<p> An existing transient network model for wicked heat pipes was extended to incorporate the effects of axial heat transfer along the wall and wick, heat transfer in the surrounding media, and non-condensable gases in the vapour region. The thermal resistance of the different components was broken down into a larger number of smaller resistances in both axial and radial directions to account for the axial conduction and to handle non-uniform boundary conditions. Two sets of experiments were performed on copper-water wicked heat pipes to evaluate the effect of non-condensable gases, axial conduction, surrounding media and non-uniform boundary conditions. In the first set of experiments, the heat pipes were electrically heated at one end and cooled on the other end using a water jacket. This set of experiments was used to investigate the effect of non-condensable gases, axial conduction and surrounding media on the steady state and transient performance. The effect of the surrounding media was investigated by heating the heat pipe through two different sized aluminum blocks mounted around then heat pipe evaporator section. In the second set of experiments, the effect of using a finned condenser on the steady state performance of the heat pipes were tested in a wind tunnel. The condenser section of the heat pipes in this case was mounted in the test section of the wind tunnel and cooled at different air velocities. Three fin densities were tested along with a heat pipe with no fins. The model predictions of the steady and transient response of the vapour and wall temperature of the heat pipes were in good agreement with the experimental results. </p> <p> The presence of non-condensable gases inside the heat pipe increased the overall thermal resistance of the heat pipe. While the non-condensable gases did not notably affect the transient response during the heat-up phase, it significantly slows down the cool-down phase. The axial conduction through the pipe wall and the wick structure decreases the overall thermal resistance of the heat pipe. The axial conduction did not have a great influence on the time response during the heat-up phase, but was very important in the cooldown phase, especially with the presence of non-condensable gases. The wick structure was found to be the most dominant component in the transient performance of the heat pipe. The evaporator block was the dominant capacitance in the overall conjugate system, and significantly affects the transient response. The experimental results from the finned condenser study showed that the internal resistance increased slightly with the fin density. There was some nonuniformity in the condenser surface temperature at the locations of the fins. However, this non-uniformity did not propagate to other parts of the heat pipe. </p> / Thesis / Master of Applied Science (MASc)

Adversarial network coding studies the transmission of data over networks affected by adversarial noise. In this realm, the noise is modeled by an omniscient adversary who is restricted to corrupting a proper subset of the network edges. In 2018, Ravagnani and Kschischang established a combinatorial framework for adversarial networks. The study was recently furthered by Beemer, Kilic and Ravagnani, with particular focus on the one-shot capacity: a measure of the maximum number of symbols that can be transmitted in a single use of the network without errors. In this thesis, both bounds and capacity-achieving schemes are provided for families of adversarial networks in multiple transmission rounds. We also demonstrate scenarios where we transmit more information using a network multiple times for communication versus using the network once. Some results in this thesis are joint work with Giuseppe Cotardo (Virginia Tech), Gretchen Matthews (Virginia Tech) and Alberto Ravagnani (Eindhoven University of Technology). / Master of Science / We study how to best transfer data across a communication network even if there is adversarial interference using network coding. Network coding is used in video streaming, autonomous vehicles, 5G and NextG communications, satellite networks, and Internet of Things (IoT) devices among other applications. It is the process that encodes data before sending it and decodes it upon receipt. It brings advantages such as increased network efficiency, improved reliability, reduced redundancy, enhanced resilience, and energy savings. We seek to enhance this valuable technique by determining optimal ways in which to utilize network coding schemes. We explore scenarios in which an adversary has partial access to a network. To examine the maximum data that can be communicated over one use of a network, we require the intermediate parts of the network process the information before forwarding it in a process called network decoding. In this thesis, we focus on characterizing when using a network multiple times for communication increases the amount of information that is received regardless of the worst-case adversarial attack, building on prior work that shows how underlying structure influences capacity. We design efficient methods for specific networks, to communicate at capacity.

