Defense Notices

Packets are transferred between nodes within a network. However, a packet can be dropped while trying to join the queue of a node it was routed to. In networking, this is referred to as packet loss. It can be caused by buffer scarcity in a congested network. Such phenomenon results in a reduced data rate and a delay increase due to packet retransmissions.

In this work, we propose an algorithm to perform load balancing on a network of queues via SDN to prevent packet loss. It implements a parameter K, based on the queues occupancy and traffic flow, to control an iterative packet redistribution process. In different experiments conducted on network models in which the queues varied in number, size and occupancy, our algorithm outperformed a load balancer using the Round-Robin technique.

Unmanned aerial vehicles (UAVs) and autonomous vehicles are becoming more ubiquitous than ever before. From medical to delivery drones, to space exploration rovers and self-driving taxi services, these vehicles are starting to play a prominent role in society as well as in our day to day lives.

 Efficient computation and communication strategies are paramount to the effective functioning of these vehicles. Mobile Edge Computing (MEC) is an innovative network technology that enables resource-constrained devices - such as UAVs and autonomous vehicles - to offload computationally intensive tasks to a nearby MEC server. Moreover, vehicles such as self-driving cars must reliably and securely relay and receive latency-sensitive information to improve traffic safety. Extensive research performed on vehicle to vehicle (V2V) and vehicle to everything (V2X) communication indicates that they will both be further enhanced by the widespread usage of 5G technology.

 We consider two relevant problems in mobile edge computing for unmanned vehicles. The first problem was to satisfy resource-constrained UAV's need for a resource-efficient offloading policy. To that end, we implemented both a computation and an energy consumption model and trained a DQN agent that seeks to maximize task completion and minimize energy consumption. The second problem was establishing communication between two autonomous vehicles and between an autonomous vehicle and an MEC server. To accomplish this goal, we experimented by leveraging an autonomous vehicle's server to send and receive custom messages in real time. These experiments will serve as a stepping stone towards enabling mobile edge computing and device-to-device communication and computation.

Binary analysis is difficult, as most of semantic and syntactic information available at source-level gets lost during the compilation process. If the binary is stripped and/ or optimized, then it negatively affects the efficacy of binary analysis frameworks. Moreover, handwritten assembly, obfuscation, excessive indirect calls or jumps, etc. further degrade their accuracy. Thus, it is important to investigate and assess the challenges to improve the binary analysis. One way of doing that is by studying security techniques implemented at binary-level.

In this dissertation we propose to implement existing compiler-level techniques for binary executables and thereby evaluate how does the loss of information at binary-level affect the performance of existing compiler-level techniques in terms of both efficiency and effectiveness.

With the increased adoption of machine learning algorithms across many different fields, powerful computing platforms have become necessary to meet their computational needs. Multicore platforms are a popular choice due to their ability to provide greater computing capabilities and still meet the different size, weight, and power (SWaP) constraints. As a result, multicore systems are also being employed at an increasing rate. However, contention for hardware resources between the multiple cores is a significant challenge as it can lead to interference and unpredictable timing behaviors. Furthermore, this contention can be intentionally induced by malicious actors with the specific goals of inhibiting system performance and increasing the execution time of safety-critical tasks. This is done by performing Denial-of-Service (DoS) attacks that target shared resources in order to prevent other cores from accessing them. When done properly, these DoS attacks can have significant impacts to performance and can threaten system safety. For example, we find that DoS attacks can cause >300X slowdown on the popular Raspberry Pi 3 embedded platform. Due to the inherent risks, it is vital that we discover and understand the mechanisms through which shared resource contention can occur and develop solutions that mitigate or prevent the potential impacts.

In this work, we investigate and evaluate shared resource contention on multicore platforms and the impacts it can have on the performance of real-time tasks. Leveraging this contention, we propose various Denial-of-Service attacks that each target different shared resources in the memory hierarchy with the goal of causing as much slowdown as possible. We show that each attack can inflict significant temporal slowdowns to victim tasks on target platforms by exploiting different hardware and software mechanisms. We then develop and analyze techniques for providing shared resource isolation and temporal performance guarantees for safety-critical tasks running on multicore platforms. In particular, we find that bandwidth throttling mechanisms are effective solutions against many DoS attacks and can protect the performance of real-time victim tasks.

One of the biggest challenges in nutrient management in corn (Zea mays) production is determining the amount of plant-available nitrogen (N) that will be supplied to the crop by the soil. Measuring a soil’s N-supplying power is quite difficult and approximations are often used in-lieu of intensive soil testing. This can lead to under/over-fertilization of crops, and in turn increased risk of crop N-deficiencies or environmental degradation. In this paper, we propose a deep learning algorithm to predict the inorganic-N content of the soil on a given day of the growing season. Since the historic data for inorganic nitrogen (IN) is scarce, deep learning has not yet been implemented in predicting fertilizer content. To overcome this hurdle, Generative Adversarial Network (GAN) is used to produce synthetic IN data and is trained using offline simulation data from the Decision Support System for Agrotechnology Transfer (DSSAT). Additionally, the time-series prediction problem is solved using long-short term memory (LSTM) neural networks. This model proves to be economical as it gives an estimate without the need for comprehensive soil testing, overcomes the issue of limited available data, and the accuracy makes it reliable for use.

