Computer Engineering Commons^™

A Tool For Biometric Interpretation Of Forensic Str Dna Profiles, Ahmad Jamal Baroudi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Rapid DNA biometric identification applications are becoming more essential and widely used in human identity validation processes. Despite their powerful identification capabilities, processing a sample to generate a forensic DNA profile still takes longer compared with other rapid biometric technologies. Methods used to speed up the analysis could lead to signal artifacts similar to those arising from low copy or degraded DNA samples, making the electropherogram unsuitable for forensic interpretation and analysis. The goal of this research effort is to apply biometrics and mathematical approaches to forensic STR (Short Tandem Repeat) profiles. To accomplish this goal, a multi-function software tool …

An Efficient Ar Model-Based Method For The Detection Of Forced Oscillations In Power Networks: Implementation And Analysis, Maria Waleska Suarez

Graduate Theses, Dissertations, and Problem Reports (ETD)

An active research topic is the detection of various oscillations that may lead to instability and potential disruption in the operation of a power network. Forced Oscillations (FOs) play a unique role in power system stability among various oscillations. They are perturbances that change the system’s state and are caused for many reasons, including but not limited to persistent load changes and oscillatory load or generation, fault, triplane, and other mechanical anomalies. These factors can hugely affect the power grid by either increasing or decreasing the amplitude, causing corrupt modes leading to blackouts, affecting the equipment involved, delivering poor power …

Synthesizing Realistic Substitute Data For A Law Enforcement Database Using A Python Library, Anthony Carrola

Graduate Theses, Dissertations, and Problem Reports (ETD)

In many databases, there is private or sensitive data that should not be accessible to any but a few individuals, such as HIPAA (Health Insurance Portability and Accountability Act) protected or LE (law enforcement) data. However, there is often a need to work with the data or change it for proper and thorough testing, especially for the developers . In some cases, the developers may be authorized to access and view the data, but it is rarely allowable for that data to be changed. Further, it is unlikely, especially on a large project, that all of the developers will have …

Generation Of High Performing Morph Datasets, Kelsey Lynn O'Haire

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition systems play a vital role in our everyday lives. We rely on this technology from menial tasks to issues as vital as national security. While strides have been made over the past ten years to improve facial recognition systems, morphed face images are a viable threat to the reliability of these systems. Morphed images are generated by combining the face images of two subjects. The resulting morphed face shares the likeness of the contributing subjects, confusing both humans and face verification algorithms. This vulnerability has grave consequences for facial recognition systems used on international borders or for law …

Improving Robotic Decision-Making In Unmodeled Situations, Nicholas Scott Ohi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Existing methods of autonomous robotic decision-making are often fragile when faced with inaccurate or incompletely modeled distributions of uncertainty, also known as ambiguity. While decision-making under ambiguity is a field of study that has been gaining interest, many existing methods tend to be computationally challenging, require many assumptions about the nature of the problem, and often require much prior knowledge. Therefore, they do not scale well to complex real-world problems where fulfilling all of these requirements is often impractical if not impossible. The research described in this dissertation investigates novel approaches to robotic decision-making strategies which are resilient to …

A Novel Computational Network Methodology For Discovery Of Biomarkers And Therapeutic Targets, Qing Ye

Graduate Theses, Dissertations, and Problem Reports (ETD)

Lung cancer has the second highest cancer incidence rate and the top cancer-related mortality worldwide. An estimate from the American Cancer Society shows that, in 2022, there will be about 236,740 lung cancer cases (117,910 men and 118,830 women) in the US. To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. There is an unmet clinical need to identify patients with early-stage NSCLC who are likely to develop recurrence and to predict their therapeutic responses. This dissertation developed a novel computational methodology for modeling molecular gene association networks …

Nbp 2.0: Updated Next Bar Predictor, An Improved Algorithmic Music Generator, Belinda M. Dungan, Proceso L. Fernandez Jr

