Students – San Diego State University Internet of Things Laboratory

Somayeh Komeylian
Ph.D Electrical Engineering Candidate
Research area: THz regime metasurfaces for 6G communication

S. Komeylian and C. Paolini, “Performance Evaluation of a Fractal Plasmonic Bowtie Nano-Antenna: Optical and Far-field Properties,” in IEEE Transactions on Nanotechnology, doi: 10.1109/TNANO.2023.3332555.

Komeylian, S.; Paolini, C. Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform. Sensors 2023, 23, 1742. https://doi.org/10.3390/s23031742

Rene Orellana
M.S. Electrical Engineering Candidate (graduated spring 2024)
EE798 project topic:

Naga Dheeraj Kurapati
M.S. Electrical Engineering Candidate (graduated fall 2024)
EE798 project topic: Roadway image segmentation for calculating Post Encroachment Time

Soumya Konery Satheeshkumar
M.S. Electrical Engineering Candidate (graduated fall 2023)
Thesis topic: Detection of heat stress in plants using machine learning regression models

S. K. Satheeshkumar, C. Paolini and M. Sarkar, “Subsurface Heat stress detection in plants using machine learning regression models,” 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS), Valencia, Spain, 2023, pp. 57-64, doi: 10.1109/ICCNS58795.2023.10193174.

James Bunnell
M.S. Electrical Engineering Candidate
Thesis topic: Machine- and Deep-Learning Architectures for Classifying 2D and 3D Material Interaction in Novel Semiconductor Devices

Deeksha Kondi Udayashankar
M.S. Electrical Engineering Graduate (graduated summer 2023)
Thesis topic: Sampling Acceleration and Orientation using SPI between the iCE40UP FPGA and the LSM6DSOX IMU for in situ Fall Detection and Classification

Jingxiao Tian
PhD Electrical Engineering Candidate
Research focus: Fall Prediction and Detection in At-Risk Older Adults through Inferencing at the Edge
Lab hours: Monday – Friday, 11:00AM to 4:00PM

Jingxiao Tian, Patrick Mercier, Christopher Paolini, Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons, Smart Health, Volume 33, 2024, 100498, ISSN 2352-6483, https://doi.org/10.1016/j.smhl.2024.100498.

Tian, J., Mercier, P., and Paolini, C., Fall Detection through Inferencing at the Edge, International Symposium on Intelligent Computing and Networking 2024 (ISICN 2024), March 18-20, 2024, Puerto Rico.

Shreyas Narasimhiah Ramesh
M.S. Electrical Engineering Graduate (graduated spring 2024)
Research focus: Embedded Anchor-to-Joint (A2J) Pose Estimation from Depth Images on the Xilinx Kria KV260 Vision AI board
Lab hours: Monday – Friday, 11:00AM to 4:00PM

S. N. Ramesh, M. Sarkar, M. Audette and C. Paolini, “Efficient Real-time Fall Prediction and Detection using Privacy-Centric Vision-based Human Pose Estimation on the Xilinx® Kria K26 SOM,” 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Toronto, ON, Canada, 2023, pp. 1-5, doi: 10.1109/BioCAS58349.2023.10388590.

Priyanka Partane
M.S. Electrical Engineering Graduate (graduated spring 2022)
EE798 project topic: Line-speed Packet Capture and Feature Extraction for Training Asymmetric Stacked Autoencoders for Network Anomaly Detection

Vineet Kandunuri
M.S. Electrical Engineering Candidate
Thesis topic: MIST: A Machine Intelligent Sensor Topology Fog Computing Architecture to Mitigate the Propagation of Cascading Power Outages

Vineet Kandunuri, Mahasweta Sarkar, and Christopher Paolini, Fog-compute capable smart metering infrastructure, IEEE International Conference on Advanced Networks and Telecommunications Systems, 13-16 December 2021, IDRBT, Hyderabad, India.

Veena Keeranagi
M.S. Electrical Engineering Graduate (graduated fall 2020)
EE798 project topic: Optimal LoRa Gateway Placement

Erfan Chowdhury Shourov
M.S. Electrical Engineering Graduate (graduated spring 2022)
Thesis topic: Deep Learning Architectures for Skateboarder-Pedestrian Surrogate Safety Measures
Publications:

Shourov, C.E.; Sarkar, M.; Jahangiri, A.; Paolini, C. Deep Learning Architectures for Skateboarder-Pedestrian Surrogate Safety Measures. Future Transp. 2021, 1, 387-413. 10.3390/futuretransp1020022
E. C. Shourov and C. Paolini, “Laying the Groundwork for Automated Computation of Surrogate Safety Measures (SSM) for Skateboarders and Pedestrians using Artificial Intelligence,” 2020 Third International Conference on Artificial Intelligence for Industries (AI4I), Irvine, CA, USA, 2020, pp. 19-22, doi: 10.1109/AI4I49448.2020.00011.
C. E. Shourov and C. Paolini, “Skateboarder and Pedestrian Conflict Zone Detection Dataset,” 14-Nov-2020. [Online]. Available: osf.io/nyhf7, DOI 10.17605/OSF.IO/NYHF7.
C. Paolini, C. E. Shourov, A. Jahangiri, and S. G. Machiani, “Skateboarder and Pedestrian Dataset,” 30-Jan-2020. [Online]. Available: osf.io/cqd9z, DOI 10.17605/OSF.IO/CQD9Z.

Ugur Emre Dogan
M.S. Electrical Engineering Graduate (graduated summer 2021)
EE798 project topic: On-device vehicle detection, classification, and tracking for Surrogate Safety Measures

Arya Yazdani
M.S. Electrical Engineering Graduate (graduated summer 2021)
EE798 project topic: Edge computing vision architectures for Surrogate Safety Measurements
Report: Real-time vehicle and pedestrian object detection and classification on the Coral EdgeTPU Board for Surrogate Safety Measurements

Jared Brzenski
Computational Science PhD Candidate
Jared is supported under NSF Office of Advanced Cyberinfrastructure (OAC) CC* Storage Grant 1659169 Implementation of a Distributed, Shareable, and Parallel Storage Resource at San Diego State University to Facilitate High-Performance Computing for Climate Science. The goals and objectives of the project will be to implement parallel shared-file I/O capabilities (i.e. each process performs I/O to a single file which is shared) in our Geologic CO₂ Sequestration and Coastal Ocean Modeling applications, at multiple layers, using (1) parallel I/O libraries (HDF5, Parallel netCDF), (2) a middleware layer (MPI-IO), and a parallel file system (BeeGFS). Publications:

Brzenski, J., Paolini, C., and Castillo, J. E., Improving the I/O of Large Geophysical Models using PnetCDF and BeeGFS, Parallel Computing, 2021, ISSN 0167-8191, 10.1016/j.parco.2021.102786

Christopher Johnson
Computer Engineering Undergraduate
Undergraduate research project: Advanced Persistent Threat (APT) Detection. Packet capture and inspection using the Xilinx U250 Alveo Data Center accelerator card on the UC San Diego / UC Berkeley PRP Kubernetes cluster.