Computer Engineering Commons^™

Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon

Electronic Thesis and Dissertation Repository

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.

One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …

A Framework For Stable Robot-Environment Interaction Based On The Generalized Scattering Transformation, Kanstantsin Pachkouski

Electronic Thesis and Dissertation Repository

This thesis deals with development and experimental evaluation of control algorithms for stabilization of robot-environment interaction based on the conic systems formalism and scattering transformation techniques. A framework for stable robot-environment interaction is presented and evaluated on a real physical system. The proposed algorithm fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction. The framework is based on the recently developed non-planar conic systems formalism and generalized scattering-based stabilization methods. A comprehensive theoretical background on the scattering transformation techniques, planar and non-planar conic systems is presented. …

Efficient Discovery And Utilization Of Radio Information In Ultra-Dense Heterogeneous 3d Wireless Networks, Mattaka Gamage Samantha Sriyananda

Electronic Thesis and Dissertation Repository

Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization …

Anonymization & Generation Of Network Packet Datasets Using Deep Learning, Spencer K. Vecile

Electronic Thesis and Dissertation Repository

Corporate networks are constantly bombarded by malicious actors trying to gain access. The current state of the art in protecting networks is deep learning-based intrusion detection systems (IDS). However, for an IDS to be effective it needs to be trained on a good dataset. The best datasets for training an IDS are real data captured from large corporate networks. Unfortunately, companies cannot release their network data due to privacy concerns creating a lack of public cybersecurity data. In this thesis I take a novel approach to network dataset anonymization using character-level LSTM models to learn the characteristics of a dataset; …

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai

Electronic Thesis and Dissertation Repository

High-Impedance Faults (HIFs) are a hazard to public safety but are difficult to detect because of their low current amplitude and diverse characteristics. Supervised machine learning techniques have shown great success in HIF detection; however, these approaches rely on resource-intensive signal processing techniques and fail in presence of non-HIF disturbances and even for scenarios not included in training data. This thesis leverages unsupervised learning and proposes a Convolutional Autoencoder framework for HIF Detection (CAE-HIFD). In CAE-HIFD, Convolutional Autoencoder learns only from HIF signals by employing cross-correlation; consequently, eliminating the need for diverse non-HIF scenarios in training. Furthermore, this thesis proposes …

Development Of A Wearable Haptic Feedback Device For Upper Limb Prosthetics Through Sensory Substitution, Marco B.S. Gallone

Electronic Thesis and Dissertation Repository

Haptics can enable a direct communication pipeline between the artificial limb and the brain; adding haptic sensory feedback for prosthesis wearers is believed to improve operation without drawing too much of the user's attention. Through neuroplasticity, the brain can become more cognizant of the information delivered through the skin and may eventually interpret it as inherently as other natural senses. In this thesis, a wearable haptic feedback device (WHFD) is developed to communicate prosthesis sensory information. A 14-week, 6-stage, between subjects study was created to investigate the learning trajectory as participants were stimulated with haptic patterns conveying joint proprioception. 37 …

A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa

Electronic Thesis and Dissertation Repository

Additively homomorphic encryption is a public-key primitive allowing a sum to be computed on encrypted values. Although limited in functionality, additive schemes have been an essential tool in the private function evaluation toolbox for decades. They are typically faster and more straightforward to implement relative to their fully homomorphic counterparts, and more efficient than garbled circuits in certain applications. This thesis presents a novel method for extending the functionality of additively homomorphic encryption to allow the private evaluation of functions of restricted domain. Provided the encrypted sum falls within the restricted domain, the function can be homomorphically evaluated “for free” …

Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell

Electronic Thesis and Dissertation Repository

An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang

Electronic Thesis and Dissertation Repository

The development of reliable state estimation algorithms for autonomous navigation systems is of great interest in the control and robotics communities. This thesis studies the state estimation problem for autonomous navigation systems. The first part of this thesis is devoted to the pose estimation on the Special Euclidean group $\SE(3)$. A generic globally exponentially stable hybrid estimation scheme for pose (orientation and position) and velocity-bias estimation on $\SE(3)\times \mathbb{R}^6$ is proposed. Moreover, an explicit hybrid observer, using inertial and landmark position measurements, is provided.

