Browsing by Author "Michaels, Alan J."

Now showing 1 - 20 of 37
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    Analysis of Lightweight Cryptographic Primitives
    George, Kiernan Brent (Virginia Tech, 2021-05-05)
    Internet-of-Things (IoT) devices have become increasingly popular in the last 10 years, yet also show an acceptance for lack of security due to hardware constraints. The range of sophistication in IoT devices varies substantially depending on the functionality required, so security options need to be flexible. Manufacturers typically either use no security, or lean towards the use of the Advanced Encryption Standard (AES) with a 128-bit key. AES-128 is suitable for the higher end of that IoT device range, but is costly enough in terms of memory, time, and energy consumption that some devices opt to use no security. Short development and a strong drive to market also contribute to a lack in security. Recent work in lightweight cryptography has analyzed the suitability of custom protocols using AES as a comparative baseline. AES outperforms most custom protocols when looking at security, but those analyses fail to take into account block size and future capabilities such as quantum computers. This thesis analyzes lightweight cryptographic primitives that would be suitable for use in IoT devices, helping fill a gap for "good enough" security within the size, weight, and power (SWaP) constraints common to IoT devices. The primitives have not undergone comprehensive cryptanalysis and this thesis attempts to provide a preliminary analysis of confidentiality. The first is a single-stage residue number system (RNS) pseudorandom number generator (PRNG) that was shown in previous publications to produce strong outputs when analyzed with statistical tests like the NIST RNG test suite and DIEHARD. However, through analysis, an intelligent multi-stage conditional probability attack based on the pigeonhole principle was devised to reverse engineer the initial state (key) of a single-stage RNS PRNG. The reverse engineering algorithm is presented and used against an IoT-caliber device to showcase the ability of an attacker to retrieve the initial state. Following, defenses based on intentional noise, time hopping, and code hopping are proposed. Further computation and memory analysis show the proposed defenses are simple in implementation, but increase complexity for an attacker to the point where reverse engineering the PRNG is likely no longer viable. The next primitive proposed is a block cipher combination technique based on Galois Extension Field multiplication. Using any PRNG to produce the pseudorandom stream, the block cipher combination technique generates a variable sized key matrix to encrypt plaintext. Electronic Codebook (ECB) and Cipher Feedback (CFB) modes of operation are discussed. Both system modes are implemented in MATLAB as well as on a Texas Instruments (TI) MSP430FR5994 microcontroller for hardware validation. A series of statistical tests are then run against the simulation results to analyze overall randomness, including NIST and the Law of the Iterated Logarithm; the system passes both. The implementation on hardware is compared against a stream cipher variation and AES-128. The block cipher proposed outperforms AES-128 in terms of computation time and consumption for small block sizes. While not as secure, the cryptosystem is more scalable to block sizes used in IoT devices.
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    An Analysis of Radio Frequency Transfer Learning Behavior
    Wong, Lauren J.; Muller, Braeden; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-06-03)
    Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how the training domain and task, characterized by the transmitter (Tx)/receiver (Rx) hardware and channel environment, impact radio frequency (RF) TL performance for example automatic modulation classification (AMC) and specific emitter identification (SEI) use-cases. Through exhaustive experimentation using carefully curated synthetic and captured datasets with varying signal types, channel types, signal to noise ratios (SNRs), carrier/center frequencys (CFs), frequency offsets (FOs), and Tx and Rx devices, actionable and generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, our results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks, but also on the relative difficulty of the source and target domains/tasks. Results also discuss the impacts of channel environment and hardware variations on RF TL performance and compare RF TL performance using head re-training and model fine-tuning methods.
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    Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
    Wong, Lauren J.; Muller, Braeden P.; McPherson, Sean; Michaels, Alan J. (MDPI, 2024-07-25)
    The use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance and reduced training time, but has yet to be fully utilized in applications of machine learning (ML) and deep learning (DL) techniques and applications related to wireless communications, a field loosely termed radio frequency machine learning (RFML). This work examines whether existing transferability metrics, used in other modalities, might be useful in the context of RFML. Results show that the two existing metrics tested, Log Expected Empirical Prediction (LEEP) and Logarithm of Maximum Evidence (LogME), correlate well with post-transfer accuracy and can therefore be used to select source models for radio frequency (RF) domain adaptation and to predict post-transfer accuracy.
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    Attacks and Defenses for Single-Stage Residue Number System PRNGs
    Vennos, Amy; George, Kiernan; Michaels, Alan J. (MDPI, 2021-06-25)
    This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.
