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Hi ๐Ÿ‘‹, I'm Josiah

A passionate signal processing and deep learning enthusiast.

josiahwsmith10

PSA: I no longer have access to my university email address (josiah.smith@utdallas.edu or jws160130@utdallas.edu), so please contact me at josiah.radar@gmail.com.

  • ๐Ÿ”ญ Iโ€™m currently working on several research publications in deep learning, computer vision, radar signal processing, and imaging.

  • ๐Ÿ“ซ How to reach me josiah.radar@gmail.com

  • ๐Ÿ“„ Know about my experiences www.linkedin.com/in/josiahwsmith

  • โšก Fun fact I love music, hiking, camping, and have lived around the world.

  • I completed my PhD from The University of Texas at Dallas in 2022

PhD Dissertation

  • Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems (arXiv)

Papers

  • Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution (arXiv, DOI)
  • Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar Imaging (arXiv, DOI)
  • A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-Resolution (arXiv, DOI)
  • Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries (arXiv, DOI)
  • An FCNN-Based Super-Resolution mmWave Radar Framework for Contactless Musical Instrument Interface (arXiv, DOI)
  • Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks using Novel Sterile Training Technique (arXiv, DOI)
  • Near-Field MIMO-ISAR Millimeter-Wave Imaging (arXiv, DOI)

Organization of Code (Notable repositories are highlighted in italics):

  • Complex-Valued Neural Networks
  • Simulating Radar and SAR Data
    • (V1) SAR Simulation (code)
    • (V2) FMCW MIMO-SAR Simulation and Image Reconstruction Toolbox (code)
    • (V3) THz and Sub-THz Imaging Toolbox (code)
  • Data-Driven Radar Image Enhancement (Computer Vision for Radar)
    • (V1) SAR CNN Enhancement (code)
    • (V2) Improved SAR CNN Enhancement (code)
    • (V2.1) Improved SAR CNN Enhancement-v2 (code)
  • MATLAB User Interfaces for Controlling SAR Scanners
    • Dual Radar (Also Applicable for Single Radar)
      • Dual Radar GUI (code)
    • SAR User Interface
      • RSAR GUI (code)
      • MATLAB SAR Scanner API (code)
    • Texas Instruments mmWave Studio
      • TI mmWave Studio MATLAB GUI (code)
  • Embedded Synchronization Software for SAR Scanner Controllers
    • (V1) TI Radar HW Trigger using ESP32 (code)
    • (V2) Dual Radar Synchronizer (code)
  • Documentation and Introduction to Radar and SAR Principles
    • Introduction to Near-Field SAR in MATLAB (code)
    • Introduction to MIMO-FMCW Radar (doc)
  • Projects
    • FCNN Audio Denoising (code)
    • Deep Learning-Enhanced BLE Ranging (code)
    • SVM-MUSIC Algorithm for DoA Estimation (code)
    • FIR and IIR Filtering for Audio Denoising (code)
    • Nyquist Sampling and Sunspots (code)
  • WISLAB-Specific
  • Code for Papers
    • Efficient 3-D Near-Field MIMO-SAR Imaging for Irregular Scanning Geometries (arXiv, DOI, code)
    • An FCNN-Based Super-Resolution mmWave Radar Framework for Contactless Musical Instrument Interface (arXiv, DOI, code)

BibTeX Citations

@article{smith2023deep,
	title        = {Deep Learning-Based Multiband Signal Fusion for {3-D} {SAR} Super-Resolution},
	author       = {Smith, J. W. and Torlak, M.},
	year         = 2023,
	month        = apr,
	journal      = {IEEE Trans. Aerosp. Electron. Syst.},
	pages        = {1--17}
}
@phdthesis{smith2022novel,
	title        = {Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems},
	author       = {Smith, J. W.},
	year         = 2022,
	month        = apr,
	address      = {Richardson, Texas, USA},
	note         = {Available at \url{https://arxiv.org/abs/2306.15341}},
	school       = {Dept. Elect. Comput. Eng., Univ. Texas Dallas},
	type         = {PhD dissertation}
}
@article{smith2022efficient,
	title        = {Efficient {3-D} Near-Field {MIMO-SAR} Imaging for Irregular Scanning Geometries},
	author       = {Smith, J. W. and Torlak, M.},
	year         = 2022,
	month        = jan,
	journal      = {IEEE Access},
	volume       = 10,
	pages        = {10283--10294}
}
@inproceedings{smith2022vision,
	title        = {A Vision Transformer Approach for Efficient Near-Field {SAR} Super-Resolution under Array Perturbation},
	author       = {Smith, J. W. and Alimam, Y. and Vedula, G. and Torlak, M.},
	year         = 2022,
	month        = apr,
	booktitle    = {Proc. IEEE Tex. Symp. Wirel. Microw. Circuits Syst. (WMCS)},
	address      = {Waco, TX, USA},
	pages        = {1--6}
}
@inproceedings{vasileiou2022efficient,
	title        = {Efficient {CNN}-Based Super Resolution Algorithms for {mmWave} Mobile Radar Imaging},
	author       = {Vasileiou, C. and Smith, J. W. and Thiagarajan, S. and Nigh, M. and Makris, Y. and Torlak, M.},
	year         = 2022,
	month        = oct,
	booktitle    = {Proc. IEEE Int. Conf. Image Process. (ICIP)},
	address      = {Bourdeaux, France},
	pages        = {3803--3807}
}
@article{smith2021improved,
	title        = {Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique},
	author       = {Smith, J. W. and Thiagarajan, S. and Willis, R. and Makris, Y. and Torlak, M.},
	year         = 2021,
	month        = jan,
	journal      = {IEEE Access},
	volume       = 9,
	pages        = {10893--10902}
}
@article{smith2021fcnn,
	title        = {An {FCNN}-Based Super-Resolution {mmWave} Radar Framework for Contactless Musical Instrument Interface},
	author       = {Smith, J. W. and Furxhi, O. and Torlak, M.},
	year         = 2021,
	month        = may,
	journal      = {IEEE Trans. Multimedia},
	volume       = 24,
	pages        = {2315--2328}
}
@inproceedings{smith2020near,
	title        = {Near-Field {MIMO-ISAR} Millimeter-Wave Imaging},
	author       = {Smith, J. W. and Yanik, M. E. and Torlak, M.},
	year         = 2020,
	month        = sep,
	booktitle    = {Proc. IEEE Radar Conf. (RadarConf)},
	address      = {Florance, Italy},
	pages        = {1--6}
}

Languages and Tools:

arduino c cplusplus firebase gcp git linux matlab opencv python pytorch scikit_learn tensorflow

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