Defense Notices

Although the Center for Remote Sensing of Ice Sheets (CReSIS) performs several radar calibration steps to produce Operation IceBridge (OIB) radar depth sounder data products, these datasets are not radiometrically calibrated and the swath array processing uses ideal (rather than measured [calibrated]) steering vectors. Any errors in the steering vectors, which describe the response of the radar as a function of arrival angle, will lead to errors in positioning and backscatter that subsequently affect estimates of basal conditions, ice thickness, and radar attenuation. Scientific applications that estimate physical characteristics of surface and subsurface targets from the backscatter are limited with the current data because it is not absolutely calibrated. Moreover, changes in instrument hardware and processing methods for OIB over the last decade affect the quality of inter-seasonal comparisons. Recent methods which interpret basal conditions and calculate radar attenuation using CReSIS OIB 2D radar depth sounder echograms are forced to use relative scattering power, rather than absolute methods.

As an active target calibration is not possible for past field seasons, a method that uses natural targets will be developed. Unsaturated natural target returns from smooth sea-ice leads or lakes are imaged in many datasets and have known scattering responses. The proposed method forms a system of linear equations with the recorded scattering signatures from these known targets, scattering signatures from crossing flight paths, and the radiometric correction terms. A least squares solution to optimize the radiometric correction terms is calculated, which minimizes the error function representing the mismatch in expected and measured scattering. The new correction terms will be used to correct the remaining mission data. The radar depth sounder data from all OIB campaigns can be reprocessed to produce absolutely calibrated echograms for the Arctic and Antarctic. A software simulator will be developed to study calibration errors and verify the calibration software. The software for processing natural targets will be made available in CReSIS’s open-source polar radar software toolbox. The OIB data will be reprocessed with new calibration terms, providing to the data user community a complete set of radiometrically calibrated radar echograms for the CReSIS OIB radar depth sounder for the first time.

The surface electromagnetic waves that propagate along a metal-dielectric or a metal-air interface are called surface plasmon polaritons (SPPs). However, as the tangential wavevector component is larger than what is permitted for the homogenous plane wave in the dielectric medium this poses a phase-matching issue. In other words, the available spatial vector in the dielectric at a given frequency is smaller than what is required by SPP to be excited. The most commonly known technique to bypass this problem is by using the Otto and Kretschmann configurations. A glass prism is used to increase the available spatial vector in dielectric/air. Other methods are evanescent field directional coupling and optical grating. Even with all these methods, it is still challenging to couple the SPPs having a large propagation constant.  

A novel way to efficiently inject the power into SPPs is via temporal modulation of the dielectric adhered to the metal. The dielectric constant is modulated in time using an incident pump field. As a result of the induced changes in the dielectric constant, spatial vector shortage is eliminated. In other words, there is enough spatial vector in the dielectric to excite SPPs. As SPPs applicability is widely studied in numerous applications, this method gives a new way of evoking SPPs. Hence, this technique opens new possibilities in the surface plasmon polariton study. One of the applications that we discuss in details is the optical limiting.  

Waveform diversity desires to optimize the Radar waveform given the constraints and objectives of a particular task or scenario. Recent advances in electronics have significantly expanded the design space of waveforms. The resulting waveforms of various waveform diverse approaches possess complex structures which have temporal, spectral, and spatial extents. The utilization of optimization in many of these approaches results in complex signal structures that are not imagined a priori, but are instead the product of algorithms. Traditional waveform analysis using the frequency spectrum, autocorrelation, and beampatterns of waveforms provide the majority of metrics of interest. But as these new waveforms’ structure increases in complexity, and the constraints of their use tighten, further aspects of the waveform’s structure must be considered, especially the true occupancy of the waveforms in the transmission hyperspace. Time-Frequency analysis can be applied to these waveforms to better understand their behavior and to inform future design. These tools are especially useful for spectrally shaped random FM waveforms as well as spatially shaped spatial beams. Both linear and quadratic transforms are used to study the emissions in time, frequency, and space dimensions. Insight on waveform generation is observed and future design opportunities are identified.

