Applied Signal Technology
Ross Dalke is a Digital Signal Processing Software Engineer II at Applied Signal Technology since November 2022, contributing to major project software requirements and designing C++ modules for real-time embedded systems. Previously, during a Master's Thesis project at California Polytechnic State University-San Luis Obispo, Ross developed a neural network-based breast cancer diagnosis system using TensorFlow and Keras, experimenting with various architectures and data augmentation. Ross's experience includes internships at Applied Signal Technology, Wolfspeed, Sharpell Technologies Inc., and Cornerstone Engineering, focusing on software development, data quality assessment, and engineering submittals. Ross holds a Master of Science in Electrical Engineering and an undergraduate degree in the same field from California Polytechnic State University-San Luis Obispo.
Applied Signal Technology
Applied Signal Technology, Inc. (AST) (NASDAQ: APSG) is a full-service intelligence, surveillance, and reconnaissance (ISR) provider, serving national priorities in defense, intelligence, and homeland security with over $200 million in annual revenues. AST is an innovation center, bringing cutting-edge science and engineering together to enhance global security. We provide expertise in the areas of signals intelligence (SIGINT), broadband communications, cyber security, and sensor signature processing. Our SIGINT competencies include communications intelligence (COMINT) and electronic intelligence (ELINT). Our broadband communication technology enables secure high speed communication networks. Our cyber security activities include network monitoring, intrusion detection, and countermeasures. Our remote sensing expertise includes processing information from electro-optic, sonar, radar, magnetic, and seismic sensors to detect changes in the environment and provide real-time alerts of potential threats. We specialize in the collection, processing, and understanding of signals for ISR missions with low size, weight, and power (SWAP) configurations to enable increased deployment on unmanned platforms.