Computer Engineering Commons^™

Review Of Deep Learning Methods In Robotic Grasp Detection, Shehan Caldera, Alexander Rassau, Douglas Chai

Research outputs 2014 to 2021

For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods …

An Entropy-Histogram Approach For Image Similarity And Face Recognition, Mohammed Aljanabi, Zahir Hussain, Song F. Lu

Research outputs 2014 to 2021

Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing. It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper. In this paper, we designed two new measures for image similarity and image recognition simultaneously. The proposed measures are based mainly on a combination of information theory and joint histogram. Information theory has a high capability to predict the relationship between …

Extraction Of Patterns In Selected Network Traffic For A Precise And Efficient Intrusion Detection Approach, Priya Naran Rabadia

Theses: Doctorates and Masters

This thesis investigates a precise and efficient pattern-based intrusion detection approach by extracting patterns from sequential adversarial commands. As organisations are further placing assets within the cyber domain, mitigating the potential exposure of these assets is becoming increasingly imperative. Machine learning is the application of learning algorithms to extract knowledge from data to determine patterns between data points and make predictions. Machine learning algorithms have been used to extract patterns from sequences of commands to precisely and efficiently detect adversaries using the Secure Shell (SSH) protocol. Seeing as SSH is one of the most predominant methods of accessing systems it …

Gender Effects Of Robotics Programs On Stem-Related Self-Efficacy Of High School Students, Sandra Hall-Lay

Walden Dissertations and Doctoral Studies

Lack of STEM-related self-efficacy has impeded the growth of women in STEM fields. Out of school (OST) robotics programs and other STEM-related OST programs provide secondary students with opportunities to work in groups, brainstorm, and formulate ideas that require communication and teamwork. The purpose of this quantitative, quasi-experimental study was to compare STEM-related self-efficacy scores among male and female students who participated in OST robotics programs or other OST STEM-related programming. Independent variables were type of OST STEM program and gender. The dependent variable was students' STEM-related self-efficacy as measured by the Coping Self-Efficacy Scale. Responses from 149 students in …

Mitigating The Effect Of Misspeculations In Superscalar Processors, Zhaoxiang Jin

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors highly rely on the speculative execution which speculatively executes instructions and then verifies. If the prediction is different from the execution result, a misspeculation recovery is performed. Misspeculation recovery penalties still account for a substantial amount of performance reduction. This work focuses on the techniques to mitigate the effect of recovery penalties and proposes practical mechanisms which are thoroughly implemented and analyzed.

In general, we can divide the misspeculation penalty into four parts: misspeculation detection delay; stale instruction elimination delay; state restoration delay and pipeline fill delay. This dissertation does not consider the detection delay, instead, we …

Resource Optimization In Wireless Sensor Networks For An Improved Field Coverage And Cooperative Target Tracking, Husam Sweidan

Dissertations, Master's Theses and Master's Reports

There are various challenges that face a wireless sensor network (WSN) that mainly originate from the limited resources a sensor node usually has. A sensor node often relies on a battery as a power supply which, due to its limited capacity, tends to shorten the life-time of the node and the network as a whole. Other challenges arise from the limited capabilities of the sensors/actuators a node is equipped with, leading to complication like a poor coverage of the event, or limited mobility in the environment. This dissertation deals with the coverage problem as well as the limited power and …

Mpart: Miss Ratio Curve Guided Partitioning In Key-Value Stores, Daniel Byrne

Dissertations, Master's Theses and Master's Reports

Web applications employ key-value stores to cache the data that is most commonly accessed. The cache improves an web application’s performance by serving its requests from memory, avoiding fetching them from the backend database. Since the memory space is limited, maximizing the memory utilization is a key to delivering the best performance possible. This has lead to the use of multi-tenant systems, allowing applications to share cache space. In addition, application data access patterns change over time, so the system should be adaptive in its memory allocation. In this thesis, we address both multi-tenancy (where a single cache is used …

Implementing Write Compression In Flash Memory Using Zeckendorf Two-Round Rewriting Codes, Vincent T. Druschke

