Joshua Ash

Josh Ash 

Josh Ash
Assistant Professor
Department of Electrical Engineering
Wright State University

Research: Large-scale Bayesian inference and graphical models, signal processing, machine learning, synthetic aperture radar, hyperspectral imaging

Publications

  1. Bayesian models for highly accelerated phase contrast MRI
    A. Rich, L.C. Potter, N. Jin, J. Ash, O. Simonetti, and R. Ahmad
    Magnetic Resonance in Medicine, 2015, (to appear)

  2. Factor graphs for inverse problems: Accelerated phase contrast magnetic resonance imaging
    A. Rich, L. Potter, J. Ash, and R. Ahmad
    Proc. IEEE Int. Conf. Image Proc., Quebec City, Sept. 2015

  3. A bayesian approach for accelerated phase-contrast MRI
    A. Rich, L.C. Potter, N. Jin, J. Ash, O. Simonetti, and R. Ahmad
    Proc. 23rd Annual Meeting & Exhibition, International Society for Magnetic Resonance and Medicine, Toronto, 30 May–5 June 2015

  4. A unifying perspective of coherent and non-coherent change detection
    J. Ash
    Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery XXI, May 2014, vol. 9093

  5. Wide-angle synthetic aperture radar imaging: Models and algorithms for anisotropic scattering
    L. Potter, J. Ash, and E. Zelnio
    IEEE Sig. Proc. Mag.; Special Issue on Advances in Synthetic Aperture Radar Imaging, vol. 31, no. 4, pp. 16–26, July 2013

  6. Three-dimensional position accuracy in circular synthetic aperture radar
    L. Moore, L. Potter, and J. Ash
    IEEE Aero. Elect. Sys. Mag., vol. 29, no. 1, Jan. 2014.

  7. Estimation of spin-echo relaxation time
    F. Golub, L. Potter, J. Ash, A. Blank, and R. Ahmad
    Journal of Magnetic Resonance, vol. 237, pp. 17–22, Dec. 2013

  8. Dynamic dictionary algorithms for model order and parameter estimation
    C. Austin, J. Ash, and R. Moses
    IEEE Trans. Sig. Proc., vol. 61, no. 20, pp. 5117–5130, Oct. 2013

  9. Joint imaging and change detection for robust exploitation in interrupted SAR environments
    J. Ash
    Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery XX, Baltimore, MD, May 2013, vol. 87460J

  10. Application of model-based change detection to airborne VNIRSWIR hyperspectral imagery
    J. Meola, M. Eismann, R. Moses, and J. Ash
    IEEE Trans. Geoscience and Remote Sensing, vol. 50, no. 10, pp. 3693–3706, Oct. 2012/

  11. Incorporating spatial structure into hyperspectral scene analysis
    J. Ash and J. Meola
    Proc. IEEE Statistical Signal Processing Workshop, Aug. 2012, pp. 5–8.

  12. Empirical space-time statistical models for inhomogeneous acoustic propagation environments
    J. Ash
    Proc. Unattended Ground, Sea, and Air Sensor Tech. and App. XIV, SPIE Defense and Security Symposium, May 2012, vol. 8388

  13. Wide angle SAR data for target discrimination research
    K. Dungan, J. Ash., J. Nehrbass, J. Parker, L. Gorham, and S. Scarborough
    Proc. Algorithms for Synthetic Aperture Radar Imagery XIX, SPIE Defense and Security Symposium, April 2012, vol. 8394-21

  14. An autofocus method for backprojection imagery in synthetic aperture radar
    J. Ash
    IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 1, pp. 104–108, Jan. 2012

  15. Detecting changes in hyperspectral imagery using a model-based approach
    J. Meola, M. Eismann, R. Moses, and J. Ash
    IEEE Trans. Geo, vol. 49, no. 7, pp. 2647–2661, 2011

  16. An autoregressive formulation for SAR backprojection imaging
    R. Moses and J. Ash
    IEEE Trans. on Aerospace & Elect. Sys., vol. 47, no. 4, pp. 2860–2873, Oct. 2011

  17. Modeling and estimation of signal-dependent noise in hyperspectral imagery
    Joseph Meola, Michael Eismann, Randolph Moses, and Joshua Ash
    Applied Optics, vol. 50, no. 21, pp. 3829–3846, 2011

  18. Performance analysis of sparse 3D SAR imaging
    Christian D. Austin, Joshua N. Ash, and Randolph L. Moses
    Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery XVIII, April 2011

  19. In silico synchronization of cellular populations through expression data deconvolution
    Marisa C. Eisenberg, Joshua N. Ash, and Dan Seigal-Gaskins
    Proc. IEEEACM 48th Design Automation Conference, June 2011/

  20. Extension and implementation of a model-based approach to hyperspectral change detection
    Joseph Meoloa, Michael T. Eismann, Randolph L. Moses, and Joshua N. Ash
    Proc. SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, April 2011

  21. Parameter estimation using sparse reconstruction with dynamic dictionaries
    Christian Austin, Joshua Ash, and Randolph Moses
    IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2011

