Engineering Commons^™

Objective Estimation Of Tracheoesophageal Speech Quality, Yousef S Ettomi Ali

Electronic Thesis and Dissertation Repository

Speech quality estimation for pathological voices is becoming an increasingly important research topic. The assessment of the quality and the degree of severity of a disordered speech is important to the clinical treatment and rehabilitation of patients. In particular, patients who have undergone total laryngectomy (larynx removal) produce Tracheoesophageal (TE) speech. In this thesis, we study the problem of TE speech quality estimation using advanced signal processing approaches. Since it is not possible to have a reference (clean) signal corresponding to a given TE speech (disordered) signal, we investigate in particular the non-intrusive techniques (also called single-ended or blind approaches) …

Cluster-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian

Electronic Thesis and Dissertation Repository

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through …

Research And Implementation Of Driving Concern Area Detection Based On Deep Learning, Jihua Ye, Shuxia Shi, Hanxi Li, Shimin Wang, Siyu Yang

Journal of System Simulation

Abstract: As a key technology of intelligent driving, driving concern area detection method has an important impact on the performance of intelligent driving or intelligent early warning system. In view of the shortcomings of the existing methods, this paper proposes an effective method for driving concern area detection based on the deep learning. We obtain the camera internal and external parameters by using camera self-calibration method based on camera model, use the Canny edge detection and Bisecting K-means clustering to realize the vanishing point estimation, and establish the road detection model based on the obtained estimates. We obtain the depth …

Amodal Instance Segmentation And Multi-Object Tracking With Deep Pixel Embedding, Yanfeng Liu

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

This thesis extends upon the representational output of semantic instance segmentation by explicitly including both visible and occluded parts. A fully convolutional network is trained to produce consistent pixel-level embedding across two layers such that, when clustered, the results convey the full spatial extent and depth ordering of each instance. Results demonstrate that the network can accurately estimate complete masks in the presence of occlusion and outperform leading top-down bounding-box approaches.

The model is further extended to produce consistent pixel-level embeddings across two consecutive image frames from a video to simultaneously perform amodal instance segmentation and multi-object tracking. No post-processing …

Self-Driving Toy Car Using Deep Learning, Fahim Ahmed, Suleyman Turac, Mubtasem Ali

Publications and Research

Our research focuses on building a student affordable platform for scale model self-driving cars. The goal of this project is to explore current developments of Open Source hardware and software to build a low-cost platform consisting of the car chassis/framework, sensors, and software for the autopilot. Our research will allow other students with low budget to enter into the world of Deep Learning, self-driving cars, and autonomous cars racing competitions.

Extracting Patterns In Medical Claims Data For Predicting Opioid Overdose, Ryan Sanders

Graduate Theses and Dissertations

The goal of this project is to develop an efficient methodology for extracting features from time-dependent variables in transaction data. Transaction data is collected at varying time intervals making feature extraction more difficult. Unsupervised representational learning techniques are investigated, and the results compared with those from other feature engineering techniques. A successful methodology provides features that improve the accuracy of any machine learning technique. This methodology is then applied to insurance claims data in order to find features to predict whether a patient is at risk of overdosing on opioids. This data covers prescription, inpatient, and outpatient transactions. Features created …

Multiple Face Detection And Recognition System Design Applying Deep Learning In Web Browsers Using Javascript, Cristhian Gabriel Espinosa Sandoval

Computer Science and Computer Engineering Undergraduate Honors Theses

Deep learning has advanced progressively in the last years and now demonstrates state-of-the-art performance in various fields. In the era of big data, transformation of data into valuable knowledge has become one of the most important challenges in computing. Therefore, we will review multiple algorithms for face recognition that have been researched for a long time and are maturely developed, and analyze deep learning, presenting examples of current research.

To provide a useful and comprehensive perspective, in this paper we categorize research by deep learning architecture, including neural networks, convolutional neural networks, depthwise Separable Convolutions, densely connected convolutional networks, and …

Seer: An Explainable Deep Learning Midi-Based Hybrid Song Recommender System, Khalil Damak, Olfa Nasraoui

Faculty Scholarship

State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learning for song recommendation, none has taken advantage of the sequential nature of songs by learning sequence models that are based on content. Aside from the importance of prediction accuracy, other significant aspects are important, such as explainability and solving the cold start problem. In this work, we propose a hybrid deep learning …

Scatter Reduction By Exploiting Behaviour Of Convolutional Neural Networks In Frequency Domain, Carlos Ivan Jerez Gonzalez

Theses and Dissertations

In X-ray imaging, scattered radiation can produce a number of artifacts that greatly

undermine the image quality. There are hardware solutions, such as anti-scatter grids.

