Ultra-Wideband Communication is increasingly being considered as an attractive solution for high data rate short range wireless and position location applications. Knowledge of the statistical nature of the channel is necessary to design wireless systems that provide optimum performance. This thesis investigates the spatial characteristics of the channel based on measurements conducted using UWB pulses in an indoor office environment. The statistics of the received signal energy illustrate the low spatial fading of UWB signals. The distribution of the Angle of arrival (AOA) of the multipath components is obtained using a two-dimensional deconvolution algorithm called the Sensor-CLEAN algorithm. A spatial channel model that incorporates the spatial and temporal features of the channel is developed based on the AOA statistics. The performance of the Sensor-CLEAN algorithm is evaluated briefly by application to known artificial channels. UWB systems co-exist with narrowband and other wideband systems. Even though they enjoy the advantage of processing gain (the ratio of bandwidth to data rate) the low energy per pulse may cause these narrow band interferers (NBI) to severely degrade the UWB system's performance. A technique to suppress NBI using multiple antennas is presented in this thesis which exploits the spatial fading characteristics. This method exploits the vast difference in fading characteristics between UWB signals and NBI by implementing a simple selection diversity scheme. It is shown that this simple scheme can provide strong benefits in performance. / Master of Science

Central Appalachian headwater streams in coalfield areas are prone to mining disturbances, and compensatory mitigation is required in cases of documented impacts. Stream construction on reclaimed mines is a common mitigation strategy. Streams constructed as compensatory mitigation are meant to restore structural and functional attributes of headwater streams and are often evaluated by measuring structural ecosystem characteristics. However, replacement of stream ecosystem functions is essential for mitigation of mining disturbances from an ecosystem perspective. This research compared selected structural and functional measures in eight constructed streams on mined areas to those of four forested reference streams across two years. Three organic matter functions were evaluated: riparian litterfall input, leaf breakdown, and periphyton accrual. Constructed streams were typically warmer than reference streams and also had elevated specific conductance, elevated oxidized nitrogen concentrations, depressed benthic macroinvertebrate richness, and lower levels of canopy cover. Functionally, litterfall input and total leaf breakdown means for constructed streams were approximately 25% and 60% of reference means, respectively. Leaf breakdown in constructed streams appeared to be inhibited as a result of reduced processing by benthic macroinvertebrates as well as inhibition of microbial and physicochemical pathways. Constructed streams with total breakdown rates most similar to reference-stream levels had the coldest stream temperatures. Areal periphyton biomass, benthic algal standing crop, and senescent autotrophic organic matter in constructed streams were roughly quadruple, double, and quintuple those of reference streams, respectively. Indicator ratios also suggested stream-type differences in periphyton structure. Mean algal accrual was greater in constructed streams than in reference streams during leaf-on seasons. My results suggest that light is likely the primary factor driving accrual rate differences during summer and fall, but that temperature may also be important during fall. Planting a diverse assemblage of native riparian trees and ensuring their successful development can inhibit benthic irradiance and thermal energy inputs while providing similar quantity and quality of OM to constructed streams, thereby fostering replacement of reference-like OM functions in some streams. / Ph. D.

In this dissertation novel methods are proposed for communicating in interference limited environments as well as detecting such interference. The methods include introducing redundancies into multicarrier signals to make them more robust, applying a novel filtering structure for mitigating radar interference to orthogonal frequency division multiplexing (OFDM) signals and for exploiting the cyclostationary nature of signals to whiten the spectrum in blind signal detection. Data symbols are repeated in both time and frequency across orthogonal frequency division multiplexing (OFDM) symbols, creating a cyclostationary nature in the signal. A Frequency Shift (FRESH) filter can then be applied to the cyclostationary signal, which is the optimal filter and is able to reject interference much better than a time-invariant filter such as the Wiener filter. A novel time-varying FRESH filter (TV-FRESH) filter is developed and its Minimum Mean Squared Error (MMSE) filter weights are found. The repetition of data symbols and their optimal combining with the TV-FRESH filter creates an effect of improving the Bit Error Rate (BER) at the receiver, similar to an error correcting code. The important distinction for the paramorphic method is that it is designed to operate within cyclostationary interference, and simulation results show that the symbol repetition can outperform other error correcting codes. Simulated annealing is used to optimize the signaling parameters, and results show that a balance between the symbol repetition and error correcting codes produces a better BER for the same spectral efficiency than what either method could have achieved alone. The TV-FRESH filter is applied to a pulsed chirp radar signal, demonstrating a new tool to use in radar and OFDM co-existence. The TV-FRESH filter applies a set of filter weights in a periodically time-varying fashion. The traditional FRESH filter is periodically time-varying due to the periodicities of the frequency shifters, but applies time-invariant filters after optimally combine any spectral redundancies in the signal. The time segmentation of the TV-FRESH filter allows spectral redundancies of the radar signal to be exploited across time due to its deterministic nature. The TV-FRESH filter improves the rejection of the radar signal as compared to the traditional FRESH filter under the simulation scenarios, improving the SINR and BER at the output of the filter. The improvement in performance comes at the cost of additional filtering complexity. A time-varying whitening filter is applied to blindly detect interference which overlaps with the desired signal in frequency. Where a time-invariant whitening filter shapes the output spectrum based on the power levels, the proposed time-varying whitener whitens the output spectrum based on the spectral redundancy in the desired signal. This allows signals which do not share the same cyclostationary properties to pass through the filter, improving the sensitivity of the algorithm and producing higher detection rates for the same probability of false alarm as compared to the time-invariant whitener. / Ph. D. / This dissertation proposes novel methods for building robust wireless communication links which can be used to improve their reliability and resilience while under interference. Wireless interference comes from many sources, including other wireless transmitters in the area or devices which emit electromagnetic waves such as microwaves. Interference reduces the quality of a wireless link and depending on the type and severity may make it impossible to reliably receive information. The contributions are both for communicating under interference and being able to detect interference. A novel method for increasing the redundancy in a wireless link is proposed which improves the resiliency of a wireless link. By transmitting additional copies of the desired information the wireless receiver is able to better estimate the original transmitted signal. The digital receiver structure is proposed to optimally combine the redundant information, and simulation results are used to show its improvement over other analogous methods. The second contribution applies a novel digital filter for mitigating interference from a radar signal to an Orthogonal Frequency Division Multiplexing (OFDM) signal, similar to the one which is being used in Long Term Evolution (LTE) mobile phones. Simulation results show that the proposed method out performs other digital filters at the most of additional complexity. The third contribution applies a digital filter and trains it such that the output of the filter can be used to detect the presence of interference. An algorithm which detects interference can tip off an appropriate response, and as such is important to reliable wireless communications. Simulation results are used to show that the proposed method produces a higher probability of detection while reducing the false alarm rate as compared to a similar digital filter trained to produce the same effect.

The penetration of social media today enables the rapid spread of breaking news and other developments to millions of people across the globe within hours. However, such pervasive use of social media by the general masses to receive and consume news is not without its attendant negative consequences as it also opens opportunities for nefarious elements to spread rumors or misinformation. A rumor generally refers to an interesting piece of information that is widely disseminated through a social network and whose credibility cannot be easily substantiated. A rumor can later turn out to be true or false or remain unverified. The spread of misinformation and fake news can lead to deleterious effects on users and society. The objective of the proposed research is to develop a range of machine learning methods that will effectively detect and characterize rumor veracity in social media. Since users are the primary protagonists on social media, analyzing the characteristics of information spread w.r.t. users can be effective for our purpose. For our first problem, we propose a method of computing user embeddings from underlying social networks. For our second problem, we propose a long short-term memory (LSTM) based model that can classify whether a story discussed in a thread can be categorized as a false, true, or unverified rumor. We demonstrate the utility of user features computed from the first problem to address the second problem. For our third problem, we propose a method that uses user profile information to detect rumor veracity. This method has the advantage of not requiring the underlying social network, which can be tedious to compute. For the last problem, we investigate a rumor mitigation technique that recommends fact-checking URLs to rumor debunkers, i.e., social network users who are very passionate about disseminating true news. Here, we incorporate the influence of other users on rumor debunkers in addition to their previous URL sharing history to recommend relevant fact-checking URLs. / Doctor of Philosophy / A rumor is generally defined as an interesting piece of a story that cannot be authenticated easily. On social networks, a user can generally find an interesting piece of news or story and may share (retweet) it. A story that initially appears plausible can later turn out to be false or remain unverified. The propagation of false rumors on social networks has a deteriorating effect on user experience. Therefore, rumor veracity detection is important, and drawing interest in social network research. In this thesis, we develop various machine learning models that detect rumor veracity. For this purpose, we exploit different types of information regarding users, such as profile details and connectivity with other users etc. Moreover, we propose a rumor mitigation technique that recommends fact-checking URLs to social network users who are passionate about debunking rumors. Here, we leverage similar techniques used in e-commerce sites for recommending products to solve this problem.

