Other Electrical and Computer Engineering Commons^™

Variable Autonomy Assignment Algorithms For Human-Robot Interactions., Christopher Kevin Robinson

Electronic Theses and Dissertations

As robotic agents become increasingly present in human environments, task completion rates during human-robot interaction has grown into an increasingly important topic of research. Safe collaborative robots executing tasks under human supervision often augment their perception and planning capabilities through traded or shared control schemes. However, such systems are often proscribed only at the most abstract level, with the meticulous details of implementation left to the designer's prerogative. Without a rigorous structure for implementing controls, the work of design is frequently left to ad hoc mechanism with only bespoke guarantees of systematic efficacy, if any such proof is forthcoming at …

Distributed Neural Network Based Architecture For Dddos Detection In Vehicular Communication Systems, Nicholas Jaton

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

With the continued development of modern vehicular communication systems, there is an ever growing need for cutting edge security in these systems. A misbehavior detection systems (MDS) is a tool developed to determine if a vehicle is being attacked so that the vehicle can take steps to mitigate harm from the attacker. Some attacks such as distributed denial of service (DDoS) attacks are a concern for vehicular communication systems. During a DDoS attack, multiple nodes are used to flood the target with an overwhelming amount of communication packets. In this thesis, we investigated the current MDS literature and how it …

An Improved Earned Value Management Method Integrating Quality And Safety, Brian Briggs

LSU Doctoral Dissertations

The construction industry invests significant time and money to improve quality and safety while reducing cost and schedule impacts. The industry has a sincere desire to improve construction project management methods to improve efficiency. Historically, quality and safety underperformances result from undermanaged quality control and safety activities. The cost and schedule impacts associated with poor quality work have always had an impact on construction operations. The unprecedented challenges and uncertainties of COVID-19 highlighted the need to improve the Earned Value Management (EVM) method within construction to reflect these quality and safety activities. The central goal of this dissertation is to …

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 …

Pier Ocean Pier, Brandon J. Nowak

Computer Engineering

Pier Ocean Peer is a weatherproof box containing a Jetson Nano, connected to a cell modem and camera, and powered by a Lithium Iron Phosphate battery charged by a 50W solar panel. This system can currently provide photos to monitor the harbor seal population that likes to haul out at the base of the Cal Poly Pier, but more importantly it provides a platform for future expansion by other students either though adding new sensors directly to the Jetson Nano or by connecting to the jetson nano remotely through a wireless protocol of their choice.

Ai Alzheimer's Early Detection Via Mri Processing, Luke Frey, Siddharth Sharma, Arti Jain

Electrical Engineering

Alzheimer’s Disease (AD) is an irreversible, progressive brain disorder that impairs memory, thinking, and language. Known as the most common form of dementia, AD is the 6th leading cause of death in the United States. It is estimated that currently, nearly 6 million Americans suffer from AD and moreover, the prevalence of AD is projected to grow to 13.8 million being diagnosed by 2050. Considering these projections, hospitals are expected to be diagnosing nearly half a million patients a year. This high volume will lead to technological growth within the diagnosis process along with more effective treatments.

As of now, …

Pilltank, Lucas Chang, Hayden Tam, Aaron Teh, Krista Round

Electrical Engineering

Imagine an elderly family member, going through their daily routine of taking their pills. They find their pill box; however, they are having trouble identifying all the pills in there. Is there a name on the tablet? Can they read what it says? Do they just trust that the medication in their box is correct? How can they properly take care of themselves if they can not even confirm that what they are taking is the right medication? To combat this issue that many face, we present PillTank.

To decrease the risk of consuming the wrong medication, PillTank identifies the …

Wildfire Early Detection System (Weds), Mason Mciver, Vincent Liang, Jeanreno Racines

Electrical Engineering

With climate change causing an increase in temperature over the past several decades, wildfires have been burning hotter and moving quicker leaving a trail of destruction in their path. Detecting a wildfire early allows firefighters to respond efficiently and effectively to ensure containment. With the rise of advanced computer vision and algorithms, autonomous systems can be used to monitor and report any fire activity. Having multiple devices spread out across a large area will allow first responders to map out the fire location and track the fire. By utilizing smart technologies, property damage can be minimized and residents living in …

Robot Path Planning System, Filippo Cheein, Tim Berry, Bruce Rogstad, Bob Entezar, Jamari Ducre

Electrical Engineering

The future is autonomous. It is estimated that by 2025 there will be 8 million autonomous cars in the market. Millions more will be autonomous devices employed in homes, malls, offices, and warehouses. There are many aspects of these future devices that are necessary for proper autonomous functionality, with none potentially more critical than the devices’ path planning ability. This project aims to create an effective, reliable, and safe path planning algorithm for the autonomous devices of the future. Indoor autonomous devices, such as warehouse or office robots, benefit from being able to always use the same floor plan rather …

Online Laboratory Course Using Low Tech Supplies To Introduce Digital Logic Design Concepts, Dhanya Nair

Engineering Faculty Articles and Research

This paper describes a Digital Logic Design Laboratory Course developed to engage students with hardware systems within an online setting. This is a junior level core course for students from Computer Science (CS), Computer Engineering (CE) and Electrical Engineering (EE). Hence, the laboratories are designed to provide the hands-on experience of breadboarding, testing and debugging essential to CE and EE while accommodating CS students with no prior hardware experience. Commercially available low-cost electronic trainers (portable workstations) are loaned to the students in addition to basic electronic components. To ensure a strong foundation in debugging, prior to utilizing these workstations, students …

First Order Self-Oscillating Class-D Circuit With Triangular Wave Injection, Matthew J. Carroll

Master's Theses

An investigation into performance improvements to the modulator stage of a class-D amplifier is conducted in this thesis. Two of the standard topologies, namely class-D open-loop pulse-width modulation (PWM), and the improved self-oscillating feedback system are benchmarked against a topology which includes both a hysteretic comparator in a feedback loop and triangle wave injection. Circuit performance is analyzed by comparing how the triangle injection circuit handles known issues with open-loop and self-oscillating circuits. Using this analysis, it is shown that the triangle injection topology offers an improved power supply rejection ratio relative to open-loop PWM and reduces distortion generated by …

An Artificial Neural Network For Bankruptcy Prediction, Walter D. Magdefrau

Master's Theses

Assessing the financial health of organizations remains a topic of great interest to economists, financial institutions, and invested stakeholders. For more than a century, research into financial distress has focused primarily on traditional applications of statistical analysis; however, modern advances in computational efficiency have created a significant opportunity for more sophisticated approaches. This thesis investigates the application of artificial intelligence on company bankruptcy prediction. The proposed neural network model is evaluated using the Polish Companies Bankruptcy dataset and yields a 5-year prediction accuracy of 96.5% and an AUC (area under receiver operating characteristic curve) measure of 92.4%.

Electricity Generation Utilising Solar Energy: A Bibliometric Review And Prospects For Future Research, Ayushi Kamboj, Harikrishnan R

Library Philosophy and Practice (e-journal)

Anthropogenic global warming, deforestation, and resource depletion have been probably the most important challenges affecting the planet today. To address problems, we'll need to make significant modifications to our power connectivity. The author of this paper demonstrates the effectiveness of using sunlight, a green energy source, whose goal is to provide global power for all applications (electricity, transport infrastructure, heat pumps, and several others). As a consequence, energy is important to both the global economy and everyday life. Notwithstanding the increasing demand and productivity, the electrical power grid has held constant throughout the last 20 years. However, the implementation and …

Semantics-Guided Human Motion Modeling In Virtual Reality Environment, Matthew Korban

LSU Doctoral Dissertations

Human Motion Modeling is essential in Computer Animation and Human-Computer Interaction. This dissertation studies how to enhance the speed and robustness of Human Motion Modeling in Virtual Reality (VR) environments. Specifically, we aim to design a pipeline to effectively capture and use semantic action information to guide the motion capturing from users in physical worlds and its transfer onto digital avatars in VR environments. To recognize the user's action, we first proposed a new Dynamic Directed Graph Convolutional Network (DDGCN) to model spatial and temporal features from users' skeletal representations. The DDGCN consists of several dynamic feature modeling modules to …

