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Enhancing Active Detection Using Semi-Supervised Contrastive Learning in Remote Sensing
Full text linkThis research aims to develop a semi-supervised model leveraging contrastive learning for remote sensing, with a primary focus on sonar data processing and potential adaptation to radar systems. Remote sensing technologies like sonar and radar rely on the detection of objects and environments using reflected signals—sonar with sound waves for underwater mapping and radar with electromagnetic waves for atmospheric or terrestrial detection. The focus of this work is on contrastive learning, which enables the model to differentiate between objects detected in sonar scans, such as buoys or gates, by learning distinct representations for each object. The dataset comprises sonar scans representing a 400-gradian environment, capturing intensity readings at various distances, along with timestamp and angle data. These sonar scans are processed sequentially using Long Short-Term Memory (LSTM) layers, which capture temporal patterns while compressing and denoising the data, thus reducing computational load while improving object detection accuracy. Additional features like normalized scan data, angle differences, and time shifts are incorporated to enhance the model’s performance. Although the research is currently centered on sonar, the contrastive learning framework, alongside the deep learning techniques employed, is highly applicable to radar systems. Both sonar and radar face similar challenges in signal processing and object detection. This research highlights how advancements in sonar data processing through contrastive learning and RNN Autoencoders offer a unified framework for enhanced object detection and environmental mapping across remote sensing technologie
Flight crew and Aviation Sustainability
Full text linkSustainable Development
The concept of sustainable development was defined in the World Commission on Environment and Development’s (WCED) 1987 Brundtland Report ‘’Our Common Future’’. The Brundtland Commission aimed to help world nations towards sustainable development. Then, sustainable development became an essential concept in the vocabulary of politicians, practitioners, and planners
DENEB 2 Liquid Engine Research
No full textThe Deneb 2 Liquid Engine Research project aims to better understand the performance of the Janus series pintle injectors developed on campus. The engine being researched is the Janus 4.2 pintle injector. This injector will fly the Deneb 2 vehicle to 100,000 feet and therefore must generate 2,000 pounds of thrust and operate for 20 seconds using an ablatively cooled combustion chamber. The injector uses kerosene and liquid oxygen as propellants, with an oxidizer-to-fuel ratio of 2.4. Previous tests on Janus injectors have shown that the injector has underperformed relative to expectations, prompting this study to explore the injector\u27s performance metrics. The research will identify key factors that can be optimized to maximize the Janus 4.2 injector\u27s efficiency and reliability, ensuring that the Deneb 2 vehicle can achieve its flight objectives
Boost Your Research Impact: A Quick Guide to Bibliometrics
No full textEver wondered how to effectively measure, demonstrate, and communicate the impact of your research? The answer lies in bibliometrics! In this engaging 30-minute workshop, you\u27ll get a concise yet comprehensive introduction to research impact metrics—covering journal, author, and article-level metrics. Learn how to navigate top databases like Web of Science, Scopus, Dimensions, and Journal Citation Reports (JCR) to find and interpret key metrics. Whether you\u27re a researcher, faculty member, or student, this session will equip you with the tools to showcase your research influence. Don\u27t miss out on this session