Electronics, Computer, and Communications Engineering Faculty Publications

Deep neural network advancements have enabled machines to produce melodies emulating human-composed music. However, the implementation of such machines is costly in terms of resources. In this paper, we present NBP 2.0, a refinement of the previous model next bar predictor (NBP) with two notable improvements: first, transforming each training instance to anchor all the notes to its musical scale, and second, changing the model architecture itself. NBP 2.0 maintained its straightforward and lightweight implementation, which is an advantage over the baseline models. Improvements were assessed using quantitative and qualitative metrics and, based on the results, the improvements from these …

Multimodal Adversarial Learning, Uche Osahor

Graduate Theses, Dissertations, and Problem Reports (ETD)

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models …

High Energy And Power Density Peptidoglycan Musclesthrough Super-Viscous Nanoconfined Water, Haozhen Wang, Zhi-Lun Liu, Jianpei Lao, Sheng Zhang, Rinat Abzalimov, Tong Wang, Xi Chen

Advanced Science Research Center

Water-responsive (WR) materials that reversibly deform in response to humidity changes show great potential for developing muscle-like actuators for miniature and biomimetic robotics. Here, it is presented that Bacillus (B.) subtilis’ peptidoglycan (PG) exhibits WR actuation energy and power densities reaching 72.6 MJ m−3 and 9.1 MW m−3, respectively, orders of magnitude higher than those of frequently used actuators, such as piezoelectric actuators and dielectric elastomers. PG can deform as much as 27.2% within 110 ms, and its actuation pressure reaches ≈354.6 MPa. Surprisingly, PG exhibits an energy conversion efficiency of ≈66.8%, which can be attributed to its super-viscous nanoconfined …

A Reynolds Number Based Sampling Technique For 3-D Vector Fields In Computational Fluid Dynamic Environments, Trent James Schweitzer

Graduate Student Theses, Dissertations, & Professional Papers

Effective visualization of unsteady, time-dependent vector fields in a virtual environment is not a trivial task. This is due to the fact that most visualization techniques require the user to have a prior understanding of how the vector field will behave to set the parameters used to create the visualization. In this thesis we will take air flow data from a computational fluid dynamic simulations to calculate the amount of turbulence (represented as Reynolds numbers) to identify regions of interest. We then calculate wind pathlines that will intersect with these points sampled from these regions. We address the issue of …

Factors Influencing The Effectiveness Of Managing Human–Robot Teams, Theodore B. Terry

Walden Dissertations and Doctoral Studies

Certain factors can influence the capabilities of a robot–human team by affecting their social and behavioral dynamics in a work environment. But these factors were not known due to the progressive nature of human–robot partnerships and a lack of peer-reviewed literature on the topic. This e-Delphi study aimed to identify and understand these unknown influential factors based on the participants’ insights. The overarching research question asked about the need to determine factors that might influence the effectiveness of managing human-robot teams. The basis for the conceptual framework for this study was the theory of communication used in organizational management. Twelve …

Jitim Front Cover Vol 31 1 2022

Journal of International Technology and Information Management

Table of Content JITIM Vol 31. issue 1, 2022

Performance Of Sensor Fusion For Vehicular Applications, Nikola Janevski

Graduate Theses, Dissertations, and Problem Reports (ETD)

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The perfor-
mance of the sensor fusion depends on many factors such as the sensors used, the kinematic
model used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type of
road, the density of vehicles, and the gating methods. The interactions between parameters
and the extent to which individual parameters contribute to the overall accuracy of a sensor
fusion system can be difficult to assess.
In this study, a full-factorial experimental evaluation of a sensor fusion system based
on a real vehicle was performed. …

Learning Representations For Human Identification, Sinan Sabri

Graduate Theses, Dissertations, and Problem Reports (ETD)

Long-duration visual tracking of people requires the ability to link track snippets (a.k.a. tracklets) based on the identity of people. In lack of the availability of motion priors or hard biometrics (e.g., face, fingerprint, or iris), the common practice is to leverage soft biometrics for matching tracklets corresponding to the same person in different sightings. A common choice is to use the whole-body visual appearance of the person, as determined by the clothing, which is assumed to not change during tracking. The problem is challenging because distinct images of the same person may look very different, since no restrictions are …