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    Cognitive Radar Applied To Target Tracking Using Markov Decision Processes
    Selvi, Ersin Suleyman (Virginia Tech, 2018-01-30)
    The radio-frequency spectrum is a precious resource, with many applications and users, especially with the recent spectrum auction in the United States. Future platforms and devices, such as radars and radios, need to be adaptive to their spectral environment in order to continue serving the needs of their users. This thesis considers an environment with one tracking radar, a single target, and a communications system. The radar-communications coexistence problem is modeled as a Markov decision process (MDP), and reinforcement learning is applied to drive the radar to optimal behavior.
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    Design and Analysis of L-Band Reconfigurable Liquid-Metal Alloy Antennas
    Thews, Jonathan Tyler (Virginia Tech, 2017-06-09)
    Efficient reconfigurable antennas are highly sought after in all communication applications for the ability to reduce space cost while maintaining the ability to control the frequency, gain, and polarization. The ability to control these parameters allows a single antenna to maximize its performance in a wide range of scenarios to satisfy changing operating requirements. This thesis will describe reconfigurable antennas using liquid-metal alloys that give the system this ability by injecting or retracting the liquid metal from various channels. After simulations were performed in an electromagnetic simulation software, proof-of-concept models were built, tested, and compared to the simulations to verify the results.
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    Designing a Block Cipher in Galois Extension Fields for IoT Security
    George, Kiernan; Michaels, Alan J. (MDPI, 2021-11-05)
    This paper focuses on a block cipher adaptation of the Galois Extension Fields (GEF) combination technique for PRNGs and targets application in the Internet of Things (IoT) space, an area where the combination technique was concluded as a quality stream cipher. Electronic Codebook (ECB) and Cipher Feedback (CFB) variations of the cryptographic algorithm are discussed. Both modes offer computationally efficient, scalable cryptographic algorithms for use over a simple combination technique like XOR. The cryptographic algorithm relies on the use of quality PRNGs, but adds an additional layer of security while preserving maximal entropy and near-uniform distributions. The use of matrices with entries drawn from a Galois field extends this technique to block size chunks of plaintext, increasing diffusion, while only requiring linear operations that are quick to perform. The process of calculating the inverse differs only in using the modular inverse of the determinant, but this can be expedited by a look-up table. We validate this GEF block cipher with the NIST test suite. Additional statistical tests indicate the condensed plaintext results in a near-uniform distributed ciphertext across the entire field. The block cipher implemented on an MSP430 offers a faster, more power-efficient alternative to the Advanced Encryption Standard (AES) system. This cryptosystem is a secure, scalable option for IoT devices that must be mindful of time and power consumption.
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    Enhanced Implementations for Arbitrary-Phase Spread Spectrum Waveforms
    Fletcher, Michael John (Virginia Tech, 2019-06-18)
    The use of practically non-repeating spreading codes to generate sequence-based spread spectrum waveforms is a strong method to improve transmission security, by limiting an observers opportunity to cross-correlate snapshots of the signal into a coherent gain. Such time-varying codes, particularly when used to define multi-bit resolution arbitrary-phase waveforms, also present significant challenges to the intended receiver, which must synchronize correlator processing to match the code every time it changes. High-order phase shift keying (PSK) spread modulations do, however, provide an overall whiter spectral response than legacy direct sequence spread spectrum (DSSS) signals. Further, the unique ability to color the output signal spectrum offers new advantages to optimize transmission in a non-white frequency channel and to mitigate observed interference. In high data rate applications, the opportunity to inject a time-aligned co-channel underlay-based watermark for authentication at the receiver is an effective method to enhance physical layer (PHY) security for virtually any primary network waveform. This thesis presents a series of options to enhance the implementation of arbitrary-phase chaotic sequence-based spread spectrum waveforms, including techniques to significantly reduce fallthrough correlator hardware resources in low-power sensing devices for only minor performance loss, capabilities for programming chosen frequency domain spectra into the resulting spread spectrum signal, and design considerations for underlay watermark-based PHY-layer firewalls. A number of hardware validated prototypes were built on an Intel Arria 10 SoC FPGA to provide measurable results, achieving substantial computational resource gains and implementation flexibility.
