Computer Engineering Commons^™

Deep Learning Based Brain Tumor Classification And Detection System, Ali̇ Ari, Davut Hanbay

Turkish Journal of Electrical Engineering and Computer Sciences

The brain cancer treatment process depends on the physician's experience and knowledge. For this reason, using an automated tumor detection system is extremely important to aid radiologists and physicians to detect brain tumors. The proposed method has three stages, which are preprocessing, the extreme learning machine local receptive fields (ELM-LRF) based tumor classification, and image processing based tumor region extraction. At first, nonlocal means and local smoothing methods were used to remove possible noises. In the second stage, cranial magnetic resonance (MR) images were classified as benign or malignant by using ELM-LRF. In the third stage, the tumors were segmented. …

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 …

Can Deep Learning Techniques Improve The Risk Adjusted Returns From Enhanced Indexing Investment Strategies, Anthony Grace

Dissertations

Deep learning techniques have been widely applied in the field of stock market prediction particularly with respect to the implementation of active trading strategies. However, the area of portfolio management and passive portfolio management in particular has been much less well served by research to date. This research project conducts an investigation into the science underlying the implementation of portfolio management strategies in practice focusing on enhanced indexing strategies. Enhanced indexing is a passive management approach which introduces an element of active management with the aim of achieving a level of active return through small adjustments to the portfolio weights. …

Deep Learning And Transfer Learning In The Classification Of Eeg Signals, Jacob M. Williams

School of Computing: Dissertations, Theses, and Student Research

Deep learning is seldom used in the classification of electroencephalography (EEG) signals, despite achieving state of the art classification accuracies in other spatial and time series data. Instead, most research has continued to use manual feature extraction followed by a traditional classifier, such as SVMs or logistic regression. This is largely due to the low number of samples per experiment, high-dimensional nature of the data, and the difficulty in finding appropriate deep learning architectures for classification of EEG signals. In this thesis, several deep learning architectures are compared to traditional techniques for the classification of visually evoked EEG signals. We …

Cyclist Detection, Tracking, And Trajectory Analysis In Urban Traffic Video Data, Farideh Foroozandeh Shahraki

UNLV Theses, Dissertations, Professional Papers, and Capstones

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to …

An Ensemble Deep Convolutional Neural Network Model With Improved D-S Evidence Fusion For Bearing Fault Diagnosis, Shaobo Li, Guokai 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

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 …

Multi-View Face Recognition From Single Rgbd Models Of The Faces, Donghun Kim, Bharath Comandur, Henry P. Medeiros, Noha M. Elfiky, Avinash Kak

Electrical and Computer Engineering Faculty Research and Publications

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based …

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 …

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, …

Vehicle Make And Model Recognition For Intelligent Transportation Monitoring And Surveillance., Faezeh Tafazzoli

Electronic Theses and Dissertations

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, …

Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in …

3d Body Tracking Using Deep Learning, Qingguo Xu

Theses and Dissertations--Computer Science

This thesis introduces a 3D body tracking system based on neutral networks and 3D geometry, which can robustly estimate body poses and accurate body joints. This system takes RGB-D data as input. Body poses and joints are firstly extracted from color image using deep learning approach. The estimated joints and skeletons are further translated to 3D space by using camera calibration information. This system is running at the rate of 3 4 frames per second. It can be used to any RGB-D sensors, such as Kinect, Intel RealSense [14] or any customized system with color depth calibrated. Comparing to the …

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 …

Respiratory Prediction And Image Quality Improvement Of 4d Cone Beam Ct And Mri For Lung Tumor Treatments, Seonyeong Park

Theses and Dissertations

Identification of accurate tumor location and shape is highly important in lung cancer radiotherapy, to improve the treatment quality by reducing dose delivery errors. Because a lung tumor moves with the patient's respiration, breathing motion should be correctly analyzed and predicted during the treatment for prevention of tumor miss or undesirable treatment toxicity. Besides, in Image-Guided Radiation Therapy (IGRT), the tumor motion causes difficulties not only in delivering accurate dose, but also in assuring superior quality of imaging techniques such as four-dimensional (4D) Cone Beam Computed Tomography (CBCT) and 4D Magnetic Resonance Imaging (MRI). Specifically, 4D CBCT used in CBCT …

Comparative Study Of Dimension Reduction Approaches With Respect To Visualization In 3-Dimensional Space, Pooja Chenna

Master of Science in Computer Science Theses

In the present big data era, there is a need to process large amounts of unlabeled data and find some patterns in the data to use it further. If data has many dimensions, it is very hard to get any insight of it. It is possible to convert high-dimensional data to low-dimensional data using different techniques, this dimension reduction is important and makes tasks such as classification, visualization, communication and storage much easier. The loss of information should be less while mapping data from high-dimensional space to low-dimensional space. Dimension reduction has been a significant problem in many fields as …

Improving Engagement Assessment By Model Individualization And Deep Learning, Feng Li

Electrical & Computer Engineering Theses & Dissertations

This dissertation studies methods that improve engagement assessment for pilots. The major work addresses two challenging problems involved in the assessment: individual variation among pilots and the lack of labeled data for training assessment models.

Task engagement is usually assessed by analyzing physiological measurements collected from subjects who are performing a task. However, physiological measurements such as Electroencephalography (EEG) vary from subject to subject. An assessment model trained for one subject may not be applicable to other subjects. We proposed a dynamic classifier selection algorithm for model individualization and compared it to other two methods: base line normalization and similarity-based …