United States

The fusion of Artificial Intelligence (AI) with signal processing has highlighted signal labeling as an essential part of the workflow. This poster/presentation aims to explore the methodologies in signal labeling and the integration of deep learning techniques to automate and optimize the process, while focusing on an example of automated labeling of flight maneuvers. While some flight test data sets are already labeled, a large set of historical data may be missing or have less accurate labels. Using AI/ML methods to iteratively label these data sets allows the recovery of this historic data for use in analysis.

NAML 2026

Enhancing AI Applications with Automated Signal Labeling – A Flight Test Data Application

signal labeling

sensors

time series

deep learning

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
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EMNLP 2022

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

poster

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The 10th Annual Workshop on Naval Applications of Machine Learning (NAML 2026) was held in San Diego, California, March 2-5, 2026. NAML is a forum for interdisciplinary experts from government, industry, and academia to discuss results of ongoing research on theory and applications of machine learning to address some of the most challenging problems faced by our naval forces. 



Integrated photonics is an innovative technology that merges optical components with traditional electronic circuits, leveraging mature, cost-effective semiconductor manufacturing processes to enable high-speed data transmission and lower power consumption than conventional electrical interconnects. Currently, the market is experiencing robust growth, primarily in data centers and high-performance computing, where photonics-based transceivers are essential for meeting the escalating bandwidth demands of cloud services and AI workloads. Looking ahead, envisioned applications extend beyond telecommunications to include highly integrated solutions like co-packaged optics (CPO) placed directly alongside silicon chips and analog computation systems for lightspeed, energy-efficient matrix multiplication. However, scaling such systems does not come without challenges, such as modulation, loss, noise, and footprint. To address such concerns, we have employed advanced numerical optimization techniques, commonly called inverse design, to the engineering of photonic devices and circuits. Inverse design methods can be used to optimize a user-defined photonic metric with respect to thousands of design parameters using computationally efficient algorithms such as the adjoint state method, leading to, for example, photonic devices and circuits that have near-unity energy efficiency and minimal chip footprints. Furthermore, recent revelations in statistical learning methods such as artificial intelligence and scientific machine learning can be used to enhance inverse design algorithms. We will present our work on integrating the latest automatic differentiation tools into photonic inverse design workflows, constructing physics-informed neural operators as data-free surrogate partial differential equation (PDE) models, and ultra sample efficient optimization methods leveraging low-accuracy surrogates.

Engineering State-of-the-Art Photonic Devices and Circuits with AI-Enhanced Inverse Design

Major theoretical and experimental advances in designing controllable quantum systems have brought us closer to Richard Feynman's vision of using quantum mechanics to simulate nature. Today, we can access up to hundreds of qubits across a range of device types, including superconducting qubits, neutral atoms, trapped ions, photonics, and more. But to unlock the full potential of quantum computing (QC)—meaningful speedups for real-world problems like simulating chemistry and materials science—we need to scale these quantum systems to orders of magnitude more qubits and do so cost-effectively. This milestone, known as quantum utility, marks the point where quantum computers become truly useful. To achieve quantum utility, it will be necessary to tightly integrate quantum processors (QPUs) with classical, high-performance computing (HPC) systems. This is because, rather than acting as general-purpose processors, QPUs function more like accelerators or coprocessors, designed to tackle highly specialized tasks that are beyond the reach of classical computers. This hybrid HPC-QC model will be essential not only in the near term—the noisy, intermediate-scale quantum (NISQ) era—but also for future fault-tolerant quantum computation (FTQC), as quantum error-correction schemes will rely heavily on classical co-processors. To achieve so-called utility-scale quantum computing, it will be necessary to integrate with existing heterogeneous HPC infrastructures and develop a hybrid quantum-classical full computing stack. In other words, we will need to build a quantum supercomputer. 

We present a full quantum-classical stack with tight integration of HPC and QC in multiple layers. At the highest layer, we extend familiar HPC programming environments to include QC capabilities, making it easier for developers to run quantum tasks alongside classical, high-performance ones. This includes a quantum accelerator API, which allows seamless execution of quantum algorithms, and a smart scheduler that efficiently distributes quantum workloads across available resources. At a lower level, we provide an HPC solution for circuit compilation and real-time quantum error correction that uses known hardware profiles to synthesize optimized fault-tolerant circuits. Integration at this layer will require fast communication between the quantum and classical components, and can be enabled by ultra-low latency interconnects. Novel algorithms and heterogeneous coprocessing—including CPU/GPUs, QPUs, and probabilistic processing units (PPUs)—allow the system to partition a particular problem into subproblems and send them out to the right coprocessors, enabling even more efficient execution. To aid in designing and evaluating the hybrid stack, we provide resource estimates for utility-scale (FTQC) problems in materials science as well as demonstrate HPC-QC integration workflows for several near-term (NISQ) applications.

