Engineering Commons^™

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego

Electrical & Computer Engineering Theses & Dissertations

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …

Transfer Learning, Model Interpretation, And Dataset Bias Analysis For Automated Violence Detection From Video, Erik Clemens

Master's Theses (2009 -)

Many communities have installed surveillance cameras in an effort to deter and respond to violence.Due to the difficulty of constantly monitoring such camera feeds, these systems are rarely used to provide real-time information. To enable rapid alerts and information for first responders, this thesis develops a proof-of-concept system capable of automatically detecting violence from video footage. This system is developed by fine-tuning a convolutional neural network that has previously demonstrated success on general action recognition tasks. This thesis explores two new techniques to improve the accuracy of the fine-tuned model. The first is a data augmentation technique that generates aspect …

Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang

Theses and Dissertations

Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …

Computer Vision Aided Hotspot Creation In Virtual Environments, Lama A. Affara, Bilal E. Nakhal

Architecture and Planning Journal (APJ)

Hotspot creation is one of the most important modules within virtual environments which helps show the navigators of these environments some information about semantic elements within it and facilitate the navigation between the virtual spaces. In this paper, a system for automatic hotspot proposals and creation in virtual environments is proposed. The system uses computer vision modules to automatically propose hotspot locations in addition to identifying and creating these hotspots with candidate labels. Two main modules used in the system are object detection and scene segmentation. The scene segmentation helps give candidate hotspot areas and provides an overall understanding of …

Using Deep Learning To Generate Front And Backyards In Landscape Architecture, Mehmet O. Senem, Mustafa Koç, Hayriye E. Tunçay, İmdat As

Architecture and Planning Journal (APJ)

The use of artificial intelligence (AI) engines in the design disciplines is a nascent field of research, which became very popular over the last decade. In particular, deep learning (DL) and related generative adversarial networks (GANs) proved to be very promising. While there are many research projects exploring AI in architecture and urban planning, e.g., in order to generate optimal floor layouts, massing models, evaluate image quality, etc., there are not many research projects in the area of landscape architecture - in particular the design of two-dimensional garden layouts. In this paper, we present our work using GANs to generate …

Autonomous 3d Urban And Complex Terrain Geometry Generation And Micro-Climate Modelling Using Cfd And Deep Learning, Tewodros F. Alemayehu

Electronic Thesis and Dissertation Repository

Sustainable building design requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction between complex urban geometries and the microclimate is characterized by complex transport mechanisms. The challenge to generate geometric and physics boundary conditions in an automated manner is hindering the progress of computational methods in urban design. Thus, the challenge of modelling realistic and pragmatic numerical urban micro-climate for wind engineering, environmental, and building energy simulation applications should address …

Tempnet – Temporal Super-Resolution Of Radar Rainfall Products With Residual Cnns, Muhammed Ali Sit, Bongchul Seo, Ibrahim Demir

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the …

Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar

Theses and Dissertations

The uncertainty of lightning constantly threatens many weather-sensitive fields where the slightest presence of lightning can endanger valuable personnel and assets. The consequences of delaying operations have incited the research of methods that can accurately predict the location of future lightning strikes from the current weather conditions. High-dimensional remote sensing modalities contain information capable of detecting significant patterns and intensities within storms that could indicate the presence of lightning. This thesis induces sparsity into convolutional neural networks (CNNs) and remote sensing modalities through a combination of regularization and tensor decomposition techniques to call attention to sparse features that are most …

3d Point Cloud Classification With Acgan-3d And Vacwgan-Gp, Onur Ergün, Yusuf Sahi̇lli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP). Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models. We consequently aim to increase the success of the model by …

Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix

Electronic Thesis and Dissertation Repository

When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing repetitive stress injuries (RSI). Symptoms usually begin slowly and become more intense and constant over time. If the motions that lead to RSI are recognized early, these injuries can be prevented. A preventative approach could be implemented in factories to warn workers about possible injuries. By detecting the movements that can cause RSI, the worker can be alerted to stop carrying out those movements. For this purpose, machine learning models can detect human motion with the human activity recognition (HAR) model. HAR models typically require …

Noise2clean: Cross-Device Side-Channel Traces Denoising With Unsupervised Deep Learning, Honggang Yu, Mei Wang, Xiyu Song, Haoqi Shan, Hongbing Qiu, Junyi Wang, Kaichen Yang

