Computer Sciences Commons^™

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)

Electrical & Computer Engineering Faculty Publications

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides

Electrical & Computer Engineering Faculty Publications

This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.

Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …

Social-Engineering, Bio-Economies, And Nation-State Ontological Security: A Commentary, Brandon Griffin, Keitavius Alexander, Xavier-Lewis Palmer, Lucas Potter

Electrical & Computer Engineering Faculty Publications

Biocybersecurity is an evolving discipline that aims to identify the gaps and risks associated with the convergence of Biology (the science of life and living organisms) and cybersecurity (the science, study, and theory of cyberspace and cybernetics) to protect the bioeconomy. The biological industries’ increased reliance on digitization, automation, and computing power has resulted in benefits for the scientific community, it has simultaneously multiplied the risk factors associated with industrial espionage and the protection of data both commercial and proprietary. The sensitive and potentially destructive power of this data and its access inherently poses a risk to the national and …

Implications Of Cyberbiosecurity In Advanced Agriculture, Simone Stephen, Keitavius Alexander, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, …

A Reflection Of Typology And Verification Flaws In Consideration Of Biocybersecurity/Cyberbiosecurity: Just Another Gap In The Wall, Luke Potter, Kim Mossberg, Xavier Palmer

Electrical & Computer Engineering Faculty Publications

Verification is central to any process in a functional and enduring cyber-secure organization. This verification is how the validity or accuracy of a state of being is assessed (Schlick, 1936; Balci, 1998). Conversely, breakdown in verification procedures is core to the interruption of normal operations for an organization. A key problem for organizations that utilize biology as an interlock within their systems is that personnel lack sufficient ability to verify all practically relevant biological information for procedures such as a nurse logging a blood draw, or a molecular biology technician preparing agar to culture microbes for study. This has several …

Understanding And Assessing Demographic (In)Equity Resulting From Extreme Heat Exposure Due To Lack Of Tree Canopies In Norfolk, Va Using Agent-Based Modeling, Virginia Zamponi, Kevin O'Brien, Erik Jensen, Brandon Feldhaus, Russell Moore, Christopher J. Lynch, Ross Gore

VMASC Publications

Prolonged exposure to extreme heat can result in illness and death. In urban areas of dense concentrations of pavement, buildings, and other surfaces that absorb and retain heat, extreme heat conditions can arise regularly and create harmful environmental exposures for residents daily during certain parts of the year. Tree canopies provide shade and help to cool the environment, making mature trees with large canopies a simple and effective way to reduce urban heat. We develop a demographically representative 1 (agent): 1 (person) agent-based model to understand the extent to which different demographics of residents in Norfolk, VA are equitably shaded …

A Structured Narrative Prompt For Prompting Narratives From Large Language Models: Sentiment Assessment Of Chatgpt-Generated Narratives And Real Tweets, Christopher J. Lynch, Erik J. Jensen, Virginia Zamponi, Kevin O'Brien, Erika Frydenlund, Ross Gore

VMASC Publications

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents' perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt …

Efficient Maritime Object Detection And Validation For Enhancing Safety Of Uncrewed Marine Systems, Ahmed Saglam, Yiannis Papelis

VMASC Publications

Safe operation of uncrewed maritime systems is a major concern in the presence of other vehicles or obstacles. Typically, perception algorithms utilize sensor data to identify obstacles that must be avoided, and AI algorithms are used to interpret raw sensor data for use in navigation and object avoidance algorithms. However, perception algorithms are typically computationally expensive. In this paper, we present an efficient method for detecting obstacles using raw lidar data in the form of range or Point Cloud, employing computationally efficient techniques that do not depend on trained models or AI matching. The approach
converts the sensor readings into …

Energy-Efficient Multi-Rate Opportunistic Routing In Wireless Mesh Networks, Mohammad Ali Mansouri Khah, Neda Moghim, Nasrin Gholami, Sachin Shetty

VMASC Publications

Opportunistic or anypath routing protocols are focused on improving the performance of traditional routing in wireless mesh networks. They do so by leveraging the broadcast nature of the wireless medium and the spatial diversity of the network. Using a set of neighboring nodes, instead of a single specific node, as the next hop forwarder is a crucial aspect of opportunistic routing protocols, and the selection of the forwarder set plays a vital role in their performance. However, most opportunistic routing protocols consider a single transmission rate and power for the nodes, which limits their potential. To address this limitation, this …