How to Build a Quantum Supercomputer: Enabling Utility-Scale Quantum Computing with HPC-QC Integration

Predictive maintenance is a high value application of machine learning, enabling early detection of degradation and reducing unscheduled failures. Across domains such as rotating machinery, HVAC systems, batteries, and unmanned platforms, ML driven programs report 18–25% reductions in sustainment cost and up to 40% reductions in downtime in mature systems. Naval and industrial assets, however, often operate in distributed, communication-limited environments where centralized training or cloud-based inference is impractical.

Distributed Predictive Maintenance (DPM) addresses this constraint by enabling edge nodes to analyze local sensor data, detect anomalies, and forecast failures using onboard ML models. Rather than sending raw data to a central server, nodes exchange compact model updates that allow a global model to emerge collaboratively. Our approach employs a fully decentralized learning algorithm, removing the parameter-server dependencies common in federated learning and enabling low latency inference even when networks are degraded, partitioned, or intermittently connected.

Communication efficiency is a central challenge: scalable model exchange requires many nodes to share updates without overwhelming RF resources. Traditional networks assume orthogonal channels per device, which becomes infeasible as sensor density grows to hundreds or thousands of shipboard or expeditionary IIoT nodes. To address this, Trabus Technologies developed the Spectrally Efficient Peer-to-Peer (SEPP) waveform, which allows many nodes to communicate simultaneously within the same band and reduces the overhead associated with distributed optimization.

Building on this capability, SEPP-DPM integrates SEPP with decentralized learning algorithms leveraging current research from Chris Brinton at Purdue University in topology-aware and consensus-based optimization. This enables autonomous condition-based maintenance driven by real-time performance trends rather than fixed schedules. The architecture remains resilient to communication loss, node churn, and non-IID data distributions typical of naval environments.

A key innovation is SEPP-DPM’s dual-model architecture:
• the Distributed System Health Model (DSHM), an online trained representation of equipment behavior; and
• the Distributed Communication Channel Model (DCCM), which captures time varying RF conditions that influence model-exchange strategies.

These models co-evolve, enabling joint optimization over sensing, communication, and learning. Nodes adapt update frequency, peer selection, and aggregation schedules based on channel estimates, yielding an ML system that is both data efficient and communication aware.

By unifying decentralized learning with efficient communications, SEPP-DPM provides a scalable framework for distributed fault detection, anomaly prediction, and resilience in naval environments, supporting higher availability and ML driven decision superiority at the tactical edge.


Spectrally Efficient Peer-Peer Networking for Enhanced Distributed Predictive Maintenance

Cyber-physical systems (CPS) increasingly require real-time intelligence and autonomous operation in environments where centralized analytics cannot meet latency, bandwidth, and reliability constraints. Traditional architectures that rely on cloud, or server-based processing may introduce delays and vulnerabilities that compromise system responsiveness during critical events. We propose an on-edge intelligent architecture utilizing expert device-layer agents, each powered by a specialized Small Language Model (SLM) fine-tuned with physics and behavior informed data. Each agent will be deployed directly to a compact embedded platform, e.g. Raspberry Pi unit, interfaced with individual field devices. 

Our approach integrates two adapter types: one trained on device-specific operational physics and another trained on historical behavioral data. This dual-adapter design enables increased contextual awareness for anomaly detection, data diagnostics and natural-language explanations. 

The experimental framework compares the deployed agents with a centralized baseline solution running on a remote server, creating controlled comparisons between conventional systems and distributed devices. This architecture allows for direct measurement of latency reduction from local inference, and an increase in accuracy performance from physics and behavior aware expert agents. 

Validation is performed using hardware-in-the-loop, power-grid simulators with physical SEL hardware to generate representative data streams. Two sets of three distinct SEL hardware devices are deployed. One set will operate with the proposed decentralized method, and the other set will operate with the typical centralized solution. This work introduces a pathway towards highly interpretable, low-latency, and resilient on-edge intelligence for mission-critical CPS applications.

