Capabilities - R4C Tech

Our Expertise Domains

Advanced cyber-physical intelligence software systems for defense and aerospace applications.

R4C Tech specializes in developing advanced cyber-physical intelligence software systems that detect and characterize threats, extract advanced insights, automate processes, and enable data-driven decision making. Our expertise spans multiple domains with proven methodologies and reliable, high-performance solutions.

Space Domain Awareness

Development of systems capable of real-time threat detection and characterization to enhance situational awareness and response strategies.

Early indicators and warning systems
Multi-sensor fusion technologies
Real-time hostility assessments
System Architecting and integration

Signals Processing and Intelligence

Real-time sensor data processing for event detection, characterization, and localization.

Distributed sensor networks
Signal processing algorithms
Signature detection and identification
Real-time data pipelines

Cyber-Physical Security

Real-time intrusion detection and health monitoring for critical assets spanning both cyber and physical domains.

Real-time intrusion detection
Health monitoring systems
Critical asset protection
Cyber-physical threat mitigation

Artificial Intelligence & Machine Learning

Predictive analytics and pattern recognition capabilities for defense and aerospace applications.

Predictive analytics
Pattern recognition
AI model development
Web Applications

Recent Publications

Sharing our expertise and findings with the broader scientific community.

Machine learning-based surrogates for eVTOL performance prediction and design optimization

Rao, J. P., & Chimata, S. N.

Metascience Aerosp, Vol. 1, pp. 246-267, 2024

This research develops computationally efficient machine learning surrogate models trained on nearly 50,000 eVTOL simulations to rapidly predict vehicle endurance and range, replacing resource-intensive physics-based models for design optimization.

Machine Learning eVTOL Performance Prediction Design Optimization

Smart buildings in the IoT Era--necessity, challenges, and opportunities

Heidary, R., Rao, J. P., & Fischer, O. J. P.

Handbook of Smart Energy Systems, pp. 1-21, 2023, Springer

This chapter examines how IoT-enabled smart buildings can address urbanization and climate challenges, reviewing current sensing capabilities, applications, and the need for interoperability between smart buildings and smart cities.

Smart Buildings IoT Energy Systems Building Management

Detecting Manufacturing Defects in PCBs via Data-Centric Machine Learning on Solder Paste Inspection Features

Prasad-Rao, J., Heidary, R., & Williams, J.

arXiv preprint arXiv:2309.03113, 2023

This study demonstrates a data-centric machine learning approach to detect PCB manufacturing defects across 15,000 PCBs, combining pin, component, and PCB-level analysis to improve automated quality control in electronics manufacturing.

Manufacturing Defects PCB Inspection Machine Learning XGBoost Data-Centric AI

Attrition Risk and Aircraft Suitability Prediction in US Navy Pilot Training Using Machine Learning

Prasad-Rao, J., Fischer, O. J. P., Rowe, N. C., Williams, J. R., Puranik, T. G., Mavris, D. N., Natali, M. W., Tindall, M. J., & Atkinson, B. W.

Aerospace, Vol. 10, Issue 4, pp. 379, 2023, MDPI

This research uses machine learning on 10 years of U.S. Navy pilot training data to predict aviator attrition risk and recommend suitable aircraft types, achieving 50% attrition prediction accuracy with 4% false positives and potential savings of USD 20 million annually.

Machine Learning Pilot Training Attrition Prediction U.S. Navy Aircraft Suitability

A Reusable Framework for Fault Detection and Isolation in Small Satellites

Rao, J. P., Pace, J., Williams, J., Mackey, R., & He, L.

37th Annual Small Satellite Conference, 2023

This NASA-supported research presents a reusable, data-driven framework using machine learning and Dynamic Time Warping to detect and isolate satellite anomalies in real-time, achieving 90% detection rates with less than 1% false positives by monitoring correlations between battery metrics and system operational variables.

Small Satellites Fault Detection Machine Learning Anomaly Detection NASA

Unsupervised anomaly detection using batteries in electric aerial vehicle propulsion test-bed

Pace, J., Rao, J. P., Williams, J., & He, L.

Annual Conference of the PHM Society, Vol. 14, Issue 1, 2022

This research presents BDAV (Battery-based Diagnosis for Aerial Vehicles), an unsupervised anomaly detection framework that uses battery measurements as a root of trust to diagnose other vehicle subsystems through machine learning models that capture physical dependencies between battery and operational variables.

Anomaly Detection Electric Aircraft Battery Systems Unsupervised Learning Vehicle Diagnostics

Experimental study into optimal configuration and operation of two-four rotor coaxial systems for EVTOL vehicles

Rao, J. P., Holzsager, J. E., Maia, M. M., & Diez, J. F.

Aerospace, Vol. 9, Issue 8, pp. 452, 2022, MDPI

This experimental study analyzes coaxial rotor configurations for eVTOL vehicles, finding that optimal performance in two-rotor systems requires sequential operation (back motor first), while four-rotor systems achieve maximum efficiency with gradually increasing propeller pitch values from first to last rotor.

eVTOL Coaxial Rotors Experimental Study Propulsion Systems Performance Optimization

Capabilities & Research