# of Displayed Technologies: 5 / 5

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Machine Learning Techniques for the Identification of Geomorphological Features to Identify Subsurface Natural Hydrogen Reservoirs
TS-054820 — Naturally occurring hydrogen is found in subsurface formations. Successful exploration for subsurface resources requires a systematic understanding of the occurrence and evolution of the resource within various geologic environments. Remote sensing (acquiring, processing, and interpreting images a…
  • College: College of Arts & Sciences
  • Inventors: Darrah, Thomas; Howat, Ian; Moortgat, Joachim; Whyte, Colin
  • Licensing Officer: Ashouripashaki, Mandana

Point Prognostics: Closed Loop Particle Forecasting Platform for Decision Support and System Prognostics
TS-049903 — A scalable, adaptive computational platform (software) that performs accurate, predictive computer simulations in less time, with the end goal of supporting a decision-making agency
A report by IOT Analytics evaluated the predictive maintenance (PdM) market at $1.5 billion in 2016 and anticipated a growth of 39% annually to $10.96 billion by 2022. Particle methods (broadly known as "Monte Carlo'' methods, or, MC) are a class of computational algorithms used in Pd…
  • College: College of Engineering (COE)
  • Inventors: Kumar, Mrinal; Yang, Chao "Chao"
  • Licensing Officer: Zinn, Ryan

Closed Loop Particle Forecasting Platform for Decision Support and System Prognostics.
TS-042531 — A computational platform for forecasting the state of evolving systems and processes.
Particle methods are a class of computational algorithms used to estimate potential outcomes of a system or process. Although popular for their simplicity and scalability, the use of fixed sized "particle ensembles" renders the simulations unable to provide performance guarantees in quan…
  • College: College of Engineering (COE)
  • Inventors: Kumar, Mrinal; Yang, Chao "Chao"
  • Licensing Officer: Zinn, Ryan

Machine-Learning Algorithm for Improved Speech Intelligibility in Noise
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.
A primary complaint of hearing-impaired (HI) listeners is poor speech recognition in background noise. This issue can be quite debilitating and persists despite considerable efforts to improve hearing technology. Despite considerable effort, monaural (single-microphone) algorithms capable of incre…
  • College: College of Arts & Sciences
  • Inventors: Healy, Eric; Vasko, Jordan
  • Licensing Officer: Willson, Christopher

Machine-Learning Algorithm for Improved Speech Intelligibility in Noise
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.
Wireless carriers receive daily complaints about poor speech recognition in background noise during calls and are constantly looking for methods to improve especially in light of recent forays into VOIP. The ability to discriminate between speech and noise in an audio signal then is an extremely i…
  • College: College of Arts & Sciences
  • Inventors: Healy, Eric; Vasko, Jordan
  • Licensing Officer: Willson, Christopher

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