Our Research Projects

Framatome – ECE Sr Design Fall2019-Spring2020 – Underwater Robot Blind Vision Recognition

Bobby Leonard Compton
08/29/2019 - 05/31/2020

Develop a means to detect objects that a robot encounters that cannot be visually seen by the operator thorough traditional camera means. Also, provide a means to notify the operator that the robot has encountered the unseen object.

Potentiometric Detection of Neuropeptides for Non-Invasive Monitoring of Stress

Spyridon Pavlidis
01/01/2020 - 12/31/2020

The objective of this proposal is to demonstrate the detection of neuropeptide Y (NPY), a biomarker for stress found in human sweat, using gold-based potentiometric sensors that are compatible with ASSIST’s Health and Environmental Tracker (HET) 2.0 biochemical sensor architecture.

Cybersecurity for Electric Power Systems

Mesut E. Baran, David M. Shafer, & Aranya Chakrabortty
10/01/2019 - 09/30/2022

Through multidisciplinary doctoral education in Cybersecurity for Electric Power Systems (CEPSE), North Carolina State University (NCSU) will increase its commitment to graduate training in two areas designated by the GAANN Program as critical to national need: Cybersecurity and Electrical Engineering. The goal of is to enlarge the pool of U.S. citizens and permanent residents who will pursue teaching and research careers in cybersecurity for electric power systems, thereby promoting workforce development and technological innovation impacting, national security, energy security, and environmental sustainability.

Fellowship for Graduate Student Sariful Islam Working on FREEDM Core Project Entitled “Next Generation Lightweight Electric Machines”, FREEDM Enhancement Project

Iqbal Husain
07/01/2018 - 06/30/2020

The objective of this project is to develop lightweight electric machines through a combination of approaches and explore the opportunities and challenges.

Broadband Wireless Access and Applications Center (BWAC) Membership Pool Agreement

Ismail Guvenc
10/01/2019 - 09/30/2021

A Fully Ultrasonic Approach for Combined Functional Imaging and Neuromodulation in Behaving Animals

Gianmarco Pinton, Omer Oralkan
09/01/2019 - 05/31/2022

Two very recent advancements have been transforming the field of medical ultrasound. First, the revolutionary discovery of ultrasound neuromodulation, which non-invasively targets and modulates activity in specific regions of the brain. Second, contrast-enhanced super-resolution, which can image microvessels at resolutions as small as ten microns, an order of magnitude smaller than the ultrasound diffraction limit, and at greater depths. To achieve this generational leap in performance super-resolution contrast imaging requires that tens of thousands of frames of data be rapidly acquired and analyzed, making this technique much more computationally and algorithmically intensive than standard ultrasound imaging. Furthermore, the skull presents a unique challenge because it aberrates and generates reverberations, which reduce the resolution detectability of contrast agents. Consequently, transcranial super-resolution imaging would be difficult if not impossible to translate to the brain in its current form with current clinical hardware, especially if 3-D imaging is desired (which it is for functional imaging). Combining ultrasonic neuromodulation with functional imaging relies on MRI to target the ultrasound focus and to assess the brain’s functional response. However, confinement in a magnet bore, which typically requires anesthesia and limits the range observable behavioral scenarios. Furthermore, fMRI is slow compared to the time scale of the neural response, which is on the order of tens to hundreds of milliseconds. There is a solution to these limitations, which our group proposes to achieve in this project by developing a fully ultrasonic approach that combines 3-D super-resolution functional imaging with neuromodulation in a single integrated ultrasound platform that can be used on behaving animals. Time reversal, in conjunction with a highly accurate acoustic simulation tool that we have developed, can correct for the aberrations induced by the skull morphology accurately focus ultrasound and improve detectability. New software and implementation approaches designed at UNC Chapel Hill, including our innovative adaptive multi-focus beamforming approach, will simultaneously target multiple regions of the brain and enable full 3-D volume acquisitions at volume frame rates over 5000 FPS, suitable for rapid (hundreds of milliseconds) functional imaging. Recent advances in ultrasound hardware will enable ultra-high frame rate processing. Our research team at UNC Chapel Hill is partnering with a world-leading transducer group at NCSU to develop a lightweight wearable neurostimulation array. Ultra-fast processors, large RAM buffers, GPUs, and high-bandwidth data transfer hardware will be utilized to handle challenging adaptive beamforming tasks and massive data acquisition. Our approach will be validated in partnership with Vanderbilt, who have been pioneering the field of neuromodulation in non-human primates. Our motivation is to develop an integrated ultrasound platform as a new approach for neurostimulation and blood flow-based functional ultrasound imaging in the whole brain, with a non-ionizing, non-invasive, low-cost technology that could be used for monitoring and modulation in behaving animals.

Membership in Broadband Wireless Access and Applications Center (BWAC) – NCSU Research Site

Ismail Guvenc
10/01/2019 - 09/30/2021

BWAC membership

Towards Ultra-Reliable Low-Latency Communications for 5G UAV Ecosystems: Collaborative Research Planning among NC State, NU and AU

Shih-Chun Lin
04/01/2019 - 12/31/2019

As one of the 5G envisioned services, ultra-reliable and low-latency communications (URLLC) aim to provide secure data transmissions from one end to another with ultra-high reliability and deadline-based low latency requirements, enabling tactile Internet, mission-critical Internet of Things, and vehicle safety applications. Meanwhile, unmanned aerial vehicles (UAVs) for wireless communications has drawn much attention as the mass production of high-performance, low-cost, intelligent UAVs become more practical and feasible, which empowers more functional diversity for 5G networks. Based on the PIs’ expertise at the three institutions, Dr. Lin at North Carolina State University (NC State), Dr. Kobayashi at Nagoya University (NU), and Dr. Shi at The University of Adelaide (AU), this project will initiate collaborative research discussion and external grant planning for introducing a holistic software-defined wireless architecture that ensures URLLC in 5G UAV ecosystems. Several teleconferences and onsite discussion at three institutions will be established. The project will also organize an international workshop in the International Conference on Materials and Systems for Sustainability (ICMaSS) 2019 in Nagoya, Japan, to bring together researchers in all relevant areas. The PIs will actively seek the feedback from industry partners to work with us on developing, testing and deploying the 5G UAV framework. Moreover, PIs plan to develop a visiting/exchange Ph.D. student program designed to allow graduate students to spend a semester (preferably in summer) at the PIs’ institutions for academic exchanges to enhance the cross-linkage between the lines of research pursued by the team members.

10kV SiC Integrated VSD Motor Drive

Subhashish Bhattacharya
08/01/2016 - 07/31/2020

The project team will develop an integrated MV SiC VSD drive and high speed motor for oil and gas industry compression system applications. To meet the power density and environmental requirements of an integrated drive, the team will develop and package a variable frequency drive topology utilizing 15kV SiC MOSFET devices, high-frequency inductors and dv/dt filters, and other customized peripherals including high temperature capacitors. The team will develop drive architectures, device controls and electrical integration techniques to take advantage of high speed SiC switching. Eaton’s state-of-the-art MV converter packaging and innovative integrated cooling concepts will produce a design capable of being fully integrated into a high speed motor with hermetically sealed enclosure. The team will use advanced machine design techniques to identify best candidate motors for integration based on system and motor performance goals, TRL, drive integration requirements and will develop solutions to address identified technology gaps.

2D Hybrid Material Architectures for Terahertz (2D-HyMaTer)

Ki Wook Kim
08/15/2019 - 03/14/2023

Theoretical modeling of the proposed 2D/3D hybrid structure and its application to the hot electron transistors will be undertaken in close coordination with the experimental effort for accurate understanding and optimization of the performance. Considering the large mismatch in the vertical and lateral dimensions of the device, a hierarchical approach will be adopted for the analysis. In particular, an atomistic modeling based on first-principles density functional theory and non-equilibrium Green’s function methods will be pursued to describe hot electron dynamics in the emitter-collector junctions. Detailed information such as electronic structures, band offset, and tunnel barrier potential will be extracted as a function of layer thickness, stacking sequence, atomic termination, etc. A variety of material combinations will be explored for the hybrid structure with a focus on 2D nitrides. The impact of the defects will also be examined numerically.