Face Recognition With Attention Mechanisms, Qiangchang Wang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Face recognition has been widely used in people’s daily lives due to its contactless process and high accuracy. Existing works can be divided into two categories: global and local approaches. The mainstream global approaches usually extract features on whole faces. However, global faces tend to suffer from dramatic appearance changes under the scenarios of large pose variations, heavy occlusions, and so on. On the other hand, since some local patches may remain similar, they can play an important role in such scenarios. Existing local approaches mainly rely on cropping local patches around facial landmarks and then extracting corresponding local representations. …

Face Representation Learning And Its Applications: From Image Editing To 3d Avatar Animation, Xudong Liu

Graduate Theses, Dissertations, and Problem Reports (ETD)

Face representation learning is one of the most popular research topics in the computer vision community, as it is the foundation of face recognition and face image generation. Numerous representation learning frameworks have been integrated into applications in daily life, such as face recognition, image editing, and face tracking. Researchers have developed advanced algorithms for face recognition with successful commercial productions, for example, FaceID on the smartphone. The performance record on face recognition is constantly updated and becoming saturated with the help of large-scale datasets and advanced computational resources. Thanks to the robust representation in face recognition, in this dissertation, …

System Development Of An Unmanned Ground Vehicle And Implementation Of An Autonomous Navigation Module In A Mine Environment, Jonas Amoama Bredu Jnr

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine …

Incentive Analysis Of Blockchain Technology, Rahul Reddy Annareddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Blockchain technology was invented in the Bitcoin whitepaper released in 2008. Since then, several decentralized cryptocurrencies and applications have become mainstream. There has been an immense amount of engineering effort put into developing blockchain networks. Relatively few projects backed by blockchain technology have succeeded and maintained a large community of developers, users, and customers, while many popular projects with billions of dollars in funding and market capitalizations have turned out to be complete scams.

This thesis discusses the technological innovations introduced in the Bitcoin whitepaper and the following work of the last fifteen years that has enabled blockchain technology. A …

Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon

Publications

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were …

A Graph-Based Approach To Boundary Estimation With Mobile Sensors, Sean Onufer Stalley, Dingyu Wang, Gautam Dasarathy, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

We consider the problem of adaptive sampling for boundary estimation, where the goal is to identify the two dimensional spatial extent of a phenomenon of interest. Motivated by applications in estimating the spread of wildfires with a mobile sensor, we present a novel graph-based algorithm that is efficient in both the number of samples taken and the distance traveled. The key idea behind our approach is that by sampling locations close to known cut edges (edges whose vertices lie on opposite sides of the boundary), we can reliably find additional cut edges. Our approach repeats this process of using the …

Leveraging Machine Learning For Detecting Iot-Based Interference In Operational Wifi Networks, Josh Pulse

Honors Program Theses

IoT (Internet of Things) devices have become increasingly popular in recent years while WiFi continues to serve as primary network provider indoors. With the advancements in technology, the networks of IoT devices continue to weave closely with indoor WiFi network deployments. Both kinds of these networks primarily operate in 2.4 GHz ISM Band (though latest WiFi standards can operate in 5 GHz and 60 GHz bands, too). With the multitude of tiny IoT sensors being deployed indoors alongside operational WiFi networks, severe interference scenarios cannot be ruled out. As a result of this interference, performance of WiFi networks is bound …

Using Satellite Images Datasets For Road Intersection Detection In Route Planning, Fatmaelzahraa Eltaher, Susan Mckeever, Ayman Taha, Jane Courtney

Datasets

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the …

Computer Vision Based Classification Of Fruits And Vegetables For Self-Checkout At Supermarkets, Khurram Hameed

Theses: Doctorates and Masters

The field of machine learning, and, in particular, methods to improve the capability of machines to perform a wider variety of generalised tasks are among the most rapidly growing research areas in today’s world. The current applications of machine learning and artificial intelligence can be divided into many significant fields namely computer vision, data sciences, real time analytics and Natural Language Processing (NLP). All these applications are being used to help computer based systems to operate more usefully in everyday contexts. Computer vision research is currently active in a wide range of areas such as the development of autonomous vehicles, …

