Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Machine Learning (2)
- Machine learning (2)
- Optimization (2)
- Aerosol Synthesis (1)
- Colloidal particles (1)
-
- Computational Methods (1)
- Conducting Polymer (1)
- Cyber-physical systems (1)
- Data Sonification (1)
- Deep learning (1)
- Edge computing (1)
- Electrical conductivity (1)
- Electronic design automation (1)
- Ensemble Control (1)
- Global optimization (1)
- Industrial internet of things (1)
- Infinite-Dimensional System (1)
- Integrated circuits (1)
- Large-Scale Systems (1)
- Linear Systems (1)
- Medical imaging (1)
- Networked control systems (1)
- Nonlinear dynamical systems (1)
- Organic processability (1)
- Resonant machine learning (1)
- Statistical Learning (1)
- Statistics (1)
- Task-based image quality (1)
- Vapor Phase synthesis (1)
- Wireless sensor networks (1)
Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
McKelvey School of Engineering Theses & Dissertations
Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …
Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao
Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao
McKelvey School of Engineering Theses & Dissertations
Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …
Algebraic, Computational, And Data-Driven Methods For Control-Theoretic Analysis And Learning Of Ensemble Systems, Wei Miao
McKelvey School of Engineering Theses & Dissertations
In this thesis, we study a class of problems involving a population of dynamical systems under a common control signal, namely, ensemble systems, through both control-theoretic and data-driven perspectives. These problems are stemmed from the growing need to understand and manipulate large collections of dynamical systems in emerging scientific areas such as quantum control, neuroscience, and magnetic resonance imaging. We examine fundamental control-theoretic properties such as ensemble controllability of ensemble systems and ensemble reachability of ensemble states, and propose ensemble control design approaches to devise control signals that steer ensemble systems to desired profiles. We show that these control-theoretic properties …
Aerosol Vapor Synthesis Of Organic Processable Pedot Particles And Measuring Electric Conductivity Using A 3d Printed Probe Station, Yang Lu
McKelvey School of Engineering Theses & Dissertations
Conducting polymers are organic semiconductors characterized by conjugated backbones (alternating single-double bonds) that enable mixed ionic-electronic conductivity. Their polymeric nature, tunable band structure and reversible redox capability have demonstrated fundamental advances in the fields ranging from electrochemical energy storage, sensing, to electro/photo catalysis and neuromorphic engineering. Conjugated backbones, the origin of all the unique physical and chemical properties associated with conducting polymers, prevent their solubility due to high lattice energy which hinders processing. Current solution utilizes a long-chain polymer (PSS) as dopants to render conducting polymer water dispersible (PEDOT:PSS). Nonetheless, PSS is highly acidic and hydrophilic limiting applicability with acid-incompatible …
Holistic Control For Cyber-Physical Systems, Yehan Ma
Holistic Control For Cyber-Physical Systems, Yehan Ma
McKelvey School of Engineering Theses & Dissertations
The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …
Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
McKelvey School of Engineering Theses & Dissertations
It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …
Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi
McKelvey School of Engineering Theses & Dissertations
A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …