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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 & Silverman, 1988). My study analyzes the traditional approach to learning

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.
ContributorsVidaure, Destiny Vanessa (Author) / Meuth, Ryan (Thesis director) / Yang, Yezhou (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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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 and robot intentions in the form of visual cues. A

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.
ContributorsKalpagam Ganesan, Ramsundar (Author) / Ben Amor, Hani (Thesis advisor) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2017
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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 can be recovered using

compressive sensing, these iterative solutions are slow

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.
ContributorsGupta, Mayank (Author) / Turaga, Pavan (Thesis advisor) / Yang, Yezhou (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
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Description
Deep neural network-based methods have been proved to achieve outstanding performance on object detection and classification tasks. Deep neural networks follow the ``deeper model with deeper confidence'' belief to gain a higher recognition accuracy. However, reducing these networks' computational costs remains a challenge, which impedes their deployment on embedded devices.

Deep neural network-based methods have been proved to achieve outstanding performance on object detection and classification tasks. Deep neural networks follow the ``deeper model with deeper confidence'' belief to gain a higher recognition accuracy. However, reducing these networks' computational costs remains a challenge, which impedes their deployment on embedded devices. For instance, the intersection management of Connected Autonomous Vehicles (CAVs) requires running computationally intensive object recognition algorithms on low-power traffic cameras. This dissertation aims to study the effect of a dynamic hardware and software approach to address this issue. Characteristics of real-world applications can facilitate this dynamic adjustment and reduce the computation. Specifically, this dissertation starts with a dynamic hardware approach that adjusts itself based on the toughness of input and extracts deeper features if needed. Next, an adaptive learning mechanism has been studied that use extracted feature from previous inputs to improve system performance. Finally, a system (ARGOS) was proposed and evaluated that can be run on embedded systems while maintaining the desired accuracy. This system adopts shallow features at inference time, but it can switch to deep features if the system desires a higher accuracy. To improve the performance, ARGOS distills the temporal knowledge from deep features to the shallow system. Moreover, ARGOS reduces the computation furthermore by focusing on regions of interest. The response time and mean average precision are adopted for the performance evaluation to evaluate the proposed ARGOS system.
ContributorsFarhadi, Mohammad (Author) / Yang, Yezhou (Thesis advisor) / Vrudhula, Sarma (Committee member) / Wu, Carole-Jean (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2022
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Description
Recent advances in autonomous vehicle (AV) technologies have ensured that autonomous driving will soon be present in real-world traffic. Despite the potential of AVs, many studies have shown that traffic accidents in hybrid traffic environments (where both AVs and human-driven vehicles (HVs) are present) are inevitable because of the unpredictability

Recent advances in autonomous vehicle (AV) technologies have ensured that autonomous driving will soon be present in real-world traffic. Despite the potential of AVs, many studies have shown that traffic accidents in hybrid traffic environments (where both AVs and human-driven vehicles (HVs) are present) are inevitable because of the unpredictability of human-driven vehicles. Given that eliminating accidents is impossible, an achievable goal of designing AVs is to design them in a way so that they will not be blamed for any accident in which they are involved in. This work proposes BlaFT – a Blame-Free motion planning algorithm in hybrid Traffic. BlaFT is designed to be compatible with HVs and other AVs, and will not be blamed for accidents in a structured road environment. Also, it proves that no accidents will happen if all AVs are using the BlaFT motion planner and that when in hybrid traffic, the AV using BlaFT will be blame-free even if it is involved in a collision. The work instantiated scores of BlaFT and HV vehicles in an urban road scape loop in the 'Simulation of Urban MObility', ran the simulation for several hours, and observe that as the percentage of BlaFT vehicles increases, the traffic becomes safer. Adding BlaFT vehicles to HVs also increases the efficiency of traffic as a whole by up to 34%.
ContributorsPark, Sanggu (Author) / Shrivastava, Aviral (Thesis advisor) / Wang, Ruoyu (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
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Description
It is not merely an aggregation of static entities that a video clip carries, but alsoa variety of interactions and relations among these entities. Challenges still remain for a video captioning system to generate natural language descriptions focusing on the prominent interest and aligning with the latent aspects beyond observations. This work presents

It is not merely an aggregation of static entities that a video clip carries, but alsoa variety of interactions and relations among these entities. Challenges still remain for a video captioning system to generate natural language descriptions focusing on the prominent interest and aligning with the latent aspects beyond observations. This work presents a Commonsense knowledge Anchored Video cAptioNing (dubbed as CAVAN) approach. CAVAN exploits inferential commonsense knowledge to assist the training of video captioning model with a novel paradigm for sentence-level semantic alignment. Specifically, commonsense knowledge is queried to complement per training caption by querying a generic knowledge atlas ATOMIC, and form the commonsense- caption entailment corpus. A BERT based language entailment model trained from this corpus then serves as a commonsense discriminator for the training of video captioning model, and penalizes the model from generating semantically misaligned captions. With extensive empirical evaluations on MSR-VTT, V2C and VATEX datasets, CAVAN consistently improves the quality of generations and shows higher keyword hit rate. Experimental results with ablations validate the effectiveness of CAVAN and reveals that the use of commonsense knowledge contributes to the video caption generation.
ContributorsShao, Huiliang (Author) / Yang, Yezhou (Thesis advisor) / Jayasuriya, Suren (Committee member) / Xiao, Chaowei (Committee member) / Arizona State University (Publisher)
Created2022
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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 explore in environments, distinguish and localize a requested target and

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.
ContributorsZheng, Shibin (Author) / Yang, Yezhou (Thesis advisor) / Zhang, Wenlong (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2019
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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. Specifically, when team robots follow

the same motion rule based on

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
ContributorsKang, Sehyeok (Author) / Pavlic, Theodore P (Thesis advisor) / Richa, Andréa W. (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
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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 data compression, compressive sensing and reconstruction, and compressed learning (learning

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.
ContributorsXu, Kai (Author) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
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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 with practical systems that are constrained in multiple ways, such

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.
ContributorsLarsen, Andrei (Author) / Askin, Ronald (Thesis advisor) / Sefair, Jorge (Thesis advisor) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021