A Risk-Averse Mechanism For Suicidality Assessment On Social Media, Ramit Sawhney, Atula Tejaswi Neerkaje, Manas Gaur

Publications

Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% …

Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …

Wise Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study In Suicidality Diagnosis, Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Sanjay Chandrasekar, Amit Sheth

Publications

The COVID-19 Pandemic has highlighted the gap between the number of mental health care seekers and care providers. Netizens have taken to internet-based platforms such as Reddit to express their experiences. Mental illness diagnosis processes have clinically accepted causal interpretations and semantics. Curiously, mental illness diagnosis accuracy is low relative to similar well-studied illnesses. Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced (WISE) causal models, inspired by the wisdom of the crowd idea that learns from a collective agreement among causal models and their semantics for mental illness diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline …

Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Publications

Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …

การพัฒนาเครื่องมืออัตโนมัติสำหรับสร้างแบบจำลองความสัมพันธ์ของส่วนต่อประสานโปรแกรมประยุกต์เว็บเซอร์วิสแบบเรสต์ฟูล, วิภาดา กลึงเทศ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในอุตสาหกรรมซอฟต์แวร์ นิยมนำซอฟต์แวร์กลับมาใช้ใหม่เป็นจำนวนมาก เนื่องจากเป็นการลดต้นทุนในการพัฒนาซอฟต์แวร์ เอกสารต่าง ๆ ในการพัฒนาระบบจึงมีความสำคัญในการอ้างอิง งานวิจัยนี้ ให้ความสนใจกับแผนภาพยูเอ็มแอลที่เป็นส่วนหนึ่งของเอกสารส่วนต่อประสานโปรแกรมประยุกต์ จึงได้นำเสนอการออกแบบ และพัฒนาเครื่องมืออัตโนมัติสำหรับการสร้างแบบจำลองความสัมพันธ์ของส่วนต่อประสานโปรแกรมประยุกต์เว็บเซอร์วิสแบบเรสต์ฟูลโดยใช้เครื่องมือเสริม PlantUML ที่เป็นเครื่องมือเสริมสำหรับการสร้างแผนภาพ ซึ่งงานวิจัยนี้ได้นำเครื่องมือเสริม PlantUML มาสร้างแผนภาพแบบจำลองความสัมพันธ์ระหว่างคอนโทรลเลอร์ เมธอด และคุณลักษณะภายในของพารามิเตอร์ ที่นำเสนอในรูปแบบของแผนภาพยูเอ็มแอล ดังนั้น เมื่อนำเครื่องมือที่พัฒนาขึ้นไปประยุกต์ใช้ จะช่วยให้ปรับปรุงเอกสารส่วนต่อประสานโปรแกรมประยุกต์ได้ง่ายยิ่งขึ้น และยังช่วยให้เอกสารตรงกับรหัสต้นฉบับ จากการประยุกต์ใช้เครื่องมือกับโครงการ ทำให้ได้ผลลัพธ์ของความถูกต้องเป็น 100%

Improving Feature Learning Capability And Interpretability Of Unsupervised Neural Networks, Chathurika S. Wickramasinghe Brahmana

Theses and Dissertations

The motivation for this dissertation is two-prong. Firstly, the current state of machine learning imposes the need for unsupervised Machine Learning (ML). Secondly, once such models are developed, a deeper understanding of ML models is necessary for humans to adapt and use such models.

Real-world systems generate massive amounts of unlabeled data at rapid speed, limiting the usability of state-of-the-art supervised machine learning approaches. Further, the manual labeling process is expensive, time-consuming, and requires the expertise of the data. Therefore, the existing supervised learning algorithms are unable to take advantage of the abundance of real-world unlabeled data. Thus, relying on …

Continual Learning From Stationary And Non-Stationary Data, Lukasz Korycki

Theses and Dissertations

Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals.

Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty …