110 research outputs found
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Without sufficient data, the quantity of information available for supervised
training is constrained, as obtaining sufficient synthetic aperture radar (SAR)
training data in practice is frequently challenging. Therefore, current SAR
automatic target recognition (ATR) algorithms perform poorly with limited
training data availability, resulting in a critical need to increase SAR ATR
performance. In this study, a new method to improve SAR ATR when training data
are limited is proposed. First, an embedded feature augmenter is designed to
enhance the extracted virtual features located far away from the class center.
Based on the relative distribution of the features, the algorithm pulls the
corresponding virtual features with different strengths toward the
corresponding class center. The designed augmenter increases the amount of
information available for supervised training and improves the separability of
the extracted features. Second, a dynamic hierarchical-feature refiner is
proposed to capture the discriminative local features of the samples. Through
dynamically generated kernels, the proposed refiner integrates the
discriminative local features of different dimensions into the global features,
further enhancing the inner-class compactness and inter-class separability of
the extracted features. The proposed method not only increases the amount of
information available for supervised training but also extracts the
discriminative features from the samples, resulting in superior ATR performance
in problems with limited SAR training data. Experimental results on the moving
and stationary target acquisition and recognition (MSTAR), OpenSARShip, and
FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR
performance of the proposed method in response to limited SAR training data
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
Vehicle recognition is a fundamental problem in SAR image interpretation.
However, robustly recognizing vehicle targets is a challenging task in SAR due
to the large intraclass variations and small interclass variations.
Additionally, the lack of large datasets further complicates the task. Inspired
by the analysis of target signature variations and deep learning
explainability, this paper proposes a novel domain alignment framework named
the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve
robustness under various operating conditions. Concisely, HDANet integrates
feature disentanglement and alignment into a unified framework with three
modules: domain data generation, multitask-assisted mask disentanglement, and
domain alignment of target features. The first module generates diverse data
for alignment, and three simple but effective data augmentation methods are
designed to simulate target signature variations. The second module
disentangles the target features from background clutter using the
multitask-assisted mask to prevent clutter from interfering with subsequent
alignment. The third module employs a contrastive loss for domain alignment to
extract robust target features from generated diverse data and disentangled
features. Lastly, the proposed method demonstrates impressive robustness across
nine operating conditions in the MSTAR dataset, and extensive qualitative and
quantitative analyses validate the effectiveness of our framework
Feedback-assisted automatic target and clutter discrimination using a Bayesian convolutional neural network for improved explainability in SAR applications
DATA AVAILABILITY STATEMENT : The NATO-SET 250 dataset is not publicly available; however, the MSTAR
dataset can be found at the following url: https://www.sdms.afrl.af.mil/index.php?collection=mstar
(accessed on 5 January 2022).In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.The Radar and Electronic Warfare department at the CSIR.http://www.mdpi.com/journal/remotesensinghj2023Electrical, Electronic and Computer Engineerin
NASA Adaptive Multibeam Phased Array (AMPA): An application study
The proposed orbital geometry for the adaptive multibeam phased array (AMPA) communication system is reviewed and some of the system's capabilities and preliminary specifications are highlighted. Typical AMPA user link models and calculations are presented, the principal AMPA features are described, and the implementation of the system is demonstrated. System tradeoffs and requirements are discussed. Recommendations are included
Analysis of nuclear waste disposal in space, phase 3. Volume 2: Technical report
The options, reference definitions and/or requirements currently envisioned for the total nuclear waste disposal in space mission are summarized. The waste form evaluation and selection process is documented along with the physical characteristics of the iron nickel-base cermet matrix chosen for disposal of commercial and defense wastes. Safety aspects of radioisotope thermal generators, the general purpose heat source, and the Lewis Research Center concept for space disposal are assessed as well as the on-pad catastrophic accident environments for the uprated space shuttle and the heavy lift launch vehicle. The radionuclides that contribute most to long-term risk of terrestrial disposal were determined and the effects of resuspension of fallout particles from an accidental release of waste material were studied. Health effects are considered. Payload breakup and rescue technology are discussed as well as expected requirements for licensing, supporting research and technology, and safety testing
Annual report of the town of Loudon of the selectmen, treasurer, town clerk, tax collector, highway agent, librarian, library trustees, auditors, trustees of trust funds, conservation commission, police department, fire department, solid waste and recycling committees, planning board, zoning board of adjustment, recreation committee, historical society, economic development committee, compliance/health officer for the year ending December 31, 1991.
This is an annual report containing vital statistics for a town/city in the state of New Hampshire
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Nostratic Dictionary
A revised edition can be found at http://www.dspace.cam.ac.uk/handle/1810/244080.Aharon Dolgopolsky is the leading authority on the Nostratic macrofamily. His 'Nostratic Dictionary' presented here is, of course, something very much more than a dictionary. It is the most thorough and extensive demonstration and documentation so far of what may be termed the Nostratic hypothesis: that several of the world's best- known language families are related in their origin, their grammar and their lexicon, and that they belong together in a larger unit, of earlier origin, the Nostratic macrofamily. It should at once be noted that several elements of this enterprise are controversial. For while the Nostratic hypothesis has many supporters, it has been criticized on rather fundamental grounds by a number of distinguished linguists. The matter was reviewed some years ago in a symposium held at the McDonald Institute, and positions remain very much polarized. It was a result of that meeting that the decision was taken to invite Aharon Dolgopolsky to publish his Dictionary - a much more substantial treatise than any work hitherto undertaken on the subject - at the McDonald Institute. For it became clear that the diversities of view expressed at that symposium were not likely to be resolved by further polemical exchanges. Instead, a substantial body of data was required, whose examination and evaluation could subsequently lead to more mature judgments. Those data are presented here, and that more mature evaluation can now proceed.McDonald Institute for Archaeological Research
Alfred P. Sloan Foundatio
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