Computer Engineering Commons^™

Evaluating Tlb (Translation Lookaside Buffer) Performance Overhead For Nvm (Non-Volatile Memory) Hybrid System, Xiang Guo

Electronic Theses and Dissertations

As the non-volatile memory (NVM) technology offers near-DRAM performance and near-disk capacity, NVM has emerged as a new storage class. Conventional file systems, designed for hard disk drives or solid-state drives, need to be re-examined or even re-designed for NVM storage. For example, new file systems such as NOVA, HMFS, HMVFS and Ext4-DAX, have been developed and implemented to fully leverage NVM’s characteristics, such as fast fine-grained access. This thesis research uses a variety of I/O workloads to evaluate the performance overhead of the TLB (translation lookaside buffer) in various file systems on emulated NVM storage systems, in which NVM …

Imparting 3d Representations To Artificial Intelligence For A Full Assessment Of Pressure Injuries., Sofia Zahia

Electronic Theses and Dissertations

During recent decades, researches have shown great interest to machine learning techniques in order to extract meaningful information from the large amount of data being collected each day. Especially in the medical field, images play a significant role in the detection of several health issues. Hence, medical image analysis remarkably participates in the diagnosis process and it is considered a suitable environment to interact with the technology of intelligent systems. Deep Learning (DL) has recently captured the interest of researchers as it has proven to be efficient in detecting underlying features in the data and outperformed the classical machine learning …

Automatic Target Recognition With Convolutional Neural Networks., Nada Baili

Electronic Theses and Dissertations

Automatic Target Recognition (ATR) characterizes the ability for an algorithm or device to identify targets or other objects based on data obtained from sensors, being commonly thermal. ATR is an important technology for both civilian and military computer vision applications. However, the current level of performance that is available is largely deficient compared to the requirements. This is mainly due to the difficulty of acquiring targets in realistic environments, and also to limitations of the distribution of classified data to the academic community for research purposes. This thesis proposes to solve the ATR task using Convolutional Neural Networks (CNN). We …

Computational Techniques In Medical Image Analysis Application For White Blood Cells Classification., Omar Dekhil

Electronic Theses and Dissertations

White blood cells play important rule in the human body immunity and any change in their count may cause serious diseases. In this study, a system is introduced for white blood cells localization and classification. The dataset used in this study is formed by two components, the first is the annotation dataset that will be used in the localization (364 images), and the second is labeled classes that will be used in the classification (12,444 images). For the localization, two approaches will be discussed, a classical approach and a deep learning based approach. For the classification, 5 different deep learning …

Past To Present (P2p): Road Thermal Image Colorization, Yuseong Park

Electronic Theses and Dissertations

Thermal image colorization into realistic RGB image is a challenging task. Thermal cameras are easily to detect objects in particular situation (e.g. darkness and fog) that the human eyes cannot detect. However, it is difficult to interpret the thermal image with human eyes. Enhancing thermal image colorization is an important task to improve these areas. The results of the existing colorization method still have color ambiguities, distortion, and blurriness problems. This paper focused on thermal image colorization using pix2pix network architecture based on Generative Adversarial Net (GAN). Pix2pix is a model that transforms thermal image into RGB image, but our …

Semantic Segmentation Using Modified U-Net Architecture For Crack Detection, Michael Sun

Electronic Theses and Dissertations

The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation …

Xylo-Bot: A Therapeutic Robot-Based Music Platform For Children With Autism, Huanghao Feng

Electronic Theses and Dissertations

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills, including motor control, emotional facial expressions, and eye gaze / joint attention. This Ph.D. dissertation focuses on studying the feasibility and effectiveness of using a social robot, called NAO, and a toy music instrument, xylophone, at modeling and improving the social responses and behaviors of children with ASD. In our investigation, we designed an autonomous social interactive music teaching system to fulfill this mission.

