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Personalized Over-The-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces
TS-073841 — Current federated learning (FL) systems face significant challenges in efficiently aggregating model updates over wireless networks, especially in environments with diverse data and varying channel conditions. There is a critical need for a solution that enhances bandwidth efficiency, personalizat…
  • College: College of Engineering (COE)
  • Inventors: Mao, Jiayu; Yener, Aylin
  • Licensing Officer: Ashouripashaki, Mandana

Vehicle-in-Virtual-Environment (VVE) Method for Autonomous Driving System Development and Evaluation
TS-073818 — Autonomous and advanced driver-assistance systems require extensive testing across rare, hazardous, and edge-case scenarios to ensure safety and regulatory readiness. Existing approaches (pure simulation, hardware-in-the-loop, proving grounds, or public-road testing) each suffer from tradeoffs in …
  • College: College of Engineering (COE)
  • Inventors: Guvenc, Levent; Aksun Guvenc, Bilin
  • Licensing Officer: Randhawa, Davinder

PS3 Algorithm: Scalable Mixed‑Integer Optimal Control for Electrified Powertrains
TS-073768 — Electrified and hybrid vehicle powertrains are increasingly complex, integrating mechanical, electrical, thermal, and emissions subsystems with both continuous and discrete decision variables. Existing energy management and co‑optimization approaches typically rely on simplified models, sequenti…
  • College: College of Engineering (COE)
  • Inventors: Anwar, Hamza; Ahmed, Qadeer; Fahim, Muhammad
  • Licensing Officer: Ashouripashaki, Mandana

Tunable Ferrite Nanoparticles for Optimized Heating and Magnetic Performance
TS-073587 — Magnetic nanoparticles are widely used in applications such as magnetic hyperthermia, catalysis, sensing, and data storage, yet their performance is often limited by poor control over key magnetic properties. Existing materials typically rely on size or shape control alone, which provides limited …
  • College: College of Engineering (COE)
  • Inventors: Getman, Rachel; Punyapu, Rohit
  • Licensing Officer: Randhawa, Davinder

Passive Joint DOA/FOA Sensing, Tracking, and Navigation with Unknown LEO Satellites
TS-073553 — Positioning, navigation, and timing (PNT) systems increasingly seek alternatives or complements to GNSS due to vulnerability to interference, jamming, and limited performance in challenged environments. While low Earth orbit (LEO) communication satellites offer strong signals and favorable geometr…
  • College: College of Engineering (COE)
  • Inventors: Kassas, Zak; Kozhaya, Sharbel
  • Licensing Officer: Ashouripashaki, Mandana

GNSS‑Denied LEO Navigation via Online Ephemeris Error Estimation
TS-073358 — Reliable positioning, navigation, and timing (PNT) in GNSS‑denied or disrupted environments remains a critical challenge for defense, transportation, and autonomous systems. While low Earth orbit (LEO) communications satellites offer powerful signals and rapid geometry changes, they are typicall…
  • College: College of Engineering (COE)
  • Inventors: Kassas, Zak; Watchi Hayek, Samer
  • Licensing Officer: Ashouripashaki, Mandana

Long-Baseline Ephemeris Error Correction for LEO-Based PNT
TS-073339 — Positioning, navigation, and timing (PNT) resilience is increasingly critical as GNSS vulnerabilities become more apparent in contested, denied, or degraded environments. Low Earth orbit (LEO) communication satellites offer a promising alternative PNT source, but their utility is limited by poorly…
  • College: College of Engineering (COE)
  • Inventors: Kassas, Zak; Saroufim, Joe
  • Licensing Officer: Ashouripashaki, Mandana

MuAMO: an Intelligent Maintenance Optimization Framework for Safety-Critical Systems
TS-073254 — Industries operating safety‑critical and asset‑intensive systems struggle to leverage the full value of disparate maintenance data sources. Current maintenance management and optimization tools operate in silos, limiting real‑time decision‑making, automation, and scalability. No existing s…
  • College: College of Engineering (COE)
  • Inventors: Smidts, Carol; Diao, Xiaoxu; Khafizov, Marat; Pietrykowski, Michael; Vaddi, Pavan Kumar; Zhao, Yunfei
  • Licensing Officer: Giles, David

A Novel Machine Learning Approach for Classification at the Network Edge
TS-073225 — In today's world, we are increasingly using low-cost devices with limited resources (often referred to as "edge devices") which are supported by connected high-performance servers. However, these edge devices often can't handle complex tasks such as classifying data. To make this possible, we need…
  • College: College of Engineering (COE)
  • Inventors: Li, Chengzhang; Eryilmaz, Atilla; Ju, Peizhong; Shroff, Ness
  • Licensing Officer: Giles, David

Cognitive Opportunistic Navigation Using Unknown Reference Signals
TS-073173 — Modern navigation systems increasingly rely on signals of opportunity such as 5G and LEO satellite downlinks, but these signals often lack public reference‑signal specifications, may be dynamic or on‑demand, and can suffer from severe Doppler effects. Conventional receivers cannot reliably acq…
  • College: College of Engineering (COE)
  • Inventors: Kassas, Zak; Neinavaie, Mohammad
  • Licensing Officer: Ashouripashaki, Mandana