However, they are costly. A software-based solution is a better option because it is

cheaper and can achieve a higher scatter reduction. Most of the current software-based

approaches are model-based. The main issues with them are the lack of flexibility, expressivity, and the requirement of a model. In consideration of this, we decided to apply

Convolutional Neural Networks (CNNs), since they do not have any of the previously

mentioned issues.

In our approach we split …

Improved Study Of Side-Channel Attacks Using Recurrent Neural Networks, Muhammad Abu Naser Rony Chowdhury

Boise State University Theses and Dissertations

Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information.

In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs):

RQ1: Is it …

Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …

Deep Learning (Partly) Demystified, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices -- and the surprising success of deep learning in the first place -- can be explained by reasonably simple and natural mathematics.

Why Deep Learning Is More Efficient Than Support Vector Machines, And How It Is Related To Sparsity Techniques In Signal Processing, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Several decades ago, traditional neural networks were the most efficient machine learning technique. Then it turned out that, in general, a different technique called support vector machines is more efficient. Reasonably recently, a new technique called deep learning has been shown to be the most efficient one. These are empirical observations, but how we explain them -- thus making the corresponding conclusions more reliable? In this paper, we provide a possible theoretical explanation for the above-described empirical comparisons. This explanation enables us to explain yet another empirical fact -- that sparsity techniques turned out to be very efficient in signal …

Image Based Recognition Of The Monuments In Prizren, Bertan Karahoda, Annea Futko

UBT International Conference

Image classification application has recently been covering a high number of research fields. In the other hand as the performance of the mobile devices is being updated day by day, the implementation of image recognition algorithms in them, is not only being trendy but very helpful in everyday tasks. With the automatic monument recognition, visiting a city is easy and fun. This application recognizes the captured monument, gives useful information and describes that particular landmark.

In this paper there are used four historical monuments of the city of Prizren, Kosovo and the aim is to classify these four famous monuments. …

Flood Management Deep Learning Model Inputs: A Review Of Necessary Data And Predictive Tools, Jacob Hale, Suzanna Long, Steven Corns, Tom Shoberg

Engineering Management and Systems Engineering Faculty Research & Creative Works

Current flood management models are often hampered by the lack of robust predictive analytics, as well as incomplete datasets for river basins prone to heavy flooding. This research uses a State-of-the-Art matrix (SAM) analysis and integrative literature review to categorize existing models by method and scope, then determines opportunities for integrating deep learning techniques to expand predictive capability. Trends in the SAM analysis are then used to determine geospatial characteristics of the region that can contribute to flash flood scenarios, as well as develop inputs for future modeling efforts. Preliminary progress on the selection of one urban and one rural …

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning, Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu

Faculty Publications

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over …

Artificial Intelligence Based Wrist Fracture Classification, Dineep Thomas

LSU Master's Theses

The problem of predicting wrist fractures from X-rays using Artificial Intelligence (AI) methods is addressed. Wrist fractures are the most commonly misdiagnosed fractures because of the complex anatomical structure of the wrist bone which includes several different bones. This research provides a predictive solution to automate the process of wrist fracture classifications and outlines a visualization technique to identify the probable location of the fractured region on the X-rays. This thesis describes a deep learning based approach for wrist fracture classification. Deep convolutional neural network (CNN) based models have been used for wrist fracture classification by combining different optimization techniques. …

A Review Of Text Corpus-Based Tourism Big Data Mining, Qin Lin, Shaobo Li, Sen Zhang, Jie Hu, Jianjun Hu

Faculty Publications

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and …

A Review Of Text Corpus-Based Tourism Big Data Mining, Qin Li, Shaobo Li, Sen Zhang, Jie Hu, Jianhun Hu

Faculty Publications

With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of tourists’ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and …

Machine Learning Methodology Review For Computational Electromagnetics, He Ming Yao, Lijun Jiang, Huan Huan Zhang, Wei E.I. Sha

Electrical and Computer Engineering Faculty Research & Creative Works

While machine learning is revolutionizing every corner of modern technologies, we have been attempting to explore whether machine learning methods could be used in computational electromagnetic (CEM). In this paper, five efforts in line with this direction are reviewed. They include forward methods such as the method of moments (MoM) solved by the artificial neural network training process, FDTD PML (perfectly matched layer) using the hyperbolic tangent basis function (HTBF), etc. There are also inverse problems that use the deep ConvNets for the effective source reconstruction and subwavelength imaging in the far-field. Benchmarks are provided to demonstrate the feasibility of …

Model Augmented Deep Neural Networks For Medical Image Reconstruction Problems, Hongquan Zuo

