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Comparative Analysis in Acquisition of Coding Skills

Description

Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder &

Students learn in various ways \u2014 visualization, auditory, memorizing, or making analogies. Traditional lecturing in engineering courses and the learning styles of engineering students are inharmonious causing students to be at a disadvantage based on their learning style (Felder & Silverman, 1988). My study analyzes the traditional approach to learning coding skills which is unnatural to engineering students with no previous exposure and examining if visual learning enhances introductory computer science education. Visual and text-based learning are evaluated to determine how students learn introductory coding skills and associated problem solving skills. My study was conducted to observe how the two types of learning aid the students in learning how to problem solve as well as how much knowledge can be obtained in a short period of time. The application used for visual learning was Scratch and Repl.it was used for text-based learning. Two exams were made to measure the progress made by each student. The topics covered by the exam were initialization, variable reassignment, output, if statements, if else statements, nested if statements, logical operators, arrays/lists, while loop, type casting, functions, object orientation, and sorting. Analysis of the data collected in the study allow us to observe whether the traditional method of teaching programming or block-based programming is more beneficial and in what topics of introductory computer science concepts.

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2018-05

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Cognitive Mapping for Object Searching in Indoor Scenes

Description

Visual navigation is a multi-disciplinary field across computer vision, machine learning and robotics. It is of great significance in both research and industrial applications. An intelligent agent with visual navigation ability will be capable of performing the following tasks: actively

Visual navigation is a multi-disciplinary field across computer vision, machine learning and robotics. It is of great significance in both research and industrial applications. An intelligent agent with visual navigation ability will be capable of performing the following tasks: actively explore in environments, distinguish and localize a requested target and approach the target following acquired strategies. Despite a variety of advances in mobile robotics, enabling an autonomous with above-mentioned abilities is still a challenging and complex task. However, the solution to the task is very likely to accelerate the landing of assistive robots.

Reinforcement learning is a method that trains autonomous robot based on rewarding desired behaviors to help it obtain an action policy that maximizes rewards while the robot interacting with the environment. Through trial and error, an agent learns sophisticated and skillful strategies to handle complex tasks in the environment. Inspired by navigation procedures of human beings that when navigating through environments, humans reason about accessible spaces and geometry of the environment a lot based on first-person view, figure out the destination and then ease over, this work develops a model that maps from pixels to actions and inherently estimate the target as well as the free-space map. The model has three major constituents: (i) a cognitive mapper that maps the topologic free-space map from first-person view images, (ii) a target recognition network that locates a desired object and (iii) an action policy deep reinforcement learning network. Further, a planner model with cascade architecture based on multi-scale semantic top-down occupancy map input is proposed.

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2019

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Computer Vision: Improving Detection and Tracking for Occluded and Blurry Settings

Description

Computer vision and tracking has become an area of great interest for many reasons, including self-driving cars, identification of vehicles and drivers on roads, and security camera monitoring, all of which are expanding in the modern digital era. When working

Computer vision and tracking has become an area of great interest for many reasons, including self-driving cars, identification of vehicles and drivers on roads, and security camera monitoring, all of which are expanding in the modern digital era. When working with practical systems that are constrained in multiple ways, such as video quality or viewing angle, algorithms that work well theoretically can have a high error rate in practice. This thesis studies several ways in which that error can be minimized.This thesis describes an application in a practical system. This project is to detect, track and count people entering different lanes at an airport security checkpoint, using CCTV videos as a primary source. This thesis improves an existing algorithm that is not optimized for this particular problem and has a high error rate when comparing the algorithm counts with the true volume of users.
The high error rate is caused by many people crowding into security lanes at the same time. The camera from which footage was captured is located at a poor angle, and thus many of the people occlude each other and cause the existing algorithm to miss people. One solution is to count only heads; since heads are smaller than a full body, they will occlude less, and in addition, since the camera is angled from above, the heads in back will appear higher and will not be occluded by people in front. One of the primary improvements to the algorithm is to combine both person detections and head detections to improve the accuracy.
The proposed algorithm also improves the accuracy of detections. The existing algorithm used the COCO training dataset, which works well in scenarios where people are visible and not occluded. However, the available video quality in this project was not very good, with people often blocking each other from the camera’s view. Thus, a different training set was needed that could detect people even in poor-quality frames and with occlusion. The new training set is the first algorithmic improvement, and although occasionally performing worse, corrected the error by 7.25% on average.