Convolutional Neural Networks (CNNs) have significantly improved the performance on various computer vision tasks such as image recognition and segmentation based on their rich representation power. To enhance the performance of CNN, a self-attention module is embedded after each layer in the network. Recently proposed Transformer-based models achieve outstanding performance by employing a multi-head self-attention module as the main building block. However, several challenges still need to be addressed, such as (1) focusing only on class-specified limited channels in CNN; (2) limited respective field in the local transformer; and (3) addition of redundant features and lack of multi-scale features in U-Net type segmentation architecture.

In our work, we propose new strategies to address these issues. First, we propose a novel channel-based self-attention module to diversify the focus more on the discriminative and significant channels, and the module can be embedded at the end of any backbone network for image classification. Second, to limit the noise added by the shallow layers of an encoder in U-Net type architecture, we replaced the skip connections with the Adaptive Global Context Module (AGCM). In addition, we introduced the Semantic Feature Enhancement Module (SFEM) for multi-scale feature enhancement in polyp segmentation. Third, we propose a Multi-scaled Overlapped Attention (MOA) mechanism in the local transformer-based network for image classification to establish the long-range dependencies and initiate the neighborhood window communication.

The surface electromagnetic waves that propagate along a metal-dielectric or a metal-air interface are called surface plasmon polaritons (SPPs). These SPPs are advantageous in a broad range of applications, including in optical waveguides to increase the transmission rates of carrier waves, in near field optics to enhance the resolution beyond the diffraction limit, and in Raman spectroscopy to amplify the Raman signal. However, they have an inherent limitation:  as the tangential wavevector component of propagation 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 the evanescent field directional coupling, optical grating, localized scatterers, and coupling via highly focused beams. However, even with all these methods at our disposal, it is still challenging to couple SPPs that have a large propagation constant. 

As SPPs apply to a wide range of purposes, it is vitally important to overcome the SPP excitation dilemma. Presented here is a novel way to efficiently inject power into SPPs via temporal modulation of the dielectric adhered to the metal. In this configuration, the dielectric constant is modulated in time using an incident pump field. As a result of the induced changes in the dielectric constant, we show that efficient phase-matched coupling can be achieved even by a perpendicularly incident uniform plane wave. This novel method of exciting SPPs paves the way for further understanding and implementation of SPPs in a plethora of applications. For example, optical waveguides can be investigated under such excitation. Hence, this technique opens new possibilities in conventional plasmonics, as well as in the emerging field of nonlinear plasmonics. 

Code that can run asynchronously is important in a wide variety of situations, from user interfaces to communication over networks, to the use of concurrency for performance gains. One widely used method of specifying asynchronous control flow is the Promise model as used in Javascript. Promises are powerful, but can be confusing and hard-to-debug. This problem is exacerbated by Javascript’s permissive type system, where erroneous code is likely to fail silently, with values being implicitly coerced into unexpected types at runtime.

The present work implements Javascript-style Promises in Haskell, translating the model to a strongly typed framework where we can use the type system to rule out some classes of bugs.

Common errors – such as failure to call one of the callbacks of an executor, which would, in Javascript, leave the Promise in an eternally-pending deadlock state – can be detected for free by the type system at compile time and corrected without even needing to run the code.

We also demonstrate that Promises form a monad, providing a monad instance that allows code using Promises to be written using Haskell’s do notation.

Remote sensing based on radar technology has been successfully used for several decades as an effective tool of scientific discovery. A particular application of radar remote sensing instruments is the systematic monitoring of ice and snow masses in both hemispheres of the Earth. The operating requirements of these instruments are driven by factors such as science requirements and platform constraints, often necessitating the development of custom electronic components to enable the desired radar functionality.

This work focuses on component development and trade studies for two multichannel radar systems. First, this thesis presents the design and implementation of two dual-polarized ultra-wideband antennas for a ground-based dual-band ice penetrating radar. The first antenna operates at UHF (600–900 MHz) while the second antenna operates at VHF (140–215 MHz). Each antenna element is composed of two orthogonal octagon-shaped dipoles, two inter-locked printed circuit baluns and an impedance matching network for each polarization. Prototype of each band shows a VSWR of less than 2:1 at both polarizations over a fractional bandwidth exceeding 40%. The antennas developed offer cross-polarization isolation larger than 30 dB, an E-plane 3-dB beamwidth of 69 degrees, and a gain of at least 4 dBi with a variation of ± 1 dB across the bandwidth. This design with high power handling in mind also allows for straightforward adjustment of the antenna dimensions to meet other bandwidth constrains. It is being used as the basis for an airborne system.

Next, this work documents design details and measured performance of an improved and integrated x16 frequency multiplier system for an airborne snow-probing radar. This sub-system produces a 40 – 56 GHz linear frequency sweep from a 2.5 – 3.5 GHz chirp and mixes it down to the 2 – 18 GHz range.  The resulting chirp is used for transmission and analog de-chirping of the receive signal. The initial prototype developed through this work provided a higher level of integration and wider fractional bandwidth (>135%) compared to earlier versions implemented with the same frequency plan and a path to guide future realizations.

Lastly, this work documents a series of trade studies on antenna array configurations for both radar systems using electromagnetic simulation tools and measurements.

Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier.

As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification.

Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN.

The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics.