Within the broader human computer interaction research field, our lab is more specifically interested in HCI at the edge. We aim to address how humans can have rich interaction with wearable devices and embedded devices with low-cost, low-power, and with real time constraints. In that pursuit, we have primarily focused on capacitive sensing devices and models for interacting with multi-modal sensors. Below are the relevant publications from this research thrust:

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “An FPGA-Based Upper-Limb Rehabilitation Device for Gesture
Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks”, in IEEE Sensors Journal,
vol. 22, no. 4, pp. 3605-3615, 15 Feb, 2022. doi:10.1109/JSEN.2022.3141659 (Extended from ICFPT)

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “An FPGA-Based Upper-Limb Rehabilitation Device for Gesture
Recognition and Motion Evaluation using Multi-Task Recurrent Neural Networks”, in 2020 International
Conference on Field-Programmable Technology (ICFPT). Maui, HI, USA, 2020. doi:
10.1109/ICFPT51103.2020.00054.

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “FPGA-Based Gesture Recognition with Capacitive Sensor Array
using Recurrent Neural Networks”, in 28th IEEE Annual International Symposium on Field-Programmable
Custom Computing Machines (FCCM). Fayetteville, AR, USA, 2020. doi: 10.1109/FCCM48280.2020.00056.

H. Liu, E. Sanchez, J. Parkerson, A. Nelson*, “Gesture Classification with Low-Cost Capacitive Sensor Array
for Upper Extremity Rehabilitation”, in IEEE Sensors Conference 2019. Montreal, QC, Canada, 2019, pp. 1-4.
doi: 10.1109/SENSORS43011.2019.8956862. [Historic AR: 25-35]

A. Nelson*, G. Toth, D. Linders, C. Nguyen, S. Rhee “Replication of Smart-City, Internet of Things Assets in a
Municipal Deployment”, in IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6715-6724, Aug. 2019. doi:
10.1109/JIOT.2019.2911010 Impact Factor: 9.515

H. Liu, J. Parkerson, A. Nelson, “Connected Capacitive Sensor Array for Upper-Extremity Motor
Rehabilitation”, in 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and
Engineering Technologies (CHASE), Washington, DC, USA, 2018, pp. 17-18. doi: 10.1145/3278576.3278584.

A. Nelson*, S. McCombe-Waller, C. Patel, R. Robucci, N. Banerjee, “Evaluating Gesture Recognition
Algorithms for a Touchless Wearable Capacitor Sensor Array Input Device”, Journal of Rehabilitation and
Assistive Technologies Engineering, Volume 5, (2018). DOI: 10.1177/2055668318762063

S. Fager, T. Sorenson, S. Butte, A. Nelson, N. Banerjee, R. Robucci, “Integrating End-User Feedback in the
Early Development of a Novel Sensor Access System for Environmental Control”, in Disability and
Rehabilitation: Assistive Technologies (Taylor and Francis) Volume 13, (2018). DOI:
10.1080/17483107.2017.1328615.

A. Nelson*, D. Hoffman, G. Toth, C. Nguyen, S. Rhee, “Towards a Foundation for a Collaborative Replicable
Smart Cities IoT Architecture”, in Proceedings of the 2nd International Workshop on Science of Smart City
Operations and Platforms Engineering (SCOPE 2017), pp. 63-68. DOI: 10.1145/3063386.3063763.

A more recent focus of the lab has been embedded cybersecurity. Connected devices that make up the Internet of Things enable a wide array of attack surfaces on devices that have less maturity in cybersecurity. Moreover, these devices often do not have the same level of physical access security guarantees as servers and PCs. As part of a funded project by NIST, we began to investigate side-channel security of post-quantum cryptography algorithms. Below are the relevant papers:

 M. Fahr, H. Kippen, A. Kwong, T. Dang, J. Lichtinger, D. Dachman-Soled, D. Genkin, A. Nelson, R. Perlner,
A. Yerukhimovich, D. Apon, “When NIST PQC FIPS Flips: Experimental Lattice KEM Key Recovery via
Rowhammer,” (Submitted).

T. Kamucheka, A. Nelson, D. Andrews, M. Huang, “A Masked Pure-Hardware Implementation of Kyber
Cryptographic Algorithm,” (Submitted)

T. Kamucheka, M. Fahr, A. Nelson, D. Andrews, M. Huang, “Power-based Side Channel Attack Analysis on
PQC Algorithms”, in National Institutes of Standards and Technology Third PQC Standardization Conference.
Washington, DC, USA, 2021.