Accelerating A Software Defined Satnav Receiver Using Multiple Parallel Processing Schemes, Logan Reich, Sanjeev Gunawardena, Michael Braasch

Faculty Publications

Excerpt: Satnav SDRs present many benefits in terms of flexibility and configurability. However, due to the high bandwidth signals involved in satnav SDR processing, the software must be highly optimized for the host platform in order to achieve acceptable runtimes. Modules such as sample decoding, carrier replica generation, carrier wipeoff, and correlation are computationally intensive components that benefit from accelerations.

Directional Speaker Poster, Eugene Ng, Bryan Wong, Ruhaan Das

Student Works

Changi Airport is set to expand with a new terminal, Terminal 5. Currently, many of the airport's processes are manual, requiring a high dependence on staff. This proposal aims to incorporate automation and AI for a smoother passenger experience.

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Complex Systems Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Multivariate Regression And Variance In Concrete Curing Methods: Strength Prediction With Experiments, Haiyan Sally Xie, Sai Ram Gandla, Owen Shi, Pranshoo Solanki

Faculty Publications – Technology

Because concrete strengths and quality are affected by various factors, multivariate regression models are often used to analyze the differences between predicted and target outputs. However, the variableness of a predicted output and how individual input parameters affect prediction reliabilities are still uncertain in practical applications, especially for the prediction of compressive strengths of concrete. This study aims to develop multivariate models for predicting concrete strengths and providing the variance analysis of prediction results by comparisons with experiment outcomes. First, this paper provides an in-depth examination of established variance analysis methods in the context of commonly used multivariate regression models. …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Moment-Based Reinforcement Learning For Ensemble Control, Yao-Chi Yu, Vignesh Narayanan, Jr-Shin Li

Publications

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics are robustly compensated. In …

A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung

Research outputs 2022 to 2026

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …

Physical Layer Authenticated Image Encryption For Iot Network Based On Biometric Chaotic Signature For Mpfrft Ofdm System, Esam A. A. Hagras, Saad Aldosary, Haitham Khaled, Tarek Hassan

Research outputs 2022 to 2026

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform–Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which …

A Novel Approach To Detecting And Mitigating Keyloggers, Damilola Osedumbi Elelegwu

College of Graduate Studies: Theses & Dissertations

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research work introduces a novel browser extension designed to thwart keylogger spyware attacks effectively. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any …

Detection And Localization Of Data Forgery Attacks In Automatic Generation Control, Fengli Zhang, Yatish R. Dubasi, Wei Bao, Qinghua Li

Electrical Engineering Faculty Publications and Presentations

Automatic Generation Control (AGC) is a key control system to maintain the power system’s balance between load and supply by maintaining its frequency in a specific range. It collects the tie-line power flow and frequency measurements of each control area to calculate the Area Control Error (ACE) and then adjusts power generation based on the calculated ACE. However, malicious frequency or tie-line power flow measurements can be injected and then AGC is misled to make false power generation adjustments which will harm power system operations. Such attacks can be carefully designed to pass the power system’s existing bad data detection …

Neuromorphic Computing Applications In Robotics, Noah Zins

Dissertations, Master's Theses and Master's Reports

Deep learning achieves remarkable success through training using massively labeled datasets. However, the high demands on the datasets impede the feasibility of deep learning in edge computing scenarios and suffer from the data scarcity issue. Rather than relying on labeled data, animals learn by interacting with their surroundings and memorizing the relationships between events and objects. This learning paradigm is referred to as associative learning. The successful implementation of associative learning imitates self-learning schemes analogous to animals which resolve the challenges of deep learning. Current state-of-the-art implementations of associative memory are limited to simulations with small-scale and offline paradigms. Thus, …