Differential Power Analysis of Deep Neural Networks with Mitigation at the Architecture Level

Aydin Aysu, Paul D. Franzon
10/01/2019 - 09/30/2020

This proposal analyzes the vulnerability of deep neural network hardware implementations against power/electromagnetic side-channel attacks and their effective and automatic mitigation through architectural enhancements and compiler support. We will enhance RISC-V based microcontrollers through custom instruction extensions and use side-channel aware compilers to let programmers write side-channel secure software. The project will tape-out proof-of-concept chips with the proposed techniques.

SMART SiC Power ICs Scalable, Manufacturable, and Robust Technology for SiC Power Integrated Circuits

Bongmook Lee
09/01/2019 - 08/31/2022

This collaborative project aims to develop scalable, manufacturable, and robust technology for SiC integrated circuits (SMART SiC ICs). To realize this goal, disruptive designs and processes will be developed to achieve integrated circuits of large scale (> 1 cm2) SiC Complementary Metal-Oxide-Semiconductor (CMOS) and high voltage (400 – 1200 V) lateral power MOSFETs (LDMOS) on 150 mm 4H-SiC substrates. The SMART SiC ICs will enable many applications requiring wide ranges of voltages and power ratings such as automotive, industrial, telecommunication, electronic data processing, energy harvesting, and power conditioning.

PAWR Platform Full Proposal: AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless

Ismail Guvenc, Rudra Dutta, & Mihail L. Sichitiu
09/01/2019 - 08/31/2024

We propose AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, a first-of-its-kind aerial wireless experimentation platform to be developed in close partnership between NCSU, Wireless Research Center of North Carolina (WRCNC), Mississippi State University (MSU), University of South Carolina (USC), City of Raleigh, Town of Cary, Town of Holly Springs, North Carolina Department of Transportation (NCDOT), and numerous other project partners. With a major focus being on aerial communications within low altitude airspace, AERPAW will develop a software defined, reproducible, and open-access advanced wireless platform with experimentation features spanning 5G technologies and beyond. NCSU, USC, and MSU researchers have existing UAS experimentation capabilities and ongoing experimental research activities involving wireless technologies spanning software defined radios (SDRs), LTE, WiFi, ultra-wideband (UWB), IoT, and millimeter wave (mmWave), which will form the initial baseline framework for the AERPAW platform. To deploy AERPAW, NCSU will work closely with NCDOT’s Integration Pilot Program, a three-year FAA project that allows BVLOS UAS experimentation for medical supply delivery in North Carolina, in close collaboration with NCSU, several UAS companies, municipalities, and a medical institution. Initial flight tests have already started within the Raleigh area, and will be expanding to other parts of the state in 2019 and beyond. Any additional FAA permits, as necessary, will be secured by AERPAW team in close collaboration with NCDOT.

CNS Core: Small: Collaborative: Towards Surge-Resilient Hybrid RF/VLC Networks

Ismail Guvenc, Yavuz Yapici
11/01/2019 - 10/31/2022

The proposed research will study a hybrid RF/VLC network where the number of IoT users that require wireless communications is significantly
larger than the number of RF base stations (BSs). Our research is organized into three synergistic research thrusts. First, in Thrust-1, a novel, hybrid NOMA VLC/RF wireless access will be introduced, for which surge-resilient RAT/LED assignment and NOMA transmission techniques will be developed. The proposed approach can schedule UEs/MTDs based on their QoS needs under traffic surges and/or network failures. Subsequently, Thrust-2 will introduce novel, resilient cross-system learning for self-organizing resource management, and explore the network resilience against censored information, i.e., information that is not available to the network due to failures or environmental changes. The proposed cross-system learning framework allows for feedback between multiple learning algorithms operating across RATs to devise optimal resource allocation strategies that guarantee the required QoS levels across RATs, even when information is censored. Finally, Thrust-3 will introduce testbeds and SDR experimental platforms to evaluate the findings from the first two research thrusts on multihop RF/VLC communications and LED selection with NOMA.

Sub-15 μW Passive Radio for Self-powered Sensor Systems

David Ricketts
09/01/2019 - 08/31/2020

A low power radio for self-powered systems.