The Center for Remote Sensing and Integrated Systems (CReSIS) has enabled the development of several radars for measuring ice and snow depth. One of these systems is the Ultra-Wideband (UWB) Snow Radar, which operates in microwave range and can provide measurements with cm-scale vertical resolution. To date, renditions of this system demand medium to high size, weight and power (SWaP) characteristics. To facilitate a more flexible and mobile measurement setup with these systems, it became necessary to reduce the SWaP of the radar electronics. This thesis focuses on the design of several compact RF and microwave modules enabling integration of a full UWB radar system weighing < 5 lbs and consuming < 30 W of DC power. This system is suitable for operation over either 12-18 GHz or 2-8 GHz in platforms with low SWaP requirements, such as unmanned aerial systems (UAS). The modules developed as a part of this work include a VCO-based chirp generation module, downconverter modules, and a set of modules for a receiver front end, each implemented on a low-cost laminate substrate. The chirp generator uses a Phase Locked Loop (PLL) based on an architecture previously developed at CReSIS and offers a small form factor with a frequency non-linearity of 0.0013% across the operating bandwidth (12-18 GHz) using sub-millisecond pulse durations. The down-conversion modules were created to allow for system operation in the S/C frequency band (2-8 GHz) as well as the default Ku band (12-18 GHz). Additionally, an RF receiver front end was designed, which includes a microwave receiver module for de-chirping and an IF module for signal conditioning before digitization. The compactness of the receiver modules enabled the demonstration of multi-channel data acquisition without multiplexing from two different aircraft. A radar test-bed largely based on this compact system was demonstrated in the laboratory and used as part of a dual-frequency instrument for a surface-based experiment in Antarctica. The laboratory performance of the miniaturized radar is comparable to the legacy 2-8 GHz snow radar and 12-18 GHz Ku-band radar systems. The 2-8 GHz system is currently being integrated into a class-I UAS. 

With the development of Convolutional Neural Networks (CNNs), computer vision enters a new era and the performance of image classification, object detection, segmentation, and recognition has been significantly improved. Object detection, as one of the fundamental problems in computer vision, is a necessary component of many computer vision tasks, such as image and video understanding, object tracking, instance segmentation, etc. In object detection, we need to not only recognize all defined objects in images or videos but also localize these objects, making it difficult to perfectly realize in real-world scenarios.

In this work, we aim to improve the performance of object detection and localization by adopting more efficient and effective CNN models. (1) We propose an effective and efficient approach for real-time detection and tracking of small golf balls based on object detection and the Kalman filter. For this purpose, we have collected and labeled thousands of golf ball images to train the learning model. We also implemented several classical object detection models and compared their performance in terms of detection precision and speed. (2) To address the domain shift problem in object detection, we propose to employ generative adversarial networks (GANs) to generate new images in different domains and then concatenate the original RGB images and their corresponding GAN-generated fake images to form a 6-channel representation of the image content. (3) We propose a strategy to improve label assignment in modern object detection models. The IoU (Intersection over Union) thresholds between the pre-defined anchors and the ground truth bounding boxes are significant to the definition of the positive and negative samples. Instead of using fixed thresholds or adaptive thresholds based on statistics, we introduced the predictions into the label assignment paradigm to dynamically define positive samples and negative samples so that more high-quality samples could be selected as positive samples. The strategy reduces the discrepancy between the classification scores and the IoU scores and yields more accurate bounding boxes.

Deep learning leads the performance in many areas of computer vision. Deep neural networks usually require a large amount of data to train a good model with the growing number of parameters. However, collecting and labeling a large dataset is not always realistic, e.g. to recognize rare diseases in the medical field. In addition, both collecting and labeling data are labor-intensive and time-consuming. In contrast, studies show that humans can recognize new categories with even a single example, which is apparently in the opposite direction of current machine learning algorithms. Thus, data-efficient learning, where the labeled data scale is relatively small, has attracted increased attention recently. According to the key components of machine learning algorithms, data-efficient learning algorithms can also be divided into three folders, data-based, model-based, and optimization-based. In this study, we investigate two data-based models and one model-based approach.

First, to collect more data to increase data quantity. The most direct way for data-efficient learning is to generate more data to mimic data-rich scenarios. To achieve this, we propose to integrate both spatial and Discrete Cosine Transformation (DCT) based frequency representations to finetune the classifier. In addition to the quantity, another property of data is the quality to the model, different from the quality to human eyes. As language carries denser information than natural images. To mimic language, we propose to explicitly increase the input information density in the frequency domain. The goal of model-based methods in data-efficient learning is mainly to make models converge faster. After carefully examining the self-attention modules in Vision Transformers, we discover that trivial attention covers useful non-trivial attention due to its large amount. To solve this issue, we proposed to divide attention weights into trivial and non-trivial ones by thresholds and suppress the accumulated trivial attention weights. Extensive experiments have been performed to demonstrate the effectiveness of the proposed models.