Dissertations, Master's Theses and Master's Reports

Flash memory has become increasingly popular as the underlying storage technology for high-performance nonvolatile storage devices. However, while flash offers several benefits over alternative storage media, a number of limitations still exist within the current technology. One such limitation is that programming (altering a bit from its default value) and erasing (returning a bit to its default value) are asymmetric operations in flash memory devices: a flash memory can be programmed arbitrarily, but can only be erased in relatively large batches of storage bits called blocks, with block sizes ranging from 512K up to several megabytes. This creates a situation …

Anomaly Inference Based On Heterogeneous Data Sources In An Electrical Distribution System, Yachen Tang

Dissertations, Master's Theses and Master's Reports

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as …

A Distributed Intrusion Detection Model Via Nondestructive Partitioning And Balanced Allocation For Big Data, Xiaonian Wu, Chuyun Zhang, Runlian Zhang, Yujue Wang, Jinhua Cui

Research Collection School Of Computing and Information Systems

There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as …

Ms. An (Meeting Students’ Academic Needs): A Socially Adaptive Robot Tutor For Student Engagement In Math Education, Karina Liles

Theses and Dissertations

This research presents a new, socially adaptive robot tutor, Ms. An (Meeting Students’ Academic Needs). The goal of this research was to use a decision tree model to develop a socially adaptive robot tutor that predicted and responded to student emotion and performance to actively engage students in mathematics education. The novelty of this multi-disciplinary project is the combination of the fields of HRI, AI, and education. In this study we 1) implemented a decision tree model to classify student emotion and performance for use in adaptive robot tutoring-an approach not applied to educational robotics; 2) presented an intuitive interface …

Tracking, Detection And Registration In Microscopy Material Images, Hongkai Yu

Theses and Dissertations

Fast and accurate characterization of fiber micro-structures plays a central role for material scientists to analyze physical properties of continuous fiber reinforced composite materials. In materials science, this is usually achieved by continuously crosssectioning a 3D material sample for a sequence of 2D microscopic images, followed by a fiber detection/tracking algorithm through the obtained image sequence.

To speed up this process and be able to handle larger-size material samples, we propose sparse sampling with larger inter-slice distance in cross sectioning and develop a new algorithm that can robustly track large-scale fibers from such a sparsely sampled image sequence. In particular, …

Ontology-Guided Pre-Release Inference Disruption, Mark Stephen Daniels

Theses and Dissertations

We investigate privacy violations occurring when non-confidential patient data is combined with medical domain ontologies to disclose a patient’s protected health information (PHI). We propose a framework that detects privacy violations and eliminates undesired inferences. Our inference channel removal process is based on controlling the release of the data items that lead to undesired inferences. These data items are either blocked from release or generalized to eliminate the disclosure of the PHI. We show that our method is sound and complete. Soundness means the only inference paths generated logically follow from released data and corresponding domain knowledge. Completeness means we …

Authenticating Users With 3d Passwords Captured By Motion Sensors, Jing Tian

Theses and Dissertations

Authentication plays a key role in securing various resources including corporate facilities or electronic assets. As the most used authentication scheme, knowledgebased authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. Thus, we propose study several behavioral biometrics (i.e., mid-air gestures) for authentication which does not have the same privacy or availability concerns as of physiological biometrics.

In this dissertation, we first propose a user-friendly authentication system Kin- Write that allows users to choose arbitrary, short and …

Bytecode-Based Multiple Condition Coverage: An Initial Investigation, Srujana Bollina

Theses and Dissertations

Masking occurs when one condition prevents another condition from influencing the output of a Boolean expression. Logic-based adequacy criteria such as Multiple Condition Coverage (MCC) are designed to overcome masking at the within-expression level, but can offer no guarantees about masking in subsequent expressions. As a result, a Boolean expression written as a single complex statement will yield test cases that are more likely to overcome masking than when the expression is written as series of simple statements. Many approaches to automated analysis and test case generation for Java systems operate not on the source code representation of code, but …

