Browsing by Author "Dale, Ashley"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Engineering and Informatics Student Multidisciplinary Learning using 3D Visualization and 3D Display of Radio Frequency (RF) Concepts
    (IEEE, 2018-10) Christopher, Lauren; William, Albert; Rao, Anusha S.; Dale, Ashley; Chase, Anthony; Joshi, Mihir Piyush; Krogg, Wendy; Abernathy, Bree; Electrical and Computer Engineering, School of Engineering and Technology
    This full paper addresses the Innovative Practice Category. We discuss our multidisciplinary approach to create a truly 3D representation and 3D display of RF signals in space through the development of two different training tools to enhance student understanding of Radio Communications. Both tools show the data on 3D autostereoscopic displays rather than rendered back to 2D displays. The first new tool is a series of 3D stereoscopic animations created by a multidisciplinary team of students from the Media Arts and Sciences (School of Informatics) and Electrical Engineering (School of Engineering) programs for use with an autostereoscopic display, where each animation focuses on a single topic within RF communication learning, using real-world examples. The second innovative tool models the Navy use-case of Electronic Warfare (EW) using examples with 3D antenna radiation patterns of signal propagation using U.S. Navy's SIMDIS interactive 3D visualization environment. The developed scenarios are displayed on an autostereoscopic display, allowing students to manipulate RF signals in a 3D environment. Learning gains were assessed via a 2x2 crossover experimental design an engineering student group. Compared to the control group, students showed gains in understanding of the 3D shape of dipole antennas and understanding of the multiple RF antennas in a cell phone, and the connections between mobile phone antennas and cell towers. The results from these interventions collectively indicate that a truly 3D representation in space can be used to enhance students' understanding of antennas and RF signals.
  • Thumbnail Image
    Item
    Novel Materials for Spintronic Devices
    (2024-12) Dale, Ashley; Cheng, Ruihua; Petrache, Horia I.; Gavrin, Andrew D.; Amin, Vivek Pravin; Dowben, Peter A.
    Spintronic devices are devices which use the spin properties of atoms to store information. Such devices are predicted to be lower power. In this work, two Fe- II spin crossover molecules are analyzed as candidate novel materials for spintronic devices through computational and experimental work. The molecular activation energy for spin crossover molecule [Fe{H2B(pz)2}2(bipy)] in thin film on a ferroelectric substrate is estimated using Monte Carlo simulations of an Ising-like Model to be 88 meV when the substrate is polarized towards the thin film surface and 198 meV when the substrate is polarized away from the thin film surface. Additional Monte Carlo simulations suggests that beyond-nearest neighbor interactions are required for observed spin-state domain formation. The magnetic anisotropy and spectroscopic g factor for [Fe{H2B(pz) 2}2 (bipy)] molecules are characterized using X-ray magnetic circular dichroism measurements; thin films were dominated by magnetocrystalline anisotropy and the spin-orbit coupling energy was found to be 1.47 kJ m-3. Spin and orbital moment anisotropy estimations from the XMCD measurement were 30.9 and 5.04 meV molecule-1, respectively, and the zero-field g factor was estimated to be gz = 2.26. The optical energy gap for [Fe{HB(tz)3}2] is estimated to be 1.93±0.15 eV using UV-Vis spectroscopic measurements and a newly derived Tauc method for large molecules in solvent. Finally, the [Fe{HB(tz)3}2] molecule in thin film form is confirmed to have increased resistance in the high-spin state, with an on-off ratio of 103. These measurements contribute to the development of novel spintronic devices.
  • Thumbnail Image
    Item
    Trusted Data Anomaly Detection (TaDA) in Ground Truth Image Data
    (IEEE, 2022-10) Boler, William; Dale, Ashley; Christopher, Lauren; Electrical and Computer Engineering, School of Engineering and Technology
    Current state-of-the-art Artificial Intelligence (AI) anomaly detection from images is primarily used for defect detection and relies on relatively homogeneous datasets of images with similar foregrounds and backgrounds. This type of anomaly detection uses human labelled ground-truth data. In our research, we have extremely heterogeneous datasets and want to identify outliers. We use self-supervised Variational Autoencoders (VAEs) to identify anomalies in the latent vector feature space. Understanding the outliers in a large training data set is important for establishing trustworthiness of the AI models learned from these data, a strong requirement for military AI applications. Our study uses 8984 examples from Kaggle military planes and 4300 examples from Kaggle landscape data. We present the results of the combined heterogeneous dataset on the localized methods, with one such result exhibiting inliers as landscapes/backgrounds and outliers as all aircraft, detecting aircraft as anomalies with a 0.87 AUC. Results also include the inter-class AUC across the different aircraft classes. Our contribution to the state-of-the-art is to apply isolation forests to the latent space data after UMAP embeddings in a strongly heterogeneous image dataset for military applications to identify anomalies.