Work with thought leaders and academic experts in signal processing
Companies can benefit from working with Signal Processing experts in various ways. These experts can provide innovative solutions to complex problems, optimize signal processing algorithms for better performance, develop advanced signal processing techniques for data analysis, and design efficient communication systems. They can also contribute to the development of cutting-edge technologies such as image and speech recognition, radar and sonar systems, and biomedical signal processing. By collaborating with Signal Processing researchers, companies can gain a competitive edge, improve product quality, enhance data processing capabilities, and accelerate technological advancements.
Researchers on NotedSource with backgrounds in signal processing include Siddharth Maddali, Aruna Ranaweera, Edoardo Airoldi, Vladimir Shapiro, Ph.D., Dmitry Batenkov, Ph.D., Tim Osswald, Lee Weinstein, Dhritiman Das, Ph.D., Vivek Singh, Dr. Haikun Huang, Ph.D., Anit Kumar Sahu, Ayse Oktay, and Athul Prasad.
Aruna Ranaweera
Professor at University of Kelaniya, PhD(Kyung Hee University, South Korea)
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Other Research Interests (16)
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32 total publications
Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing
Electronics / Oct 04, 2021
Piyumal, K., Ranaweera, A., Kalingamudali, S., & Kularatna, N. (2021). Supercapacitor Assisted Hybrid PV System for Efficient Solar Energy Harnessing. Electronics, 10(19), 2422. https://doi.org/10.3390/electronics10192422
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Edoardo Airoldi
Professor of Statistics & Data Science Temple University & PI, Harvard University
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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
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Vladimir Shapiro, Ph.D.
PRINCIPAL AI/COMPUTER VISION DATA SCIENTIST; EXPERIENCED SOFTWARE (PYTHON, C/C++, R) DEVELOPER; ADJUNCT UNIVERSITY PROFESSOR
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Other Research Interests (14)
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Most Relevant Publications (4+)
38 total publications
Handwritten document image segmentation and analysis
Pattern Recognition Letters / Jan 01, 1993
Shapiro, V., Gluhchev, G., & Sgurev, V. (1993). Handwritten document image segmentation and analysis. Pattern Recognition Letters, 14(1), 71–78. https://doi.org/10.1016/0167-8655(93)90134-y
Accuracy of the straight line Hough Transform: The non-voting approach
Computer Vision and Image Understanding / Jul 01, 2006
Shapiro, V. (2006). Accuracy of the straight line Hough Transform: The non-voting approach. Computer Vision and Image Understanding, 103(1), 1–21. https://doi.org/10.1016/j.cviu.2006.02.001
On the hough transform of multi-level pictures
Pattern Recognition / Apr 01, 1996
A. Shapiro, V. (1996). On the hough transform of multi-level pictures. Pattern Recognition, 29(4), 589–602. https://doi.org/10.1016/0031-3203(95)00116-6
Motion analysis via interframe point correspondence establishment
Image and Vision Computing / Mar 01, 1995
Shapiro, V., Backalov, I., & Kavardjikov, V. (1995). Motion analysis via interframe point correspondence establishment. Image and Vision Computing, 13(2), 111–118. https://doi.org/10.1016/0262-8856(95)93152-i
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Dmitry Batenkov, Ph.D.
A highly experienced applied mathematician working in academia (faculty) and industry (consulting), with 15+ years of research and teaching expertise in inverse problems, signal processing, and data science.
