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Articles 1 - 6 of 6
Full-Text Articles in Electrical and Computer Engineering
Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii
Neural Networks For Improved Signal Source Enumeration And Localization With Unsteered Antenna Arrays, John T. Rogers Ii
Theses and Dissertations
Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case …
Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman
Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman
UNLV Theses, Dissertations, Professional Papers, and Capstones
The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.
Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …
An Automated, Deep Learning Approach To Systematically & Sequentially Derive Three-Dimensional Knee Kinematics Directly From Two-Dimensional Fluoroscopic Video, Viet Dung Nguyen
An Automated, Deep Learning Approach To Systematically & Sequentially Derive Three-Dimensional Knee Kinematics Directly From Two-Dimensional Fluoroscopic Video, Viet Dung Nguyen
Doctoral Dissertations
Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient …
Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi
Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi
All Theses
The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …
Deep Face Morph Detection Based On Wavelet Decomposition, Poorya Aghdaie
Deep Face Morph Detection Based On Wavelet Decomposition, Poorya Aghdaie
Graduate Theses, Dissertations, and Problem Reports
Morphed face images are maliciously used by criminals to circumvent the official process for receiving a passport where a look-alike accomplice embarks on requesting a passport. Morphed images are either synthesized by alpha-blending or generative networks such as Generative Adversarial Networks (GAN). Detecting morphed images is one of the fundamental problems associated with border control scenarios. Deep Neural Networks (DNN) have emerged as a promising solution for a myriad of applications such as face recognition, face verification, fake image detection, and so forth. The Biometrics communities have leveraged DNN to tackle fundamental problems such as morphed face detection. In this …
Establishing The Foundation To Robotize Complex Welding Processes Through Learning From Human Welders Based On Deep Learning Techniques, Rui Yu
Theses and Dissertations--Electrical and Computer Engineering
As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).
This …