United States

The intensity of atmospheric turbulence has a profound effect on the performance of laser weapon systems. Proper configuration and operation of these systems requires nearly real-time knowledge of the atmosphere’s turbulent intensity. Determination of this characteristics is, however, difficult in operational settings. Models exist based on macro-meteorological measurements but these have been shown to have questionable accuracy, especially in the maritime and near-maritime environment. Current techniques for directly determining atmospheric turbulent intensity are also impractical in an operational setting, typically requiring two-sided systems with both a transmitter and receiver, one at the source and one at the target. For these reasons alternative techniques for determining the intensity of atmospheric intensity are desirable. Specifically, a one-sided system, only requiring information acquisition from the source side would be particularly attractive.
The disruption of laser weapon performance due to atmospheric turbulence affects other phenomenon based on the propagation of light, namely imaging. As turbulent intensity increases, the quality of images taken through the atmosphere (a one-sided technique) decreases. Consequently, images blurred by atmospheric turbulence carry information about its intensity. As atmospheric turbulence increases, image quality decreases. The current work uses convolutional neural networks (CNN), in combination with a synthetically generated data set of turbulently blurred images, to predict turbulent intensity from only blurred images. Specifically, a training and validation data set is created using a pristine image which is subsequently blurred using software that simulates the effect of the turbulent atmosphere. The data is then used to train CNNs to predict the turbulent intensity of “unknown” images in the validation set. The results are encouraging and demonstrate the ability of CNNs to predict the intensity of atmospheric turbulence using only a blurred image.

NAML 2022

Determining the Intensity of Atmospheric Turbulence Using Blurred Images and a CNN

image blur

convolution neural networks

optical turbulence

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


<figure class="video_container">
  <iframe src="https://s3.amazonaws.com/pf-user-files-01/u-59356/uploads/2022-03-22/hk13mxo/NAML_ReceptionWithtitle.mp4" width=826  height=526  frameborder="0" allowfullscreen="true"> </iframe>
</figure>

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Traditional software safety techniques rely on verifying software against a deductively defined specification of how the software should behave. In the case of AI systems, however, specifications are often implicit (e.g., in the case of expert systems, linear and higher-order programs, and rules engines, where behavior is induced by describing facts, deduction rules, or objective functions and allowing an inference engine or solver to recombine them according to pre-set combination schemas) or inductively defined (e.g., in machine learning systems, the specification is derived from the data set and perhaps some parameters of the model). Data-driven methods are subject to systematic bias and are epistemically limited: practical datasets cannot describe all possible events in any open-world problem. Traditional software verification and validation approaches may not apply directly to these novel systems, complicating the operation of systems safety analysis (e.g., MIL-STD 882E). However, AI offers advanced capabilities, and these capabilities must be safe to employ. When AI is deployed in a weapon, robot, or planning system, unwanted events may occur. Several techniques can be used to support the evaluation process for understanding and making risk decisions for military employment. This research considers those techniques – including software analysis, simulation environments, and mathematical determinations – and evaluates which are most employable, useful, and correct.

Understanding and Assessing Safety in Artificial Intelligence Systems

A viable approach to algorithm development and acquisition takes into account the efficacy of the development and deployment cycle, the reproducibility of models and predictions, the ease of diagnosing algorithm failures and inadequacies, verifiability of compliance with requirements, scalability of both the evaluation system and the algorithm under evaluation, the ability to collaborate in order to accelerate development, efficiency and accuracy of performance benchmarking, and the difficulty of moving an algorithm from an evaluation to an operational environment. Taken together, these concepts support solid engineering practice and due diligence in algorithm acquisition. When applied to machine learning approaches, they are commonly termed the MLOps (Machine Learning Operations) cycle. They are an integral part of viable acquisition and management of software algorithms. As part of the software life cycle an acquisition process must manage a set of operationally meaningful performance tests and their relationship to versioned software implementations of algorithms. This paper describes the Seascape system and its place in such a process.

At the program level, the acquisition of algorithms takes place at a distance from the development work. Vendors of algorithms may be defense companies, national laboratories, collaborative research partners, or other independent groups. The distance between the vendor of the algorithm and the program manager produces a need for an objective way to assess and compare any algorithms with which they are presented. It is not guaranteed that the individual in charge of acquisition will be an expert in all types of algorithms under consideration, further highlighting the need for an objective framework that permits standardized comparisons. Ideally such a system is highly scalable, allowing for parallel evaluation of multiple algorithms in an efficient fashion. Data should be carefully curated and stored, to provide an objective frame of reference for an algorithm, and the analysis should be done in an automated way, ensuring that all algorithms are assessed in the same way every time they are changed.