  22. Supersonic projectile models for asynchronous shooter localization
    Richard Kozick, Joshua Ash, and Gene Whipps
    Proc. SPIE Unattended Ground, Sea, and Air Sensor Technologies and Applications XIII, Defense and Security Symposium, April 2011

  23. On the relation between sparse reconstruction and parameter estimation with model order selection
    Christian Austin, Randolph Moses, Joshua Ash, and Emre Ertin
    IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 3, pp. 560–570, June 2010

  24. Analysis of motion disambiguation using multi-channel circular SAR
    Ahmed Fasih, Carl Rossler, Joshua Ash, and Randolph Moses
    Proc. SPIE, Algorithms for Synthetic Aperture Radar Imagery XVII, April 2010, vol. 7699

  25. A model-based approach to hyperspectral change detection
    Joe Meola, Joshua Ash, Randolph Moses, and Michael Eismann
    Proc. SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, April 2010, vol. 7695

  26. Performance of shockwave-based shooter localization under model misspecification
    Joshua N. Ash, Gene T. Whipps, and Richard J. Kozick
    Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, March 2010

  27. Model-based deconvolution of cell cycle timeseries data reveals gene expression details at high resolution
    Dan Siegal-Gaskins, Joshua Ash, and Sean Crosson
    PLoS Computational Biology, vol. 5, no.8, Aug. 2009

  28. Time-delay estimation in a time-warping environment
    Joshua Ash and Randolph Moses
    Proc. SPIE Unattended Ground, Sea, and Air Sensor Tech. and App. XI, Defense and Security Symposium, 2009, Orlando, FL

  29. On the relation between sparse sampling and parametric estimation
    Christian D. Austin, Emre Ertin, Joshua N. Ash, and Randolph L. Moses
    Proc. IEEE DSP Workshop, Jan. 2009, Marco Island, FL

  30. Self-calibration of sensor networks
    Joshua Ash and Randolph Moses
    in Handbook on Array Processing and Sensor Networks, S. Haykin and K.J.R. Liu, Eds. IEEE-Wiley, 2010

  31. SAR focusing performance for moving objects with random motion components
    Ahmed R. Fasih, Emre Ertin, Joshua N. Ash, and Randolph L. Moses
    Proc. Asilomar Conference on Signals, Systems, and Computers, Oct. 2008, Pacific Grove, CA

  32. Recursive SAR imaging
    R. L. Moses and J. N. Ash
    Proc. of SPIE, Algorithms for Synthetic Aperture Radar Imagery, vol. 6970, March 2008

  33. On optimal anchor node placement in sensor localization by optimization of subspace principal angles
    Joshua N. Ash and Randolph L. Moses
    Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 2289–2292, Mar. 30–Apr. 4 2008

  34. On the relative and absolute positioning errors in self localization systems
    Joshua N. Ash and Randolph L. Moses
    IEEE Trans. Signal Process., vol. 56, no. 11, pp. 5668–5679, 2008

  35. Sensor localization error decomposition: Theory and applications
    Joshua N. Ash and Randolph L. Moses
    /Proc. IEEE Statistical Signal Processing Workshop, Aug. 2007, pp. 660–664, (invited)

  36. Robust system multiangulation using subspace methods
    Joshua N. Ash and Lee C. Potter
    Proc. Information Processing in Sensor Networks (IPSN), April 2007, pp. 61–68

  37. Relative and absolute errors in sensor network localization
    Joshua N. Ash and Randolph L. Moses
    IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1033–1036, April 15–20 2007

  38. Locating the nodes: cooperative localization in wireless sensor networks
    Neal Patwari, Joshua N. Ash, Spyros Kyperountas, Alfred O. Hero III, Randolph L. Moses, and Neiyer S. Correal
    IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 54–69, July 2005

  39. Outlier compensation in sensor network self-localization via the EM algorithm
    Joshua N. Ash and Randolph L. Moses
    Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 749–752, March 19–23 2005

  40. Acoustic time delay estimation and sensor network self localization: Experimental results
    Joshua N. Ash and Randolph L. Moses
    Journal of the Acoustical Society of America, vol. 118, no. 2, pp. 841–850, 2005

  41. Sensor network localization via received signal strength measurements with directional antennas
    Joshua Ash and Lee Potter
    Proc. 42nd Annual Allerton Conference on Communication, Control, and Computing, pp. 1861–1870, Sept. 2004

  42. Position and orientation for distributed sensors: the PODIS network
    Jason Wilden, Jim Agniel, Randolph L. Moses, and Joshua N. Ash
    Proc. of the 2004 MSS Battlefield Acoustics Symposium, Aug. 2004

  43. Acoustic sensor network self-localization: Experimental results
    Joshua N. Ash and Randolph L. Moses
    Proc. Military Sensing Symposia (MSS) Specialty Group on Battlefield Acoustic and Seismic Sensing, Magnetic and Electric Field Sensors, Oct. 2003

Teaching

Software