Expert Device-Layer Agents for Enhanced On-Edge Intelligence in Cyber Physical Systems

A transfer learning approach was adopted, using a VGG16 neural network model to classify earthquakes from non-earthquake events in North-East China close to the North Korean test site. Magnitudes below 4 are considered for classification of tectonic events (earthquakes) from explosions. Because of the scarcity of observed explosion data, synthetic seismograms were generated using SW4 to augment the observed data so that overfitting doesn’t happen during validation. Our preliminary experiment shows promising results which may help us identify any potential future small event.

Machine-Learning Event Classification in North-East China using augmented data

The Small Weapons Automated Scoring (SWAS) system is a cost-effective, computer vision-based solution designed to modernize and enhance marksmanship training. SWAS automates the traditionally manual, time-consuming, and subjective process of scoring bullet impacts on targets. By leveraging a standard IP camera and a sophisticated machine learning model, the system provides shooters with immediate, objective, and data-rich feedback on their performance. The application processes this video feed in real-time, feeding each frame into a purpose-built computer vision model trained to detect and locate new bullet impacts. Key outcomes include improved training efficiency, detailed performance analytics for individual and programmatic review, and a significant reduction in the personnel and resources required for range operations.

The system's primary objective is to deploy a low-cost, lane-based automated scoring system that provides instantaneous feedback and robust data logging. SWAS empowers instructors and shooters with precise, actionable data, accelerating skill development and creating a comprehensive digital record of performance over time.

Small Weapons Automated Scoring

In support of developing a credible digital twin for prognostic health management of Navy-relevant equipment, one must first assess the quality of captured data before processing for either digital twin development or health assessment. This paper focuses on assessment of data quality in time-series vibration sensor data. Accelerometers are common in many machinery and equipment health management systems. Sources of poor quality, or corrupted vibration sensor data include internal sensor faults, wiring and connector problems, and incorrect or loose sensor mounting. Other common types of sensors used for health management, such as current or pressure sensors may have similar failure modes. The signal path from measurand to health management processing algorithms are themselves systems that require monitoring and assessment.

Numerous computational techniques exist to varying levels of maturity for monitoring the health of a system from which one can acquire data, including variants of statistical process control and scientific Machine Learning. Those techniques are ubiquitous for assessing equipment health. Whatever the chosen technique to monitor a process, one must curate the data appropriately. In collaboration with the Naval Surface Warfare Center Carderock Division, our team has developed a classifier using machine learning to distinguish between “good” and “bad” data from piezoelectric accelerometers. Algorithms extract salient features from vibration data and predict the class of data quality.

This paper describes the preprocessing, feature extraction and training of the classifier for data quality. We demonstrate our methodology on data sets acquired from machinery on a representative ship platform, from a shaker in a laboratory, and a combination thereof. A secondary goal of the research was to explore the use of simulated sensor data (in this case from a shaker) to supplement the relatively small amounts of actual faulty sensor data. The methodology also includes the use of real modeled sensor data to generate representative laboratory data for model development. This is a common problem in the development of health monitoring systems, for sensor as well as machinery faults. The results show both the ability to detect sensor quality issues and the successful use of supplemental data in model training. 


Feature Engineering and Classifier Training Method for Vibration Data Quality Assessment

My team at the University of Arizona’s SpaceTREx Laboratory has been working extensively with planetary scientists on advancing spacecraft technologies for high-priority reconnaissance missions. Among them are plans for fast flyby of small bodies, moons, and interstellar objects. For these application areas, we have been proposing spacecraft swarms that fly by a body or inspect it, providing rapid 3D mapping and imagery. The organization and coordination of these swarms are achieved through the use of Machine Learning algorithms that evolve the craft design, swarm organization, and trajectories of multiple crafts. Building on these flyby mission concepts, we are beginning to explore alternative techniques, including rendezvous, impactor/ramming, and landing/docking. Many of these architectural and operational techniques may be applicable for robotic UUVs. Using a swarm or fractionated modular craft, we may split groups depending on needs to take on specialized tasks, including maintaining communications links, inspecting a craft, or performing analysis from a complex debris field. Given a target, maybe an uncooperative craft, one or a few may get close, but others in the swarm would maintain a safe distance. If the craft is uncooperative and is on a collision course with vital assets, one consideration may be to use a ramming spacecraft that impacts and conjoins with the uncooperative craft and moves it off onto a safe trajectory. In other examples, we have used a spacecraft swarm to perform multipoint observation of a complex 3D event without any craft being a single point of failure for the evolving mission. These projects are early-stage and currently supported by NASA and Brinson Foundation funding. We have made promising progress in simulation and are planning towards an early-stage hardware demonstration in the laboratory through external support in preparation for field demonstrations.