The second part of this thesis is devoted to the problem of simultaneous estimation of the …

Design And Implementation Of Anomaly Detections For User Authentication Framework, Iman Abu Sulayman

Electronic Thesis and Dissertation Repository

Anomaly detection is quickly becoming a very significant tool for a variety of applications such as intrusion detection, fraud detection, fault detection, system health monitoring, and event detection in IoT devices. An application that lacks a strong implementation for anomaly detection is user trait modeling for user authentication purposes. User trait models expose up-to-date representation of the user so that changes in their interests, their learning progress or interactions with the system are noticed and interpreted. The reason behind the lack of adoption in user trait modeling arises from the need of a continuous flow of high-volume data, that is …

Leveraging Cloud-Based Nfv And Sdn Platform Towards Quality-Driven Next-Generation Mobile Networks, Hassan Hawilo

Electronic Thesis and Dissertation Repository

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand …

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 …

A Wearable Mechatronic Device For Hand Tremor Monitoring And Suppression: Development And Evaluation, Yue Zhou

Electronic Thesis and Dissertation Repository

Tremor, one of the most disabling symptoms of Parkinson's disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human.

It has been reported in …

Classifying Appliances Operation Modes Using Dynamic Time Warping (Dtw) And K Nearest Neighbors (Knn), Abdelkareem M. Jaradat

Electronic Thesis and Dissertation Repository

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance …

Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez

Electronic Thesis and Dissertation Repository

Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …

Nonlinear Attitude And Pose Filters With Superior Convergence Properties, Hashim Abdellah Hashim Mohamed

Electronic Thesis and Dissertation Repository

In this thesis, several deterministic and stochastic attitude filtering solutions on the special orthogonal group SO(3) are proposed. Firstly, the attitude estimation problem is approached on the basis of nonlinear deterministic filters on SO(3) with guaranteed transient and steady-state measures. The second solution to the attitude estimation problem considers nonlinear stochastic filters on SO(3) with superior convergence properties with two filters being developed in the sense of Ito, and one in the sense of Stratonovich.

This thesis also presents several deterministic and stochastic pose filtering solutions developed on the special Euclidean group SE(3). The first solution includes two nonlinear deterministic …

Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo

Electronic Thesis and Dissertation Repository

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …

Generalized Scattering-Based Stabilization Of Nonlinear Interconnected Systems, Anastasiia Usova

Electronic Thesis and Dissertation Repository

The research presented in this thesis is aimed at development of new methods and techniques for stability analysis and stabilization of interconnections of nonlinear systems, in particular, in the presence of communication delays. Based on the conic systems' formalism, we extend the notion of conicity for the non-planar case where the dimension of the cone's central subspace may be greater than one. One of the advantages of the notion of non-planar conicity is that any dissipative system with a quadratic supply rate can be represented as a non-planar conic system; specifically, its central subspace and radius can be calculated using …

Optimization And Management Techniques For Geo-Distributed Sdn-Enabled Cloud Datacenters' Provisioning, Khaled M. Alhazmi

Electronic Thesis and Dissertation Repository

Cloud computing has become a business reality that impacts technology users around the world. It has become a cornerstone for emerging technologies and an enabler of future Internet services as it provides on-demand IT services delivery via geographically distributed data centers. At the core of cloud computing, virtualization technology has played a crucial role by allowing resource sharing, which in turn allows cloud service providers to offer computing services without discrepancies in platform compatibility.

At the same time, a trend has emerged in which enterprises are adopting a software-based network infrastructure with paradigms, such as software-defined networking, gaining further attention …

Recurrent Neural Network Architectures Toward Intrusion Detection, Wafaa Anani

Electronic Thesis and Dissertation Repository

Recurrent Neural Networks (RNN) show a remarkable result in sequence learning, particularly in architectures with gated unit structures such as Long Short-term Memory (LSTM). In recent years, several permutations of LSTM architecture have been proposed mainly to overcome the computational complexity of LSTM. In this dissertation, a novel study is presented that will empirically investigate and evaluate LSTM architecture variants such as Gated Recurrent Unit (GRU), Bi-Directional LSTM, and Dynamic-RNN for LSTM and GRU specifically on detecting network intrusions. The investigation is designed to identify the learning time required for each architecture algorithm and to measure the intrusion prediction accuracy. …

