Search for: All records

The objective of this paper is to provide a holistic summary of ongoing research related to the development, implementation, assessment, and continuous refinement of an augmented reality (AR) app known as Vectors in Space. This Unity-based app was created by the authors and provides a self-guided learning experience for students to learn about fundamental vector concepts routinely encountered in undergraduate physics and engineering mechanics courses. Vectors are a fundamental tool in mechanics courses as they allow for the precise and comprehensive description of physical phenomena such as forces, moments, and motion. In early engineering coursework, students often perceive vectors as an abstract mathematical concept that requires spatial visualization skills in three dimensions (3D). The app aims to allow students to build these tacit skills while simultaneously allowing them to learn fundamental vector concepts that will be necessary in subsequent coursework. Three self-paced, guided learning activities systematically address concepts that include: (a) Cartesian components of vectors, (b) unit vectors and directional angles, (c) addition, (d) subtraction, (e) cross product using the right-hand rule, (f) angle between vectors using the dot product, and (g) vector projections using the dot product. The authors first discuss the app's scaffolding approach with special attention given to the incorporation of Mayer's principles of multimedia learning as well as the use of animations. The authors' approach to develop the associated statics learning activities, practical aspects of implementation, and lessons learned are shared. The effectiveness of the activities is assessed by applying analysis of covariance (ANCOVA) to pre- and post-activity assessment scores for control and treatment groups. Though the sample sizes are relatively small (less than 50 students), the results demonstrate that AR had a positive impact on student learning of the dot product and its applications. Larger sample sizes and refinements to the test instruments will be necessary in the future to draw robust conclusions regarding the other vector topics and operations. Qualitative feedback from student focus groups conducted with undergraduate engineering students identified the app's strengths as well as potential areas of improvement. 

Paleomagnetic, rock magnetic, or geomagnetic data found in the MagIC data repository from a paper titled: High‐Fidelity Archeointensity Results for the Late Neolithic Period From Central China 

Scanned images of patent or historical documents often contain localized zigzag noise introduced by the digitizing process; yet when viewed as a whole image, global structures are apparent to humans, but not to machines. Existing denoising methods work well for natural images, but not for binary diagram images, which makes feature extraction difficult for computer vision and machine learning methods and algorithms. We propose a topological graph-based representation to tackle this denoising problem. The graph representation emphasizes the shapes and topology of diagram images, making it ideal for use in machine learning applications such as classification and matching of scientific diagram images. Our approach and algorithms provide essential structure and lay important foundation for computer vision such as scene graph-based applications, because topological relations and spatial arrangement among objects in images are captured and stored in our skeleton graph. In addition, while the parameters for almost all pixel-based methods are not adaptive, our method is robust in that it only requires one parameter and it is adaptive. Experimental comparisons with existing methods show the effectiveness of our approach.