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    Fallthrough Correlation Techniques for Arbitrary-Phase Spread Spectrum Waveforms
    Fletcher, Michael; Michaels, Alan J.; Ridge, Devin (IEEE, 2019-09-11)
    The use of practically non-repeating spreading codes to generate sequence-based spread spectrum waveforms is a strong method to improve transmission security, by limiting an observer's opportunity to cross-correlate snapshots of the signal into a coherent gain. Such time-varying codes, particularly when used to define multi-bit resolution arbitrary-phase waveforms, present significant challenges to the intended receiver, who must synchronize acquisition processing to match the time-varying code each time it changes. This paper presents a series of options for optimizing the traditional brute-force matched-filter preamble correlator for burst-mode arbitrary-phase spread spectrum signals, achieving significant computational gains and flexibility, backed by measurable results from hardware prototypes built on an Intel Arria 10 Field Programmable Gate Array (FPGA). The most promising of which requires no embedded multipliers and reduces the total hardware logic by more than 76%. Extensions of the core fallthrough correlator techniques are considered to support low-power asynchronous reception, underlay-based physical layer rewall functions, and Receiver-Assigned Code Division Multiple Access (RA-CDMA) protocols in Internet of Things (IoT)-caliber devices.
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    Foundations of Radio Frequency Transfer Learning
    Wong, Lauren Joy (Virginia Tech, 2024-02-06)
    The introduction of Machine Learning (ML) and Deep Learning (DL) techniques into modern radio communications system, a field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when compared to traditional signal processing techniques and has broad utility in both the commercial and defense sectors. Existing RFML systems predominately utilize supervised learning solutions in which the training process is performed offline, before deployment, and the learned model remains fixed once deployed. The inflexibility of these systems means that, while they are appropriate for the conditions assumed during offline training, they show limited adaptability to changes in the propagation environment and transmitter/receiver hardware, leading to significant performance degradation. Given the fluidity of modern communication environments, this rigidness has limited the widespread adoption of RFML solutions to date. Transfer Learning (TL) is a means to mitigate such performance degradations by re-using prior knowledge learned from a source domain and task to improve performance on a "similar" target domain and task. However, the benefits of TL have yet to be fully demonstrated and integrated into RFML systems. This dissertation begins by clearly defining the problem space of RF TL through a domain-specific TL taxonomy for RFML that provides common language and terminology with concrete and Radio Frequency (RF)-specific example use- cases. Then, the impacts of the RF domain, characterized by the hardware and channel environment(s), and task, characterized by the application(s) being addressed, on performance are studied, and methods and metrics for predicting and quantifying RF TL performance are examined. In total, this work provides the foundational knowledge to more reliably use TL approaches in RF contexts and opens directions for future work that will improve the robustness and increase the deployability of RFML.
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    FPGA Implementation of a Pseudo-Random Aggregate Spectrum Generator for RF Hardware Test and Evaluation
    Baweja, Randeep Singh (Virginia Tech, 2020-10-09)
    Test and evaluation (TandE) is a critically important step before in-the-field deployment of radio-frequency (RF) hardware in order to assure that the hardware meets its design requirements and specifications. Typically, TandE is performed either in a lab setting utilizing a software simulation environment or through real-world field testing. While the former approach is typically limited by the accuracy of the simulation models (particularly of the anticipated hardware effects) and by non-real-time data rates, the latter can be extremely costly in terms of time, money, and manpower. To build upon the strengths of these approaches and to mitigate their weaknesses, this work presents the development of an FPGA-based TandE tool that allows for real-time pseudo-random aggregate signal generation for testing RF receiver hardware (such as communication receivers, spectrum sensors, etc.). In particular, a framework is developed for an FPGA-based implementation of a test signal emulator that generates randomized aggregate spectral environments containing signals with random parameters such as center frequencies, bandwidths, start times, and durations, as well as receiver and channel effects such as additive white Gaussian noise (AWGN). To test the accuracy of the developed spectrum generation framework, the randomization properties of the framework are analyzed to assure correct probability distributions and independence. Additionally, FPGA implementation decisions, such as bit precision versus accuracy of the generated signal and the impact on the FPGA's hardware footprint, are analyzed.This analysis allows the test signal engineer to make informed decisions while designing a hardware-based RF test system. This framework is easily extensible to other signal types and channel models, and can be used to test a variety of signal-based applications.
