NOEMAC

Pioneering the future of maritime defense and medical imaging

Where beamforming meets Artificial Intelligence.

From deep-sea awareness to field-ready diagnostics — redefining precision through AI-driven acoustics.

ASW Poseidon TRL 9

Advanced passive long-array sonar to improve underwater situational awareness for Anti-Submarine Warfare.

Portable Ultrasound TRL 6

Field-ready volumetric ultrasound technology addressing a critical capability gap for battlefield and emergency medicine.

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About NOEMAC

Established July 2025 Subsidiary of ADICUS4D Ltd (Canada)

NOEMAC Ltd was established as a subsidiary of the Canadian corporation ADICUS4D Ltd. Our mission is to complete the development and commercialization of advanced geometrically multidimensional 3D beamformers and integrate them with AI decision-support systems.

The technologies were conceived and developed by Prof. Stergios Stergiopoulos (Senior & Chief Defence Scientist, DRDC Toronto, retired) and Prof. Konstantinos N. Plataniotis (University of Toronto).

Focus areas

Anti-Submarine Warfare (ASW) surveillance systems based on multidimensional beamforming.
Field-deployable 4D ultrasound with automated diagnostic capabilities for combat casualty and trauma care, and civilian emergency medicine.

Why it matters

Multidimensional beamformers fused with AI decision support deliver higher sensitivity, better localization, and faster operator decisions across maritime sensing and point-of-care medicine.

Developed Technologies

ASW Poseidon

TRL 9

ASW POSEIDON is NOEMAC’s next-generation antisubmarine warfare (ASW) system—a state-of-the-art passive surveillance sonar operating at TRL 9. The system deploys either a sea-bottom fixed long hydrophone array or a 200 m towed array at depths of approximately 100 m. For quiet submarines, the fixed sea-bottom configuration provides detection ranges up to 30 nautical miles (~55 km).

The signal-processing architecture integrates advanced adaptive and synthetic aperture beamformers, spectral analysis modules, and a Passive Localization Assistant (PLA) designed to address the rapid emergence of UUVs, their evolving communication and swarm behaviors, and the expanding operational domains of undersea warfare. The system’s AI-based command-and-control (C2) framework enhances underwater situational awareness—analogous in role to an AWACS in the aerial domain.

Performance Characteristics:

Advanced beamformers (adaptive and synthetic aperture) for both narrowband and broadband targets.
Adaptive beamformers minimize strong noise from naval vessels to improve detection of quiet broadband targets.
Acoustic synthetic-aperture beamformers increase array gain for narrowband signals by approximately 3.5 dB.
Spectral analysis supports classification, target persistence, and situational assessment.
Passive Localization Assistant (PLA) provides location estimation and C2 integration for UUVs and swarm behaviors.
AI-powered target classification automates detection and identification processes for enhanced operational response.
Signal trackers maintain robust tracking of narrow-band and broad-band contacts.

Integrated chain — Fig. 1: Integrated signal-processing chain

Fig. 6: Sea-bottom fixed array configuration

Delivery Timeline: A complete sea-bottom mounted system—including the hydrophone line array, supporting electronics, full signal-processing chain, and integrated C2 system—can be delivered within 16 months.

Scope Note: Deployment of the sea-bottom hydrophone array is not included. NOEMAC provides consultations for:

Numerical assessment of sonar-equation parameters for optimum performance.
Deployment procedures for sea-bottom or towed array configurations.
Training of sonar operators in system operation and interpretation.
Maintenance and technical support for hardware and software subsystems.

Stergiopoulos, S. (1998). Implementation of Adaptive and Synthetic Aperture Beamformers in Sonar Systems. Proceedings of the IEEE, 86(2), 358–396.
Stergiopoulos, S. (Ed.). (2009). Advanced Beamformers, in Handbook on Advanced Signal Processing for Sonar, Radar and Medical Imaging Systems (2nd ed.). CRC Press.
Stergiopoulos, S. (1990). Optimum bearing resolution for a moving towed array and extension of its physical aperture. J. Acoust. Soc. Am., 87(5), 2128–2140.
Stergiopoulos, S., & Sullivan, E. J. (1989). Extended towed array processing by overlapped correlator. J. Acoust. Soc. Am., 86(1), 158–171.
Stergiopoulos, S., & Ashley, A. T. (1997). Split-beam processing as a broadband bearing estimator. J. Acoust. Soc. Am., 102(6), 3556–3563.
Stergiopoulos, S. (1995). Noise normalization technique for beamformed towed array data. J. Acoust. Soc. Am., 97(4), 2334–2345.
Stergiopoulos, S., & Urban, H. (1992). Forming a long synthetic aperture in the sea. IEEE J. Oceanic Eng., 17(1), 62–72.
Stergiopoulos, S. (1991). Limitations on towed array gain in a non-isotropic ocean. J. Acoust. Soc. Am., 90(6), 3161–3172.