Scanning hosts on the internet for vulnerable devices and services is a key step in numerous cyberattacks. Previous work has shown that scanning is a widespread phenomenon on the internet and commonly targets web application/server deployments. Given that automated scanning is a crucial step in many cyberattacks, it would be beneficial to make it more difficult for adversaries to perform such activity.

In this work, we propose Web-Armour, a mitigation approach to adversarial reconnaissance and vulnerability scanning of web deployments. The proposed approach relies on injecting scanning impeding delays to infrequently or rarely used portions of a web deployment. Web-Armour has two goals: First, increase the cost for attackers to perform automated reconnaissance and vulnerability scanning; Second, introduce minimal to negligible performance overhead to benign users of the deployment. We evaluate Web-Armour on live environments, operated by real users, and on different controlled (offline) scenarios. We show that Web-Armour can effectively lead to thwarting reconnaissance and internet-wide scanning.

Resource scheduling plays a vital role in High-Performance Computing (HPC) systems. However, most scheduling research in HPC has focused on only a single type of resource (e.g., computing cores or I/O resources). With the advancement in hardware architectures and the increase in data-intensive HPC applications, there is a need to simultaneously embrace a diverse set of resources (e.g., computing cores, cache, memory, I/O, and network resources) in the design of runtime schedulers for improving the overall application performance. This thesis performs an empirical evaluation of a recently proposed multi-resource scheduling algorithm for minimizing the overall completion time (or makespan) of computational workflows comprised of moldable parallel jobs. Moldable parallel jobs allow the scheduler to select the resource allocations at launch time and thus can adapt to the available system resources (as compared to rigid jobs) while staying easy to design and implement (as compared to malleable jobs). The algorithm was proven to have a worst-case approximation ratio that grows linearly with the number of resource types for moldable workflows. In this thesis, a comprehensive set of simulations is conducted to empirically evaluate the performance of the algorithm using synthetic workflows generated by DAGGEN and moldable jobs that exhibit different speedup profiles. The results show that the algorithm fares better than the theoretical bound predicts, and it consistently outperforms two baseline heuristics under a variety of parameter settings, illustrating its robust practical performance.

Carrier synchronization is an essential part of digital communication systems. In essence, carrier synchronization is the process of estimating and correcting any carrier phase and frequency differences between the transmitted and received signals. Typically, carrier synchronization is achieved using a phase lock loop (PLL) system; however, this method is unreliable when experiencing frequency offsets larger than 30 kHz. This thesis evaluates the FPGA implementation of a combined FFT and PLL-based carrier phase synchronization system. The algorithm includes non-data-aided, FFT-based, frequency estimator used to initialize a data-aided, PLL-based phase estimator. The frequency estimator algorithm employs a resource-efficient strategy of averaging several small FFTs instead of using one large FFT, which results in a rough estimate of the frequency offset. Since it is initialized with a rough frequency estimate, this hybrid design allows the PLL to start in a state close to frequency lock and focus mainly on phase synchronization. The results show that the algorithm demonstrates comparable performance, based on performance metrics such as bit-error rate (BER) and estimator error variance, to alternative frequency estimation strategies and simulation models. Moreover, the FFT-initialized PLL approach improves the frequency acquisition range of the PLL while achieving similar BER performance as the PLL-only system.

Natural Language Processing (NLP) is an application of Machine Learning (ML) which focuses on deriving useful and underlying facts through the semantics in articles to automatically extract insights about how information can be pictured, presented, and interpreted.  Knowledge graphs, as a promising medium for carrying the structured linguistical piece, can be a desired target for learning and visualization through artificial neural networks, in order to identify the absent information and understand the hidden transitive relationship among them. In this study, we aim to construct Temporal Knowledge Graphs of sematic information to facilitate better visualization of editorial data. Further, A neural network-based approach for link prediction is carried out on the constructed knowledge graphs. This study uses news articles in English language, from New York Times (NYT) collected over a period of time for experiments. The sentences in these articles can be decomposed into Part-Of-Speech (POS) Tags to give a triple t = {sub, pred, obj}. A directed Graph G (V, E) is constructed using POS tags, such that the Set of Vertices is the grammatical constructs that appear in the sentence and the Set of Edges is the directed relation between the constructs. The main challenge that arises with knowledge graphs is the storage constraints that arise in lieu of storing the graph information. The study proposes ways by which this can be handled. Once these graphs are constructed, a neural architecture is trained to learn the graph embeddings which can be utilized to predict the potentially missing links which are transitive in nature. The results are evaluated using learning-to-rank metrics such Mean Reciprocal Rank (MRR).