DPRA: Dynamic Probabilistic Risk Assessment for Cyber Security Risk Analysis
TS-073146 — As industrial systems become increasingly digital and interconnected, traditional risk assessment tools struggle to capture how cyber threats interact with physical processes in real time. Existing methods typically assess hardware failures or isolated cyber events, but they cannot model how attac…
  • College: College of Engineering (COE)
  • Inventors: Smidts, Carol; Diao, Xiaoxu; Vaddi, Pavan Kumar; Zhao, Yunfei
  • Licensing Officer: Giles, David

Model-Based, Multi-Criteria Optimization for Sensor Placement and Selection
TS-073138 — Designing online monitoring (OLM) for safety‑critical systems is constrained by scarce early‑stage operational data and by quantitative models that are slow to build, brittle across configurations, and costly to iterate. This creates expensive sensor networks with blind spots, poor diagnosabil…
  • College: College of Engineering (COE)
  • Inventors: Smidts, Carol; Diao, Xiaoxu; Olatubosun, Samuel; Rownak, Md Ragib; Vaddi, Pavan Kumar
  • Licensing Officer: Giles, David

Cooperative Navigation Strategy for Safer, Smarter Urban Intersections
TS-073111 — Urban intersections are among the highest‑risk environments for automated and human‑driven vehicles due to occlusions, complex right‑of‑way, mixed traffic, and inconsistent connectivity. On‑board perception alone often misses beyond‑line‑of‑sight actors, while centralized or game 
  • College: College of Engineering (COE)
  • Inventors: Khan, Rahan; Ahmed, Qadeer; Hanif, Athar
  • Licensing Officer: Ashouripashaki, Mandana

Immersive VR Platform for Human Reliability Assessment for Physical Security
TS-073103 — Physical protection remains a major driver of nuclear plant operations and maintenance costs, yet current security risk models rely on conservative assumptions and sparse empirical data on how defenders and operators actually behave under extreme threat. They rarely capture errors of commission, k…
  • College: College of Engineering (COE)
  • Inventors: Smidts, Carol; Dechasuravanit, Atitarn; Diao, Xiaoxu; Olatubosun, Samuel; Rownak, Md Ragib; Shafieezadeh, Abdollah; Yilmaz, Alper; Zhao, Yunfei
  • Licensing Officer: Giles, David

Risk‑Informed Markov Decision Framework for Industrial Asset Management
TS-073031 — Operators of large, complex facilities struggle to balance revenue, maintenance, and regulatory safety constraints under uncertainty. Existing tools typically optimize only a subset of factors without a unified, real‑time view of component health and future degradation. Advanced reactors and oth…
  • College: College of Engineering (COE)
  • Inventors: Zhao, Yunfei; Smidts, Carol
  • Licensing Officer: Giles, David

Propagation-Based Fault Detection and Sensor Optimization for Complex Industrial Systems
TS-072175 — The Need Modern industrial and energy systems are increasingly complex, making timely fault detection and discrimination critical for safety, reliability, and cost control. Existing fault diagnosis methods often struggle with transient states, require extensive historical data, or lack interpretabil…
  • College: College of Engineering (COE)
  • Inventors: Smidts, Carol; Diao, Xiaoxu; Li, Boyuan
  • Licensing Officer: Giles, David

SPARKLE: Machine Learning Platform for Rapid Organic Battery Material Discovery
TS-071386 — The Need The search for sustainable, high-performance battery materials is hindered by reliance on finite metal-based resources and slow, trial-and-error development cycles. Organic electrode materials (OEMs), composed of earth-abundant elements, offer a more sustainable path but present challenges …
  • College: College of Engineering (COE)
  • Inventors: Paulson, Joel; Muthyala, Madhav; Park, Jay; Sorourifar, Farshud; Zhang, Shiyu
  • Licensing Officer: Mess, David

VerDiff: Automated Vulnerability Version Detection for Open Source Security
TS-071385 — The Need Open source software is foundational to modern development, yet it introduces significant security risks due to outdated dependencies and inaccurate vulnerability advisories. Public databases often fail to identify all affected versions of software, leaving organizations exposed. With vulne…
  • College: College of Engineering (COE)
  • Inventors: Anwar, Md Sakib; Lin, Zhiqiang; Yagemann, Carter
  • Licensing Officer: Mess, David

Efficient Machine Learning Prediction of Solvation Thermodynamics
TS-071267 — The Need Modeling solvent effects on catalytic surfaces is critical for designing industrial processes like biomass conversion, fuel synthesis, and electrocatalysis. Traditional multiscale simulations combining density functional theory (DFT) and molecular dynamics (MD) offer accuracy but are comp…
  • College: College of Engineering (COE)
  • Inventors: Getman, Rachel; Punyapu, Rohit; Shi, Jiexin
  • Licensing Officer: Randhawa, Davinder

A Generalized Mistuning Model for Bladed Disk Systems
TS-070971 — The Need Modern gas turbines and compressors rely on bladed disks, which are highly sensitive to mistuning caused by manufacturing tolerances, wear, or damage. Existing modeling tools are fragmented, complex, and often limited to specific mistuning types. Industry will greatly benefit from a unified…
  • College: College of Engineering (COE)
  • Inventors: D'Souza, Kiran; Krizak, Troy
  • Licensing Officer: Giles, David

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