การจำลองกำหนดการเดินเรือโดยใช้ไทม์ออโตมาตาแบบที่มีความน่าจะเป็น, รัตชนก เธียรปุญญธนากุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในอุตสาหกรรมการขนส่งทางทะเลที่มีการจัดการด้านความเสี่ยงในการเกิดความล่าช้าในการเดินเรือตามกำหนดเป็นปัญหาที่ซับซ้อน และเกิดความเสี่ยงและเกิดค่าเสียหายผลจากถึงกำหนดล่าช้าที่จะต้องประสบกับค่าใช้จ่ายของต้นทุนที่สูงขึ้นจากปัญหาความล่าช้า จึงให้ความสนใจที่ปัญหาเหล่านี้อยู่ที่การให้ความสำคัญกับความน่าจะเป็นจากความไม่แน่นอนและเวลาในการเดินเรือ ซึ่งเป็นปัจจัยที่สำคัญในการวางแผนและจัดการตารางเดินเรือให้เหมาะสมและมีประสิทธิภาพและเหมาะสมกับเงื่อนไขและปัจจัยที่แปรผันในอุตสาหกรรมการขนส่งทางทะเล งานวิจัยนี้ จึงเล็งเห็นความสำคัญของการนำไทม์ออโตมาตาแบบที่มีความน่าจะเป็น Probabilistic Timed Automata (PTA) มาใช้ในการจำลองกำหนดการตารางเดินเรือ (Vessel Scheduling) เพื่อช่วยให้สามารถจำลองและประเมินผลของปัจจัยต่าง ๆ ที่ส่งผลต่อการเดินเรือได้อย่างเป็นระบบ และการช่วยให้ผู้วางแผนสามารถทำการปรับปรุงและวิเคราะห์ตารางเดินเรือ โดยมีผลจากการปรับปรุงค่าความนาจะเป็นและทำการทวนสอบผลที่ได้จากสถิติข้อมูลที่ใช้จำลองไม่เกิน 10% ผ่านการเขียนโปรแกรมด้วยภาษา PRISM โดยใช้ PRISM Model Checker โดยเครื่องมือสามารถจำลองพฤติกรรมการเดินเรือตามแบบจำลอง PTA ที่ออกแบบไว้ โดยคำนึงถึงปัจจัยของความน่าจะเป็นที่ส่งผลให้เกิดความล่าช้าและทำการทวนสอบด้วยสูตร PCTL ได้

An Optimized And Scalable Blockchain-Based Distributed Learning Platform For Consumer Iot, Zhaocheng Wang, Xueying Liu, Xinming Shao, Abdullah Alghamdi, Md. Shirajum Munir, Sujit Biswas

School of Cybersecurity Faculty Publications

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing …

Robustembed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training, Javad Asl, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have demonstrated their exceptional performance across a wide range of natural language processing tasks. The utilization of PLM-based sentence embeddings enables the generation of contextual representations that capture rich semantic information. However, despite their success with unseen samples, current PLM-based representations suffer from poor robustness in adversarial scenarios. In this paper, we propose RobustEmbed, a self-supervised sentence embedding framework that enhances both generalization and robustness in various text representation tasks and against diverse adversarial attacks. By generating high-risk adversarial perturbations to promote higher invariance in the embedding space and leveraging the perturbation within a novel contrastive …

Evaluating Ai Sentiment Analysis, Aakriti Shah

Honors Program Theses

This paper presents a comparative analysis of human and AI performance on a sentiment analysis task involving the coding of qualitative data from community program transcripts. The results demonstrate promising but imperfect agreement between two AI models, Claude and Bing, versus three human annotators and one expert annotator using the Community Capitals framework categories. While both models achieved fair alignment with human judgment, confusion patterns emerged involving metaphorical language and text overlapping multiple categories. The findings provide a case study for benchmarking conversational AI systems against human baselines to reveal limitations and target improvements. Key gaps center around distinguishing between …

Exploration Of Robotics Need In The Medical Field And Robotic Arm Operation Via Glove Control, Aditi Vijayvergia

Master’s Theses

This thesis project is an exercise in getting hands-on experience in redesigning and modifying a robotic system. It also involves understanding the current need for robotic applications in hospital settings. To achieve the above, a thorough literature review of the current state of robotics in a hospital setting was conducted. Moreover, a number of interviews with medical care professionals were completed. Three main themes were obtained from the literature review and five main themes were obtained from the interviews which will be presented in this thesis report. The next phase of the project involved redesigning a system that is composed …