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    Framework for Evaluating the Severity of Cybervulnerability of a Traffic Cabinet
    Ernst, Joseph M.; Michaels, Alan J. (National Academy of Sciences, 2017)
    The increasing connectivity in transportation infrastructure is driving a need for additional security in transportation systems. For security decisions in a budget-constrained environment, the possible effect of a cyberattack must be numerically characterized. The size of an effect depends on the level of access and the vehicular demand on the intersections being controlled. This paper proposes a framework for better understanding of the levels of access and the effect that can be had in scenarios with varying demand. Simulations are performed on a simplistic corridor to provide numerical examples of the possible effects. The paper concludes that the possibility of some levels of cyberthreat may be acceptable in locations where traffic volumes would not be able to create an unmanageable queue. The more intimate levels of access can cause serious safety concerns by modifying the settings of the traffic controller in ways that encourage red-light running and accidents. The proposed framework can be used by transportation professionals and cybersecurity professionals to prioritize the actions to be taken to secure the infrastructure.
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    Further Analysis of PRNG-Based Key Derivation Functions
    McGinthy, Jason M.; Michaels, Alan J. (IEEE, 2019)
    The Internet of Things (IoT) is growing at a rapid pace. With everyday applications and services becoming wirelessly networked, security still is a major concern. Many of these sensors and devices have limitations, such as low power consumption, reduced memory storage, and reduced fixed point processing capabilities. Therefore, it is imperative that high-performance security primitives are used to maximize the lifetime of these devices while minimally impacting memory storage and timing requirements. Previous work presented a residue number system (RNS)-based pseudorandom number generator (PRNG)-based key derivation function (KDF) (PKDF) that showed good initial energy-efficient performance for the IoT devices. This paper provides additional analysis on the PRNG-based security and draws a comparison to a current industry-standard KDF. Subsequently, embedded software implementations were performed on an MSP430 and MSP432 and compared with the transport layer security (TLS) 1.3 hash-based message authentication code (HMAC) key derivation function (HKDF); these results demonstrate substantial computational savings for the PKDF approach, while both pass the NIST randomness quality tests. Finally, hardware translation for the PKDF is evaluated through the Mathworks' HDL Coder toolchain and mapping for throughput and die area approximation on an Intel (R) Arria 10 FPGA.
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    The Importance of Data in RF Machine Learning
    Clark IV, William Henry (Virginia Tech, 2022-11-17)
    While the toolset known as Machine Learning (ML) is not new, several of the tools available within the toolset have seen revitalization with improved hardware, and have been applied across several domains in the last two decades. Deep Neural Network (DNN) applications have contributed to significant research within Radio Frequency (RF) problems over the last decade, spurred by results in image and audio processing. Machine Learning (ML), and Deep Learning (DL) specifically, are driven by access to relevant data during the training phase of the application due to the learned feature sets that are derived from vast amounts of similar data. Despite this critical reliance on data, the literature provides insufficient answers on how to quantify the data training needs of an application in order to achieve a desired performance. This dissertation first aims to create a practical definition that bounds the problem space of Radio Frequency Machine Learning (RFML), which we take to mean the application of Machine Learning (ML) as close to the sampled baseband signal directly after digitization as is possible, while allowing for preprocessing when reasonably defined and justified. After constraining the problem to the Radio Frequency Machine Learning (RFML) domain space, an understanding of what kinds of Machine Learning (ML) have been applied as well as the techniques that have shown benefits will be reviewed from the literature. With the problem space defined and the trends in the literature examined, the next goal aims at providing a better understanding for the concept of data quality through quantification. This quantification helps explain how the quality of data: affects Machine Learning (ML) systems with regard to final performance, drives required data observation quantity within that space, and impacts can be generalized and contrasted. With the understanding of how data quality and quantity can affect the performance of a system in the Radio Frequency Machine Learning (RFML) space, an examination of the data generation techniques and realizations from conceptual through real-time hardware implementations are discussed. Consequently, the results of this dissertation provide a foundation for estimating the investment required to realize a performance goal within a Deep Learning (DL) framework as well as a rough order of magnitude for common goals within the Radio Frequency Machine Learning (RFML) problem space.