Advancing Machine Learning Directed Design and Organization of Spacecraft Swarms Concepts Towards Terrestrial and Underwater Applications

Maritime vessel controls and operational technology (OT) systems are getting more complex and interconnected. With industry trends aiming to reduce crew, automate tasks, and improve efficiency, these networks are expanding in scale, intricacy, and criticality for vessel operation and maintenance. The standard controller area network (CAN) bus for maritime vessel networks, developed by the National Marine Electronics Association (NMEA), known as NMEA2000. NMEA2000 is an application layer network protocol built on the ISO11783 standard and compatible with automotive SAEJ1939, it uses unique message identifiers known as Parameter Group Number, to define the data within each communication frame.

Despite its widespread use, NMEA2000 remains a relatively unexplored domain, particularly in understanding normal versus abnormal network behavior, due to the unavailability of open-source datasets. To address this gap, we constructed a NMEA2000 system consisting of five nodes: GPS/Radar, Wind Speed/Direction sensor, and Multifunction Display. Using this setup, we collected datasets to analyze system behavior and developed an intelligent fingerprinting techniques for each sensor, establishing a baseline of the normal operating system. We subject the system to controlled attacks to evaluate the accuracy and effectiveness of the fingerprints. This work represents a foundational step towards enhancing security and reliability in maritime OT systems.

Intelligent NMEA2000 Network Fingerprinting

The Citizen Broadband Radio Service (CBRS) utilizes the 3.55-3.7 GHz band for dynamic spectrum sharing, enabling commercial cellular network operations without causing harmful interference to high-priority Navy incumbent systems. This protection is facilitated by a dedicated Environmental Sensing Capability (ESC) that continuously monitors for incumbent activity and triggers network responses. As spectrum sharing extends to additional frequency bands beyond CBRS, augmenting dedicated sensing with complementary sensing modalities may be necessary for enhancing detection performance and response times. 
This presentation proposes a novel approach for sensing that leverages commercial cellular base stations as distributed sensors to detect incumbent spectral utilization. The approach employs the You Only Look Once (YOLO) real-time object detection architecture applied to a time–frequency representation (spectrogram) of received signals. YOLO models are trained on a combination of synthetic and measured radar datasets that encompass diverse radar types, pulse structures, and signal-to-noise ratios (SNRs). The framework simultaneously detects radar emissions and classifies their type, enabling efficient and reliable identification of incumbent activity.
In contrast to signal processing and classical machine learning methods that rely on handcrafted features and fixed detection thresholds, the proposed deep learning pipeline adapts to complex, nonstationary environments and maintains high detection performance across varying SNRs and interference conditions. The sensing capability is validated using a laboratory testbed comprising an NVIDIA Aerial Platform-based 5G base station with a GPU-accelerated physical layer integrated with the Open-Air Interface (OAI) software stack. Radar waveforms specified by the National Telecommunications and Information Administration (NTIA) for the ESC certification were utilized for performance testing. Executing advanced machine learning (ML) inference on a GPU-powered base station yields end-to-end latencies that are significantly better than current CBRS deployments. For this study, we target a chirp radar waveform as specified by NTIA for ESC certification. Our YOLO-based radar detector achieves approximately 98% precision and 95% recall at moderate-to-high SNRs, with sub-10 ms ML model inference per spectrogram. The improved sensing performance enables commercial networks to initiate timely mitigation actions that can protect incumbent systems while maintaining the cellular communication quality of service.
The proposed YOLO-based distributed sensing framework demonstrates that commercial 5G base stations can serve as efficient, real-time spectrum sensors, achieving improved detection accuracy and low latency. The experimental results establish deep learning–based sensing and AI-driven spectrum agility as a practical foundation for next-generation dynamic spectrum sharing systems. 


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