Our Research Projects

Grid-forming Battery Energy Storage System Characterization and Testing

Srdjan Miodrag Lukic
11/01/2018 - 08/01/2019

The goal of this project is to study the loading capabilities of an inverter operating in grid forming mode. A Battery Energy Storage System (BESS) may need to power up a microgrid after an outage, thus supplying all of the magnetizing currents to line-start machines as well as isolation transformers in the microgrid. There is a need to understand the capabilities of the state-of-the art BESS inverters to support all of these loads. Though simulating such behavior is feasible, experimental validation is required to guarantee that the system will operate as expected, and the BESS inverter protection will not trip.

Learning Deep Grammar Networks for Visual Question Answering

Tianfu Wu
01/08/2019 - 12/31/2019

this proposal study deep grammar networks in visual question answering (VQA) tasks. In addition to publicly available VQA benchmarks, this project will also work on a new VQA dataset which has been created and are gradually scaling up through the Critical Events Project in the Visual Narrative Cluster at NC State University with which PI Wu is affiliated.

CRII: SaTC: Secure Instruction Set Extensions for Lattice-Based Post-Quantum Cryptosystems

Aydin Aysu
02/15/2019 - 01/31/2021

The emergence of quantum computers poses a serious threat for existing cryptographic systems and necessitates deploying new encryption schemes relying on different mathematical principles to protect electronic devices in the post-quantum era of computing. While theoretical security of these systems are being thoroughly analyzed, attacks on their practical implementations are largely unexplored. The primary research goal of this project is to develop secure implementations for lattice-based cryptosystems—a major class of post-quantum encryption proposals. This work specifically addresses power and electromagnetic side-channel vulnerabilities on physical implementations of lattice-based cryptosystems that can extract secret information by observing its correlation to these computation effects.

To advance the understanding of secure lattice-based cryptosystem implementations, this project proposes a framework that uses instruction set extensions (ISEs) that is designed to mitigate power and EM side-channels and integrated into a customized processor which can map security-critical computations to the ISE. As a result, lattice encryption software can be composed from a set of secure hardware operations and the proposed framework can therefore be automated to secure and benchmark different lattice-based post-quantum proposals. The project will disseminate publications, open-source hardware and software, and it is targeted to bridge the computer architecture and hardware security research communities. This work will also help the ongoing post-quantum standardization effort in US.

Smart Battery Gauge for Continuous Battery Health Assessment at Butler Farm

Mo-Yuen Chow
08/16/2018 - 08/15/2019

The energy storage systems at Butler Farms are currently being monitored and maintained by NCEMC using the Smart Battery Gauge developed during Phase I of this project. The Smart Battery Gauge was developed to continuously monitor and provide live feedback about the State of Charge of the energy storage system at a rack level. This project will develop a State of Health (SOH) estimation algorithm that can provide meaningful insights into appropriate energy storage system operation in the microgrid to increase Remaining Useful Life (RUL) and State of Function (SOF).

Information Sciences: Computing Science: Intelligent Systems: Tractable Deep Learning: Structure vs. Scale in Data

Chau-Wai Wong
03/28/2019 - 03/27/2022

Many well-established problems in data science, such as data classification and clustering, raise unprecedented challenges in presence of complex and high dimensional data. Our interest is in inference applications, and more generally analysis problems, which in turn often resort to representation theory. Our goal is to build on the many previous and more recent accomplishments in Machine Learning and data science to develop the tradeoff of structure versus deep scale when representing data for either feature characterization and exploitation or inference applications. The vast array of applications in data science we typically encounter, and of interest herein, invariably seek to glean/use an extensive number of features of the data, which may also invoke the scale information whose necessary depth, as in deep learning (DL), remains an open problem We propose to develop an analytical framework for deep structure understanding using the existing Deep Leaning development as a source of inspiration. We proposed to investigate the tradeoffs of structure versus depth, noted above , as well as the known and open challenges of DL, such as Universal Approximation Property, and convergence issues. We expect our resulting algorithmic development to hence present great advantages in relation to the state of the art, with equal or better performance, with predictable behavior.

