Computer Sciences Commons^™

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

Convex Relaxations For Particle-Gradient Flow With Applications In Super-Resolution Single-Molecule Localization Microscopy, Hesam Mazidisharfabadi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Single-molecule localization microscopy (SMLM) techniques have become advanced bioanalytical tools by quantifying the positions and orientations of molecules in space and time at the nanoscale. With the noisy and heterogeneous nature of SMLM datasets in mind, we discuss leveraging particle-gradient flow 1) for quantifying the accuracy of localization algorithms with and without ground truth and 2) as a basis for novel, model-driven localization algorithms with empirically robust performance. Using experimental data, we demonstrate that overlapping images of molecules, a typical consequence of densely packed biological structures, cause biases in position estimates and reconstruction artifacts. To minimize such biases, we develop …

First Year Students' Experience In A Cyber World Course - An Evaluation, Frank Breitinger, Ryan Tully-Doyle, Kristen Przyborski, Lauren Beck, Ronald S. Harichandran

Electrical & Computer Engineering and Computer Science Faculty Publications

Although cybersecurity is a major present concern, it is not a required subject in University. In response, we developed Cyber World which introduces students to eight highly important cybersecurity topics (primarily taught by none cybersecurity experts). We embedded it into our critical thinking Common Course (core curriculum) which is a team-taught first-year experience required for all students. Cyber World was first taught in Fall 2018 to a cohort of over 150 students from various majors at the University of New Haven. This article presents the evaluation of our Fall taught course. In detail, we compare the performance of Cyber World …

Hail Detection Using Dual Polarization Weather Radar, Alfonso Ladino Rincon

International Programs

This poster highlights how active remote sensors such as weather radar are completely useful for hail detection given its feature and the information they produce. Hail detection is already well studied by the atmospheric scientific community and dual polarimetric variables values for hail signature are presented according to those advances. Then, a supervised classification technique is showed to illustrated how machine learning can be integrated to radar information for automatic hail detection. However, this fuzzy logic algorithm has the capability to distinguish between meteorological and non-meteorological echoes. This automatic information might help forecasters from National Weather Services – NWS to …

A Framework To Support Automatic Certification For Self-Adaptive Systems, Ioannis Nearchou

Masters Theses

Presently, cyber-physical systems are increasingly being integrated into societies, from the economic sector to the nuclear energy sector. Cyber-physical systems are systems that combine physical, digital, human, and other components, which operate through physical means and software. When system errors occur, the consequences of malfunction could negatively impact human life. Academic studies have relied on the MAPE-K feedback loop model to develop various system components to satisfy the self-adaptive features, such that violation of the safety requirements can be minimized. Assurance of system requirement satisfaction is argued through an industrial standard form, called an assurance case, which is usually applied …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Graduate Student Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Three-Dimensional (3d) Reconstruction Of Ultrasound Foetal Images Using Visualisation Toolkit (Vtk), Henry Dusim Grace Anabela

Student Works (2020-2029)

Three-dimensional (3D) reconstruction of two-dimensional (2D) ultrasound images has been used for easier analysing of anatomy related to abnormalities. 3D image reconstruction is required as it helps doctors interpret results and assess any defects quickly. Not only does it improve the accuracy of diagnosis, but it also saves a lot of time. In this thesis, 295 slices of 2D ultrasound images of a foetal are used to do 3D reconstruction using surface rendering, volume rendering, and multiplanar rendering technique. A similar software known as 3D Slicer is also used to reconstruct the ultrasound image for comparison purpose. VTK can easily …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Advanced Parallel Algorithms In Computational Electromagnetics, Shu Wang

Electrical and Computer Engineering ETDs

The rapid development of high performance computing has pushed the computational electromagnetic(CEM) towards high accuracy, high fidelity and extreme computational scales. There is a great need for existing CEM solvers to have enhanced parallelism and scaling capability. The purpose of this dissertation is to investigate advanced parallel algorithms for both frequency and time domain solvers.

In frequency domain, this work first develop the underpinnings of parallel preconditioning technique and high-order transmission condition in the context of multi-solver scheme. The result is a computing resource-aware and implementation wise compact solver. Then this work targeted at developing efficient algorithms for cases where …

Distributed Adaptive State Estimation And Tracking Scheme For Nonlinear Systems Using Active Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel adaptive neural network (NN) based distributed state estimation scheme for a heterogeneous sensor network (HSN), to estimate the state vector of an unknown nonlinear process/target by using sensed output when the target input remains unknown. The active nodes in the HSN can sense the target output based on the detection range. By using a connected graph, the active nodes will communicate their estimated state vector from their adaptive NN observer to other passive nodes in the neighborhood that cannot sense the target, so that they can estimate the target state vector. Next, a subset of …

Optimal Control Of Linear Continuous-Time Systems In The Presence Of State And Input Delays With Application To A Chemical Reactor, Rohollah Moghadam, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the optimal regulation of linear continuous-time systems with state and input delays is introduced by utilizing a quadratic cost function and state feedback. The Lyapunov-Krakovskii functional incorporating state and input delays is defined as a value function. Next, the Bellman type equation is formulated, and a delay Algebraic Riccati equation (DARE) over infinite time horizon is derived. By using the stationarity condition for the Bellman type equation, the optimal control input is obtained. It is demonstrated that the proposed optimal control input makes the closed-loop system asymptotically stable. Finally, simulation results confirm the theoretical claims by applying …

Dynamic Trajectory Generation And A Robust Controller To Intercept A Moving Ball In A Game Setting, Ravi Prakash, Laxmidhar Behera, Santhakumar Mohan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Complex and interactive robot manipulation skills, such as playing a game of table tennis against a human opponent, are a novel problem with multifaceted challenges. Accurate dynamic trajectory generation in order to respond to the tennis ball from the opponent and a novel control scheme for robust and high-performance tracking of the ball in such dynamic situations is a prerequisite to winning the game. In this paper, the dynamic movement primitives (DMPs) are employed for the stable generation of dynamic trajectories in the presence of environmental uncertainties such as ball position and velocity, opponent position and velocity and so on. …

Availability-Resilient Control Of Uncertain Linear Stochastic Networked Control Systems, Chandreyee Bhowmick, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

The resilient output feedback control of linear networked control (NCS) system with uncertain dynamics in the presence of Gaussian noise is presented under the denial of service (DoS) attacks on communication networks. The DoS attacks on the sensor-to-controller (S-C) and controller-to-actuator (C-A) networks induce random packet losses. The NCS is viewed as a jump linear system, where the linear NCS matrices are a function of induced losses that are considered unknown. A set of novel correlation detectors is introduced to detect packet drops in the network channels using the property of Gaussian noise. By using an augmented system representation, the …

Online Optimal Adaptive Control Of A Class Of Uncertain Nonlinear Discrete-Time Systems, Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis …

A Multicriteria Aware Optimal Routing Approach For Enhancing Quality Of Service In Device To Device Communication, Tilwari Valmik

Student Works (2020-2029)

As the world is moving towards the digitalization era, increasing demands of higher data rates, energy effiffifficiency, and seamless connectivity are skyrocketing. Device-to-Device (D2D) communication is one of the key technologies for future Fifth Generation (5G) network. D2D communication technology enhances network coverage, boosts spectral efficiency, has low latency, and enables the devices to communicate with each other, with partial or none involvement of network infrastructure. Therefore, factors of such nature make D2D communication a promising medium guarantying reliability to several telecommunications scenarios. D2D caters to all the needs of it’s users, from the high demand of peer-to-peer users for …

Next-Generation Self-Organizing Communications Networks: Synergistic Application Of Machine Learning And User-Centric Technologies, Chetana V. Murudkar

USF Tampa Graduate Theses and Dissertations

The telecommunications industry is going through a metamorphic journey where the 5G and 6G technologies will be deeply rooted in the society forever altering how people access and use information. In support of this transformation, this dissertation proposes a fundamental paradigm shift in the design, performance assessment, and optimization of wireless communications networks developing the next-generation self-organizing communications networks with the synergistic application of machine learning and user-centric technologies.

This dissertation gives an overview of the concept of self-organizing networks (SONs), provides insight into the “hot” technology of machine learning (ML), and offers an intuitive understanding of the user-centric (UC) …

Cup-Net: Compressed Ultrafast Photography Using Convolutional Neural Networks, Matthew Parker

ENGS 88 Honors Thesis (AB Students)

Compressed ultrafast photography (CUP) is a cutting-edge imaging technique that uses a variation of the traditional streak camera to obtain video at 100 billion frames per second with a single exposure. In order to achieve this level of temporal detail, CUP leverages compressed sensing (CS). Compressed sensing theory states that a compressed representation of an image can be directly acquired using a non-adaptive measurement matrix so long as the encoding matrix follows certain properties such as restrictive isometry and incoherence. This compressed representation of the original scene can later be reconstructed back into the original form. CUP applies CS by …

Monte Carlo Tree Search Applied To A Modified Pursuit/Evasion Scotland Yard Game With Rendezvous Spaceflight Operation Applications, Joshua A. Daughtery

Theses and Dissertations

This thesis takes the Scotland Yard board game and modifies its rules to mimic important aspects of space in order to facilitate the creation of artificial intelligence for space asset pursuit/evasion scenarios. Space has become a physical warfighting domain. To combat threats, an understanding of the tactics, techniques, and procedures must be captured and studied. Games and simulations are effective tools to capture data lacking historical context. Artificial intelligence and machine learning models can use simulations to develop proper defensive and offensive tactics, techniques, and procedures capable of protecting systems against potential threats. Monte Carlo Tree Search is a bandit-based …

Work-In-Progress: Augmented Reality System For Vehicle Health Diagnostics And Maintenance, Yuzhong Shen, Anthony W. Dean, Rafael Landaeta

