Engineering Commons^™

Uncovering And Mitigating Spurious Features In Domain Generalization, Saeed Karimi, Hamdi̇ Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Domain generalization (DG) techniques strive to attain the ability to generalize to an unfamiliar target domain solely based on training data originating from the source domains. Despite the increasing attention given to learning from multiple training domains through the application of various forms of invariance across those domains, the enhancements observed in comparison to ERM are nearly insignificant under specified evaluation rules. In this paper, we demonstrate that the disentanglement of spurious and invariant features is a challenging task in conventional training since ERM simply minimizes the loss and does not exploit invariance among domains. To address this issue, we …

Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation …

Ai And 6g Into The Metaverse: Fundamentals, Challenges And Future Research Trends, Muhammad Zawish, Fayaz Ali Dharejo, Sunder Ali Khowaja, Saleem Raza, Steven Davy, Kapal Dev, Paolo Bellavista

Articles

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

Precision Spraying Using Variable Time Delays And Vision-Based Velocity Estimation, Paolo Rommel Sanchez, Hong Zhang

Henry M. Rowan College of Engineering Faculty Scholarship

Traditionally, precision farm equipment often relies on real-time kinematics and global positioning systems (RTK-GPS) for accurate position and velocity estimates. This approach proved effective and widely adopted in developed regions where RTK-GPS satellite and base station availability and visibility are not limited. However, RTK-GPS signal can be limited in farm areas due to topographic and economic constraints. Thus, this study developed a precision sprayer that estimated the travel velocity locally by tracking the relative motion of plants using a deep-learning-based machine vision system. Sprayer valves were then controlled by variable time delay (VTD) queuing and dynamic filtering. The proposed velocity …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Detection, Tracking, And Classification Of Aircraft And Birds From Multirotor Small Unmanned Aircraft Systems, Chester Valentine Dolph

Electrical & Computer Engineering Theses & Dissertations

The ability for small Unmanned Aircraft Systems (sUAS) to safely operate beyond visual line of sight (BVLOS) is of great interest to governments, businesses, and scientific research. One critical element for sUAS to operate BVLOS is the capability to avoid other air traffic. While many aircraft will be cooperative and broadcast their locations using Automatic Dependent Surveillance Broadcast (ADS-B), it is expected that many aircraft will remain non-cooperative – meaning they do not communicate position or flight plan to other aircraft. Avoiding mid-air collisions with non-cooperative aircraft is a critical limitation to widespread sUAS flying BVLOS. Examples of non-cooperative traffic …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

Automatic Testing Of Organic Strain Gauge Tactile Sensors., Brian P. Goulet

Electronic Theses and Dissertations

Human-Robot Interaction is a developing field of science, that is posed to augment everything we do in life. Skin sensors that can detect touch, temperature, distance, and other physical interaction parameters at the human-robot interface are very important to enhancing the collaboration between humans and machines. As such, these sensors must be efficiently tested and characterized to give accurate feedback from the sensor to the robot. The objective of this work is to create a diversified software testing suite that removes as much human intervention as possible. The tests and methodology discussed here provide multiple realistic scenarios that the sensors …

Intra-Hour Solar Forecasting Using Cloud Dynamics Features Extracted From Ground-Based Infrared Sky Images, Guillermo Terrén-Serrano

Electrical and Computer Engineering ETDs

Due to the increasing use of photovoltaic systems, power grids are vulnerable to the projection of shadows from moving clouds. An intra-hour solar forecast provides power grids with the capability of automatically controlling the dispatch of energy, reducing the additional cost for a guaranteed, reliable supply of energy (i.e., energy storage). This dissertation introduces a novel sky imager consisting of a long-wave radiometric infrared camera and a visible light camera with a fisheye lens. The imager is mounted on a solar tracker to maintain the Sun in the center of the images throughout the day, reducing the scattering effect produced …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …

Heterogeneous Collaborative Mapping For Autonomous Mobile Systems, Sooraj Sunil

Electronic Theses and Dissertations

An accurate map of the environment is essential for autonomous robot navigation. During collaborative simultaneous localization and mapping, the individual robots usually represent the environment as probabilistic occupancy grid maps. These maps can be exchanged among robots and fused to reduce the overall exploration time, which is the main advantage of the collaborative systems. Such fusion is challenging due to the unknown initial correspondence problem. This thesis presents a novel feature-based map fusion approach through detecting, describing, and matching geometrically consistent features present in the overlapping region between the maps. The main drawback of usual feature-based approaches is the incapability …

Ai-Driven Automated Medical Imaging Analysis, Jingya Liu

Dissertations and Theses

Medical imaging has been applied widely in many clinical diagnoses to detect and differentiate abnormalities by revealing the internal structure of the human body at normal anatomical and physiological levels. Manual analyzing medical images demands attention and is time-consuming, requiring well-trained expertise. The speed, fatigue, and experience may limit the diagnostic performance, leading to delays and even false diagnoses that significantly impact patient treatment. Therefore, accurate systematic systems based on medical image analysis are crucial for timely clinical diagnosis.

This dissertation focuses on advancing automatic computer-aided diagnosis systems to detect cancer, assisting radiologists with early intervention to improve survival rates. …

Lapnitor: A Web Service That Protects Your Laptop From Theft., Michael Ameteku

Williams Honors College, Honors Research Projects

Laptop theft is an issue worldwide. According to an article from 2018, Security Boulevard stated that a laptop is stolen every 53 seconds. Using a laptop's camera, we can monitor the surroundings of the laptop and reduce a laptop's probability of being stolen. According to the University of Pittsburgh, a laptop has a 1-in- 10 chance of being stolen and nearly half of these thefts occur in offices or classrooms. These thefts mostly occur when a laptop owner leaves their device unattended for a certain period of time to maybe go visit the restroom or attend to a call when …

Magic: The Gathering Card Virtualizer, Vincent Garbonick, Jacen C. Conlan, Jaret A. Varn

Williams Honors College, Honors Research Projects

Any well-versed Magic: The Gathering (MTG) player or collector knows how difficult it can be to keep track of all cards in their collection. Some spend hours searching for that one specific card, and others are constantly scouring the internet for how much their collection costs. However, this issue does not only affect casual fans. Resale companies spend hours a day determining the costs of cards, and tournament judges painstakingly check players’ decks to ensure they are not cheating. To assist with these struggles, the design team proposed to create the MTG Card Virtualizer. This device scans MTG playing cards …

Towards Semantic Integration Of Machine Vision Systems To Aid Manufacturing Event Understanding, Kaishu Xia, Clint Saidy, Max Kirkpatrick, Noble Anumbe, Amit Sheth, Ramy Harik

Publications

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

A Brief Bibliometric Survey On Night Vision Bot Using Dynamic Ir And Object Detection, Devesh Abhyankar Mr., Gurumoorty Suresh Mr., Hrithik Sambhaji Karjule Mr., Parth Bhardwaj Mr., Harish Muleva Mr., Anurag Mahajan Dr.

Library Philosophy and Practice (e-journal)

This study aims to analyse the work done in the field of Night Vision Robots using IR and Object Detection from 2011 to 2021, using the bibliometric methods. This paper presents a Scopus database review on "Night Vision Bot using Dynamic IR and Object Detection". The necessity for doing this bibliometric survey is that to know how the technology in the field of mobile robotics and night vision, as well as to object detection, has evolved over the years. This paper shows the importance of Night Vision Robot from the year 2011 and continued up to 2021 April. The database …

Learning To Detect Pedestrian Flow In Traffic Intersections From Synthetic Data, Abhijit Baul

Theses and Dissertations

Detecting pedestrian flow in different directions at at traffic-intersection has always been a challenging task. Challenges include different weather conditions, different crowd densities, occlusions, lack of available data, and so on. The emergence of deep learning and computer vision algorithms has shown promises to deal with these problems. Most of the recent works only focus on either detecting combined pedestrian flow or counting the total number of pedestrians. In this work, we have tried to detect not only combined pedestrian flow but also pedestrian flow indifferent directions. Our contributions are, 1) we are introducing a synthetic pedestrian dataset that we …