Mitigating climate change necessitates a rapid transition away from fossil fuels and toward renewable carbon-neutral energy sources such as wind and solar. This doctoral research addresses fundamental limitations of first-principles computational methods for the design and discovery of new processes and materials to accelerate industrial decarbonization and the global transition to a clean and sustainable energy economy by developing practical methods that build on thermodynamics and leverage foundational advances in machine learning. Recent breakthroughs in artificial intelligence for materials design and discovery aim to screen entire material libraries for desirable properties and to predict novel materials with target properties. Because of the scarcity of available thermodynamic data, designing materials for thermodynamic conditions far away from absolute zero temperature and pressure has proven particularly challenging. In principle, machine learning can speed up materials modeling by providing surrogate models, learning the relationship between structure/composition features and material properties, and training the model to predict desired properties. Due to a lack of experimental data, these models rely heavily on synthetic data from first-principles approaches such as electronic density functional theory. Designing high-temperature processes is also problematic because of the intrinsic limitations of conventional density functional theory calculations, which are strictly correct only at zero temperature. To overcome these data and methodological limitations, I integrated thermodynamic relationships with machine learning models to augment results from first-principles calculations. Additionally, I identified materials descriptor spaces that provide natural representations of structures and compositions for materials discovery. Chapter 1 introduces in more detail the motivation for this doctoral research and for the combination of computational materials thermodynamics and machine learning. Chapter 2 reviews computational materials science methods that I employed. Chapter 3 showcases how the melting temperatures of materials can be predicted with a combination of electronic structure theory and machine learning. In Chapter 4, our approach for Gibbs free energy predictions is discussed. Chapter 5 deals with the representation learning of materials, dimensionality reduction, quantifying the information content of materials representation spaces, and constructing property-aware materials descriptors. I conclude the thesis with a summary and a discussion of future directions.

As climate change exacerbates heat stress during the summer season, it becomes increasingly critical to develop effective strategies to safeguard the productivity of tomato plants (Solanum Lycopersicon L.). This research delves into the tools and techniques aimed at enhancing the cultivation of summer tomatoes in the coastal region of Virginia. The study explores the optimization of transplant dates, the implementation of reflective mulches, and the application of kaolin clay particle films. Field trials spanning two seasons were carried out, comparing different planting dates in May, June, and July, as well as the use of reflective, black, and white plastic mulches, both with and without foliar kaolin sprays. The findings of this study underscore the impact of transplanting tomatoes in May, demonstrating a substantial increase in yields when compared to transplanting in June and July. Reflective mulches enhanced plant height and fruit production relative to the conventional black plastic mulch. The combination of kaolin clay sprays with standard black mulch, resulting in yield increases of over 35%, rivaling the outcomes achieved with reflective and white mulch treatments. The application of kaolin did not significantly affect leaf-level physiological processes. These results highlight the significant potential of strategic early planting and the adoption of emerging heat mitigation technologies, such as kaolin clay films, in sustaining and enhancing the productivity of summer tomatoes. This becomes particularly relevant as growing conditions continue to evolve due to rising temperatures and the increasing extremity of weather events resulting from climate change. / Master of Science in Life Sciences / With the challenge of hotter summers due to climate change, finding effective ways to grow tomatoes is more crucial than ever. In our two-season study in Virginia's coastal region, we experimented with various methods to improve tomato growth in these warmer conditions. What we discovered was quite promising. Planting tomatoes in early May resulted in significantly better yields than later planting times. Using reflective mulch was beneficial too; it helped the plants grow taller and produce more fruit compared to traditional black mulch. However, the most impressive result came from combining kaolin clay spray with black mulch. This approach led to a matching of the performance of black plastic plus the combination of kaolin clay against reflective and white mulches. It's interesting to note that the kaolin spray didn't alter the basic functioning of the plants at the leaf level. These findings are encouraging. They suggest that early planting and innovative approaches like kaolin clay sprays can effectively boost tomato production, even as we contend with rising temperatures and evolving climate patterns. Embracing these strategies could be key to successful tomato farming in an era of climate change