<p dir="ltr">As the demand for IoT devices continues to grow, our reliance on networks in daily life increases. Whether we are considering individual users or large multinational companies, networks have become an essential asset for people across various industries. However, this dependence on networks also exposes us to security vulnerabilities when traffic is not adequately filtered. A successful attack on the network could have severe consequences for its users. Therefore, the implementation of a network intrusion detection system (IDS) is crucial to safeguard the well-being of our modern society.</p><p dir="ltr">While AI-based IDS is a new force in the field of intrusion detection, it outperforms some traditional approaches. However, it is not without its flaws. The performance of ML-based IDS decreases when applied to a different dataset than the one it was trained on. This decrease in performance hinders the ML-based IDS's ability to be used in a production environment, as the data generated in a production environment also differs from the data that is used to train the IDS. This paper aims to devise an ML-based IDS that is generalizable to a different environment.</p>

Allocating resources in computer systems is a significant challenge due to constraints on resources, coordinating access to those resources, and tolerating malicious behavior. This dissertation investigates two fundamental problems concerning resource allocation. The first addresses the general challenge of sharing server resources among multiple clients, where an adversary may deny the availability of these resources; this is known as a denial-of-service (DoS) attack. Here, we propose a deterministic algorithm that employs resource burning (RB)—the verifiable expenditure of a network resource—to defend against DoS attacks. Specifically, our solution forces an adversary to incur higher RB costs compared to legitimate clients. Next, we develop a general policy-driven framework that utilizes machine learning classification to tune the amount of RB used for mitigating DoS attacks. Finally, we expand the application of RB to defend against DoS attacks on hash tables, which are a popular data structure in network applications. The second problem deals with resource allocation in wireless systems; specifically, the sharing of the wireless medium among multiple participants competing to transmit data. While modern WiFi and cellular standards do solve this problem, several recent theoretical results suggest that superior solutions are possible. Here, we investigate the viability of these solutions and discover that they fall short of their promised performance in practice. Consequently, we identify the cause of this shortcoming and quantify the discrepancy through a combination of analytical and simulation work. Ultimately, we propose a revised theoretical model that aligns better with practical observations.

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / One of the core philosophies of the integrated Network Enhanced Telemetry (iNET) project is to leverage standard networking technologies whenever possible to both reduce development cost and to allow standard networking applications to function. This paper presents decisions about the system's behavioral design and other decisions affecting the selection and design of system components. The TmNS is a network of networks that must be integrated into existing range processes. An overall guiding tenet for the TmNS is enhancement rather than replacement. As such, this enhancement is melded with pre-existing devices, approaches, and technologies. Overall, the pre-existing Pulse Code Modulation (PCM) data delivery mechanism is augmented with bi-directional, reliable, TmNS-provided communication.

ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, Nevada / This paper discusses how IRIG 106 Chapter 10 recording techniques could be employed in a network-centric environment, while maintaining as many of the strengths of the traditional approach. In the course of that discussion, aspects of the published standard which would have to be disregarded or reinterpreted for a network-centric approach to be adopted are illustrated.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / With the ever increasing need for faster data rates and the emergence of faster network interfaces such as Fiber Distributed Data Interface (FDDI), the task of adding new network interfaces to a telemetry system and supporting existing ones is becoming increasingly more complex. This complexity can be eliminated if the data acquisition hardware and software allows new network interfaces to be easily integrated into a telemetry system. It is the purpose of this paper to address the issues involved when dealing with multiple, heterogeneous, networking environments in telemetry systems. The paper will show how the use of flexible telemetry hardware and software will simplify the integration of new networks into an existing system, and how this flexibility can allow data acquisition applications to take advantage of a heterogeneous network.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / Our telemetry department has an application for a data categorization/compression of a high speed transient signal in a short period of time. Categorization of the signal reveals important system performance and compression is required because of the terminal nature of our telemetry testing. Until recently, the hardware for the system of this type did not exist. A new exploratory device from Intel has the capability to meet these extreme requirements. This integrated circuit is an analog neural network capable of performing 2 billion connections per second. The two main advantages of this chip over traditional hardware are the obvious computation speed of the device and the ability to compute a three layer feed-forward neural network classifier. The initial investigative development work using the Intel chip has been completed. The results from this proof of concept will show data categorization/compression performed on the neural network integrated circuit in real time. We will propose a preliminary design for a transient measurement system employing the Intel integrated circuit.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Prototyping an Internet Protocol (IP) compliant architecture will demonstrate a realistic basis for satellite communication design. The prototype IP architecture should prove seamless and secure communications between the satellites and ground stations. Using commercial off the shelf (COTS) equipment, design and development of satellite communications becomes easier and less expensive than developing specialized equipment. IP space applications will improve communications while minimizing development costs.