CAREER: Reconfigurable Microfluidic-Microbalance Sensors to Monitor and Optimize the Performance of Microphysiological Models

Michael Daniele
02/15/2019 - 01/31/2024

Microphysiological Model (MPM) systems, i.e. organ-on-a-chip systems, are poised to revolutionize pre-clinical testing by predicting human response better than animal models. We will develop a Vascularization Monitoring System (VMS) which controls perfusion, while monitoring cell proliferation. The VMS will contain sensors for continuous monitoring of perfusion pressure, perfusion flow rate, and electrical impedance.

FoMR: Post-Silicon Microarchitecture

Eric Rotenberg
08/01/2018 - 07/31/2021

This project explores new ways to exploit field-programmable hardware (FPGA) building blocks when they are co-mingled within and among the pipeline stages of various general-purpose core types, including superscalar cores, wide-vector cores, and trace processors. In particular, the PI addresses the topics called-out in the FoMR RFP under: “Utilization of new microarchitecture building blocks, such as reconfigurable logic to boost IPC”.

EAGER: RF Switches Using 2D Phase Change Materials

Spyridon Pavlidis
08/15/2018 - 01/31/2020

Supplemental funds to support research experiences for undergraduates (REUs) are sought. These undergraduates will participate in the development of synthesis and characterization tools for 2D phase change materials.

Proof-of-Concept Study for Integrated Patch Recorder for Fetal Health Monitoring

Michael Daniele, Edgar J Lobaton
08/15/2018 - 08/14/2020

Proof of concept investigation for the engineering of novel acoustic-based fetal heart rate monitors.

NSF Workshop on Reconfigurable Sensor Systems Integrated with Artificial Intelligence and Data Harnessing to Enable Personalized Medicine

Michael Daniele, Veena Misra
09/15/2018 - 08/31/2019

The focus of this multi-phased workshop is to determine future strategies for advancing the fundamental understanding and engineering of reconfigurable sensor systems by integrating hardware with data harnessing, real-time learning, and artificial intelligence capabilities. Specifically, this workshop will define the state-of-the-art, necessary innovations, and future challenges facing the research and development of reconfigurable sensor systems for applications in understanding of human physiology, pathophysiology, metacognition, cognition, and behavioral psychology.

Continuation of Nutrient Effects on Sweetpotato Yield and Shape

Michael D. Boyette, Natalie Genevieve Nelson, & Jonathan R. Schultheis
03/01/2018 - 02/28/2020

The objectives of this research continue and build upon findings of a project begun in 2016 to investigate the influence of different cultural practices, especially different nitrogen types and timing of applications, on the shape and size development and yields of multiple sweetpotatoes varieties.
Additional specific aims are to:
1. Continue to fine tune the unique equipment and analytical methodologies that have been developed in the last two years to scan and analyze the shape and size characteristics of different treatment populations of sweetpotatoes.
2. The ultimate aim of this work is to minimize the production of over/under sized and misshappened sweetpotatoes by identifying those numerous factors that influence size and shape.

Wearable Physiological Monitors for Measuring Canine Stimulus Responses

David L Roberts, Alper Yusuf Bozkurt, & Margaret E. Gruen
07/01/2018 - 09/30/2019

This work will provide a coding system and results of coding behavioral responses of dogs during stimulus testing trials conducted by ORNL staff. The aim is to provide expert interpretation of dogs’ behavioral responses captured on video by ORNL staff during experimental sessions. NCSU will provide a system for coding behaviors pertinent to the behavioral responses identified in video recordings, as well as the result of the application of that system to a corpus of video data provided by ORNL. Design and application of the system will follow existing practices commonly used in scientific studies of veterinary behavior.

