TS-067434 — The Need Spatial aggregation is crucial in numerous industries where data from low-level spatial units, such as census blocks, must be grouped into larger, meaningful regions. Traditional approaches often struggle with the computational complexity of these tasks and tend to focus on finding a singl…

TS-065412 — The Need: In contemporary cybersecurity landscapes, the conventional methods of employing Physically Unclonable Functions (PUFs) necessitate maintaining a secure challenge-response database. However, this practice poses significant security risks as access to this database could lead to the comprom…

TS-064260 — The Need: Dance researchers, educators, choreographers, and movement practitioners face the challenge of accessing and editing Laban movement notation scores on modern, portable devices. With the discontinuation of LabanWriter, the industry lacked a touch-screen application tailored to the latest te…

TS-062341 — The Need: In the world of dance education and performance analysis, there is a pressing need for innovative tools that cater to diverse learning styles and intelligences. Traditional teaching methods often fall short in engaging students fully and providing a comprehensive understanding of dance mov…

TS-062332 — The Need: In the fast-paced and ever-evolving landscape of graduate education, universities and academic institutions face mounting challenges in efficiently managing their graduate students' academic journeys. With diverse departmental requirements, it becomes crucial to have a centralized solu…

TS-051417 — Traditional regression analysis has been a staple in predicting cause-effect relationships. Counltess industries use these methods to predict rare events, from economic issues to cancer research, causality is often a desired result. Given the vast amount of potential variables it sometimes becomes…

TS-042266 — A monaural machine-learning algorithm for classifying time-frequency units in an unknown signal, which results in marked speech-intelligibly improvements in noisy signals.

TS-038074 — A monaural machine-learning algorithm for classifying time-frequency units in an unknown signal, which results in marked speech-intelligibly improvements in noisy signals.