This thesis addresses the significance of speech features within the task of speaker recognition. Motivated by the perception of simple attributes like `loud', `smooth', `fast', more than 70 new speech features are developed. A set of basic speech features like pitch, loudness and speech speed are combined together with these new features in a feature set, one set per utterance. A neural network classifier is used to evaluate the significance of these features by creating a speaker recognition system and analysing the behaviour of successfully trained single-speaker networks. An in-depth analysis of network weights allows a rating of significance and feature contribution. A subjective listening experiment validates and confirms the results of the neural network analysis. The work starts with an extended sentence analysis; ten sentences are uttered by 630 speakers. The extraction of 100 speech features is outlined and a 100-element feature vector for each utterance is derived. Some features themselves and the methods of analysing them have been used elsewhere, for example pitch, sound pressure level, spectral envelope, loudness, speech speed and glottal-to-noise excitation. However, more than 70 of the 100 features are derivatives of these basic features and have not yet been described and used before in the speakerr ecognition research,e speciallyyn ot within a rating of feature significance. These derivatives include histogram, 3`d and 4 moments, function approximation, as well as other statistical analyses applied to the basic features. The first approach assessing the significance of features and their possible use in a recognition system is based on a probability analysis. The analysis is established on the assumption that within the speaker's ten utterances' single feature values have a small deviation and cluster around the mean value of one speaker. The presented features indeed cluster into groups and show significant differences between speakers, thus enabling a clear separation of voices when applied to a small database of < 20 speakers. The recognition and assessment of individual feature contribution jecomes impossible, when the database is extended to 200 speakers. To ensure continous vplidation of feature contribution it is necessary to consider a different type of classifier. These limitations are overcome with the introduction of neural network classifiers. A separate network is assigned to each speaker, resulting in the creation of 630 networks. All networks are of standard feed-forward backpropagation type and have a 100-input, 20- hidden-nodes, one-output architecture. The 6300 available feature vectors are split into a training, validation and test set in the ratio of 5-3-2. The networks are initially trained with the same 100-feature input database. Successful training was achieved within 30 to 100 epochs per network. The speaker related to the network with the highest output is declared as the speaker represented by the input. The achieved recognition rate for 630 speakers is -49%. A subsequent preclusion of features with minor significance raises the recognition rate to 57%. The analysis of the network weight behaviour reveals two major pointsA definite ranking order of significance exists between the 100 features. Many of the newly introduced derivatives of pitch, brightness, spectral voice patterns and speech speed contribute intensely to recognition, whereas feature groups related to glottal-to-noiseexcitation ratio and sound pressure level play a less important role. The significance of features is rated by the training, testing and validation behaviour of the networks under data sets with reduced information content, the post-trained weight distribution and the standard deviation of weight distribution within networks. The findings match with results of a subjective listening experiment. As a second major result the analysis shows that there are large differences between speakers and the significance of features, i. e. not all speakers use the same feature set to the same extent. The speaker-related networks exhibit key features, where they are uniquely identifiable and these key features vary from speaker to speaker. Some features like pitch are used by all networks; other features like sound pressure level and glottal-to-noise excitation ratio are used by only a few distinct classifiers. Again, the findings correspond with results of a subjective listening experiment. This thesis presents more than 70 new features which never have been used before in speaker recognition. A quantitative ranking order of 100 speech features is introduced. Such a ranking order has not been documented elsewhere and is comparatively new to the area of speaker recognition. This ranking order is further extended and describes the amount to which a classifier uses or omits single features, solely depending on the characteristics of the voice sample. Such a separation has not yet been documented and is a novel contribution. The close correspondence of the subjective listening experiment and the findings of the network classifiers show that it is plausible to model the behaviour of human speech recognition with an artificial neural network. Again such a validation is original in the area of speaker recognition