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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

Robust Training of Spiking Neural Networks via Generative AI
TS-073717 — Spiking Neural Networks (SNNs) promise ultra-low-power, low-latency AI for edge and neuromorphic computing, but their adoption is constrained by fundamental training challenges. SNN performance is highly sensitive to how training data are collected (e.g., lighting, sensor settings, noise), leading…
  • College: College of Engineering (COE)
  • Inventors: Baietto, Anthony; Stewart, Christopher
  • Licensing Officer: Randhawa, Davinder

Data‑Driven Powertrain Recommender Systems (PRS) for Optimized Truck Fleets
TS-073692 — Fleet operators face increasing pressure to reduce operating costs and emissions while maintaining performance and reliability. Choosing the “right” truck (diesel, alternative fuel, or battery electric) for a specific duty cycle remains largely heuristic, conservative, and error‑prone. As a …
  • College: College of Engineering (COE)
  • Inventors: Ahmed, Qadeer; Subraya-Hegde, Sharat; Villani, Manfredi
  • 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

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