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Image Processing for an Autonomous Throwing Arm and Smart Catching System

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In this paper, we propose an autonomous throwing and catching system to be developed as a preliminary step towards the refinement of a robotic arm capable of improving strength and motor function in the limb. This will be accomplished by

In this paper, we propose an autonomous throwing and catching system to be developed as a preliminary step towards the refinement of a robotic arm capable of improving strength and motor function in the limb. This will be accomplished by first autonomizing simpler movements, such as throwing a ball. In this system, an autonomous thrower will detect a desired target through the use of image processing. The launch angle and direction necessary to hit the target will then be calculated, followed by the launching of the ball. The smart catcher will then detect the ball as it is in the air, calculate its expected landing location based on its initial trajectory, and adjust its position so that the ball lands in the center of the target. The thrower will then proceed to compare the actual landing position with the position where it expected the ball to land, and adjust its calculations accordingly for the next throw. By utilizing this method of feedback, the throwing arm will be able to automatically correct itself. This means that the thrower will ideally be able to hit the target exactly in the center within a few throws, regardless of any additional uncertainty in the system. This project will focus of the controller and image processing components necessary for the autonomous throwing arm to be able to detect the position of the target at which it will be aiming, and for the smart catcher to be able to detect the position of the projectile and estimate its final landing position by tracking its current trajectory.

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2018-05

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Automatic Recording of Children's Activity Within a Classroom: A Study of Levy Flights

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The diagnosis for an attention deficit/hyperactivity disorder (ADHD) in children is heavily based on teacher or parent opinion, and not on scientific evidence. This causes children to be wrongly diagnosed with a disorder and be prescribed medicine that they do

The diagnosis for an attention deficit/hyperactivity disorder (ADHD) in children is heavily based on teacher or parent opinion, and not on scientific evidence. This causes children to be wrongly diagnosed with a disorder and be prescribed medicine that they do not need to be taking. This paper discusses a project that was completed for the Child Study Lab (CSL) preschool at Arizona State University (ASU), in which children’s activity within a classroom was automatically recorded using ultra-wideband technology. This project’s goal was to gather location data on the children in the CSL and analyze and assess the collected data for any patterns of behavior. The hope was that if a child’s data displayed a pattern that strayed from the norm, that this analysis could pose as a more objective way to indicate that a child may have an attention deficit problem. Fractal Dimensions and Levy Flights were researched and applied to the data analysis portion of this project.

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2020-05

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The Development of a Power System for the Phoenix CubeSat

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The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that

The Phoenix CubeSat is a 3U Earth imaging CubeSat which will take infrared (IR) photos of cities in the United Stated to study the Urban Heat Island Effect, (UHI) from low earth orbit (LEO). It has many different components that need to be powered during the life of its mission. The only power source during the mission will be its solar panels. It is difficult to calculate power generation from solar panels by hand because of the different orientations the satellite will be positioned in during orbit; therefore, simulation will be used to produce power generation data. Knowing how much power is generated is integral to balancing the power budget, confirming whether there is enough power for all the components, and knowing whether there will be enough power in the batteries during eclipse. This data will be used to create an optimal design for the Phoenix CubeSat to accomplish its mission.

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2017-05

Wireless Machine-learning Enabled Reconfigurable ""Button-type"" Pressure Sensors for Gait Analysis

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This paper introduces a wireless reconfigurable “button-type” pressure sensor system, via machine learning, for gait analysis application. The pressure sensor system consists of an array of independent button-type pressure sensing units interfaced with a remote computer. The pressure sensing unit

This paper introduces a wireless reconfigurable “button-type” pressure sensor system, via machine learning, for gait analysis application. The pressure sensor system consists of an array of independent button-type pressure sensing units interfaced with a remote computer. The pressure sensing unit contains pressure-sensitive resistors, readout electronics, and a wireless Bluetooth module, which are assembled within footprint of 40 × 25 × 6mm3. The small-footprint, low-profile sensors are populated onto a shoe insole, like buttons, to collect temporal pressure data. The pressure sensing unit measures pressures up to 2,000 kPa while maintaining an error under 10%. The reconfigurable pressure sensor array reduces the total power consumption of the system by 50%, allowing extended period of operation, up to 82.5 hrs. A robust machine learning program identifies the optimal pressure sensing units in any given configuration at an accuracy of up to 98%.

