The continued development of integrated photonic devices requires low-power, small volume all-optical modulators. The weak nonlinear optical response of conventional optical materials requires the use of high intensities and large interaction volumes in order to achieve significant light modulation, hindering the miniaturization of all-optical switches and the development of lightweight transmission optics with nonlinear optical response. These challenges may be addressed using plasmonic nanostructures due to their unique ability to confine and enhance electric fields in sub-wavelength volumes. The ultrafast nonlinear response of free electrons in such plasmonic structures and the fast thermal nonlinear optical response of metal nanoparticles, as well as the plasmon enhanced nonlinear Kerr-type response of the host material surrounding the nanostructures could allow ultrafast all-optical modulation with low modulation energy. In this thesis, we investigate the linear and nonlinear optical response of engineered effective media containing coupled metallic nanoparticles. The fundamental interactions in systems containing coupled nanoparticles with size, shape, and composition dissimilarity, are evaluated analytically and numerically, and it is demonstrated that under certain conditions the achieved field enhancement factors can exceed the single-particle result by orders of magnitude in a process called cascaded plasmon resonance. It is demonstrated that these conditions can be met in systems containing coupled nanospheres, and in systems containing non-spherical metal nanoparticles that are compatible with common top-down nanofabrication methods such as electron beam lithography and nano-imprint lithography. We show that metamaterials based on such cascaded plasmon resonance structures can produce enhanced nonlinear optical refraction and absorption compared to that of conventional plasmonic nanostructures. Finally, it is demonstrated that the thermal nonlinear optical response of metal nanoparticles can be enhanced in carefully engineered heterogeneous nanoparticle clusters, potentially enabling strong and fast thermal nonlinear optical response in system that can be produced in bulk through chemical synthesis.

Globally, mining is vital to human interests, but its practice can cause landscape alteration which may look unnatural or engineered. The reintroduction of native plants to these areas is needed to restore the visual appeal and ecological function back into these altered mine lands. Cercocarpus ledifolius (curl-leaf mountain mahogany) is one desirable native species in the Intermountain West that is prized for its potential to grow on step and rocky hillsides and for the habitat it provides for wildlife. Unfortunately, C. ledifolius does not establish well from seed, which has been attributed to seed dormancy. The first objective of this study was to determine if scarification and gibberellic acid (GA3) treatments improve germination by alleviating seed dormancy. We also aimed to determine if a combination of fungicide and hydrophobic seed coatings increased emergence and establishment of C. ledifolius seedlings in mine overburden by reducing loss from fungal pathogens and premature germination. We found that two treatments, GA3 and GA3 + hydrophobic coatings, improved emergence compared to untreated seed, producing 1.8 (P = 0.0682), and 2.2 (P = 0.0751) more seedlings per meter, respectively. The second objective of this study was to make improvements in the laboratory to treatments explored in the field trial. We found that C. ledifolius seed responded inconsistently to treatments applied in the lab. The 15-minute acid scarified seed in combination with various GA3 seed coatings had significantly higher germination than untreated seed in one trial but had no difference in a second trial. Overall, these results indicate that seed enhancement technologies have the potential to improve C. ledifolius emergence in reclaimed mine lands, but additional research is needed to understand the species' dormancy characteristics better and improve the efficacy of the applied seed treatments.

This study investigates the connection between self-enhancement and self-verification and confirmation and emotional support. The hypotheses predicted that there is a positive relationship between confirmation and self-enhancement and self-verification; people feel good about themselves when confirmed by friends, people feel that friends know them well when they are confirmed. The hypotheses also predicted that there would be a positive relationship between emotional support and self-enhancement and self-verification; people feel good when friends provide emotional support, and people feel that friends know them well when provided emotional support. A research question was also posed: Does family functioning have an effect on perceptions of self-enhancement and self-verification messages? To find the answers, a questionnaire was completed by 279 individuals. The results indicate two types of enhancement messages; a more specific and positive form of enhancement and more global (and negative) self perception of rejection. The findings are interesting and unique to self-enhancement in communication research which provides many avenues for continued research. Results also suggest that different elements of confirming communication influences perceptions of enhancement in different ways, emotional support predicts verification.

The increased demand for advanced sludge stabilization in wastewater treatment facilities over the past decade has led to the implementation of various pretreatment techniques prior to anaerobic digestion. In an attempt to reduce sludge volumes and improve sludge conditioning properties, the use of thermal hydrolysis process before anaerobic digestion has been adopted with an increase in solids destruction, COD removal, and methane gas. In this study, the evaluation of thermal hydrolysis process as a viable pretreatment strategy to anaerobic digestion has been conducted in order to assess its capacity for solids solubilization. Solubilization experiments were conducted at temperatures ranging from 130 to 170℃ and reaction times between 10 and 60 min. Anaerobic biogas production by thermally pre-treated sludge was carried out through a mesophilic anaerobic digester. The results showed that solids solubilization increased with increases in temperature and time, while temperatures above 160℃ for 30 min strongly affected the sludge characteristics. Ammonia production via deamination by thermal hydrolysis was less significant than protein solubilization at a temperature of 170℃. Both protein and carbohydrate solubilization were more dependent on temperature than reaction time. The enhancement of the biogas production was achieved with increases in temperature as pretreatment of 170℃ yielded 20% more biogas than at 130℃. However, it seems the enhancement was linked to the initial biodegradability of the sludge. / Master of Science

In real-world scenarios, speech signals are often contaminated with environmental noises, and reverberation, which degrades speech quality and intelligibility. Lately, the development of deep learning algorithms has marked milestones in speech- based research fields e.g. speech recognition, spoken language understanding, etc. As one of the crucial topics in the speech processing research area, speech enhancement aims to restore clean speech signals from noisy signals. In the last decades, many conventional speech enhancement statistical-based algorithms had been pro- posed. However, the performance of these approaches is limited in non-stationary noisy conditions. The raising of deep learning-based approaches for speech enhancement has led to revolutionary advances in their performance. In this context, speech enhancement is formulated as a supervised learning problem, which tackles the open challenges introduced by the speech enhancement conventional approaches. In general, deep learning speech enhancement approaches are categorized into frequency-domain and time-domain approaches. In particular, we experiment with the performance of the Wave-U-Net model, a solid and superior time-domain approach for speech enhancement. First, we attempt to improve the performance of back-end speech-based classification tasks in noisy conditions. In detail, we propose a pipeline that integrates the Wave-U-Net (later this model is modified to the Dilated Encoder Wave-U-Net) as a pre-processing stage for noise elimination with a temporal convolution network (TCN) for the intent classification task. Both models are trained independently from each other. Reported experimental results showed that the modified Wave-U-Net model not only improves the speech quality and intelligibility measured in terms of PESQ, and STOI metrics, but also improves the back-end classification accuracy. Later, it was observed that the dis-joint training approach often introduces signal distortion in the output of the speech enhancement module. Thus, it can deteriorate the back-end performance. Motivated by this, we introduce a set of fully time- domain joint training pipelines that combine the Wave-U-Net model with the TCN intent classifier. The difference between these architectures is the interconnections between the front-end and back-end. All architectures are trained with a loss function that combines the MSE loss as the front-end loss with the cross-entropy loss for the classification task. Based on our observations, we claim that the JT architecture with equally balancing both components’ contributions yields better classification accuracy. Lately, the release of large-scale pre-trained feature extraction models has considerably simplified the development of speech classification and recognition algorithms. However, environmental noise and reverberation still negatively affect performance, making robustness in noisy conditions mandatory in real-world applications. One way to mitigate the noise effect is to integrate a speech enhancement front-end that removes artifacts from the desired speech signals. Unlike the state-of-the-art enhancement approaches that operate either on speech spectrogram, or directly on time-domain signals, we study how enhancement can be applied directly on the speech embeddings, extracted using Wav2Vec, and WavLM models. We investigate a variety of training approaches, considering different flavors of joint and disjoint training of the speech enhancement front-end and of the classification/recognition back-end. We perform exhaustive experiments on the Fluent Speech Commands and Google Speech Commands datasets, contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, as well as on LibriSpeech, contaminated with noises from the MUSAN dataset, considering intent classification, keyword spotting, and speech recognition tasks respectively. Results show that enhancing the speech em-bedding is a viable and computationally effective approach, and provide insights about the most promising training approaches.

Lockout Tagout (LOTO) is a safety procedure instated by the Occupational Safety and Health Administration (OSHA) when doing maintenance on dangerous machinery and hazardous power sources. In this procedure, authorized workers shut off the machinery and use physical locks and tags to prevent operation during maintenance. LOTO has been the industry standard for 32 years since it was instantiated, being used in many different industries such as industrial work, mining, and agriculture. However, LOTO is not without its issues. The LOTO procedure requires employees to be trained and is prone to human error. As well, there is a clash between the technological advancement of machinery and the requirement of physical locks and tags required for LOTO. In this thesis, we propose a digital LOTO system to help streamline the LOTO procedure and increase the safety of the workers with machine learning. We first discuss what LOTO is, its current requirements, limitations, and issues. Then we look at current IoT locks and digital LOTO solutions and compare them to the requirements of traditional LOTO. Then we present our proposed digital LOTO system which will enhance the safety of workers and streamline the LOTO process with machine learning. Our digital LOTO system uses a rule-based system that enforces and streamlines the LOTO procedure and uses machine learning to detect potential violations of LOTO standards. We also validate that our system fulfills the requirements of LOTO and that the combination of machine learning and rule-based systems ensures the safety of workers by detecting violations with high accuracy. Finally, we discuss potential future work and improvements on this system as this thesis is part of a larger collaboration with Chevron, which plans to implement a digital LOTO system in their oil fields.

Yes / In this paper, a simple folded loop antenna (FLA) for handsets with relatively wide- band impedance, designed and optimized using genetic algorithms (GA). The FLA dimensions were optimized and evaluated using GA in collaboration with NEC-2 source code. Configuration of optimal FLA with excellent VSWR covering entirely the required GSM1800 frequency bands was found within the maximum generation. A prototype antenna was tested to verify and validate the GA-optimized antenna structure. The measured data have shown good agreement with predicted ones. Moreover, the capabilities of GA are shown as an e±cient optimisation tool for selecting globally optimal parameters to be used in simulations with an electromagnetic antenna design code, seeking convergence to designated specifications.

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