Self-powered Time-Keeping and Time-of-Occurrence Sensing

Self-powered and passive Internet-of-Things (IoT) devices (e.g. RFID tags, financial assets, wireless sensors and surface-mount devices) have been widely deployed in our everyday and industrial applications. While diverse functionalities have been implemented in passive systems, the lack of a reference clock limits the design space of such devices used for applications such as time-stamping sensing, recording and dynamic authentication. Self-powered time-keeping in passive systems has been challenging because they do not have access to continuous power sources. While energy transducers can harvest power from ambient environment, the intermittent power cannot support continuous operation for reference clocks. The thesis of this dissertation is to implement self-powered time-keeping devices on standard CMOS processes. In this dissertation, a novel device that combines the physics of quantum tunneling and floating-gate (FG) structures is proposed for self-powered time-keeping in CMOS process. The proposed device is based on thermally assisted Fowler-Nordheim (FN) tunneling process across high-quality oxide layer to discharge the floating-gate node, therefore resulting in a time-dependent FG potential. The device was fully characterized in this dissertation, and it does not require external powering during runtime, making it feasible for passive devices and systems. Dynamic signature based on the synchronization and desynchronization behavior of the FN timer is proposed for authentication of IoT devices. The self-compensating physics ensure that when distributed timers are subjected to identical environment variances that are common-mode noise, they can maintain synchronization with respect to each other. On the contrary, different environment conditions will desynchronize the timers creating unique signatures. The signatures could be used to differentiate between products that belong to different supply-chains or products that were subjected to malicious tampering. SecureID type dynamic authentication protocols based on the signature generated by the FN timers are proposed and they are proven to be robust to most attacks. The protocols are further analyzed to be lightweight enough for passive devices whose computational sources are limited. The device could also be applied for self-powered sensing of time-of-occurrence. The prototype was verified by integrating the device with a self-powered mechanical sensor to sense and record time-of-occurrence of mechanical events. The system-on-chip design uses the timer output to modulate a linear injector to stamp the time information into the sensing results. Time-of-occurrence can be reconstructed by training the mathematical model and then applying that to the test data. The design was verified to have a high reconstruction accuracy

Quasi-Self-Powered Piezo-Floating-Gate Sensing Technology for Continuous Monitoring of Large-Scale Bridges

Developing a practical framework for long-term structural health monitoring (SHM) of large structures, such as a suspension bridge, poses several major challenges. The next generation of bridge SHM technology needs to continuously monitor conditions and issue early warnings prior to costly repair or catastrophic failures. Additionally, the technology has to interpret effects of rare, high-impact events like earthquakes or hurricanes. The development of this technology has become an even higher priority due to the fact that many of the world's bridges are reaching the end of their designed service lives. Current battery-powered wireless SHM methods use periodic sampling with relatively long sleep-cycles to increase a sensor's operational life. However, long sleep-cycles make the technology vulnerable to missing or misinterpreting the effect of a rare event. To address these practical issues, we present a novel quasi-self-powered sensing solution for long-term and cost-effective monitoring of large-scale bridges. The approach we propose combines our previously reported and validated self-powered Piezo-Floating-Gate (PFG) sensor in conjunction with an ultra-low-power, long-range wireless interface. The physics behind the PFG's operation enable it to continuously capture and store local, cumulative information regarding dynamic loading conditions of the bridge in non-volatile memory. Using extensive numerical and laboratory studies, we demonstrate the capabilities of the PFG sensor for predicting structural conditions. We then present a system level design that adapts PFG sensing for SHM in bridges. A challenging aspect of SHM in large-scale bridges is the need for long-range wireless interrogation, as many portions of the structure are not easily accessible for continual inspection and portions of the bridge cannot be frequently taken out-of-service. We show that by combining self-powered PFG sensors with a small battery and optimized long-range active wireless interface, we can realize a quasi-self-powered system that easily achieves a continuous operating lifespan in excess of 20 years. The efficiency and feasibility of the proposed method is verified in a case study of the Mackinac Bridge in Michigan, the longest suspension bridge across anchorages in the Western Hemisphere. Associated data from the deployment are discussed, in addition to limitations, challenges, and additional considerations for widespread field deployment of the proposed SHM framework

Convergence of Intelligent Data Acquisition and Advanced Computing Systems

This book is a collection of published articles from the Sensors Special Issue on "Convergence of Intelligent Data Acquisition and Advanced Computing Systems". It includes extended versions of the conference contributions from the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, as well as external contributions

Concurrent Measurements of Inflow, Power Performance and Loads for a Grid-Synchronized Cross-Flow Turbine Operating in a Tidal Estuary

The adaptation of sustainable fluid energy conversion technologies, such as wind or tidalenergy, requires numerical modeling tools that are able to accurately predict device performance and loading in an effort to reduce the costs of turbines, deployment platforms and mooring structures. To validate models, data sets from turbines operating in real flow environments are required. Particularly for tidal energy, data sets of inflow (tidal current resource), power performance (electrical power and shaft speed), and thrust loading for any scale device are rare because the work to date has largely been funded by private developers and the data is not made publicly available. This “silos” the development of knowledge around operating devices to individual developers, which slows the pace of commercialization for the technology sector as a whole. The research project presented here utilized an existing tidal turbine, a modified New EnergyCorp EVG-025 vertical axis cross flow turbine (3.2m dia. X 1.7m tall), deployed at the UNH Tidal Energy Test Site at the Memorial Bridge in Portsmouth, NH. Significant improvements were made to the existing system, including the first grid synchronous operation, the development of a new data acquisition system (DAQ) and adding time synchronization across new and existing DAQ’s to allow for accurate performance and load characterization of the device. A significant data acquisition campaign was conducted during the fall of 2021, with over 750kWh hours of renewable tidal energy delivered to the NH grid during 29 days of turbine operation. Turbine power performance and thrust loading was characterized over a range of inflow operating conditions. Spectral analysis indicates the effects of turbulent structures on thrust loading and power output. The results further highlight the need for accurate instrument location and temporal resolution for accurate tidal resource characterization when siting new projects. This data set with all the concurrent measurements is sufficiently detailed for numerical model validation in real tidal flows. After significant quality control (QC) processing, the data set has been published in a public database, MHKR/PRIMRE. (Link: MHKDR-394

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program

The activities are reported of the NASA Biomedical Applications Team at Southwest Research Institute between 25 August, 1972 and 15 November, 1973. The program background and methodology are discussed along with the technology applications, and biomedical community impacts

ICP Enhanced HIPIMS System Design and Characterisation

A custom inductively coupled plasma assisted high-power impulse magnetron sputtering system was designed and assembled for the study of E x B plasma spoke instabilities. The magnetron plasma was characterized using time-resolved Langmuir probe, floating probe, optical emission spectroscopy and high-speed camera methods. Fluctuations in the floating potential of the plasma were observed, this was attributed to localized rotating spokes. Localized spoke structures were observed in the camera footage, the shape and size of these spokes were found to be influenced by argon gas rarefaction

Proceedings of the 2009 Coal Operators\u27 Conference

Proceedings of the 2009 Coal Operators\u27 Conference. All papers in these proceedings are peer reviewed. ISBN: 978 1 920806 95 8

Advances in Batteries, Battery Modeling, Battery Management System, Battery Thermal Management, SOC, SOH, and Charge/Discharge Characteristics in EV Applications

The second-generation hybrid and Electric Vehicles are currently leading the paradigm shift in the automobile industry, replacing conventional diesel and gasoline-powered vehicles. The Battery Management System is crucial in these electric vehicles and also essential for renewable energy storage systems. This review paper focuses on batteries and addresses concerns, difficulties, and solutions associated with them. It explores key technologies of Battery Management System, including battery modeling, state estimation, and battery charging. A thorough analysis of numerous battery models, including electric, thermal, and electro-thermal models, is provided in the article. Additionally, it surveys battery state estimations for a charge and health. Furthermore, the different battery charging approaches and optimization methods are discussed. The Battery Management System performs a wide range of tasks, including as monitoring voltage and current, estimating charge and discharge, equalizing and protecting the battery, managing temperature conditions, and managing battery data. It also looks at various cell balancing circuit types, current and voltage stressors, control reliability, power loss, efficiency, as well as their advantages and disadvantages. The paper also discusses research gaps in battery management systems.publishedVersio

An Optogenetic Brain-machine Interface for Spatiotemporal Neuromodulation

Direct neural stimulation has recently become a standard therapy for neurological disorders such as Parkinson\u27s Disease, Essential Tremors, and Dystonia. Currently, deep brain electro-stimulation and neuro-pharmaceutical treatments are the dominant therapeutic options available to the public. As our understanding of brain function and neurological diseases improves, we are able to develop more advanced neuromodulation techniques. These methods could become viable treatment solutions for treating brain dysfunction. Optogenetics, first introduced by a research team led by Karl Deisseroth at Stanford University, has proved to be a versatile technique with remarkable potential to be used in treatments for brain disorders, dysfunction, and injuries. Optogenetics makes use of light-gated ion channels and pumps, originally derived from certain types of algae or bacteria, to bi-directionally modulate the activity of neurons in mammals. By adopting new advances in the field of optics and photonics, including high-speed high-resolution spatial light modulators, solid-state lasers, and ultra-low noise photodetectors, we can build sophisticated devices which allow precise and resolute optical patterning in both the spatial and temporal domains. In this thesis, I present an optogenetic brain-machine interface that offers high spatiotemporal neuromodulation functionality. The incorporation of imaging and sensing devices of neural activity into the system allowed us to run multiple independent experiments. These optogenetic experiments include closed-loop modulation of multiple areas of tissue, investigating the causal relationship between neural activity and blood flow, and quantifying the relationship between neural activity and cell metabolism. To understand light to brain tissue interaction in a rat brain, a device has been developed which allows one to extract the optical properties throughout the tissue. Utilizing this data, Monte Carlo software was used to predict light distribution within the brain. This has far reaching effects for the future use of optogenetics. Our approach will allow the investigator the ability to precisely understand how introduced light will be distributed within the rat brain where light-gated ion channels have been genetically expressed. This becomes noticeably important when attempting to determine which areas of the brain tissue will and won\u27t be modulated by the introduced light. Due to the many advantages optogenetics inherently provides, it is a rising prospect for novel neuromodulation therapies. With continued research and development of devices, we could create new therapies for disabilities that arise from dysfunction of the human brain