In this dissertation, three studies were done for radar and hyperspectral remote sensing applications using statistical techniques. The first study investigated a relationship between synthetic aperture radar backscatter and in situ soil properties for levee monitoring. A series of statistical analyses were performed to investigate potential correlations between three independent polarization channels of radar backscatter and various soil properties. The results showed a weak but considerable correlation between the cross-polarized (HV) radar backscatter coefficients and several soil properties. The second study performed effective statistical feature extraction for levee slide classification. Images about a levee are often very large, and it is difficult to monitor levee conditions quickly because of high computational cost and large memory requirement. Therefore, a time-efficient method to monitor levee conditions is necessary. The traditional support vector machine (SVM) did not work well on original radar images with three bands, requiring extraction of discriminative features. Gray level co-occurrence matrix is a powerful method to extract textural information from grey-scale images, but it may not be practical for a big data in terms of calculation time. In this study, very efficient feature extraction methods with spatial filtering were used, including a weighted average filter and a majority filter in conjunction with a nonlinear band normalization process. Feature extraction with these filters, along with normalized bands, yielded comparable results to gray level co-occurrence matrix with a much lower computational cost. The third study focused on the case when only a small number of ground truth labels were available for hyperspectral image classification. To overcome the difficulty of not having enough training samples, a semisupervised method was proposed. The main idea was to expand ground truth using a relationship between labeled and unlabeled data. A fast self-training algorithm was developed in this study. Reliable unlabeled samples were chosen based on SVM output with majority voting or weighted majority voting, and added to labeled data to build a better SVM classifier. The results showed that majority voting and weighted majority voting could effectively select reliable unlabeled data, and weighted majority voting yielded better performance than majority voting.

This study explored the relatively new field of public mobile handheld AR and how the touchscreen-based input of smartphones affects the way users aged 10-12 interact with the 6DOF (6 degrees of freedom) that AR provides. Two experiments were performed, one in a public museum setting and one in a private school setting. A statistical analysis was performed between non-restricted and restricted touchscreen-based input. Quantitative and qualitative data was gathered through semi structured interviews and non-participant observations. Results show no statistical significance between the physical distance moved of the smartphone and the restriction of touchscreen-based input. Qualitative data show a different software application may yield different results. / Den här studien utforskade det relativt nya området inom offentlig mobil handhållen förstärkt verklighet och hur den pekskärmsbaserade interaktionen av mobila enheter påverkar sättet användare med åldrar 10-12 interagerar med de sex frihetsgrader som förstärkt verklighet möjliggör. Två experiment utfördes, det ena i en publik miljö på ett museum och det andra i en privat miljö på en skola. En statistisk analys utfördes mellan begränsad och icke-begränsad pekskärms interaktion. Kvantitativ och kvalitativ data samlades genom semi-strukturerade intervjuer och icke-deltagande observationer. Resultatet visar på att det inte finns någon statiskt signifikant skillnad mellan den fysiska rörelsen av den mobila enheten och begränsningen av pekskärmsinteraktionen. Den kvalitativa datan visar dock att en annorlunda implementation av mjukvaran kan ge andra resultat.

Neural coding is essential for understanding how the activity of individual neurons or ensembles of neurons relates to cognitive processing of the world. Neurons can code for multiple variables simultaneously and neuroscientists are interested in classifying neurons based on the variables they represent. Building a model identification paradigm to identify neurons in terms of their coding properties is essential to understanding how the brain processes information. Statistical paradigms are capable of methodologically determining the factors influencing neural observations and assessing the quality of the resulting models to characterize and classify individual neurons. However, as neural recording technologies develop to produce data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analysis; however, they require huge training data sets, and model assessment and interpretation are more challenging than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to evaluate our approaches, we apply them to data from a population of neurons in rat hippocampus and prefrontal cortex (PFC), to characterize how spatial learning and memory processes are represented in these areas. The data consist of local field potentials (LFP) and spiking data simultaneously recorded from the CA1 region of hippocampus and the PFC of a male Long Evans rat performing a spatial alternation task on a W-shaped track. We have examined this data in three separate but related projects. In one project, we build an improved class of statistical models for neural activity by expanding a common set of basis functions to increase the statistical power of the resulting models. In the second project, we identify the individual neurons in hippocampus and PFC and classify them based on their coding properties by using statistical model identification methods. We found that a substantial proportion of hippocampus and PFC cells are spatially selective, with position and velocity coding, and rhythmic firing properties. These methods identified clear differences between hippocampal and prefrontal populations, and allowed us to classify the coding properties of the full population of neurons in these two regions. For the third project, we develop a supervised machine learning classifier based on convolutional neural networks (CNNs), which use classification results from statistical models and additional simulated data as ground truth signals for training. This integration of statistical and ML approaches allows for statistically principled and computationally efficient classification of the coding properties of general neural populations.