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    Improving Implantable Medical Device Security Through Cooperative Jamming
    Lytle, Kimberly Mirella (Virginia Tech, 2023-07-03)
    Implantable medical devices (IMDs) are medically necessary devices embedded in a human body that monitor chronic disorders or automatically deliver therapies, such as insulin pumps or pacemakers. Typically, they are small form-factor devices with limited battery and processing power. Most IMDs have wireless capabilities that allow them to share data with an offboard programming device, such as a smartphone application, that has more storage and processing power than the IMD itself. Additionally, the programming device can send commands back to the IMD to change its settings according to the treatment plan. As such, wirelessly sharing information between an IMD and offboard device can help medical providers monitor the patient's health remotely while giving the patient more insight into their condition, more autonomy, and fewer in-person appointments. However, serious security concerns have arisen as researchers have demonstrated it is possible to hack these devices to obtain sensitive information or potentially harm the patient. This is particularly easy to do as most IMDs transmit their data in the clear to avoid allocating their limited resources to encrypting their packets. As these concerns and the percentage of the American population with IMDs grows, there is another fear that bad actors could exploit the link between the programming device and IMD. Theoretically, a hacker could launch a man in the middle attack to send the IMD unauthorized commands, reprogramming it to act as a radio, sniffing signals of interest in the environment. As such, the hacker could use the IMD as a software defined radio (SDR) that captures sensitive or even classified information without the patient's knowledge. If this were to happen, it is possible an unwitting person with an IMD who has access to classified or sensitive information could be used to exfiltrate data that, in the wrong hands, could be used for corporate espionage or to the detriment of national security. While governing bodies agree that cybersecurity risks are present in IMD systems, there are no requirements for IMD manufacturers to create their devices with security measures that mitigate these risks. Researchers have proposed physical, technical, and administrative security measures for IMDs, but other existing wireless security techniques may apply to the healthcare space and need to be explored. Beamforming is an array signal processing technique that relies on individual elements of antenna arrays adjusting their phase and amplitude to create an overall effect of directing RF energy in a particular direction. Similarly, cooperative beamforming uses several physically separate "friendly" beamforming-capable devices to collectively send artificial noise to eavesdroppers while ensuring the signal is successfully received by the intended receiver. Although there are several cooperative jamming algorithms, they share the underlying principles of minimizing SINR at potential eavesdroppers while maximizing the SINR at the intended receiver. Researchers exploring cooperative jamming have largely used models to estimate its impact on channel secrecy. While RF propagation and communication system modeling provides valuable insight into system performance, many theoretical and empirical models are limited by the extent to which the operational environment matches that of the model itself. Ray tracing, alternatively, is more widely applicable as it accounts for a 3D environment and the objects a signal interacts with in that space. A ray is defined as an individual RF signal that travels in a straight line through a uniform medium; obeys the laws of reflection, refraction, and diffraction; and carries energy. As the ray interacts with objects in the environment, its energy will decrease by some amount that depends on the materials and geometry of the object. While research has predominantly focused on applications like cellular communications, the same principles of minimizing SINR at potential eavesdroppers while maximizing the SINR at the intended receiver can be applied to IMDs. As IMD use cases assume the programmer is nearby, the friendly nodes will not need to act as relays and can instead focus all their power on jamming. The number of cooperative jammers will be low to simulate the number of devices an individual might have in a workspace or office setting, like a personal phone, smart watch, or laptop, and realistic power constraints will be observed. Further, ray tracing software will provide additional visual insights into how various building materials like drywall, concrete, brick, and glass impact cooperative jamming. Through these simulations, the trade-off between secrecy rate and physical separation and layout of friendly nodes can be determined, which in turn may inform how companies or individuals can protect their proprietary and personal information.
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    Low-Latency Wireless Network Extension for Industrial Internet of Things
    Fletcher, Michael; Paulz, Eric; Ridge, Devin; Michaels, Alan J. (MDPI, 2024-03-26)
    The timely delivery of critical messages in real-time environments is an increasing requirement for industrial Internet of Things (IIoT) networks. Similar to wired time-sensitive networking (TSN) techniques, which bifurcate traffic flows based on priority, the proposed wireless method aims to ensure that critical traffic arrives rapidly across multiple hops to enable numerous IIoT use cases. IIoT architectures are migrating toward wirelessly connected edges, creating a desire to extend TSN-like functionality to a wireless format. Existing protocols possess inherent challenges to achieving this prioritized low-latency communication, ranging from rigidly scheduled time division transmissions, scalability/jitter of carrier-sense multiple access (CSMA) protocols, and encryption-induced latency. This paper presents a hardware-validated low-latency technique built upon receiver-assigned code division multiple access (RA-CDMA) techniques to implement a secure wireless TSN-like extension suitable for the IIoT. Results from our hardware prototype, constructed on the IntelFPGA Arria 10 platform, show that (sub-)millisecond single-hop latencies can be achieved for each of the available message types, ranging from 12 bits up to 224 bits of payload. By achieving one-way transmission of under 1 ms, a reliable wireless TSN extension with comparable timelines to 802.1Q and/or 5G is achievable and proven in concept through our hardware prototype.