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2021

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Solving SPDEs for Multi-Dimensional Shape Analysis

Description

Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken

Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.

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2021

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Reduced Order Models and Approximations for Hardware Acceleration of Neural Networks

Description

Many real-world engineering problems require simulations to evaluate the design objectives and constraints. Often, due to the complexity of the system model, simulations can be prohibitive in terms of computation time. One approach to overcome this issue is to construct

Many real-world engineering problems require simulations to evaluate the design objectives and constraints. Often, due to the complexity of the system model, simulations can be prohibitive in terms of computation time. One approach to overcome this issue is to construct a surrogate model, which approximates the original model. The focus of this work is on the data-driven surrogate models, in which empirical approximations of the output are performed given the input parameters. Recently neural networks (NN) have re-emerged as a popular method for constructing data-driven surrogate models. Although, NNs have achieved excellent accuracy and are widely used, they pose their own challenges. This work addresses two common challenges, the need for: (1) hardware acceleration and (2) uncertainty quantification (UQ) in the presence of input variability. The high demand in the inference phase of deep NNs in cloud servers/edge devices calls for the design of low power custom hardware accelerators. The first part of this work describes the design of an energy-efficient long short-term memory (LSTM) accelerator. The overarching goal is to aggressively reduce the power consumption and area of the LSTM components using approximate computing, and then use architectural level techniques to boost the performance. The proposed design is synthesized and placed and routed as an application-specific integrated circuit (ASIC). The results demonstrate that this accelerator is 1.2X and 3.6X more energy-efficient and area-efficient than the baseline LSTM. In the second part of this work, a robust framework is developed based on an alternate data-driven surrogate model referred to as polynomial chaos expansion (PCE) for addressing UQ. In contrast to many existing approaches, no assumptions are made on the elements of the function space and UQ is a function of the expansion coefficients. Moreover, the sensitivity of the output with respect to any subset of the input variables can be computed analytically by post-processing the PCE coefficients. This provides a systematic and incremental method to pruning or changing the order of the model. This framework is evaluated on several real-world applications from different domains and is extended for classification tasks as well.

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2021

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A Novel Battery Management & Charging Solution for Autonomous UAV Systems

Description

Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission

Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment.

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2018

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Compressive Light Field Reconstruction using Deep Learning

Description

Light field imaging is limited in its computational processing demands of high

sampling for both spatial and angular dimensions. Single-shot light field cameras

sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing

incoming rays onto a 2D sensor array. While this resolution

Light field imaging is limited in its computational processing demands of high

sampling for both spatial and angular dimensions. Single-shot light field cameras

sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing

incoming rays onto a 2D sensor array. While this resolution can be recovered using

compressive sensing, these iterative solutions are slow in processing a light field. We

present a deep learning approach using a new, two branch network architecture,

consisting jointly of an autoencoder and a 4D CNN, to recover a high resolution

4D light field from a single coded 2D image. This network decreases reconstruction

time significantly while achieving average PSNR values of 26-32 dB on a variety of

light fields. In particular, reconstruction time is decreased from 35 minutes to 6.7

minutes as compared to the dictionary method for equivalent visual quality. These

reconstructions are performed at small sampling/compression ratios as low as 8%,

allowing for cheaper coded light field cameras. We test our network reconstructions

on synthetic light fields, simulated coded measurements of real light fields captured

from a Lytro Illum camera, and real coded images from a custom CMOS diffractive

light field camera. The combination of compressive light field capture with deep

learning allows the potential for real-time light field video acquisition systems in the

future.

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Date Created
2017

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Mediating Human-Robot Collaboration through Mixed Reality Cues

Description

This work presents a communication paradigm, using a context-aware mixed reality approach, for instructing human workers when collaborating with robots. The main objective of this approach is to utilize the physical work environment as a canvas to communicate task-related instructions

This work presents a communication paradigm, using a context-aware mixed reality approach, for instructing human workers when collaborating with robots. The main objective of this approach is to utilize the physical work environment as a canvas to communicate task-related instructions and robot intentions in the form of visual cues. A vision-based object tracking algorithm is used to precisely determine the pose and state of physical objects in and around the workspace. A projection mapping technique is used to overlay visual cues on tracked objects and the workspace. Simultaneous tracking and projection onto objects enables the system to provide just-in-time instructions for carrying out a procedural task. Additionally, the system can also inform and warn humans about the intentions of the robot and safety of the workspace. It was hypothesized that using this system for executing a human-robot collaborative task will improve the overall performance of the team and provide a positive experience to the human partner. To test this hypothesis, an experiment involving human subjects was conducted and the performance (both objective and subjective) of the presented system was compared with a conventional method based on printed instructions. It was found that projecting visual cues enabled human subjects to collaborate more effectively with the robot and resulted in higher efficiency in completing the task.

