Toward Data-Driven Radar STAP

Catalyzed by the recent emergence of site-specific, high-fidelity radio frequency (RF) modeling and simulation tools purposed for radar, data-driven formulations of classical methods in radar have rapidly grown in popularity over the past decade. Despite this surge, limited focus has been directed toward the theoretical foundations of these classical methods. In this regard, as part of our ongoing data-driven approach to radar space-time adaptive processing (STAP), we analyze the asymptotic performance guarantees of select subspace separation methods in the context of radar target localization, and augment this analysis through a proposed deep learning framework for target location estimation. In our approach, we generate comprehensive datasets by randomly placing targets of variable strengths in predetermined constrained areas using RFView, a site-specific RF modeling and simulation tool developed by ISL Inc. For each radar return signal from these constrained areas, we generate heatmap tensors in range, azimuth, and elevation of the normalized adaptive matched filter (NAMF) test statistic, and of the output power of a generalized sidelobe canceller (GSC). Using our deep learning framework, we estimate target locations from these heatmap tensors to demonstrate the feasibility of and significant improvements provided by our data-driven approach in matched and mismatched settings.Comment: 39 pages, 24 figures. Submitted to IEEE Transactions on Aerospace and Electronic Systems. This article supersedes arXiv:2201.1071

Preparation of Spherical Mo5Si3 Powder by Inductively Coupled Thermal Plasma Treatment

A method was developed to fabricate spherical Mo5Si3 powder by milling and spheroidizing using inductively coupled thermal plasma. A Mo5Si3 alloy ingot was fabricated by vacuum arc melting, after which it was easily pulverized into powder by milling due to its brittle nature. The milled powders had an irregular shape, but after being spheroidized by the thermal plasma treatment, they had a spherical shape. Sphericity was increased with increasing plasma power. After plasma treatment, the percentage of the Mo3Si phase had increased due to Si evaporation. The possibility of Si evaporation was thermodynamically analyzed based on the vapor pressure of Mo and Si in the Mo5Si3 liquid mixture. By this process, spherical Mo silicide powders with high purity could be fabricated successfully

High-throughput organo-on-pillar (high-TOP) array system for three-dimensional ex vivo drug testing

The development of organoid culture technologies has triggered industrial interest in ex vivo drug test-guided clinical response prediction for precision cancer therapy. The three-dimensional culture encapsulated with basement membrane (BM) components is extremely important in establishing ex vivo organoids and drug sensitivity tests because the BM components confer essential structures resembling tumor histopathology. Although numerous studies have demonstrated three-dimensional culture-based drug screening methods, establishing a large-scale drug-screening platform with matrix-encapsulated tumor cells is challenging because the arrangement of microspots of a matrix-cell droplet onto each well of a microwell plate is inconsistent and difficult to standardize. In addition, relatively low scales and lack of reproducibility discourage the application of three-dimensional organoid-based drug screening data for precision treatment or drug discovery. To overcome these limitations, we manufactured an automated organospotter-integrated high-throughput organo-on-pillar (high-TOP) drug-screening platform. Our system is compatible with various extracellular matrices, including BM extract, Matrigel, collagen, and hydrogel. In addition, it can be readily utilized for high-content analyses by simply exchanging the bottom plates without disrupting the domes. Our system demonstrated considerable robustness, consistency, reproducibility, and biological relevancy in three-dimensional drug sensitivity analyses using Matrigel-encapsulated ovarian cancer cell lines. We also demonstrated proof-of-concept cases representing the clinical feasibility of high-TOP-assisted ex vivo drug tests linked to clinical chemo-response in ovarian cancer patients. In conclusion, our platform provides an automated and standardized method for ex vivo drug-sensitivity-guided clinical response prediction, suggesting effective chemotherapy regimens for patients with cancer.N