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    On Efficient Computer Vision Applications for Neural Networks
    Billings, Rachel Mae (Virginia Tech, 2021-04-06)
    Since approximately the dawn of the new millennium, neural networks and other machine learning algorithms have become increasingly capable of adeptly performing difficult, dull, and dangerous work conventionally carried out by humans in times of old. As these algorithms become steadily more commonplace in everyday consumer and industry applications, the consideration of how they may be implemented on constrained hardware systems such as smartphones and Internet-of-Things (IoT) peripheral devices in a time- and power- efficient manner while also understanding the scenarios in which they fail is of increasing importance. This work investigates implementations of convolutional neural networks specifically in the context of image inference tasks. Three areas are analyzed: (1) a time- and power-efficient face recognition framework, (2) the development of a COVID-19-related mask classification system suitable for deployment on low-cost, low-power devices, and (3) an investigation into the implementation of spiking neural networks on mobile hardware and their conversion from traditional neural network architectures.
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    On the Use of Convolutional Neural Networks for Specific Emitter Identification
    Wong, Lauren J. (Virginia Tech, 2018-06-12)
    Specific Emitter Identification (SEI) is the association of a received signal to an emitter, and is made possible by the unique and unintentional characteristics an emitter imparts onto each transmission, known as its radio frequency (RF) fingerprint. SEI systems are of vital importance to the military for applications such as early warning systems, emitter tracking, and emitter location. More recently, cognitive radio systems have started making use of SEI systems to enforce Dynamic Spectrum Access (DSA) rules. The use of pre-determined and expert defined signal features to characterize the RF fingerprint of emitters of interest limits current state-of-the-art SEI systems in numerous ways. Recent work in RF Machine Learning (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in place of a traditional expert-defined feature extraction process, to improve upon traditional SEI systems, by developing and analyzing two distinct approaches for performing SEI using CNNs. Neither approach assumes a priori knowledge of the emitters of interest. Further, both approaches use only raw IQ data as input, and are designed to be easily tuned or modified for new operating environments. Results show CNNs can be used to both estimate expert-defined features and to learn emitter-specific features to effectively identify emitters.
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    Optimization of Disaggregated Space Systems Using the Disaggregated Integral Systems Concept Optimization Technology Methodology
    Wagner, Katherine Mott (Virginia Tech, 2020-07-10)
    This research describes the development and application of the Disaggregated Integral Systems Concept Optimization Technology (DISCO-Tech) methodology. DISCO-Tech is a modular space system design tool that focuses on the optimization of disaggregated and non-traditional space systems. It uses a variable-length genetic algorithm to simultaneously optimize orbital parameters, payload parameters, and payload distribution for space systems. The solutions produced by the genetic algorithm are evaluated using cost estimation, coverage analysis, and spacecraft sizing modules. A set of validation cases are presented. DISCO-Tech is then applied to three representative space mission design problems. The first problem is the design of a resilient rideshare-manifested fire detection system. This analysis uses a novel framework for evaluating constellation resilience to threats using mixed integer linear programming. A solution is identified where revisit times of under four hours are achievable for $10.5 million, one quarter of the cost of a system manifested using dedicated launches. The second problem applies the same resilience techniques to the design of an expanded GPS monitor station network. Nine additional monitor stations are identified that allow the network to continuously monitor the GPS satellites even when five of the monitor stations are inoperable. The third problem is the design of a formation of satellites for performing sea surface height detection using interferometric synthetic aperture radar techniques. A solution is chosen that meets the performance requirements of an upcoming monolithic system at 70% of the cost of the monolithic system.
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    Polar Coding in Certain New Transmission Environments
    Timmel, Stephen Nicholas (Virginia Tech, 2023-05-15)
    Polar codes, introduced by Arikan in 2009, have attracted considerable interest as an asymptotically capacity-achieving code with sufficient performance advantages to merit inclusion in the 5G standard. Polar codes are constructed directly from an explicit model of the communication channel, so their performance is dependent on a detailed understanding of the transmission environment. We partially remove a basic assumption in coding theory that channels are identical and independent by extending polar codes to several types of channels with memory, including periodic Markov processes and Information Regular processes. In addition, we consider modifications to the polar code construction so that the inclusion of a shared secret in the frozen set naturally produces encryption via one-time pad. We describe one such modification in terms of the achievable frozen sets which are compatible with the polar code automorphism group. We then provide a partial characterization of these frozen sets using an explicit construction for the Linear Extension Diameter of channel entropies.