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Date Created
2017

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Comparison of Team Robot Localization by Input Difference for Deep Neural Network Model

Description

In a multi-robot system, locating a team robot is an important issue. If robots

can refer to the location of team robots based on information through passive action

recognition without explicit communication, various advantages (e.g. improving security

for military purposes) can be obtained.

In a multi-robot system, locating a team robot is an important issue. If robots

can refer to the location of team robots based on information through passive action

recognition without explicit communication, various advantages (e.g. improving security

for military purposes) can be obtained. Specifically, when team robots follow

the same motion rule based on information about adjacent robots, associations can

be found between robot actions. If the association can be analyzed, this can be a clue

to the remote robot. Using these clues, it is possible to infer remote robots which are

outside of the sensor range.

In this paper, a multi-robot system is constructed using a combination of Thymio

II robotic platforms and Raspberry pi controllers. Robots moving in chain-formation

take action using motion rules based on information obtained through passive action

recognition. To find associations between robots, a regression model is created using

Deep Neural Network (DNN) and Long Short-Term Memory (LSTM), one of state-of-art technologies.

The input data of the regression model is divided into historical data, which

are consecutive positions of the robot, and observed data, which is information about the

observed robot. Historical data is sequence data that is analyzed through the LSTM

layer. The accuracy of the regression model designed using DNN can vary depending

on the quantity and quality of the input. In this thesis, three different input situations

are assumed for comparison. First, the amount of observed data is different, second, the

type of observed data is different, and third, the history length is different. Comparative

models are constructed for each case, and prediction accuracy is compared to analyze

the effect of input data on the regression model. This exploration validates that these

methods from deep learning can reduce the communication demands in coordinated

motion of multi-robot systems

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Date Created
2020

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Learning in Compressed Domains

Description

A massive volume of data is generated at an unprecedented rate in the information age. The growth of data significantly exceeds the computing and storage capacities of the existing digital infrastructure. In the past decade, many methods are invented for

A massive volume of data is generated at an unprecedented rate in the information age. The growth of data significantly exceeds the computing and storage capacities of the existing digital infrastructure. In the past decade, many methods are invented for data compression, compressive sensing and reconstruction, and compressed learning (learning directly upon compressed data) to overcome the data-explosion challenge. While prior works are predominantly model-based, focus on small models, and not suitable for task-oriented sensing or hardware acceleration, the number of available models for compression-related tasks has escalated by orders of magnitude in the past decade. Motivated by this significant growth and the success of big data, this dissertation proposes to revolutionize both the compressive sensing reconstruction (CSR) and compressed learning (CL) methods from the data-driven perspective. In this dissertation, a series of topics on data-driven CSR are discussed. Individual data-driven models are proposed for the CSR of bio-signals, images, and videos with improved compression ratio and recovery fidelity trade-off. Specifically, a scalable Laplacian pyramid reconstructive adversarial network (LAPRAN) is proposed for single-image CSR. LAPRAN progressively reconstructs images following the concept of the Laplacian pyramid through the concatenation of multiple reconstructive adversarial networks (RANs). For the CSR of videos, CSVideoNet is proposed to improve the spatial-temporal resolution of reconstructed videos. Apart from CSR, data-driven CL is discussed in the dissertation. A CL framework is proposed to extract features directly from compressed data for image classification, objection detection, and semantic/instance segmentation. Besides, the spectral bias of neural networks is analyzed from the frequency perspective, leading to a learning-based frequency selection method for identifying the trivial frequency components which can be removed without accuracy loss. Compared with the conventional spatial downsampling approaches, the proposed frequency-domain learning method can achieve higher accuracy with reduced input data size. The methodologies proposed in this dissertation are not restricted to the above-mentioned applications. The dissertation also discusses other potential applications and directions for future research.

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2021