Michigan Tech Publications

Deep learning (DL)-based side-channel analysis (SCA) has posed a severe challenge to the security and privacy of embedded devices. During its execution, an attacker exploits physical SCA leakages collected from profiling devices to create a DL model for recovering secret information from victim devices. Despite this success, recent works have demonstrated that certain countermeasures, such as random delay interrupts or clock jitters, would make these attacks more complex and less practical in real-world scenarios. To address this challenge, we present a novel denoising scheme that exploits the U-Net model to pre-process SCA traces for “noises” (i.e., countermeasures) removal. Specifically, we …

A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas

National Training Aircraft Symposium (NTAS)

Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes' mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction …

Pt-Net: A Multi-Model Machine Learning Approach For Smarter Next-Generation Wearable Tremor Suppression Devices For Parkinson's Disease Tremor, Anas Ibrahim

Electronic Thesis and Dissertation Repository

According to the World Health Organization (WHO), Parkinson's Disease (PD) is the second most common neurodegenerative condition that can cause tremors and other motor and non motor related symptoms. Medication and deep brain stimulation (DBS) are often used to treat tremor; however, medication is not always effective and has adverse effects, and DBS is invasive and carries a significant risk of complications. Wearable tremor suppression devices (WTSDs) have been proposed as a possible alternative, but their effectiveness is limited by the tremor models they use, which introduce a phase delay that decreases the performance of the devices. Additionally, the availability …

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Research On Intelligent Prediction Method Of Wargaming Air Mission, Dayong Zhang, Jingyu Yang, Xi Wu

Journal of System Simulation

Abstract: The efficient, accurate and automatic judgment of the combat mission or intention of the enemy's air targets in the battlefield is the basis of situation awareness and the key to the allocation of auxiliary combat resources. Combined with the calculation characteristics of feed forward deep neural network and long-term and short-term memory network model, two targeted basic index learners are designed, and then the weighted combination is carried out according to the cross entropy of the basic index, which can be used to further train the evaluation index of the learner. It can not only effectively prevent the model …

Efficient Training On Alzheimer’S Disease Diagnosis With Learnable Weighted Pooling For 3d Pet Brain Image Classification, Xin Xing, Muhammad Usman Rafique, Gongbo Liang, Hunter Blanton, Zu Zhang, Chris Wang, Nathan Jacobs, Ai-Ling Lin

Computer Science Faculty Publications

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyatt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hsiang Lu, George K. Thiruvathukal, James C. Davis

Department of Electrical and Computer Engineering Faculty Publications

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …

Cooperative Deep $Q$ -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In This Article, We Address Two Key Challenges in Deep Reinforcement Learning (DRL) Setting, Sample Inefficiency and Slow Learning, with a Dual-Neural Network (NN)-Driven Learning Approach. in the Proposed Approach, We Use Two Deep NNs with Independent Initialization to Robustly Approximate the Action-Value Function in the Presence of Image Inputs. in Particular, We Develop a Temporal Difference (TD) Error-Driven Learning (EDL) Approach, Where We Introduce a Set of Linear Transformations of the TD Error to Directly Update the Parameters of Each Layer in the Deep NN. We Demonstrate Theoretically that the Cost Minimized by the EDL Regime is an Approximation …

Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi

Articles

Wheat is the most important and dominating crop in Pakistan in terms of production and acreage, which is grown on 37% of the cultivated area, accounting for 70% of the total production. However, wheat yield is highly affected by stripe rust, which is considered the most devastating fungal disease, causing 5.5 million tonnes of loss per year globally. In order to minimize this loss, the accurate and timely detection of rust disease is crucial instead of manual inspection. Towards this end, we propose a system to detect wheat rust disease and classify its infection types into four classes, including healthy, …

Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy

Articles

Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …

Artificial Intelligence In Prediction Of The Remaining Useful Life Of Wind Turbine Shaft Bearings, Jinsiang Shaw, B.J. Wu

Journal of Marine Science and Technology

Long-term periodic rotation and unstable load changes in wind turbines can cause unexpected damage to high-speed shaft bearings (HSSBs). In this study, after preprocessing of the HSSB vibration signal, four different models for predicting bearing degradation in terms of remaining useful life (RUL) in days were investigated: support vector regression (SVR), convolutional neural networks (CNN), long short-term memory (LSTM), and CNN-LSTM. The experimental results revealed that the CNN achieved the best mean absolute error (MAE), at 0.44 days, based on frequency response plot using the fast Fourier transform (FFT), while that of the CNN-LSTM model predicted using the amplitude profile …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCE Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

Cooperative Deep Q -Learning Framework For Environments Providing Image Feedback, Krishnan Raghavan, Vignesh Narayanan, Sarangapani Jagannathan

Publications

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation …

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan

Graduate Theses, Dissertations, and Problem Reports

The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., …

Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo

Articles

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …