Research NewsletterIssue: ORN-2024-51

Recent NJIT Patents Issued by US Patent and Trademark Office (USPTO)

Patent Title:   Systems and Methods for Decoding of Graph-Based Channel Codes via Reinforcement Learning

NJIT Ref No.:  21-021

Inventor(s):    Kliewer, Joerg / Beemer, Allison / Habib, Salman

Patent Application Status:  Issued Patent

Patent Issue Date: 12/10/2024

Patent No.:  12,166,504

Technology Licensing Status: Available 

NSF: NSF-AFRL REsearch in FLoquet Engineered QuanTum Systems (NSF-AFRL REFLEQTS); Water, Landscape, and Critical Zone Processes (WaLCZ); Security, Privacy, and Trust in Cyberspace (SaTC 2.0); NSF Scholarships in Science, Technology, Engineering, and Mathematics Program (S-STEM); NSF/CASIS Collaboration on Tissue Engineering and Mechanobiology on the International Space Station to Benefit Life on Earth

NIH: Exploratory/Developmental Bioengineering Research Grants (EBRG) (R21 High Impact, Interdisciplinary Science in NIDDK Research Areas (RC2 Limited Competition: Small Grant Program for the NCATS Clinical and Translational Science Award (CTSA) Program (R03 BRAIN Initiative: New Concepts and Early-Stage Research for Recording and Modulation in the Nervous System (R21): Rehabilitation Research Career Development Programs (K12); NCI Continuing Umbrella of Research Experiences (CURE) Non-Mentored Research Scientist Career Development Award (K22); In Vivo Non-Invasive Optical Imaging Approaches for Biological Systems (UG3/UH3)

Department of Defense/US Army/DARPA/ONR: Broad Agency Announcement (BAA) for the Joint Airborne Mission Survivability IPT

Department of Energy: Office of Electricity (OE) and the Office of Energy Efficiency and Renewable Energy’s Wind Energy Technologies Office (EERE WETO)

National Endowment of Humanities: Awards for Faculty

Agencies look to automation software to usher in next phase of post-quantum security: Agencies beginning the process of preparing their systems for future quantum threats are investigating how automated cryptography software can be incorporated into existing capabilities, the first step in a series of assessments to marry legacy systems with quantum-resilient technology. Leadership at participating agencies spoke with Nextgov/FCW about the path towards creating quantum-resilient networks. Following the release of the first standardized post-quantum cryptography algorithms, the National Institute of Standards and Technology and the Cybersecurity and Infrastructure Security Agency are working with industry partners to spearhead the algorithms’ adoption within federal agencies.  Crucial to this effort is the harmonization of legacy technologies embedded in the federal government with newer software solutions, namely automated cryptography discovery and inventory — or ACDI — tools. More information is posted on the NextGov website.

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Digital Twins: Transforming Government Operations with Interoperability: As defined by the Digital Twin Consortium, a digital twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. Powered by domain knowledge and real time data, digital twins provide a holistic, end-to-end view of data across an entire organization. With digital twins, agencies can simulate operations far more efficiently and cost effectively than building physical representations. Using generative artificial intelligence and machine learning, the technology can replicate everything about a physical environment or asset. These virtual representations, combined with sensors to collect real time data and live feedback, allow users to dive deep into real world operating scenarios. For example, when operational equipment needs to be deployed, it’s essential to understand how it’s operating, the potential for outages and what can be done to prevent such events. A digital twin gives agencies the ability to track all of that not only in real-time, but also in the future through predictive data analysis—allowing them to predict problems and prescribe fixes before they occur. More information is posted on the NextGov website.