Portable Ultrasound

TRL 6

The Portable 4D Ultrasound Automated Diagnostic System is a developed technology at TRL 6, designed to address a critical capability gap in battlefield medicine. It delivers instant volumetric 3D/4D imaging with AI-guided eFAST for rapid detection of non-compressible torso hemorrhage (NCTH) directly at the point of injury.

A planar matrix-array transducer integrated with GPU-accelerated beamforming (NVIDIA Jetson platform) enables approximately 20 volumes per second real-time imaging with guided acquisition and automated interpretation, enhancing decision-making and survivability in critical field operations.

Core Components:

Planar matrix-array transducer capturing a full 3D volume per acquisition.
GPU-accelerated beamforming on embedded NVIDIA Jetson for low-latency volumetric imaging (~20 vol/s).
On-device AI with guided prompts and automated interpretation for reliable eFAST workflows.
TeleConsult GUI with secure telemedicine communication to support remote consultation and training.

TRL Snapshot by Sub-system:

Matrix planar array probe — TRL 8
Parallel computing architecture (8 FPGAs) — TRL 8
3D beamformer with volumetric reconstruction — TRL 7
GUI with telemedicine (TeleConsult) — TRL 9
Automated diagnostic tool (Morison’s pouch) — TRL 6

K. L. Amezcua, et al., “Design and Testing of Ultrasound Probe Adapters for a Robotic Imaging Platform,” Scientific Reports, 14(1):5102, 2024.
Yuan Bi, et al., “Machine Learning on Robotic Ultrasound Imaging: Challenges and Perspectives,” arXiv:2401.02376, 2024.
Torres Hernandez, et al., “Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics,” Bioengineering, 11(4):392, 2024.
D. C. Hile, et al., “Is Point-of-Care Ultrasound Accurate and Useful in the Hands of Military Medical Technicians?” Military Medicine, 177(8):983–987, 2012.
J. D. Monti and M. D. Perreault, “Impact of a 4-hour Introductory eFAST Training Intervention Among Ultrasound-Naïve U.S. Military Medics,” Military Medicine, 185(5-6):e601-e608, 2020.
S. Stergiopoulos, K. Plataniotis, and M. Marsousi, “Optimal Probe Placement for FAST by Organ Segmentation in 3D Ultrasound Images,” patent filed, DRDC File: 1416-14/007CA, 2019.
P. Weber, K. Plataniotis, S. Stergiopoulos, G. Sakas, et al., “Portable, 3D/4D Ultrasound Diagnostic Imaging System (PUDIS),” NATO HFM-249 Symposium, Warsaw, April 2015.
M. Marsousi, K. Plataniotis, and S. Stergiopoulos, “An Automated Approach for Kidney Segmentation in Three-Dimensional Ultrasound Images,” IEEE JBHI, 2016.
M. Marsousi, K. Plataniotis, and S. Stergiopoulos, “Computer-Assisted 3D Ultrasound Probe Placement for Emergency Healthcare Applications,” IEEE TII, 2016.
S. Stergiopoulos and A. Dhanantwari, “High Resolution 3D Ultrasound Imaging System Deploying a Multi-Dimensional Array of Sensors,” technical report/patent notes.
S. Stergiopoulos and A. Dhanantwari, US Patent 6,719,696, 2004.
S. Stergiopoulos and A. Dhanantwari, US Patent 6,482,160, 2002.
S. Stergiopoulos, “3D Ultrasound System and Method,” US Patent Application 61/777,321, 2018 (DND ref. 1416-18003).
S. Stergiopoulos, M. Marsousi, and K. Plataniotis, “Automated Diagnostics in 3D Ultrasound System and Method,” US Patent Application 61/777,343, 2018 (DND ref. 1416-18004).