Using Motion Capture And Augmented Reality To Test Aar With Boom Occlusion, Vincent J. Bownes

Theses and Dissertations

The operational capability of drones is limited by their inability to perform aerial refueling. This can be overcome by automating the process with a computer vision solution. Previous work has demonstrated the feasibility of automated aerial refueling (AAR) in simulation. To progress this technique to the real world, this thesis conducts experiments using real images of a physical aircraft replica and a motion capture system for truth data. It also compares the error between the real and virtual experiments to validate the fidelity of the simulation. Results indicate that the current technique is effective on real images and that the …

Methods For Object Tracking With Machine Vision, Zachary Simon Stamler

Dissertations and Theses

As machine learning and deep learning systems continue to find applications in science and engineering, the problem of providing these systems with high-quality data continues to increase in importance. Many of these systems utilize machine vision as their primary source of information, and in order to maximally leverage their abilities it is important to be able to provide them with high quality, accurate data. Unfortunately, many sets of tracking data extracted from video suffer from the problem of missing frames, which can arise from a multitude of causes depending on the system. These missing frames can result in confusion between …

‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette

Electrical and Computer Engineering Publications

Hand telerehabilitation currently has limitations for accurate and remote assessment of range of motion (ROM) in small finger joints. ‘DIGITS’ application utilises the front smartphone camera to measure finger ROM in a reliable and rapid assessment protocol. Our initial beta-phase testing examined the consistency of our software measurements to in-person goniometry. 6 to 9 degrees of difference existed between the smartphone application recorded data versus the in-person measurements. This range is within acceptable 7 to 9 degree tolerance for interrater goniometry measurements. The effect of environmental factors such as hand distance, lightings and hand orientation was evaluated. The intraclass correlation …

A Deep Transfer Learning Based Model For Automatic Detection Of Covid-19from Chest X-Rays, Prateek Chhikara, Prakhar Gupta, Prabhjot Singh, Tarunpreet Bhatia

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest …

Feature Detection For Hand Hygiene Stages, Rashmi Bakshi, Jane Courtney, Damon Berry, Graham Gavin

Articles

The process of hand washing involves complex hand movements. There are six principal sequential steps for washing hands as per the World Health Organization (WHO) guidelines. In this work, a detailed description of an aluminum rig construction for creating a robust hand-washing dataset is discussed. The preliminary results with the help of image processing and computer vision algorithms for hand pose extraction and feature detection such as Harris detector, Shi-Tomasi and SIFT are demonstrated. The hand hygiene pose- Rub hands palm to palm was captured as an input image for running all the experiments. The future work will focus upon …

Bibliometric Review On Image Based Plant Phenotyping, Shrikrishna Ulhas Kolhar, Jayant Jagtap

Library Philosophy and Practice (e-journal)

Plant phenotyping is a quantitative description of structural, physiological and temporal traits of plants resulting from interaction of plant genotypes with the environment. A rapid development is in progress in the field of image-based plant phenotyping. Plant phenotyping has wide range of applications in plant breeding research, plant growth prediction, biotic and abiotic stress analysis, crop management and early disease detection. The main motive is to provide detailed bibliometric review in order to know the available literature and current research trends in the area of plant phenotyping using plant images. The bibliometric analysis is primarily based on Scopus, web of …

Atmospheric Turbulence Distortion In Video: Restoration Utilizing Sparse Analysis, Benjamin J. Sanda

Dissertations

The removal of atmospheric turbulence (AT) distortion in long range imaging is one of the most challenging areas of research in imaging processing with an immediate need for solutions in several applications such as in military and transportation systems. AT exacerbates distortion due to non-linear geometric blur and scintillations in long-distance images and videos, severely reducing image quality and information interpretation. AT negatively impacts both human and computer vision systems, compromising visibility essential for accurate object identification and tracking.

In this dissertation, a novel sparse analysis framework is developed to address efficient AT blur and scintillation removal in video. Operating …