Economical Data-Fused Grid Edge Processor (EDGEPRO) for Future Distribution Grid Control Applications

Ning Lu, David Lee Lubkeman
04/01/2019 - 02/20/2022

The proposed concept entails the research, development, and demonstration of an economical, data-fused grid edge processor (EDGEPRO) that can generate required data to support flexible grid operations by processing raw data from various existing sources (e.g., smart transformers, smart pole-top sensors, distribution automation (DA) controllers, smart inverters). The edge device will process high-speed and high-volume data by leveraging data fusion and machine learning (ML) technologies, making and executing local grid control decisions, and communica-ting certain processed data with other control systems, the cloud, and/or the utility control center. Because of its capabilities, the EDGEPRO will be able to calculate “virtual meter” data that, will eliminateing the need for installing additional grid monitoring sensors and devices at many feeder locations such as a service transformer, to and reduce the overall cost of flexible grid control. The target cost for the advanced edge data processor is $3000 USD per unit.

Intelligent, Grid-friendly 1MVA Medium Voltage Extreme Fast Charger

Srdjan Miodrag Lukic, Kenneth Allan Dulaney, & Iqbal Husain
10/01/2018 - 12/31/2021

North Carolina State University (NCSU), ABB, and New York Power Authority (NYPA) will develop and demonstrate an intelligent, medium voltage (MV) extreme fast charger with integrated generation and storage capabilities that will help avoid negative impacts of extreme fast charging on the nation’s electric grid. The charger will be capable of delivering a combined 1 MVA to five vehicles, while enabling a charge rate of up to 350 kW per stall. The system will consist of a solid-state transformer (SST), which connects directly to the medium voltage (MV) and delivers power to a shared, 1,000 V DC bus, to which multiple vehicles, generation (PV), and storage units can be connected through partial-power DC/DC converters. The DC bus will be protected using novel intelligent solid-state circuit breakers, developed by ABB for this effort. Each vehicle will interface to the DC bus through a correctly sized DC/DC converter, allowing charging in the 50 kW to 350 kW range at the vehicle battery voltage. Therefore, this station will be capable of supporting state-of-the-art electric vehicles as well as the next generation of vehicles operating at higher voltages and faster charging rates.

An Innovative Secure Millimeter Wave (mmWave) Machine to Machine (M2M) Communication Network for Operating Drones

Ismail Guvenc, Huaiyu Dai
12/18/2018 - 09/30/2019

The use of Radio Controlled (RC) Unmanned Aerial Vehicles (UAVs) or Drones, for civilian and commercial purposes have been growing steadily for the past decade. Their non-military use range from responding to Hurricane Harvey to rescuing swimmers caught in 10-ft swells, and from pizza delivery to Prime Air, the Amazon drone delivery system. UAV’s have historically had a limited flight radius dictated by line-of-sight radio controllers; however, distance limitation from which the drone operator can control them can be bypassed by using commercial wireless networks to control the drone. It is expected that billions of 5G mobile and Internet-of-Things (IoT) wireless devices will be all connected with the new millimeter wave frequency bands. We propose to analyze and validate the hypotheses that 1) using mmWave with antennas tilted upward for RF coverage in the sky can lead to a secure and reliable wireless network for UAV/Drone operation, 2) there is a unique opportunity to use NOMA (Non Orthogonal Multiple Access) with the primary mmWave beam aimed towards a swarm of Drones to control them with high security and efficiency, 3) further increase security, reliability, and spectral efficiency by using co-operative communications among the swarm of Drones and 4) this swarm of drones can be effectively used for emergency recovery of critical infrastructure such as a cyber compromised power grid that requires a black restart.

Capacitive Micromachined Ultrasonic Transducer Arrays for Air-Coupled Acoustic Microtapping

Omer Oralkan, Feysel Yalcin Yamaner
06/01/2018 - 12/31/2019

This collaborative work between the research teams of North Carolina State University (NCSU) and University of Washington (UW) aims to build a dynamic elastography imaging system for soft tissue. The imaging system developed by the UW Team has demonstrated the feasibility of a non-contact dynamic elastography imaging of the cornea. The system has mainly two components. An ultrasonic transducer that generates a focused beam to excite the soft tissue (micro-tapping) and an optical coherence tomography unit that captures the mechanical waves generated in the tissue. The work in this project covers the fabrication and system integration of the ultrasonic transducer arrays. The target prototype will have a precise transmit focus control on the surface of the soft tissue and create lateral beam oscillations for shear wave imaging.

Next-Generation Non-Surgical Neurotechnology: Multifocal Integrated Non-Invasive Device for Sensing and Stimulation (MINDSS)

Omer Oralkan
04/01/2019 - 04/25/2020

In this project we will develop capacitive micromachined ultrasound transducer (CMUT) arrays for neurostimulation, capable of focused stimulation of a

Planning Grant: Engineering Research Center for Rapid Innovations in SystEms Engineering and Agricultural Sustainability (RiseEnAg)

Cranos Williams, Michael Kudenov, & Rosangela Sozzani
09/01/2018 - 08/31/2019

We propose this planning grant to fund activities that will crystallize the engineering research theme and further define the research thrusts that are needed to accomplish the targeted societal impact of the Engineering Research Center for Accelerating Agricultural Sustainability from Seed to Table. This Engineering Research Center proposes integrative systems solutions and innovative strategies that will address the challenges associated with food security in the 21st century. We anticipate that the engineering solutions and decision support systems that are developed as part of this ERC will accelerate the discovery breeding and management strategies for increasing crop yield under current resource constraints and enhance crop robustness to minimize losses that occur at various stages of the food supply chain. The ERC will have four synergistic research thrusts: 1) Sensor Development, Calibration, and Integration; 2) Data Mining, Machine Learning, and Multiscale Modeling for Improving Plant Yield, Robustness, and Development; 3) Heterogenous Testbeds for Inducing and Monitoring Complex Growth Conditions; and 4) Data Management Cyberinfrastructure and High Speed Computing Architecture Development.

VOC for digitally-enabled early remote sensing of Fusarium Head Blight

Ralph A. Dean, Omer Oralkan, & Yeon Oh
08/01/2018 - 07/31/2019

The overall goal of this project is to develop, test and fabricate a multi-element CMUT sensor that can selectively detect VOCs produced in real-time during early infection of wheat heads by Fusarium graminearum.

Wide-Area Control of New York State Power Grid using FACTS and Wind Farms, FREEDM Core project

Aranya Chakrabortty
08/16/2018 - 06/30/2020

The objective of this research will be on improving dynamic performance of New York State (NYS) power grid using supplementary Wide-Area Damping Control (WADC) with shunt-connected FACTS devices and Wind farms as the control actuators. The NY state is moving towards more renewable generation. The state of New York already undertaken a comprehensive energy strategy, known as Reforming Energy Vision (REV), for building a clean, more resilient, and affordable energy system. One of the major goals of REV is to reach 50% renewable generation by the year 2030. Bulk wind power integration will be a major contributor in reaching this goal. Different research conducted by New York Power Authority (NYPA) and FREEDM Systems Center have shown that high wind penetration can impact the grid oscillation properties adversely and can induce poorly damped inter-area oscillatory modes. This could result in destabilization of the power grid. In wake of this scenario, NYPA is considering to implement Wide Area Controllers within their territory. NYPA has already installed multiple Phasor Measurement Units (PMUs) all across the grid. Currently, data stream from PMUs is being used to provide Wide-Area Situational Awareness (WASA) for the system operators; however, this data is not being used for any decision making and control-based remedial action for grid operation. Recently more emphasis is given to use this Wide-Area Measurement Systems (WAMS) in order to design oscillation damping controllers. This research will look into explore the feasibility, constraints and possible solutions for the real-life implementation of wide-area damping controllers through FACTS and Wind farms in New York State Grid.