Urban flooding presents a significant challenge to cities worldwide, resulting in loss of life and economic damage. Factors such as urbanization, climate change, and extreme weather events compound the vulnerability of urban areas to flooding, with rapid urbanization emerging as a primary driver of increased flood risk. In response to this pressing issue, this thesis embarks on a transformative exploration, advocating for a paradigm shift in urban stormwater management through the lens of "Hydro-Urbanism." Central to this concept is recognizing stormwater as a valuable resource rather than a mere liability. By implementing targeted strategies to curb runoff, detain stormwater, and replenish groundwater, cities can mitigate the adverse impacts of urban flooding while enhancing resilience and livability. Through a comprehensive review of existing literature and analysis of case studies, this research explores the efficacy of diverse stormwater management techniques in alleviating urban flooding and fostering sustainable urban development. In addition to technical aspects, the study delves into the socioeconomic dimensions of Hydro-Urbanism, highlighting the significance of community engagement and participatory planning in creating resilient and inclusive urban environments. Focused on Hyderabad city in Telangana, India, this project lies at the intersection of cultural heritage and modernity, confronting significant challenges posed by urban flooding amidst rapid urban expansion. By reframing the narrative around water from vulnerability to resilience and opportunity, the project aims to harness the power of stormwater as a catalyst for change. A tailored typology-based approach seeks to nurture a future where cities and water coexist harmoniously, protecting urban areas from flooding and fostering a more harmonious relationship between urban communities and the natural world. / Master of Science / Urban flooding, characterized by the inundation of urban streets, buildings, and infrastructure, arises when rainwater overwhelms drainage systems or water bodies overflow due to heavy rainfall or storms. It is a significant challenge faced by cities globally, leading to property damage, transportation disruptions, and risks to public safety. In response to the pressing issue of urban flooding, this project adopts a novel approach called "Hydro-Urbanism," emphasizing the interconnection between water and urban landscapes and aiming to transform how cities manage stormwater resources. Unlike traditional methods that view stormwater as a problem to be mitigated, Hydro-Urbanism recognizes stormwater as a valuable resource that can be harnessed for various purposes. It seeks to establish a symbiotic relationship between urban environments and water, wherein stormwater is managed strategically to mitigate flooding risks and enhance urban resilience while improving the quality of urban life. Amid rapid urbanization, exemplified vividly in cities like Hyderabad in the Telangana state of India, the balance between expanding urban sprawl and natural ecosystems has become increasingly fragile. Here, the challenge of managing stormwater looms large, threatening public safety, infrastructure integrity, and economic stability. Nevertheless, what if we could flip this narrative? What if stormwater could be a resource instead of being a menace? Focused on Hyderabad, a city at the crossroads of tradition and modernity, this project proposes a typology-based approach tailored to its unique urban fabric. By harnessing the potential of stormwater, the project aims to pave the way for a more resilient and adaptive urban future. Ultimately, the goal is to foster a harmonious coexistence between urban communities and stormwater resources, ensuring cities' long-term viability and well-being in the face of environmental uncertainties.

Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficients by solving a set of linear equations referred to as the generalized covariance equations. Conventional approaches such as Gaussian elimination or direct matrix inversion are computationally inefficient for solving such a system of equations especially when the covariance matrix has a high order. We implement two recursive algorithms for efficiently inverting the covariance matrix. First, we implement the Akaike algorithm which exploits the block-Toeplitz structure of the covariance matrix for its recursive inversion. In the second approach, we implement the Wax-Kailath algorithm that achieves a factor of 2 reduction over the Akaike algorithm in the number of recursions involved and the computational effort required to form the inverse matrix. Although a TVAR model works well for IF estimation of frequency modulated (FM) components in white noise, when the model is applied to a signal containing a finitely correlated signal in addition to the white noise, estimation performance degrades; especially when the correlated signal is not weak relative to the FM components. We propose a decorrelating TVAR (DTVAR) model based IF estimation and a DTVAR model based linear prediction error filter for FM interference rejection in a finitely correlated environment. Simulations show notable performance gains for a DTVAR model over the TVAR model for moderate to high SIRs. / Master of Science

This paper explores the role of risk management during the pre-production phase of film projects. The purpose is to find the key risk management factors in film production and find which actions top management takes in the pre-production phase to mitigate these. To address this, the following research questions are used: (1) What are the key risks associated with film projects, and what actions are taken by top management to mitigate these?Followed by our sub-questions of, (2) How are risks identified in the pre-production of film projects? (3) How does communication and stakeholder engagement contribute to risk mitigation in film projects? (4) Which are the most common and recommended risk mitigation strategies? The central problem addressed in this paper is the lack of a comprehensive understanding of how risk management practices can contribute to mitigating risks in film projects. To explore this issue, we use a qualitative research methodology, including semi-structured interviews with five top management professionals in the film industry. The interviews aimed at gathering in-depth insights into their experiences and practices related to risk management during the pre-production phase. The data collected was analyzed using thematic analysis. Our research identified key risks in film projects and what methods top management uses to identify these risks in pre-production. Furthermore, we present how communication and stakeholder management contribute to risk management in pre- production. Lastly, we provide the most common risk mitigation strategies applied in the film industry. Our findings offer practical recommendations for professionals in the film industry and for up-and-coming producers and directors who want ground pillars to stand on when starting a film project. Furthermore, our findings contribute to the existing risk management literature by focusing on a not-so-well-studied subject, namely risk management, in the film's pre-production phase.

This research investigates an important class of hardware attacks against embedded software, which uses fault injection as a hacking tool. Fault attacks use well-chosen, targeted fault injection combined with clever system response analysis to break the security of a system. In case of a fault attack on embedded software, faults are injected into the underlying processor hardware and their effects are observed in the executed software's output. This introduces an additional difficulty in mitigation of fault attack risk. Designing efficient countermeasures requires first understanding software, instruction-set, and hardware level components of fault attacks, and then, systematically addressing the vulnerabilities at each level. This research first proposes an instruction fault sensitivity model to capture effects of fault injection on embedded software. Based on the instruction fault sensitivity model, a novel fault attack method called MAFIA (Micro-architecture Aware Fault Injection Attack) is also introduced. MAFIA exploits the vulnerabilities in multiple abstraction layers. This enables an adversary to determine best points to attack during the execution as well as pinpoint the desired fault effects. It has been shown that MAFIA breaks the existing countermeasures with significantly fewer fault injections than the traditional fault attacks. Another contribution of the research is a fault attack simulator, MESS (Micro-architectural Embedded System Simulator). MESS enables a user to model hardware, instruction-set, and software level components of fault attacks in a simulation environment. Thus, software designers can use MESS to evaluate their programs against several real-world fault attack scenarios. The final contribution of this research is the fault-attack-resistant FAME (Fault-attack Aware Microprocessor Extensions) processor, which is suited for embedded, constrained systems. FAME combines fault detection in hardware and fault response in software. This allows low-cost, performance-efficient, flexible, and backward-compatible integration of hardware and software techniques to mitigate fault attack risk. FAME has been designed as an architectural concept as well as implemented as a chip prototype. In addition to protection mechanisms, the chip prototype also includes fault injection and analysis features to ease fault attack research. The findings of this research indicate that considering multiple abstraction layers together is essential for efficient fault attacks, countermeasures, and evaluation techniques. / Ph. D. / Today, we trust a range of embedded computers to process and protect our sensitive data. For instance, credit cards process sensitive financial data during electronic payment. Similarly, smartphones use and store private user data. This research investigates fault attacks, a serious threat to the security of embedded computers. In a fault attack, an adversary breaches the security by injecting intentional faults in an embedded computer. To induce faults, the adversary deliberately manipulates the operating conditions of the computer such as the supply voltage and ambient temperature. These faults interfere with the correct operation of the computer and cause temporary malfunctions in its hardware. The adversary then exploits the malfunctions to break the security. Although fault injection is a powerful hacking tool that may affect any security mechanism, there is no generic technique to deal with the security threat of faults. This research seeks a broader, deeper understanding of fault attacks and appropriate countermeasures for them. Our contributions include a novel fault modeling method, efficient fault attacks, a fault attack simulator, and a low-cost fault-attack-aware microprocessor. This research also provides a deeper understanding of causes and effects of faults, which can be utilized in the design of fault attacks, countermeasures, and metrics.