Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- TÜBİTAK (55)
- China Simulation Federation (31)
- Old Dominion University (30)
- University of South Carolina (12)
- Singapore Management University (7)
-
- University of Wisconsin Milwaukee (7)
- MBZUAI (6)
- New Jersey Institute of Technology (6)
- Technological University Dublin (5)
- University of Arkansas, Fayetteville (5)
- University of Louisville (5)
- Wright State University (5)
- Air Force Institute of Technology (4)
- Chapman University (4)
- Embry-Riddle Aeronautical University (4)
- Missouri University of Science and Technology (4)
- Edith Cowan University (3)
- University of Kentucky (3)
- University of Nevada, Las Vegas (3)
- Purdue University (2)
- The Texas Medical Center Library (2)
- Binghamton University (1)
- Boise State University (1)
- California State University, San Bernardino (1)
- Louisiana State University (1)
- Michigan Technological University (1)
- Nova Southeastern University (1)
- Rowan University (1)
- San Jose State University (1)
- Southern Methodist University (1)
- Publication Year
- Publication
-
- Turkish Journal of Electrical Engineering and Computer Sciences (55)
- Journal of System Simulation (31)
- Electrical & Computer Engineering Faculty Publications (14)
- Theses and Dissertations (13)
- Dissertations (10)
-
- Faculty Publications (9)
- Research Collection School Of Computing and Information Systems (7)
- Computer Vision Faculty Publications (6)
- Browse all Theses and Dissertations (5)
- Electrical & Computer Engineering Theses & Dissertations (5)
- Doctoral Dissertations (4)
- Electronic Theses and Dissertations (4)
- Engineering Faculty Articles and Research (4)
- Graduate Theses and Dissertations (4)
- Engineering Technology Faculty Publications (3)
- Computer Science Faculty Publications (2)
- Dissertations & Theses (Open Access) (2)
- Faculty Scholarship (2)
- Mechanical & Aerospace Engineering Theses & Dissertations (2)
- Publications (2)
- Research outputs 2022 to 2026 (2)
- Theses and Dissertations--Computer Science (2)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (2)
- Articles (1)
- Boise State University Theses and Dissertations (1)
- CCE Theses and Dissertations (1)
- Civil & Environmental Engineering Faculty Publications (1)
- Computational Modeling & Simulation Engineering Theses & Dissertations (1)
- Computer Science Faculty Research (1)
- Computer Science Theses & Dissertations (1)
- Publication Type
Articles 211 - 222 of 222
Full-Text Articles in Engineering
Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam
Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam
Electrical & Computer Engineering Theses & Dissertations
Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …
Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang
Intent Recognition In Smart Living Through Deep Recurrent Neural Networks, Xiang Zhang, Lina Yao, Chaoran Huang, Quan Z. Sheng, Xianzhi Wang
Research Collection School Of Computing and Information Systems
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided …
Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu
Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu
LSU Doctoral Dissertations
Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …
An Ensemble Deep Convolutional Neural Network Model With Improved D-S Evidence Fusion For Bearing Fault Diagnosis, Shaobo Li, Guoka Liu, Xianghong Tang, Jianguang Lu, Jianjun Hu
An Ensemble Deep Convolutional Neural Network Model With Improved D-S Evidence Fusion For Bearing Fault Diagnosis, Shaobo Li, Guoka Liu, Xianghong Tang, Jianguang Lu, Jianjun Hu
Faculty Publications
Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations …
Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee
Speech Based Machine Learning Models For Emotional State Recognition And Ptsd Detection, Debrup Banerjee
Electrical & Computer Engineering Theses & Dissertations
Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a …
Demo: Deepmon - Building Mobile Gpu Deep Learning Models For Continuous Vision Applications, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Demo: Deepmon - Building Mobile Gpu Deep Learning Models For Continuous Vision Applications, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Deep learning has revolutionized vision sensing applications in terms of accuracy comparing to other techniques. Its breakthrough comes from the ability to extract complex high level features directly from sensor data. However, deep learning models are still yet to be natively supported on mobile devices due to high computational requirements. In this paper, we present DeepMon, a next generation of DeepSense [1] framework, to enable deep learning models on conventional mobile devices (e.g. Samsung Galaxy S7) for continuous vision sensing applications. Firstly, Deep-Mon exploits similarity between consecutive video frames for intermediate data caching within models to enhance inference latency. Secondly, …
Deepmon: Mobile Gpu-Based Deep Learning Framework For Continuous Vision Applications, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan
Deepmon: Mobile Gpu-Based Deep Learning Framework For Continuous Vision Applications, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, i.e., high processing latency and power consumption. In this paper, we propose DeepMon, a mobile deep learning inference system to run a variety of deep learning inferences purely on a mobile device in a fast and energy-efficient manner. For this, we designed a suite of …
Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li
Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li
Electrical & Computer Engineering Faculty Publications
Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …
Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li
Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li
Computer Science Theses & Dissertations
Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Electrical and Computer Engineering Publications
In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is …
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Open Access Dissertations
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant …
Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose
Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose
Doctoral Dissertations
Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. …