Electrical & Computer Engineering Faculty Publications

This paper discusses undergraduate research to develop an augmented reality (AR) system for diagnostics and maintenance of the Joint Light Tactical Vehicle (JLTV) employed by U.S. Army and U.S. Marine Corps. The JLTV’s diagnostic information will be accessed by attaching a Bluetooth adaptor (Ford Reference Vehicle Interface) to JLTV’s On-board diagnostics (OBD) system. The proposed AR system will be developed for mobile devices (Android and iOS tablets and phones) and it communicates with the JLTV’s OBD via Bluetooth. The AR application will contain a simplistic user interface that reads diagnostic data from the JLTV, shows vehicle sensors, and allows users …

Efficient Hardware Implementations Of Bio-Inspired Networks, Anakha Vasanthakumaribabu

Dissertations

The human brain, with its massive computational capability and power efficiency in small form factor, continues to inspire the ultimate goal of building machines that can perform tasks without being explicitly programmed. In an effort to mimic the natural information processing paradigms observed in the brain, several neural network generations have been proposed over the years. Among the neural networks inspired by biology, second-generation Artificial or Deep Neural Networks (ANNs/DNNs) use memoryless neuron models and have shown unprecedented success surpassing humans in a wide variety of tasks. Unlike ANNs, third-generation Spiking Neural Networks (SNNs) closely mimic biological neurons by operating …

Evaluating Driving Performance Of A Novel Behavior Planning Model On Connected Autonomous Vehicles, Keyur Shah

Honors Scholar Theses

Many current algorithms and approaches in autonomous driving attempt to solve the "trajectory generation" or "trajectory following” problems: given a target behavior (e.g. stay in the current lane at the speed limit or change lane), what trajectory should the vehicle follow, and what inputs should the driving agent apply to the throttle and brake to achieve this trajectory? In this work, we instead focus on the “behavior planning” problem—specifically, should an autonomous vehicle change lane or keep lane given the current state of the system?

In addition, current theory mainly focuses on single-vehicle systems, where vehicles do not communicate with …

Reducing Run-Time Adaptation Space Via Analysis Of Possible Utility Bounds, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the “curse of dimensionality", increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be …

Side-Channel Power Resistance For Encryption Algorithms Using Implementation Diversity, Ivan M. Bow

Electrical and Computer Engineering ETDs

This thesis paper investigates countermeasures to hardware side-channel attacks and proposes a new solution to this ever growing threat against data integrity and security. The side-channel attack methods, differential power analysis and correlation power analysis, are both very powerful techniques and are used to gain access to secrets inside of a field programmable gate array that are otherwise inaccessible, in particular the cryptographic key for the Advanced Encryption Standard algorithm. To counter these attacks, we propose a method of changing the internal hardware configuration of the field programmable gate array using dynamic partial reconfiguration. Using this method, we change the …

A Real-Time Internet Of Things (Iot) Based Affective Framework For Monitoring Emotions In Infants, Alhagie Sallah

Electrical Engineering Theses

An increase in the number of working parents has led to a higher demand for remotely monitoring activities of babies through baby monitors. The baby monitors vary from simple audio and video monitoring frameworks to advance applications where we can integrate sensors for tracking vital signs such as heart rate, respiratory rate monitoring. The Internet of Things (IoT) is a network of devices where each device can is recognizable in the network. The IoT node is a sensor or device, which primarily functions as a data acquisition unit. The data acquired through the IoT nodes are wirelessly transmitted to the …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

College Of Engineering Senior Design Competition Spring 2020, University Of Nevada, Las Vegas

Fred and Harriet Cox Senior Design Competition Projects

Part of every UNLV engineering student’s academic experience, the senior design project stimulates engineering innovation and entrepreneurship. Each student in their senior year chooses, plans, designs, and prototypes a product in this required element of the curriculum. A capstone to the student’s educational career, the senior design project encourages the student to use everything learned in the engineering program to create a practical, real world solution to an engineering challenge. The senior design competition helps focus the senior students in increasing the quality and potential for commercial application for their design projects. Judges from local industry evaluate the projects on …

An Fpga-Based Hardware Accelerator For The Digital Image Correlation Engine, Keaten Stokke

Graduate Theses and Dissertations

The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when …

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

Partial Discharge Classification For Xlpe Cable Joints Using K Nearest Neighbors Algorithm, Mohd Salleh Muhammad Shairazi

Student Works (2020-2029)

Due to excellent mechanical and electrical properties, cross-linked polyethylene (XLPE) cables are commonly used in the power industry. However, cable joints are the weakest part of XLPE cables and are susceptible to insulation failures. Cable joint insulation breakup can cause large losses for power companies. It is therefore necessary to evaluate the consistency of the insulation for early detection of insulation failure. It is known that there is a link between the partial discharge (PD) and the quality of the insulation. PD analysis is an important tool for assessing the quality of insulation in cable joints. In this study, XLPE …