Many websites and products rely heavily on consumers’ usage habits to maximize profit. Therefore, this project aims to aid product development by analyzing server traffic logs. The proposed solution is a monitoring system focusing on user analytics. The website for this monitoring system contains graphical presentations of statistics on user activity, browser usage, OS usage, browser version usage and OS version usage, most common requests, and the slowest requests over a chosen time interval. Statistical calculations are made in the backend code through connections to two databases. One database contains server traffic logs and is connected to the second database through indexing. Testing on the application proved proper functionality and fulfilled the requirements. The performance testing on the application showed effectiveness with relatively low latency for most statistics methods. This latency was further reduced by 50% using indexing. / Många webbplatser och produkter är mycket beroende av konsumenter och deras användningsvanor för att maximera vinsten. Därför syftar detta projekt till att hjälpa produktutvecklingen genom att analysera servertrafikloggar. Den föreslagna lösningen är ett övervakningssystem med fokus på användaranalys. Hemsidan för detta övervakningssystem innehåller grafiska presentationer av statistik över användaraktivitet, webbläsaranvändning, OS-användning, webbläsarversionsanvändning och OS-versionsanvändning, vanligaste förfrågningar och de långsammaste förfrågningarna under ett valt tidsintervall. Statistiska beräkningar görs i backend-koden genom kopplingar till två databaser. En databas innehåller servertrafikloggar och är kopplad till den andra databasen genom indexering. Testning på applikationen visade att den fungerade korrekt och uppfyllde kraven. Prestandatestningen på applikationen visade effektivitet med relativt låg latens för de flesta statistikberäknande metoder. Denna latens reducerades ytterligare med 50 % med hjälp av indexering.

Committee meetings are a fundamental part of the legislative process in whichconstituents, lobbyists, and legislators alike can speak on proposed bills at thelocal and state level. Oftentimes, unspoken “rules” or standards are at play inpolitical processes that can influence the trajectory of a bill, leaving constituentswithout a political background at an inherent disadvantage when engaging withthe legislative process. The work done in this thesis aims to explore the extent towhich the language and phraseology of a general public testimony can influence avote, and examine how this information can be used to promote civic engagement. The Digital Democracy database contains digital records for over 40,000 realtestimonies by non-legislator public persons presented at California Legislaturecommittee meetings 2015-2018, along with the speakers’ desired vote outcomeand individual legislator votes in that discussion. With this data, we conduct alinguistic analysis that is then leveraged by the Constituent phraseology AnalysisTool (CPAT) to generate a user-based intelligent statistical comparison betweena proposed testimony and language patterns that have previously been successful. The following questions are at the core of this research: Which (if any) lan-guage features are correlated with persuasive success in a legislative context?Does the committee’s topic of discussion impact the language features that canlend to a testimony’s success? Can mirroring a legislator’s speech patterns changethe probability of the vote going your way? How can this information be used tolevel the playing field for constituents who want their voices heard? Given the 33 linguistic features developed in this research, supervised classifi-cation models were able to predict testimonial success with up to 85.1% accuracy,indicating that the new features had a significant impact on the prediction ofsuccess. Adding these features to the 16 baseline linguistic features developedin Gundala’s [18] research improved the prediction accuracy by up to 2.6%. Wealso found that balancing the dataset of testimonies drastically impacted theprediction performance metrics, with 93% accuracy achieved for the imbalanceddataset and 60% accuracy after balancing. The Constituent Phraseology AnalysisTool showed promise in the generation of linguistic analysis based on previouslysuccessful language patterns, but requires further development before achievingtrue usability. Additionally, predicting success based on linguistic similarity to alegislator on the committee produced contradictory results. Experiments yieldeda 4% increase in predictive accuracy when adding comparative language featuresto the feature set, but further experimentation with weight distributions revealedonly marginal impacts from comparative features.

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