Seascape: An Algorithm Benchmarking Framework

In this talk, we tackle a generic n-dimensional numerical computing problem and explore performance and tradeoffs between popular frameworks based on open source jupyter notebook examples. Most data science practitioners perform their work in Python because of its high level abstraction and rich set of numerical computing libraries. But the choice of library and methodology is driven by complexity-impacting constraints like problem size, latency, memory, SWAP, hardware, and others. To that end, we demonstrate that a wide selection of GPU-accelerated libraries (RAPIDS, CuPy, Numba, Dask, cuNumeric), including the development of hand tuned CUDA kernels, are accessible to data scientists without ever leaving Python.

Evaluating Your Options for Accelerated Numerical Computing in Pure Python

The work presented describes how real-time geospatial image processing can be accomplished on a SWaP (size, weight, and power)-constrained edge system. Traditionally, the data processing requirements for these workflows end-to-end are compute-intensive, and therefore challenging to deploy to power-constrained edge-based hardware. The edge platform we use is a self-hosted BF2 DPU paired with an A100 GPU, allowing for a low-power, high-performance edge solution without a host CPU. We'll demonstrate how common geospatial processing workloads such as orthorectification can be GPU-accelerated and launched from our edge BF2 system.

High Performance Geospatial Image Processing at the Edge

It is critical to always have an accurate estimate of the position of aircraft on the carrier deck. Currently, determining the pose of aircraft is done via visual inspection by several sailors who then update a digital interface with any changes to the position or status of aircraft. This task is labor intensive, requires focus for extended periods of time, and requires multiple personnel stationed during all flight operations. PATRIOT (Panoramic Asset Tracking of Real-time Information for the Ouija Tabletop) is a research endeavor to apply computer vision techniques to existing camera feeds from the carrier to identify and track aircraft, personnel, and vehicles. This work focuses on two branches used in the overall PATRIOT pipeline that estimate the pose of aircraft from synthetic and real videos: keypoint-based techniques and bounding box-based techniques. Several state-of-the-art keypoint-based methods (High-Resolution Net, Higher High-Resolution Net, and OpenPifPaf) and accompanying pose estimation algorithms are compared with a bounding box-based method using Faster R-CNN and an encoder-decoder model.

Pose Estimation Techniques for PATRIOT (Panoramic Asset Tracking of Real-time Information for the Ouija Tabletop)

Addressing quality-of-service (QoS) requirements for software applications
in undersea acoustic networks is challenging. This is in
part due to the strong dependency of the characteristics on the
acoustic propagation environment and the lack of effective congestion
control protocols for undersea acoustic networks. In this
work we propose a deep reinforcement learning (DRL) based algorithm
for allocating bandwidth resources across different traffic
types. Bandwidth allocations are periodically updated based on
traffic prioritization, QoS delay requirements, and queue occupancy
levels. A DRL agent learns the optimal bandwidth allocation policy
via a Soft Actor-Critic (SAC) algorithm. In order to test the
effectiveness of our proposed algorithm, we developed a weighted
fair queuing (WFQ) module in NS3 to enable testing in a high fidelity
simulation environment. The WFQ module enables the use of
queueing disciplines together with an NS3 library for underwater
acoustic communications and networking. The interaction between
the DRL agent and the simulated environment is enabled by the
NS3-gym framework. Numerical tests illustrate the performance of
the DRL-based policy and a use case for the new WFQ NS3 module.

Active Queue Management in Underwater Network via Deep Reinforcement Learning

Advancements in computer vision,
machine learning and deep learning have
evolved to a state where real-time
detection and performance exceeds the
human. SeeByte's approach to automatic
target recognition in forward-looking sonar
(FLS) and electro optical camera (EO)
utilizes a convolution neural network
(CNN) and single-shot detector backbone
(SSD).

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Determining the Intensity of Atmospheric Turbulence Using Blurred Images and a CNN

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Understanding and Assessing Safety in Artificial Intelligence Systems

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