Machine learning has permeated many research fields. Our lab is interested in machine learning tasks with noisy or incomplete training data as well as machine learning with low resources or with real-time deadlines. The two major projects in this area are a collaborative effort to use Terahertz spectroscopy to identify breast cancer, and the cross-cutting gesture recognition research from the HCI topic. Below are the relevant works:

H. Liu, N. Vohra, K. Bailey, M. El-Shenawee, A. Nelson*, “Semantic Segmentation of Xenograft Tumor Tissues Imaged with Pulsed Terahertz Technology”, in 2022 IEEE International Symposium on Antennas and Propagation (to appear).

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “An FPGA-Based Upper-Limb Rehabilitation Device for Gesture
Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks”, in IEEE Sensors Journal,
vol. 22, no. 4, pp. 3605-3615, 15 Feb, 2022. doi:10.1109/JSEN.2022.3141659

H. Liu, N. Vohra, M. El-Shenawee, A. Nelson*, “Deep Learning Classification of Breast Cancer Tissue from
Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning”, Journal of
Infrared, Milli Terahz Waves (2022). doi: 10.1007/s10762-021-00839-x

N. Vohra, H. Liu, A. Nelson, K. Bailey, M. El-Shenawee, “Hyperspectral Terahertz Imaging and Optical
Clearance for Cancer Classification in Breast Tumor Surgical Specimen”, J. Med. Imag. 9(1), 014002 (2022),
SPIE, doi:10.1117/1.JMI.9.1.014002

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “An FPGA-Based Upper-Limb Rehabilitation Device for Gesture
Recognition and Motion Evaluation using Multi-Task Recurrent Neural Networks”, in 2020 International
Conference on Field-Programmable Technology (ICFPT). Maui, HI, USA, 2020. doi:
10.1109/ICFPT51103.2020.00054.

H. Liu, A. Panahi, D. Andrews, A. Nelson*, “FPGA-Based Gesture Recognition with Capacitive Sensor Array
using Recurrent Neural Networks”, in 28th IEEE Annual International Symposium on Field-Programmable
Custom Computing Machines (FCCM). Fayetteville, AR, USA, 2020. doi: 10.1109/FCCM48280.2020.00056.

We would like to thank our sponsors for their support. Below are the sponsors who have supported us:

“ObiiGo Smart and Connected OBD-II System Development”, Obii Inc.
Award Amount: $100,000
Duration: July 2021 – September 2022 PI: Alexander Nelson

“Ohio River Valley Supply Chain Scenario Analysis”, Inter-Modal Holding, LLC
Award Amount: $1,500,000
Duration: July 2020 – December 2021
PI: Sarah Hernandez (Role: Co-Investigator)

“Quantitative Analysis of NIST Round 2 Post-Quantum Cryptography”, U.S. National Institutes of Standards
and Technology
Award Amount: $105,175
Duration: May 2020 – November 2021PI: Miaoqing Huang (Role: Co-Investigator)

“Machine Learning for DCIS Breast Cancer”, Arkansas Womens’ Giving Circle
Award Amount: $19,145 (Direct)
Duration: November 2019 – October 2021
PI: Alexander Nelson

“Machine Learning to Enhance Detection of Cancer in Excised Breast Tumors”, Arkansas Biosciences Institute
Award Amount: $28,977 (Direct)
Duration: July 2019 – May 2020
PI: Alexander Nelson

“Enabling Near Real-Time Imaging of Tumor Margins using Bayesian Hierarchichal Models and Deep Capsule
Networks”, University of Arkansas Chancellor’s Innovation and Collaboration Fund
Award Amount: $52,197 (Direct)
Duration: July 2019 – June 2022
PI: Alexander Nelson

“Biologically inspired feedback mechanisms for soft robotic actuators through fabric capacitor sensor arrays”,
University of Arkansas, College of Engineering
Award Amount: $24,822 (Direct)
Duration: January 2019 – December 2019
PI: Alexander Nelson

“Activity phenotypes in pregnancy, post-partum and associations with child obesity”, NIH NIGMS
Award Amount: $58,959
Duration: December 2018 – December 2019
PI: Erin K Howie Hickey (Role: Co-Investigator)

“GAANN: Securing Cognitive Edge Computing for Healthcare”, U.S. Department of Education
Award Amount: $751,914 + 187,979 (U.A. cost share)
Duration: October 2018 – October 2022
PI: Jia Di (Role: Co-Investigator)

“Montgomery County Replicable Smart City Technology” Federal ID Award #70NANB16H276, National
Institutes of Standards and Technology (NIST) Replicable Smart Cities Technology Cooperative Agreement
Program
Award Amount: $100,000 Duration: September 2016 – June 2017 PI: Daniel Hoffman, CIO Montgomery
County. (Role: Senior Personnel)

Totals: All Funded Research – $2.929M. Total Funded Research as PI – $225,141