CPS: Small: Data-Driven Reinforcement Learning Control of Large CPS Networks using Multi-Stage Hierarchical Decompositions

Aranya Chakrabortty
01/01/2020 - 12/31/2022

In the current state-of-the-art machine learning based real-time control and decision-making in large-scale complex networks such as electric power systems is largely bottlenecked by the curse of dimensionality. Even the simplest linear quadratic regulator design demands cubic numerical complexity. The problem becomes even more complex when the network model is unknown, due to which an additional learning time needs to be accommodated. In this 3-year NSF CPS proposal, we take a new stance for solving this problem, and propose a hierarchical or nested machine learning-based scheme for real-time control of extreme-dimensional networks. Our approach will be to design appropriate projection matrices by which a network can be divided into disparate sets of non-overlapping groups depending on the low-rank properties of their controllability grammian, and multiple sets of composite controllers can be learned independently for each group using model-free reinforcement learning. Accordingly, the control goals of the network will also be decomposed into local (microscopic) and global (macroscopic) reward functions. Local controllers will be designed via privacy preserving group learning, and the global controllers via model reduction and averaging. Sparsity-promoting structures will be imposed on top of the local controllers to reduce their communication complexity. Deep learning algorithms based on historical events will be used to train recurrent neural networks so that they can rapidly predicting these sparse projections after any disturbance event in the network. Throughout this entire exercise, wide-area control of power systems using streaming Synchrophasor data from Phasor Measurements Units (PMUs) will be treated as a driving example. Results will be validated using standard IEEE models, a simplified model of the Japanese power grid with high-scale solar penetration, and an Opal-RT model of the Duke Energy power grid integrated with the ExoGENI cloud computing network at the FREEDM Systems Center.

EIT Health Grant: SensUs 2019

Michael Daniele, Stefano Menegatti
01/01/2019 - 12/31/2019

As part of the 2019 SensUs Competition, the students and research of the team venture to design, construct and validate a sensor to detect and quantify biological medication for rheumatoid arthritis. SensUs provides SenseNC an opportunity to investigate a real-world issue in healthcare, while directly interfacing with the healthcare industry. These experiences will be valuable in current and future scientific endeavors for all team members.

Phase II IUCRC North Carolina State University: Broadband Wireless Access and Applications Center (BWAC)

Ismail Guvenc, Jacob James Adams, & Alexandra Duel-Hallen
09/15/2019 - 08/31/2023

The major goal of National Science Foundation’s Broadband Wireless Access and Applications Center (BWAC) lead by the University of Arizona is “advancing wireless technologies and providing cost-effective and practical solutions for next-generation (5G & beyond) wireless systems, millimeter-wave communications, wireless cybersecurity, shared-spectrum access systems, full-duplex transmissions, massive MIMO techniques, and others.” The North Carolina State University (NCSU) is planning to join NSF BWAC center starting in 2019 with at least four full industrial members. The addition of NCSU into BWAC will synergistically complement BWAC mission and vision, by introducing new and complementary research areas, including millimeter wave (mmWave) theory, circuits, antennas, and experimentation, drone based communications, visible light communications (VLC), antenna design for broadband communications, non-orthogonal multiple access (NOMA) and its variants, among other areas.

Three Dimensional Study of the Benefits of Hybrid Bonding

Paul D. Franzon
08/16/2019 - 05/15/2020

NCSU will investigate the advantages of Hybrid bonding in 3DICs.

III: Small: Collaborative Research: Cost-Efficient Sampling and Estimation from Large-Scale Networks

Do Young Eun
10/01/2019 - 09/30/2022

Recent years have witnessed that online social networks (OSNs) change the way people interact with each other and trigger a tremendous amount of attention in various disciplines because of their extensive applications and massive useful data. They are simply too large to be downloaded or stored locally, and the sheer size forces us to resort to ‘sampling’ for estimation and inference of massive networks in a compact manner.
In particular, sampling via random-walk crawling has been commonly considered as the only viable solution, which is feasible via the public yet restrictive local-neighborhood-only interfaces provided by OSNs,
for estimating the properties of users (nodes), their relationships (edges), and more sophisticated relationship among multiple users (subgraph patterns). Whereas there have been many efforts in the literature to advance our understanding on sampling via random-walk crawling, there are still important challenges that have been generally overlooked and remained unsolved.

The long-term goal of the proposed research is to build a strong theoretical foundation for the optimal sampling strategies and the optimal control of multiple random walks for the estimation and inference of massive networks in the cost-constrained environments in reality, going beyond the current Markov Chain Monte Carlo (MCMC) driven statistical theories and practices for graph sampling in the literature.