Ph.D. Thesis

My doctoral research addressed detection of underground installations in hostile environments, combining machine learning with robust signal processing in low signal-to-noise regimes. It connects to broader research interests and builds on industry experience in applied AI.

PhD thesis visual

Detection of underground installations in hostile environments.

During my PhD studies, I had the privilege of working on a project funded by the U.S. Army Research Office (ARO) and led by my adviser, Prof. Nathan Intrator, together with Nobel laureate Prof. Leon N. Cooper. The project combined machine-learning techniques with a bank of unmatched filters to estimate object distance from time-of-arrival in signal-to-noise-ratio regimes below the classical detection threshold. This approach enabled the use of low-power acoustic pulses to detect underground installations while remaining covert.

Machine Learning Signal Processing Time-of-Arrival Low SNR
Project report (PDF)

Publications

  1. Apartsin, A., & Intrator, N. (2008). A data fusion and multiple ping method for improving the resolution of low-power acoustic and seismic sensing. The Journal of the Acoustical Society of America, 124(4_Supplement), 2597-259.
    DOI
  2. Apartsin, A., Cooper, L. N., & Intrator, N. (2010, August). SNR-dependent filtering for Time Of Arrival estimation in high noise. In 2010 IEEE International Workshop on Machine Learning for Signal Processing (pp. 427-431). IEEE.
    DOI
  3. Apartsin, A., Cooper, L. N., & Intrator, N. (2011, January). Biosonar-inspired source localization in low SNR. In International Conference on Bio-inspired Systems and Signal Processing (Vol. 2, pp. 399-404). SCITEPRESS.
    DOI
  4. Apartsin, A., Cooper, L. N., & Intrator, N. (2012). Semi-coherent time of arrival estimation using regression. The Journal of the Acoustical Society of America, 132(2), 832-837.
    DOI
  5. Apartsin, A., Cooper, L. N., & Intrator, N. (2013). Time-of-flight estimation in the presence of outliers. Part I-Single echo processing. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3382-3392.
    DOI
  6. Apartsin, A., Cooper, L. N., & Intrator, N. (2013). Time-of-flight estimation in the presence of outliers. Part II-Multiple echo processing. IEEE Transactions on Geoscience and Remote Sensing, 52(7), 3843-3850.
    DOI
  7. Apartsin, A., Cooper, L. N., & Intrator, N. (2014). Energy-Efficient Time-of-Flight Estimation in the Presence of Outliers: A Machine Learning Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1306-1313.
    DOI

Award

  • 2011: The Don and Sara Maren Foundation award for outstanding achievements in Ph.D. studies.