Defense Notices

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.

Metagenomics is the study of genomic content present in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling, and peptide assembly. Unfortunately, their informational dependencies have been overlooked, and have been formulated as independent problems. 

In this work, we propose a novel de novo analysis pipeline that leverages these informational dependencies, to improve functional analysis of metagenomics data. Specifically, the pipeline will contain four novel modules: an assembly graph module, a graph-based gene calling module, a peptide assembly module, and a biosynthetic gene cluster (BGC) discovery module. The assembly graph module will be computational and memory efficient. It will be based on a combination of de Bruijn and string graphs. The assembly graphs contain important sequencing information, which can be further exploited to improve functional annotation. De novo gene-calling enables us to predict novel genes and protein sequences, that have not been previously characterized. We hypothesize that de novo gene calling can benefit from assembly graph structures, as they contain important start/stop codon information that provide stronger ORF signals. The assembly graph framework will be designed for both nucleotide and protein sequences. The resulting protein sequences from gene calling can be further assembled into longer protein contigs using our assembly framework. For the novel BGC module, the gene members of a BGC will be marked in the assembly graph. Finding a BGC can be achieved by identifying a path connecting its gene members in the assembly graph. Experimental results have shown that our proposed pipeline improved existing gene calling sensitivity on unassembled reads, achieving a 10-15% improvement in sensitivity over the state-of-the-art methods, at a high specificity (>90%). Our pipeline further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins, delivering more hypothetical protein sequences.

Quantum computing is a promising technology that can potentially demonstrate supremacy over classical computing in solving specific problems. At present, two critical challenges for quantum computing are quantum state decoherence, and low scalability of current quantum devices. Decoherence places constraints on realistic applicability of quantum algorithms as real-life applications usually require complex equivalent quantum circuits to be realized. For example, encoding classical data on quantum computers for solving I/O and data-intensive applications generally requires quantum circuits that violate decoherence constraints. In addition, current quantum devices are of small-scale having low quantum bit(qubit) counts, and often producing inaccurate or noisy measurements, which also impacts the realistic applicability of real-world quantum algorithms. Consequently, benchmarking of existing quantum algorithms and investigation of new applications are heavily dependent on classical simulations that use costly, resource-intensive computing platforms. Hardware-based emulation has been alternatively proposed as a more cost-effective and power-efficient approach. This work proposes a hardware-based emulation methodology for quantum algorithms, using cost-effective Field-Programmable Gate-Array(FPGA) technology. The proposed methodology consists of three components that are required for complete emulation of quantum algorithms; the first component models classical-to-quantum(C2Q) data encoding, the second emulates the behavior of quantum algorithms, and the third models the process of measuring the quantum state and extracting classical information, i.e., quantum-to-classical(Q2C) data decoding. The proposed emulation methodology is used to investigate and optimize methods for C2Q/Q2C data encoding/decoding, as well as several important quantum algorithms such as Quantum Fourier Transform(QFT), Quantum Haar Transform(QHT), and Quantum Grover’s Search(QGS). This work delivers contributions in terms of reducing complexities of quantum circuits, extending and optimizing quantum algorithms, and developing new quantum applications. For higher emulation performance and scalability of the framework, hardware design techniques and hardware architectural optimizations are investigated and proposed. The emulation architectures are designed and implemented on a high-performance-reconfigurable-computer(HPRC), and proposed quantum circuits are implemented on a state-of-the-art quantum processor. Experimental results show that the proposed hardware architectures enable emulation of quantum algorithms with higher scalability, higher accuracy, and higher throughput, compared to existing hardware-based emulators. As a case study, quantum image processing using multi-spectral images is considered for the experimental evaluations. 

Open-source intelligence is a branch within cybercrime investigation that focuses on information collection and aggregation. Through this aggregation, investigators and analysts can analyze the data for connections relevant to the investigation. There are many tools that assist with information collection and aggregation. However, these often require enterprise licensing. A solution to enterprise licensed tools is using open-source tools to collect information, often by scraping websites. These tools provide useful information, but they provide a large number of disjointed reports. The framework we developed automates information collection, aggregates these reports, and generates one single graphical report. By using a graphical report, the time required for analysis is also reduced. This framework can be used for different investigations. We performed a case study regarding the performance of the framework with missing person case information. It showed a significant improvement in the time required for information collection and report analysis. 