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Most Relevant Publications (3+)
51 total publications
Moment inversion problem for piecewise D -finite functions
Inverse Problems / Sep 16, 2009
Batenkov, D. (2009). Moment inversion problem for piecewise D -finite functions. Inverse Problems, 25(10), 105001. https://doi.org/10.1088/0266-5611/25/10/105001
Decimated Prony's Method for Stable Super-Resolution
IEEE Signal Processing Letters / Jan 01, 2023
Katz, R., Diab, N., & Batenkov, D. (2023). Decimated Prony’s Method for Stable Super-Resolution. IEEE Signal Processing Letters, 30, 1467–1471. https://doi.org/10.1109/lsp.2023.3324553
Stable soft extrapolation of entire functions
Inverse Problems / Dec 07, 2018
Batenkov, D., Demanet, L., & Mhaskar, H. N. (2018). Stable soft extrapolation of entire functions. Inverse Problems, 35(1), 015011. https://doi.org/10.1088/1361-6420/aaedde
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Tim Osswald
Polymers Professor - University of Wisconsin
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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
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Lee Weinstein
STEM Educator
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Other Research Interests (21)
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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
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Vivek Singh
Rutgers Professor, MIT alum, CS PhD, AI expert
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Most Relevant Publications (2+)
95 total publications
New Signals in Multimedia Systems and Applications
IEEE MultiMedia / Jan 01, 2018
Cesar, P., Singh, V., Jain, R., Sebe, N., & Oliver, N. (2018). New Signals in Multimedia Systems and Applications. IEEE MultiMedia, 25(1), 12–13. https://doi.org/10.1109/mmul.2018.011921231
Predicting Loneliness through Digital Footprints on Google and YouTube
Electronics / Nov 29, 2023
Ahmed, E., Xue, L., Sankalp, A., Kong, H., Matos, A., Silenzio, V., & Singh, V. K. (2023). Predicting Loneliness through Digital Footprints on Google and YouTube. Electronics, 12(23), 4821. https://doi.org/10.3390/electronics12234821
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Dr. Haikun Huang, Ph.D.
Chief Technology Officer at Great Victory Legends
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34 total publications
Exercise Intensity-Driven Level Design
IEEE Transactions on Visualization and Computer Graphics / Apr 01, 2018
Xie, B., Zhang, Y., Huang, H., Ogawa, E., You, T., & Yu, L.-F. (2018). Exercise Intensity-Driven Level Design. IEEE Transactions on Visualization and Computer Graphics, 24(4), 1661–1670. https://doi.org/10.1109/tvcg.2018.2793618
Automatic Optimization of Wayfinding Design
IEEE Transactions on Visualization and Computer Graphics / Sep 01, 2018
Huang, H., Lin, N.-C., Barrett, L., Springer, D., Wang, H.-C., Pomplun, M., & Yu, L.-F. (2018). Automatic Optimization of Wayfinding Design. IEEE Transactions on Visualization and Computer Graphics, 24(9), 2516–2530. https://doi.org/10.1109/tvcg.2017.2761820
Synthesizing Personalized Construction Safety Training Scenarios for VR Training
IEEE Transactions on Visualization and Computer Graphics / May 01, 2022
Li, W., Huang, H., Solomon, T., Esmaeili, B., & Yu, L.-F. (2022). Synthesizing Personalized Construction Safety Training Scenarios for VR Training. IEEE Transactions on Visualization and Computer Graphics, 28(5), 1993–2002. https://doi.org/10.1109/tvcg.2022.3150510
Mood-Driven Colorization of Virtual Indoor Scenes
IEEE Transactions on Visualization and Computer Graphics / May 01, 2022
Solah, M., Huang, H., Sheng, J., Feng, T., Pomplun, M., & Yu, L.-F. (2022). Mood-Driven Colorization of Virtual Indoor Scenes. IEEE Transactions on Visualization and Computer Graphics, 28(5), 2058–2068. https://doi.org/10.1109/tvcg.2022.3150513
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Anit Kumar Sahu
PhD from CMU working in ML/AI
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62 total publications
Federated Learning: Challenges, Methods, and Future Directions
IEEE Signal Processing Magazine / May 01, 2020
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/msp.2020.2975749
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics
IEEE Transactions on Signal and Information Processing over Networks / Jan 01, 2016
Sahu, A. K., Kar, S., Moura, J. M. F., & Poor, H. V. (2016). Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics. IEEE Transactions on Signal and Information Processing over Networks, 1–1. https://doi.org/10.1109/tsipn.2016.2618318
$\mathcal {CIRFE}$: A Distributed Random Fields Estimator
IEEE Transactions on Signal Processing / Sep 15, 2018
Sahu, A. K., Jakovetic, D., & Kar, S. (2018). $\mathcal {CIRFE}$: A Distributed Random Fields Estimator. IEEE Transactions on Signal Processing, 66(18), 4980–4995. https://doi.org/10.1109/tsp.2018.2863646
Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization
IEEE Transactions on Signal Processing / Jan 01, 2022
Wang, J., Sahu, A. K., Joshi, G., & Kar, S. (2022). Matcha: A Matching-Based Link Scheduling Strategy to Speed up Distributed Optimization. IEEE Transactions on Signal Processing, 70, 5208–5221. https://doi.org/10.1109/tsp.2022.3212536
Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference
IEEE Transactions on Signal Processing / Jan 01, 2023
Petrović, N., Bajović, D., Kar, S., Jakovetić, D., & Sahu, A. K. (2023). Large Deviations for Products of Non-Identically Distributed Network Matrices With Applications to Communication-Efficient Distributed Learning and Inference. IEEE Transactions on Signal Processing, 71, 1319–1333. https://doi.org/10.1109/tsp.2023.3263254
Guest Editorial Inference and Learning over Networks
IEEE Transactions on Signal and Information Processing over Networks / Dec 01, 2016
Matta, V., Richard, C., Saligrama, V., & Sayed, A. H. (2016). Guest Editorial Inference and Learning over Networks. IEEE Transactions on Signal and Information Processing over Networks, 2(4), 423–425. https://doi.org/10.1109/tsipn.2016.2615526
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing
IEEE Transactions on Signal Processing / Jan 01, 2016
Sahu, A. K., & Kar, S. (2016). Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing. IEEE Transactions on Signal Processing, 64(1), 89–103. https://doi.org/10.1109/tsp.2015.2478737
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Ayse Oktay
Assoc. Prof., Yildiz Technical University
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47 total publications
Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks
Biomedical Signal Processing and Control / Feb 01, 2020
Oktay, A. B., & Kocer, A. (2020). Differential diagnosis of Parkinson and essential tremor with convolutional LSTM networks. Biomedical Signal Processing and Control, 56, 101683. https://doi.org/10.1016/j.bspc.2019.101683
Human identification with dental panoramic radiographic images
IET Biometrics / Nov 06, 2017
Oktay, A. B. (2017). Human identification with dental panoramic radiographic images. IET Biometrics, 7(4), 349–355. Portico. https://doi.org/10.1049/iet-bmt.2017.0078
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Athul Prasad
5G / 6G Technology and Ventures at Samsung; D.Sc. (Tech), MBA
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75 total publications
Dynamic base station planning with power adaptation for green wireless cellular networks
EURASIP Journal on Wireless Communications and Networking / May 15, 2014
Yigitel, M. A., Incel, O. D., & Ersoy, C. (2014). Dynamic base station planning with power adaptation for green wireless cellular networks. EURASIP Journal on Wireless Communications and Networking, 2014(1). https://doi.org/10.1186/1687-1499-2014-77
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Example signal processing projects
How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on signal processing?
Optimizing Signal Processing Algorithms for Image Recognition
A company in the computer vision industry can collaborate with a Signal Processing expert to optimize their image recognition algorithms. By leveraging advanced signal processing techniques, the expert can improve the accuracy and speed of image recognition systems, enabling the company to develop more efficient and reliable computer vision solutions.
Developing Advanced Signal Processing Techniques for Data Analysis
A data analytics company can partner with a Signal Processing researcher to develop advanced signal processing techniques for data analysis. These techniques can help the company extract valuable insights from complex datasets, identify patterns and trends, and make data-driven decisions. By leveraging the expertise of the researcher, the company can enhance their data analysis capabilities and gain a competitive advantage in the market.
Designing Efficient Communication Systems
A telecommunications company can collaborate with a Signal Processing expert to design efficient communication systems. The expert can develop signal processing algorithms and protocols that optimize data transmission, reduce noise and interference, and improve overall system performance. By working with the researcher, the company can enhance the reliability and efficiency of their communication networks, leading to improved customer satisfaction and business growth.
Advancing Biomedical Signal Processing
A healthcare technology company can partner with a Signal Processing specialist to advance biomedical signal processing techniques. The researcher can develop algorithms and methods for analyzing physiological signals, such as ECG and EEG, to detect abnormalities, monitor patient health, and improve medical diagnosis. By collaborating with the expert, the company can enhance their healthcare solutions and contribute to the development of innovative medical technologies.
Improving Radar and Sonar Systems
A defense contractor can collaborate with a Signal Processing researcher to improve radar and sonar systems. The expert can develop signal processing algorithms that enhance target detection, tracking, and classification capabilities, improving the performance and accuracy of these systems. By leveraging the expertise of the researcher, the company can strengthen their defense technologies and gain a competitive edge in the market.