Owsnet: Towards Real-Time Offensive Words Spotting Network For Consumer Iot Devices, Bharath Sudharsan, Sweta Malik, Peter Corcoran, Pankesh Patel, John G. Breslin, Muhammad Intizar Ali

Publications

Every modern household owns at least a dozen of IoT devices like smart speakers, video doorbells, smartwatches, where most of them are equipped with a Keyword spotting(KWS) system-based digital voice assistant like Alexa. The state-of-the-art KWS systems require a large number of operations, higher computation, memory resources to show top performance. In this paper, in contrast to existing resource-demanding KWS systems, we propose a light-weight temporal convolution based KWS system named OWSNet, that can comfortably execute on a variety of IoT devices around us and can accurately spot multiple keywords in real-time without disturbing the device's routine functionalities.

When OWSNet …

Cognitive Digital Twins For Smart Manufacturing, Muhammad Intizar Ali, Pankesh Patel, John G. Breslin, Ramy Harik, Amit Sheth

Publications

Smart manufacturing or Industry 4.0, a trend initiated a decade ago, aims to revolutionize traditional manufacturing using technology-driven approaches. Modern digital technologies such as the Industrial Internet of Things (IIoT), Big Data Analytics, Augmented/Virtual Reality, and Artificial Intelligence (AI) are the key enablers of new smart manufacturing approaches. The digital twin is an emerging concept whereby a digital replica can be built of any physical object. Digital twins are becoming mainstream; many organizations have started to rely on digital twins to monitor, analyze, and simulate physical assets and processes. The current use of digital twins for smart manufacturing is largely …

A Reconfigurable Stretchable Liquid Metal Antenna, Phase Shifter, And Array For Wideband Applications, David M. Hensley

Electrical and Computer Engineering ETDs

While liquid metals, such as mercury, have been used in electronics for quite some time, the non-toxic gallium based liquid metals have caused an increase in research for liquid metal applications. Some of the potential applications that have been previously presented range from reconfigurable antennas, strain and pressure sensors, and speakers and microphones to name a few. The focus of this work is to provide further research into the use of gallium based liquid metals as a reconfigurable antenna, a phase shifter, and an array. This is done by designing, constructing, and characterizing each of these reconfigurable liquid metal (LM) …

Guest Editorial: Edge Intelligence For Beyond 5g Networks, Yan Zhang, Zhiyong Feng, Hassnaa Moustafa, Feng Ye, Usman Javaid, Chunfen Cui

Electrical and Computer Engineering Faculty Publications

Beyond fifth-generation (B5G) networks, or so-called "6G", is the next-generation wireless communications systems that will radically change how Society evolves. Edge intelligence is emerging as a new concept and has extremely high potential in addressing the new challenges in B5G networks by providing mobile edge computing and edge caching capabilities together with Artificial Intelligence (AI) to the proximity of end users. In edge intelligence empowered B5G networks, edge resources are managed by AI systems for offering powerful computational processing and massive data acquisition locally at edge networks. AI helps to obtain efficient resource scheduling strategies in a complex environment with …

Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal

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

Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised person ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, this thesis proposes a new iterative clustering framework that incorporates (a) two attention architectures that learn …

Classification Of Primary Versus Metastatic Pancreatic Tumor Cells Using Multiple Biomarkers And Whole Slide Imaging, Poupack Pooshang Baghery

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

Pancreatic cancer is a challenging cancer with a high mortality rate and a 5-year survival rate between 2% to 9%. The role of biomarkers is crucial in cancer prognosis, diagnosis, and predicting the possible responses to a specific therapy. The Discovery and development of various types of biomarkers have been studied intensively in the hope of determining the best treatment approaches, better management, and possibly cure of this deadly cancer. However, metastasis, responsible for about 90% of the deaths from cancer, is still poorly understood. A few research that have investigated the expression of a particular biomarker or a panel …