A novel modular robot-music teaching system consisting of three modules is presented. Module 1 provides an autonomous self-awareness positioning system for the …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Microgrid-Enabled Reactive Power Support To Enhance Grid Economics, Sarhan Hasan

Electronic Theses and Dissertations

Reactive power plays an essential role in voltage control and stability in electric power systems. Various Volt/VAR techniques are utilized in electric power systems to maintain the voltage profile within defined acceptable limits and accordingly provide reliability and stability. Reactive power has been commonly generated through large-scale synchronous generators or distributed capacitor banks to provide proper transmission and distribution level system management, however, reactive power can be further used as an effective means to reduce total system operation cost. In this dissertation, an optimal reactive power model is proposed to determine the optimal nodal reactive powers that result in the …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

Nyku: A Social Robot For Children With Autism Spectrum Disorders, Dan Stephan Stoianovici

Electronic Theses and Dissertations

The continued growth of Autism Spectrum Disorders (ASD) around the world has spurred a growth in new therapeutic methods to increase the positive outcomes of an ASD diagnosis. It has been agreed that the early detection and intervention of ASD disorders leads to greatly increased positive outcomes for individuals living with the disorders. Among these new therapeutic methods, Robot-Assisted Therapy (RAT) has become a hot area of study. Recent works have shown that high functioning ASD children have an affinity for interacting with robots versus humans. It is proposed that this is due to a less complex set of communication …

Understanding Depression During The Covid-19 Pandemic Through Social Media Data, Nusrat Armin

Electronic Theses and Dissertations

The COVID-19 pandemic has dramatically affected peoples’ daily lives all over theworld - physically, economically, and emotionally. Due to the virus, many people have died, and many hospitalized. A record number of people have lost their job, and many businesses have closed. The global economy is at risk. People are facing new realities of their lives. Studies have shown that the level of depression is three times higher than before this pandemic. Previous studies have shown that people use social media to express their emotions and feelings. The purpose of this study is to understand the depression during this COVID-19 …

Kernel-Controlled Dqn Based Cnn Pruning For Model Compression And Acceleration, Romancha Khatri

Electronic Theses and Dissertations

Apart from the accuracy, the size of convolutional neural networks (CNN) models is another principal factor for facilitating the deployment of models on memory, power and budget constrained devices. However, conventional model compression techniques require human experts to setup parameters to explore the design space which is suboptimal and time consuming. Various pruning techniques are implemented to gain compression, trading off speed and accuracy. Given a CNN model [11], we propose an automated deep reinforcement learning [9] based model compression technique that can effectively turned off kernels on each layer by observing its significance on decision making. By observing accuracy, …

Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman

Electronic Theses and Dissertations

Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). …

Quantitative Performance Assessment Of Lidar-Based Vehicle Contour Estimation Algorithms For Integrated Vehicle Safety Applications, David M. Mothershed

Electronic Theses and Dissertations

Many nations and organizations are committing to achieving the goal of `Vision Zero' and eliminate road traffic related deaths around the world. Industry continues to develop integrated safety systems to make vehicles safer, smarter and more capable in safety critical scenarios. Passive safety systems are now focusing on pre-crash deployment of restraint systems to better protect vehicle passengers. Current commonly used bounding box methods for shape estimation of crash partners lack the fidelity required for edge case collision detection and advanced crash modeling. This research presents a novel algorithm for robust and accurate contour estimation of opposing vehicles. The presented …

Instructor Activity Recognition Using Smartwatch And Smartphone Sensors, Zayed Uddin Chowdhury

Electronic Theses and Dissertations

During a classroom session, an instructor performs several activities, such as writing on the board, speaking to the students, gestures to explain a concept. A record of the time spent in each of these activities could be valuable information for the instructors to virtually observe their own style of instruction. It can help in identifying activities that engage the students more, thereby enhancing teaching effectiveness and efficiency. In this work, we present a preliminary study on profiling multiple activities of an instructor in the classroom using smartwatch and smartphone sensor data. We use 2 benchmark datasets to test out the …