Deep Convolutional Particle Filter With Adaptive Correlation Maps For Visual Tracking, Reza Jilil Mozhdehi, Yevgeniy Vladimirovich Reznichenko, Abubakar Siddique, Henry P. Medeiros

Electrical and Computer Engineering Faculty Research and Publications

The robustness of the visual trackers based on the correlation maps generated from convolutional neural networks can be substantially improved if these maps are used to employed in conjunction with a particle filter. In this article, we present a particle filter that estimates the target size as well as the target position and that utilizes a new adaptive correlation filter to account for potential errors in the model generation. Thus, instead of generating one model which is highly dependent on the estimated target position and size, we generate a variable number of target models based on high likelihood particles, which …

Efficiency Improvement Of Fault-Tolerant Three-Level Power Converters, Ramin Katebi, Jiangbiao He, Waqar A. Khan, Nathan Weise

Electrical and Computer Engineering Faculty Research and Publications

Fault-tolerant power converters play a critical role in the transportation electrification. However, fault-tolerant operation, high efficiency, and low cost usually result in design criteria that have conflicting constraints and goals. The majority of the fault-tolerant power converter topologies presented in the literature confirm these conflicts. In this paper, three types of fault-tolerant neutral-point clamped (NPC) converters are investigated. Various modulation strategies are explored to reduce the losses of the redundant phase leg. The simulation and experimental results show that the Switching Frequency Optimal Phase opposition Disposition modulation strategy is the most effective approach in minimizing the losses in the redundant …

Detection And Quantification Of Multi-Analyte Mixtures Using A Single Sensor And Multi-Stage Data-Weighted Rlse, Karthick Sothivelr, Florian Bender, Fabien Josse, Edwin E. Yaz, Antonio J. Ricco

Electrical and Computer Engineering Faculty Research and Publications

This work reports the development and experimental verification of a sensor signal processing technique for online identification and quantification of aqueous mixtures of benzene, toluene, ethylbenzene, xylenes (BTEX) and 1, 2, 4-trimethylbenzene (TMB) at ppb concentrations using time-dependent frequency responses from a single polymer-coated shear-horizontal surface acoustic wave sensor. Signal processing based on multi-stage exponentially weighted recursive leastsquares estimation (EW-RLSE) is utilized for estimating the concentrations of the analytes in the mixture that are most likely to have produced a given sensor response. The initial stages of EW-RLSE are used to eliminate analyte(s) that are erroneously identified as present in …

Stochastic Search Methods For Mobile Manipulators, Amoako-Frimpong Samuel Yaw, Matthew Messina, Henry P. Medeiros, Jeremy Marvel, Roger Bostelman

Electrical and Computer Engineering Faculty Research and Publications

Mobile manipulators are a potential solution to the increasing need for additional flexibility and mobility in industrial applications. However, they tend to lack the accuracy and precision achieved by fixed manipulators, especially in scenarios where both the manipulator and the autonomous vehicle move simultaneously. This paper analyzes the problem of dynamically evaluating the positioning error of mobile manipulators. In particular, it investigates the use of Bayesian methods to predict the position of the end-effector in the presence of uncertainty propagated from the mobile platform. The precision of the mobile manipulator is evaluated through its ability to intercept retroreflective markers using …

Luminescence Of Defects In The Structural Transformation Of Layered Tin Dichalcogenides, Peter Sutter, H.P. Komsa, A. V. Krasheninnikov, Y Huang, Eli A. Sutter

Department of Electrical and Computer Engineering: Faculty Publications

Layered tin sulfide semiconductors are both of fundamental interest and attractive for energy conversion applications. Sn sulfides crystallize in several stable bulk phases with different Sn:S ratios (SnS2, Sn2, S3, and SnS), which can transform into phases with a lower sulfur concentration by introduction of sulfur vacancies (VS). How this complex behavior affects the optoelectronic properties remains largely unknown but is of key importance for understanding light-matter interactions in this family of layered materials. Here, we use the capability to induce VS and drive a transformation between few-layer SnS2 and SnS by electron beam irradiation, combined with in-situ cathodoluminescence spectroscopy …