Word embedding has become a popular form of data representation that is used to train deep neural networks in many natural

language processing tasks, such as Machine Translation, Question Answer Generation, Named Entity Recognition, Next

Word/Sentence Prediction etc. With embedding, each word is represented as a dense vector which captures its semantic relationship

with other words and can better empower Machine Learning models to achieve state-of-the-art performance.

However, due to the memory and time intensive nature of learning such word embeddings, transfer learning has emerged as a

common practice to warm start the training process. As a result, an efficient way is to initialize with pretrained word vectors and then

fine-tune those on downstream domain specific smaller datasets. This study aims to find whether we can infer the contextual

distribution (i.e., how words cooccur in a sentence driven by syntactic regularities) of the downstream datasets given that we have

access to the embeddings from both pre-training and fine-tuning processes.

In this work, we propose a focused sampling method along with a novel model inversion architecture “Invernet” to invert word

embeddings into the word-to-word context information of the fine-tuned dataset. We consider the popular word2Vec models

including CBOW, SkipGram, and GloVe based algorithms with various unsupervised settings. We conduct extensive experimental

study on two real-world news datasets: Antonio Gulli’s News Dataset from Hugging Face repository and a New York Times dataset

from both quantitative and qualitative perspectives. Results show that “Invernet” has been able to achieve an average F1 score of 0.75

and an average AUC score of 0.85 in an attack scenario.

A concerning pattern from our experiments reveal that embedding models that are generally considered superior in different tasks

tend to be more vulnerable to model inversion. Our results suggest that a significant amount of context distribution information from

the downstream dataset can potentially leak if an attacker gets access to the pretrained and fine-tuned word embeddings. As a result,

attacks using “Invernet” can jeopardize the privacy of the users whose data might have been used to fine-tune the word embedding

model.

With the growing computational power and the enormous data available from many sectors, applications with machine learning (ML) components are widely adopted in our everyday lives. One major drawback associated with ML models is hard to guarantee same performance with changing environment. Since ML models are not traditional software that can be tested end-to-end. ML models are vulnerable against distributional shifts and cyber-attacks. Various cyber-attacks against deep neural networks (DNN) have been proposed in the literature, such as poisoning, evasion, backdoor, and model inversion. In the evasion attacks against DNN, the attacker generates adversarial instances that are visually indistinguishable from benign samples and sends them to the target DNN to trigger misclassifications.

In our work, we proposed a novel multi-view adversarial image detector, namely ‘Argos’, based on a novel observation. That is, there exist two” souls” in an adversarial instance, i.e., the visually unchanged content, which corresponds to the true label, and the added invisible perturbation, which corresponds to the misclassified label. Such inconsistencies could be further amplified through an autoregressive generative approach that generates images with seed pixels selected from the original image, a selected label, and pixel distributions learned from the training data. The generated images (i.e., the “views”) will deviate significantly from the original one if the label is adversarial, demonstrating inconsistencies that ‘Argos’ expects to detect. To this end, ‘Argos’ first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree. Our experimental results show that ‘Argos’ significantly outperforms two representative adversarial detectors in both detection accuracy and robustness against six well-known adversarial attacks.

Coq's extraction plugin is used to produce code of a general purpose

  programming language from a specification written in the Calculus of Inductive

  Constructions (CIC). Currently, this mechanism is trusted, since there is no

  formal connection between the synthesized code and the CIC terms it originated

  from. This comes from a lack of formal specifications for the target

  languages: OCaml, Haskell, and Scheme. We intend to use the formally specified

  CakeML language as an extraction target, and generate a theorem in Coq that

  relates the generated CakeML abstract syntax to the CIC terms it is generated

  from. This work expands on the techniques used in the HOL4 translator from

  Higher Order Logic to CakeML. The HOL4 translator also allows for the

  generation of stateful code from the state and exception monad. We expand on

  their techniques by extracting terms with dependent types, and generating

  stateful code for other kinds of monads, like the reader monad, depending on

  what kind of computation the monad intends to represent.