Theses and Dissertations

Solving an ill-posed inverse problem is difficult because it doesn't have a unique solution. In practice, for some important inverse problems, the conventional methods, e.g. ordinary least squares and iterative methods, cannot provide a good estimate. For example, for single image super-resolution and CT reconstruction, the results of these conventional methods cannot satisfy the requirements of these applications. While having more computational resources and high-quality data, researchers try to use machine-learning-based methods, especially deep learning to solve these ill-posed problems. In this dissertation, a model augmented recursive neural network is proposed as a general inverse problem method to solve these …

Mid To Late Season Weed Detection In Soybean Production Fields Using Unmanned Aerial Vehicle And Machine Learning, Arun Narenthiran Veeranampalayam Sivakumar

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Mid-late season weeds are those that escape the early season herbicide applications and those that emerge late in the season. They might not affect the crop yield, but if uncontrolled, will produce a large number of seeds causing problems in the subsequent years. In this study, high-resolution aerial imagery of mid-season weeds in soybean fields was captured using an unmanned aerial vehicle (UAV) and the performance of two different automated weed detection approaches – patch-based classification and object detection was studied for site-specific weed management. For the patch-based classification approach, several conventional machine learning models on Haralick texture features were …

In Vivo Human-Like Robotic Phenotyping Of Leaf And Stem Traits In Maize And Sorghum In Greenhouse, Abbas Atefi

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

In plant phenotyping, the measurement of morphological, physiological and chemical traits of leaves and stems is needed to investigate and monitor the condition of plants. The manual measurement of these properties is time consuming, tedious, error prone, and laborious. The use of robots is a new approach to accomplish such endeavors, which enables automatic monitoring with minimal human intervention. In this study, two plant phenotyping robotic systems were developed to realize automated measurement of plant leaf properties and stem diameter which could reduce the tediousness of data collection compare to manual measurements. The robotic systems comprised of a four degree …

Unsupervised-Learning Assisted Artificial Neural Network For Optimization, Varun Kote

Mechanical & Aerospace Engineering Theses & Dissertations

Innovations in computer technology made way for Computational Fluid Dynamics (CFD) into engineering, which supported the development of new designs by reducing the cost and time by lowering the dependency on experimentation. There is a further need to make the process of development more efficient. One such technology is Artificial Intelligence. In this thesis, we explore the application of Artificial Intelligence (AI) in CFD and how it can improve the process of development.

AI is used as a buzz word for the mechanism which can learn by itself and make the decision accordingly. Machine learning (ML) is a subset of …

Large-Scale Data Analysis And Deep Learning Using Distributed Cyberinfrastructures And High Performance Computing, Richard Dodge Platania

LSU Doctoral Dissertations

Data in many research fields continues to grow in both size and complexity. For instance, recent technological advances have caused an increased throughput in data in various biological-related endeavors, such as DNA sequencing, molecular simulations, and medical imaging. In addition, the variance in the types of data (textual, signal, image, etc.) adds an additional complexity in analyzing the data. As such, there is a need for uniquely developed applications that cater towards the type of data. Several considerations must be made when attempting to create a tool for a particular dataset. First, we must consider the type of algorithm required …

Inverted Cone Convolutional Neural Network For Deboning Mris, Oliver John Palumbo

Theses and Dissertations

Data plenitude is the power but also the bottleneck for data-driven approaches, including neural networks. In particular, Convolutional Neural Networks (CNNs) require an abundant database of training images to achieve a desired high accuracy. Current techniques employed for boosting small datasets are data augmentation and synthetic data generation, which suffer from computational complexity and imprecision compared to original datasets. In this thesis, we intercalate prior knowledge based on the temporal relation between the images in the third dimension. Specifically, we compute the gradient of subsequent images in the dataset to remove extraneous information and highlight subtle variations between the images. …

Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather

Master's Theses

Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States' strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe affirmative results. Our trained …

High-Performance Learning Systems Using Low-Precision Nanoscale Devices, Nandakumar Sasidharan Rajalekshmi

Dissertations

Brain-inspired computation promises a paradigm shift in information processing, both in terms of its parallel processing architecture and the ability to learn to tackle problems deemed unsolvable by traditional algorithmic approaches. The computational capability of the human brain is believed to stem from an interconnected network of 100 billion compute nodes (neurons) that interact with each other through approximately 10¹⁵ adjustable memory junctions (synapses). The conductance of synapses is modifiable allowing the network to learn and perform various cognitive functions. Artificial neural networks inspired by this architecture have demonstrated even super-human performance in many complex tasks.

Computational systems based …

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available …

Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu

Faculty Publications

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available …