Real-Time Intrusion Detection Using Multidimensional Sequence-To-Sequence Machine Learning And Adaptive Stream Processing, Gobinath Loganathan

Electronic Thesis and Dissertation Repository

A network intrusion is any unauthorized activity on a computer network. There are host-based and network-based Intrusion Detection Systems (IDS's), of which there are each signature-based and anomaly-based detection methods. An anomalous network behavior can be defined as an intentional violation of the expected sequence of packets. In a real-time network-based IDS, incoming packets are treated as a stream of data. A stream processor takes any stream of data or events and extracts interesting patterns on the fly. This representation allows applying statistical anomaly detection using sequence prediction algorithms as well as using a stream processor to perform signature-based intrusion …

Intelligent Advancements In Location Management And C-Ran Power-Aware Resource Allocation, Emad Ali Aqeeli

Electronic Thesis and Dissertation Repository

The evolving of cellular networks within the last decade continues to focus on delivering a robust and reliable means to cope with the increasing number of users and demanded capacity. Recent advancements of cellular networks such as Long-Term Evolution (LTE) and LTE-advanced offer a remarkable high bandwidth connectivity delivered to the users. Signalling overhead is one of the vital issues that impact the cellular behavior. Causing a significant load in the core network hence effecting the cellular network reliability. Moreover, the signaling overhead decreases the Quality of Experience (QoE) of users. The first topic of the thesis attempts to reduce …

Optimal Decomposition Strategy For Tree Edit Distance, Shaofeng Jiang

Electronic Thesis and Dissertation Repository

An ordered labeled tree is a tree where the left-to-right order among siblings is significant. Given two ordered labeled trees, the edit distance between them is the minimum cost edit operations that convert one tree to the other.

In this thesis, we present an algorithm for the tree edit distance problem by using the optimal tree decomposition strategy. By combining the vertical compression of trees with optimal decomposition we can significantly reduce the running time of the algorithm. We compare our method with other methods both theoretically and experimentally. The test results show that our strategies on compressed trees are …

A Framework For Tumor Localization In Robot-Assisted Minimally Invasive Surgery, Nikita Chopra

Electronic Thesis and Dissertation Repository

Manual palpation of tissue is frequently used in open surgery, e.g., for localization of tumors and buried vessels and for tissue characterization. The overall objective of this work is to explore how tissue palpation can be performed in Robot-Assisted Minimally Invasive Surgery (RAMIS) using laparoscopic instruments conventionally used in RAMIS. This thesis presents a framework where a surgical tool is moved teleoperatively in a manner analogous to the repetitive pressing motion of a finger during manual palpation. We interpret the changes in parameters due to this motion such as the applied force and the resulting indentation depth to accurately determine …

Expert-In-The-Loop Multilateral Telerobotics For Haptics-Enabled Motor Function And Skills Development, Mahya Shahbazi

Electronic Thesis and Dissertation Repository

Among medical robotics applications are Robotics-Assisted Mirror Rehabilitation Therapy (RAMRT) and Minimally-Invasive Surgical Training (RAMIST) that extensively rely on motor function development. Haptics-enabled expert-in-the-loop motor function development for such applications is made possible through multilateral telerobotic frameworks. While several studies have validated the benefits of haptic interaction with an expert in motor learning, contradictory results have also been reported. This emphasizes the need for further in-depth studies on the nature of human motor learning through haptic guidance and interaction. The objective of this study was to design and evaluate expert-in-the-loop multilateral telerobotic frameworks with stable and human-safe control loops that …

Modelling And Analysis Of Smart Grids For Critical Data Communication, Abdulfattah Noorwali

Electronic Thesis and Dissertation Repository

Practical models for the subnetworks of smart grid are presented and analyzed. Critical packet-delay bounds for these subnetworks are determined, with the overall objective of identifying parameters that would help in the design of smart grid with least end-to-end delay.

A single-server non-preemptive queueing model with prioritized critical packets is presented for Home Area Network (HAN). Closed-form expressions for critical packet delay are derived and illustrated as a function of: i) critical packet arrival rate, ii) service rate, iii) utilization factor, and iv) rate of arrival of non-critical packets. Next, wireless HANs using FDMA and TDMA are presented. Upper and …