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2018-12

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Improved Finite Sample Estimate of A Nonparametric Divergence Measure

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This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used

This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used in conjunction with a power law curve to calculate an asymptotic value of the divergence estimator. Monte Carlo estimates of Dp are found for increasing values of sample size, and a power law fit is used to relate the divergence estimates as a function of sample size. The fit is also used to generate a confidence interval for the estimate to characterize the quality of the estimate. We compare the performance of this method with the other estimation methods. The calculated divergence is applied to the binary classification problem. Using the inherent relation between divergence measures and classification error rate, an analysis of the Bayes error rate of several data sets is conducted using the asymptotic divergence estimate.

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2016-05

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Sensitivity Analysis of a Spatiotemporal Correlation Based Seizure Prediction Algorithm

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Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with

Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.

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2015-05

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A Non-Parametric Semi-Supervised f-Divergence

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Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the

Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological speech analysis.

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2016-05

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Novel Solar Array Interface Electronics for Maximum PV Power Extraction

Description

Current technology does not allow for the full amount of power produced by solar arrays (PV) on spacecraft to be utilized. The arrays are designed with non-reconfigurable architectures and sent on fifteen to twenty year long missions. They cannot be

Current technology does not allow for the full amount of power produced by solar arrays (PV) on spacecraft to be utilized. The arrays are designed with non-reconfigurable architectures and sent on fifteen to twenty year long missions. They cannot be changed once they are in space, so the arrays are designed for the end of life. Throughout their lifetime, solar arrays can degrade in power producing capabilities anywhere from 20% to 50%. Because there is such a drastic difference in the beginning and end of life power production, and because they cannot be reconfigured, a new design has been found necessary in order to increase power production. Reconfiguration allows the solar arrays to achieve maximum power producing capabilities at both the beginning and end of their lives. With the potential to increase power production by 50%, the reconfiguration design consists of a switching network to be able to utilize any combination of cells. The design for reconfiguration must meet the power requirements of the solar array. This thesis will explore different designs for reconfiguration, as well as possible switches for implementation. It will also review other methods to increase power production, as well as discuss future work in this field.

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2018-05

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Electric Field Sensing

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This project examines the science of electric field sensing and completes experiments, gathering data to support its utility for various applications. The basic system consists of a transmitter, receiver, and lock-in amplifier. The primary goal of the study was to

This project examines the science of electric field sensing and completes experiments, gathering data to support its utility for various applications. The basic system consists of a transmitter, receiver, and lock-in amplifier. The primary goal of the study was to determine if such a system could detect a human disturbance, due to the capacitance of a human body, and such a thesis was supported. Much different results were obtained when a person disturbed the electric field transmitted by the system than when other types of objects, such as chairs and electronic devices, were placed in the field. In fact, there was a distinct difference between persons of varied sizes as well. This thesis goes through the basic design of the system and the process of experimental design for determining the capabilities of such an electric field sensing system.

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Date Created
2013-05

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An Economic Perspective -- Hybrid Solar Modules: Harnessing Solar Energy for Electrical and Thermal Applications

Description

A hybrid PV/T module was built, consisting of a thermal liquid heating system and a photovoltaic module system that combine in a hybrid format. This report will discuss the work on the project from Fall 2012 to Spring 2013 and

A hybrid PV/T module was built, consisting of a thermal liquid heating system and a photovoltaic module system that combine in a hybrid format. This report will discuss the work on the project from Fall 2012 to Spring 2013 and the extended section on the economics for the Honors Thesis. Three stages of experiments were completed. Stage 1 showed our project was functional as we were able to verify our panel produced electricity and increased the temperature of water flowing in the system by 0.65°C. Stage 2 testing included “gluing” the flow system to the back of the panel resulting in an average increase of 4.76°C in the temperature of the water in the system. Stage 3 testing included adding insulating foam to the module which resulted in increasing the average temperature of the water in our flow system by 6.95°C. The economic calculations show the expected energy cost savings for Arizona residents.

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2013-05