Work with academic experts in signal processing
A scholar or researcher with expertise in signal processing can help business and industrial clients solve their signal processing problems and conduct signal processing research to get ahead on R&D. Experts on NotedSource with backgrounds in signal processing include Tim Osswald, John Santapietro, Jonathan Tamir, Edoardo Airoldi, Lee Weinstein, Daniel Greenfield, and Tamoghna Roy.
Tim Osswald
Polymers Professor - University of Wisconsin
Most Relevant Research Interests
Other Research Interests (64)
About
Most Relevant Publications (1+)
117 total publications
Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method
International Journal of Automation and Smart Technology / Dec 01, 2018
Jiang, C.-P. (2018). Technical Development of Multi-Resin Three-Dimensional Printer Using Bottom-Up Method. International Journal of Automation and Smart Technology, 8(4), 173–178. https://doi.org/10.5875/ausmt.v8i4.1840
Jonathan Tamir
Most Relevant Research Interests
Other Research Interests (11)
Most Relevant Publications (2+)
5 total publications
Computational MRI With Physics-Based Constraints: Application to Multicontrast and Quantitative Imaging
IEEE Signal Processing Magazine / Jan 01, 2020
Tamir, J. I., Ong, F., Anand, S., Karasan, E., Wang, K., & Lustig, M. (2020). Computational MRI With Physics-Based Constraints: Application to Multicontrast and Quantitative Imaging. IEEE Signal Processing Magazine, 37(1), 94–104. https://doi.org/10.1109/msp.2019.2940062
Memory-Efficient Learning for Large-Scale Computational Imaging
IEEE Transactions on Computational Imaging / Jan 01, 2020
Kellman, M., Zhang, K., Markley, E., Tamir, J., Bostan, E., Lustig, M., & Waller, L. (2020). Memory-Efficient Learning for Large-Scale Computational Imaging. IEEE Transactions on Computational Imaging, 6, 1403–1414. https://doi.org/10.1109/tci.2020.3025735
Edoardo Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
Most Relevant Research Interests
Other Research Interests (63)
About
Most Relevant Publications (1+)
106 total publications
SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series
IEEE Transactions on Signal Processing / Oct 01, 2017
Han, Q., Ding, J., Airoldi, E. M., & Tarokh, V. (2017). SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series. IEEE Transactions on Signal Processing, 65(19), 4994–5005. https://doi.org/10.1109/tsp.2017.2716898
Lee Weinstein
STEM Educator
Most Relevant Research Interests
Other Research Interests (34)
About
Most Relevant Publications (1+)
22 total publications
Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows
Smart Materials and Structures / Mar 14, 2012
Weinstein, L. A., Cacan, M. R., So, P. M., & Wright, P. K. (2012). Vortex shedding induced energy harvesting from piezoelectric materials in heating, ventilation and air conditioning flows. Smart Materials and Structures, 21(4), 045003. https://doi.org/10.1088/0964-1726/21/4/045003
Daniel Greenfield
Ph.D. candidate (anticipated Mar 2023) in Biophysics with a focus on quantitative techniques in biomedicine
Most Relevant Research Interests
Other Research Interests (13)
Most Relevant Publications (1+)
10 total publications
Wireless Wearable Sensor Paired With Machine Learning for the Quantification of Tissue Oxygenation
IEEE Internet of Things Journal / Dec 15, 2021
Cascales, J. P., Greenfield, D. A., Roussakis, E., Witthauer, L., Li, X., Goss, A., & Evans, C. L. (2021). Wireless Wearable Sensor Paired With Machine Learning for the Quantification of Tissue Oxygenation. IEEE Internet of Things Journal, 8(24), 17557–17567. https://doi.org/10.1109/jiot.2021.3081044
Tamoghna Roy
ML@DeepSig | Applied Research | Deep Learning
Most Relevant Research Interests
Other Research Interests (5)
Most Relevant Publications (1+)
15 total publications
Over-the-Air Deep Learning Based Radio Signal Classification
IEEE Journal of Selected Topics in Signal Processing / Feb 01, 2018
O’Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-Air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing, 12(1), 168–179. https://doi.org/10.1109/jstsp.2018.2797022