Improving the performance of GIS/spatial analysts though novel applications of the Emotiv EPOC EEG headset

Geospatial information systems are used to analyze spatial data to provide decision makers with relevant, up-to-date, information. The processing time required for this information is a critical component to response time. Despite advances in algorithms and processing power, we still have many “human-in-the-loop” factors. Given the limited number of geospatial professionals, analysts using their time effectively is very important. The automation and faster humancomputer interactions of common tasks that will not disrupt their workflow or attention is something that is very desirable. The following research describes a novel approach to increase productivity with a wireless, wearable, electroencephalograph (EEG) headset within the geospatial workflow.

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.

Automation of shear-wave splitting parameter determination of local earthquakes at Yellowstone : application as indicator of crustal stress and temporal variation

Shear-wave splitting can be a useful technique for determining crustal stress fields in volcanic settings and temporal variations associated with activity. Splitting parameters were determined for a subset of local earthquakes recorded from 2000-2010 at Yellowstone. Analysis was automated using an unsupervised cluster analysis technique to determine optimum splitting parameters from 270 analysis windows for each event. Six stations clearly exhibit preferential fast polarization values sub-orthogonal to the direction of minimum horizontal compression. Yellowstone deformation results in a local crustal stress field differing from the regional field dominated by NE-SW extension, and fast directions reflect this difference rotating around the caldera maintaining perpendicularity to the rim. One station exhibits temporal variations concordant with identified periods of caldera subsidence and uplift. From splitting measurements, we calculated a crustal anisotropy of ~17-23% and crack density ~0.12-0.17 possibly resulting from stress-aligned fluid filled microcracks in the upper crust and an active hydrothermal system.

Non-invasive method to predict viscosity and size using total internal reflection fluorescence microscopy (TIRFM) system

This study develops an automated analysis tool by combining total internal reflection fluorescence microscopy (TIRFM), an evanescent wave microscopic imaging technique to capture time-sequential images and the corresponding image processing Matlab code to identify movements of single individual particles. The developed code will enable us to examine two dimensional hindered tangential Brownian motion of nanoparticles with a sub-pixel resolution (nanoscale). The measured mean square displacements of nanoparticles are compared with theoretical predictions to estimate particle diameters and fluid viscosity using a nonlinear regression technique. These estimated values will be confirmed by the diameters and viscosities given by manufacturers to validate this analysis tool. Nano-particles used in these experiments are yellow-green polystyrene fluorescent nanospheres (200 nm, 500 nm and 1000 nm in diameter (nominal); 505 nm excitation and 515 nm emission wavelengths). Solutions used in this experiment are de-ionized (DI) water, 10% d-glucose and 10% glycerol. Mean square displacements obtained near the surface shows significant deviation from theoretical predictions which are attributed to DLVO forces in the region but it conforms to theoretical predictions after ~125 nm onwards. The proposed automation analysis tool will be powerfully employed in the bio-application fields needed for examination of single protein (DNA and/or vesicle) tracking, drug delivery, and cyto-toxicity unlike the traditional measurement techniques that require fixing the cells. Furthermore, this tool can be also usefully applied for the microfluidic areas of non-invasive thermometry, particle tracking velocimetry (PTV), and non-invasive viscometry.

DESIGN AND IMPLEMENT DYNAMIC PROGRAMMING BASED DISCRETE POWER LEVEL SMART HOME SCHEDULING USING FPGA

With the development and capabilities of the Smart Home system, people today are entering an era in which household appliances are no longer just controlled by people, but also operated by a Smart System. This results in a more efficient, convenient, comfortable, and environmentally friendly living environment. A critical part of the Smart Home system is Home Automation, which means that there is a Micro-Controller Unit (MCU) to control all the household appliances and schedule their operating times. This reduces electricity bills by shifting amounts of power consumption from the on-peak hour consumption to the off-peak hour consumption, in terms of different “hour price”. In this paper, we propose an algorithm for scheduling multi-user power consumption and implement it on an FPGA board, using it as the MCU. This algorithm for discrete power level tasks scheduling is based on dynamic programming, which could find a scheduling solution close to the optimal one. We chose FPGA as our system’s controller because FPGA has low complexity, parallel processing capability, a large amount of I/O interface for further development and is programmable on both software and hardware. In conclusion, it costs little time running on FPGA board and the solution obtained is good enough for the consumers.

Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Strategic Optimization Techniques For FRTU Deployment and Chip Physical Design

Combinatorial optimization is a complex engineering subject. Although formulation often depends on the nature of problems that differs from their setup, design, constraints, and implications, establishing a unifying framework is essential. This dissertation investigates the unique features of three important optimization problems that can span from small-scale design automation to large-scale power system planning: (1) Feeder remote terminal unit (FRTU) planning strategy by considering the cybersecurity of secondary distribution network in electrical distribution grid, (2) physical-level synthesis for microfluidic lab-on-a-chip, and (3) discrete gate sizing in very-large-scale integration (VLSI) circuit. First, an optimization technique by cross entropy is proposed to handle FRTU deployment in primary network considering cybersecurity of secondary distribution network. While it is constrained by monetary budget on the number of deployed FRTUs, the proposed algorithm identi?es pivotal locations of a distribution feeder to install the FRTUs in different time horizons. Then, multi-scale optimization techniques are proposed for digital micro?uidic lab-on-a-chip physical level synthesis. The proposed techniques handle the variation-aware lab-on-a-chip placement and routing co-design while satisfying all constraints, and considering contamination and defect. Last, the first fully polynomial time approximation scheme (FPTAS) is proposed for the delay driven discrete gate sizing problem, which explores the theoretical view since the existing works are heuristics with no performance guarantee. The intellectual contribution of the proposed methods establishes a novel paradigm bridging the gaps between professional communities.