Available Technologies
# of Displayed Technologies: 10 / 48
Applied Category Filter (Click To Remove): Software
GPS Independent Lane Level Vehicle Localization
TS-069654 — Technology bundle containing T2025-148 and T2024-150.
The Need
Reliable vehicle localization is critical for autonomous and human-driven vehicles, especially in GPS-denied environments such as dense urban areas, tunnels, and off-road farms and construction sites. Current localization methods relying solely on GPS are prone to signal loss and inaccurac…
- College: College of Engineering (COE)
- Inventors: Javed, Nur Uddin; Ahmed, Qadeer
- Licensing Officer: Ashouripashaki, Mandana
Motion-Guided Deep Image Prior (M-DIP) for Dynamic Image Reconstruction in Cardiovascular MRI
TS-069527 — The Need
Cardiovascular MRI (CMR) data acquisition is often time-consuming and relies on complex reconstruction methods that do not fully exploit the spatial and temporal structure of CMR data. There is a need for a more efficient, unsupervised approach that can accelerate CMR imaging while maintai…
- College: College of Engineering (COE)
- Inventors: Ahmad, Rizwan; Chen, Chong; Sultan, Muhammad
- Licensing Officer: Randhawa, Davinder
SyMANTIC – Novel Symbolic Regression to Discover Accurate Models from Data
TS-069523 — The Need
In many scientific and industrial fields, there is a critical need for interpretable and accurate models that can be derived from complex datasets. Traditional machine learning methods often produce black-box models that lack transparency and interpretability, making it difficult to unders…
- College: College of Engineering (COE)
- Inventors: Muthyala, Madhav Reddy; Paulson, Joel; Sorourifar, Farshud
- Licensing Officer: Randhawa, Davinder
Joint Activity Testing (JAT): A Testing & Evaluation Methodology for Human-Machine Teams
TS-068443 —
In high-stakes industries, the integration of humans and advanced automation systems demands evaluation methods that reliably predict performance under varying challenges. Current testing methods often focus on individual components, failing to assess how human-machine teams operate as a unit, par…
- College: College of Engineering (COE)
- Inventors: Morey, Dane; Rayo, Michael
- Licensing Officer: Zinn, Ryan
Vision System Using MDP-Based Tracking for Automated Dimensional Analysis
TS-068440 —
Accurate measurement of physical attributes, such as dimensions, alignments, and surface features, is essential for ensuring quality and consistency across industries. Traditional measurement methods, often reliant on manual tools and human oversight, are time-intensive, prone to errors, and lac…
- College: College of Engineering (COE)
- Inventors: Allen, Theodore; Rodriguezbuno, Ramiro; Zhang, Yifei
- Licensing Officer: Zinn, Ryan
Computational Design of Experiment Framework for Processing of Metal Alloys
TS-067792 — A software tool for optimizing the manufacturing process for metal alloys.
Metallic alloys are composed of a homogeneous mixture of two or more metals or of metals and nonmetal or metalloid elements to provide specific characteristics or structural properties. Alloys are used in many applications, such as aircraft, offshore drilling, automobiles, and others.
Obtaining t…
- College: College of Engineering (COE)
- Inventors: Alexandrov, Boian; Forquer, Matthew; Jang, Eun; Luo, Yuxiang; Stewart, Jeffrey
- Licensing Officer: Zinn, Ryan
Optimal and Pure Leaf Classification Trees for Machine Learning (ML) Decision-Making
TS-067550 — A method to improve the performance and accuracy of ML-based decision trees.
Decision trees are popular machine learning (ML) methods used in classification and regression problems, and they have numerous applications in the real world. Various industries use decision trees to help decide strategies, investments, and operations. In addition, they are used in healthcare to he…
- College: College of Engineering (COE)
- Inventors: Allen, Theodore; Arrey, Evelyn; Booth, Matthew; Liu, Enhao; Mashayekhi, Medhi
- Licensing Officer: Zinn, Ryan
AIDRIN (AI Data Readiness Inspector)
TS-067116 — AIDRIN (AI Data Readiness Inspector) is a system designed to comprehensively evaluate datasets through a diverse range of metrics, giving an overall perspective on their readiness for AI applications.
In the contemporary digital landscape, the explosive growth in data generation has created a pressing need for efficient and scalable database systems. Traditional databases struggle with the increasing volume, variety, and velocity of data, leading to performance bottlenecks, high operational cos…
- College: College of Engineering (COE)
- Inventors: Byna, Suren; Hiniduma, Kaveen
- Licensing Officer: Zinn, Ryan
Dust Analysis: A Novel Approach to Monitoring Viral Spread
TS-066962 — The Need
Viral disease surveillance (e.g. influenza, SARS-CoV-2) in high-risk settings faces several challenges, such as asymptomatic carriers, incomplete reporting, resource limitations, and delayed diagnosis of traditional swab test methods. These challenges could allow a virus to silently spread…
- College: College of Engineering (COE)
- Inventors: Dannemiller, Karen; Faith, Seth; Hull, Natalie; Nastasi, Nick; Renninger, Nicole
- Licensing Officer: Ashouripashaki, Mandana
Smartphone Detection Kit for Airborne Formaldehyde and Allergens
TS-066960 — The Need
Indoor air can be polluted by various sources, including building materials, furniture, cleaning products, and even people. Exposure to these resulting contaminants and allergens can lead to a variety of health problems, such as respiratory irritation, allergies, and even cancer. It is cur…
- College: College of Engineering (COE)
- Inventors: Dannemiller, Karen; Parquette, Jonathan; Qin, Rongjun
- Licensing Officer: Ashouripashaki, Mandana
