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SimulationAI -- AI-Enabled Software Solution for Physics-Based Simulations
TS-066058 — By adopting our AI-driven solution, engineering teams can achieve more in less time, push the boundaries of innovation, and significantly cut down costs, all while maintaining or increasing the reliability and accuracy of their structural and material analysis. This is not just an evolution in FEM technology—it's a revolution.
In an era where precision and efficiency drive the success of engineering projects, the finite element method (FEM) remains indispensable but is burdened by high operational and computational costs. These costs often lead to overlooked uncertainty factors, suboptimal designs, and significant finan…
  • College: College of Engineering (COE)
  • Inventors: Soghrati, Soheil; vemparala, Balavignesh; Yang, Ming
  • Licensing Officer: Zinn, Ryan

Secure Your Digital Fortress: Revolutionizing Cybersecurity with Next-Gen PUF Technology
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…
  • College: College of Arts & Sciences
  • Inventors: Gauthier, Daniel
  • Licensing Officer: Dahlman, Jason "Jay"

Automatic Mechanical Assembly Loops/Stacks Detection
TS-064578 — This software solution revolutionizes the process of tolerance stack analysis in mechanical assemblies. It automatically detects and extracts tolerance stacks as the foundational step for precise tolerance analysis and schema generation.
This software solution revolutionizes the process of tolerance stack analysis in mechanical assemblies. It automatically detects and extracts tolerance stacks as the foundational step for precise tolerance analysis and schema generation. The Need Analyzing tolerance stacks in mechanical assembl…
  • College: College of Engineering (COE)
  • Inventors: Haghighi, Payam; Shah, Jami
  • Licensing Officer: Randhawa, Davinder

Three-dimensional cellular automation codes for solidification microstructure and porosity simulation of multi-component alloys
TS-063911 — Porosity formation during the solidification of aluminum-based alloys, induced by hydrogen gas and alloy shrinkage, presents a significant challenge for industries relying on high-performance solidification products such as castings, welds, and additively manufactured components. This issue advers…
  • College: College of Engineering (COE)
  • Inventors: Luo, Alan; Gu, Cheng
  • Licensing Officer: Zinn, Ryan

Systems and Methods of Reminding Drivers of the Stalking Vehicles on the Road
TS-063308 — In today’s world, privacy and safety are paramount. Being followed by other vehicles during driving can be unnerving and potentially dangerous, leading to privacy leakage and even significant traffic accidents. There is a pressing need for a solution that can detect abnormal following vehicl…
  • College: College of Engineering (COE)
  • Inventors: Sun, Wei; Srinivasan, Kannan
  • Licensing Officer: Randhawa, Davinder

Vulnerability and Attackability analysis of automotive controllers using structural model of the system
TS-063239 — The Ohio State University has developed a vulnerability analysis technique for connected and autonomous vehicles that assesses the vulnerability and attackability of the automotive controllers to determine the security of the system. The Need Automated vehicle technologies improve safety, assis…
  • College: College of Engineering (COE)
  • Inventors: Renganathan, Vishnu; Ahmed, Qadeer
  • Licensing Officer: Ashouripashaki, Mandana

A Regularized Conditional GAN for Posterior Sampling in Inverse Problems
TS-063238 — A novel regularization technique applicable for medical imaging applications that leverages conditional generative adversarial networks (cGANs) to generate reconstructed images in significantly shorter timeframes. The Need Several techniques are used for image reconstruction in the medical aren…
  • College: College of Engineering (COE)
  • Inventors: Bendel, Matthew; Ahmad, Rizwan; Schniter, Philip
  • Licensing Officer: Hampton, Andrew

Convolutional Neural Network to Assess Phayngeal and Laryngeal Pathology and Function on Nasopharyngolaryngoscopy
TS-063154 — Worldwide, 686,000 new head and neck (H&N) cancers are diagnosed yearly, and 375,000 people will die annually. Human papillomavirus (HPV) is responsible for an increasing subset of H&N malignancies called oropharyngeal squamous cell carcinomas (OPSCC). Although it has a better prognosis than…
  • College: College of Engineering (COE)
  • Inventors: Krening, Samantha; Gifford, Ryan; Jhawar, Sachin; VanKoevering, Kyle
  • Licensing Officer: Hampton, Andrew

An information extraction, enrichment, and caching framework for augmented reality applications
TS-063109 — The Need In the age of augmented reality (AR), there's a growing opportunity for a comprehensive solution that facilitates the seamless exploration of real-world data through camera-based AR applications. These applications require the ability to extract, cache, and enrich information, enhancin…
  • College: College of Engineering (COE)
  • Inventors: Nandi, Arnab; Burley, Codi; Sarkhel, Ritesh "Ritesh"
  • Licensing Officer: Mess, David

Auditing Fairness Online through Interactive Refinement
TS-063038 — The Need In the era of machine learning, high-stakes decisions are increasingly being made by black box models, leading to concerns about accountability and fairness. These models can exhibit inherent biases, raising the need for a system that ensures accountability and fairness in decision-making …
  • College: College of Engineering (COE)
  • Inventors: Maneriker, Pranav; Burley, Codi; Parthasarathy, Srinivasan
  • Licensing Officer: Mess, David

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