color highlights. The utility of these manipulations was assessed with groups of questions that varied from easy to hard. We found main effects from the use of our salient cues. Error and response time were reduced, and the portion of eye fixations near the key information increased. An interaction between the cues and the difficulty of the questions was also observed. In addition, participants were given a baseline skills test, and we report the corresponding effects. We discuss our experimental design, our results, and implications for future work with salience in statistical graphs.

Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. Data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns.

Segmentation is usually performed in the spatial domain and is likely hindered by similar intensity, intensity inhomogeneity, and partial volume effect. In this article, a visual-selection method is proposed to carry out segmentation in the intensity space such that the aforementioned difficulties are alleviated and better results can be produced. The proposed procedure utilizes volume rendering to explore the input data and builds a transfer function, encoding the intensity distribution of the target. Then, by using this transfer function and image processing techniques, a region of interest (ROI) is constructed in the intensity field. At the following stage, a texture-based region growing computation is conducted to extract the target from the ROI. Experiments show that the proposed method produces high quality results for a phantom which is composed of plates with similar intensities and textures. It also out-performs a traditional segmentation system in separating organs and tissues from a torso CT-scan data set.

The authors introduce an integrative approach for the analysis of the high-dimensional parameter space relevant for decision-making in the context of quality control. Typically, a large number of parameters influence the quality of a manufactured part in an assembly process, and our approach supports the visual exploration and comprehension of the correlations among various parameters and their effects on part quality. We combine visualization and machine learning methods to help a user with the identification of important parameter value settings having certain effects on a part. The goal to understand the influence of parameter values on part quality is treated from a reverse engineering perspective, driven by the goal to determine what values cause what effects on part quality. The high-dimensional parameter value domain generally cannot be visualized directly, and the authors employ dimension reduction techniques to address this problem. Their prototype system makes possible the identification of regions in a high-dimensional parameter value space that lead to desirable (or non-desirable) parameter value settings for quality assurance. They demonstrate the validity and effectiveness of our methods and prototype by applying them to a sheet metal deformation example.