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Engineering | SENSIP

SenSIP Industry Consortium

SenSIP Info in a Quad Chart

The mission of the SenSIP Industry Consortium(NSF I/UCRC NCSS SenSIP Site) is to perform use-inspired research and train students in sensor and information systems, digital signal and image processing, wireless communications, machine learning, and quantum AI.

Applications addressed include information processing, software systems, integrated sensing, biomedicine and genomics, defense and homeland security, sustainability and environmental technologies, speech/audio processing and 6G+ telephony, imaging and video systems, low power realizations, real-time implementations, AI monitored solar energy, smart cameras, radar, and vehicular sensing.


SenSIP Consortium Membership Chart

Download SenSIP Consortium brochure

Net Centric-SenSIP Agreement

Net Centric-SenSIP Bylaws

Net Centric website


Andreas Spanias

Industry Advisory Board (IAB and Project Directors):

  • Mark Giampapa, Nitesh Shah Raytheon – Surveillance and Machine Learning
  • Silvia Garre , NXP – Sensor Fusion
  • Evgeni Gousev, Qualcomm – Computational Imaging Sensors
  • Diann Dow, On Semi, Machine Learning
  • Alphacore, Esko, Mikkola, Imaging  (2 memberships)
  • Steve Whaley,  Lightsense, Light Sensors, Spectroscopy
  • Joe Marvin, Prime Solutions Group, radar and machine learning
  • John Lewis, CI Government Labs
  • Ruchir Sehra, Resonea (Associate Membership), Audio Breathing Analytics
  • Steve Miller, Aperio DSP (Associate Membership), ML Applications
  • Devarajan Srinivasan, Solar Monitoring, Poundra LLC

Members at Large (Advisors) and Industry/Lab Collaborators

  • Claire Jackoski, Intel
  • Jayaraman Thiagarajan, LLNL
  • Glen Abousleman, General Dynamics
  • Mike Stanley ,  SensMACH, Tiny ML
  • Joseph Gilman, Clovity, IoT
  • Murat Ocksay, Interactive Flow (IFS) – Imaging Sensors for Blood Flow

The SenSIP industry consortium is an approved NSF funded Industry University Cooperative Research Center (I/UCRC) site. SenSIP works with the Net Centric I/UCRC of Texas with the University of North Texas, University of Texas-Dallas and Southern Methodist University.

Funded Industry Consortium Programs with SenSIP
Program with Alphacore, Imaging (active)
Program with ON Semi, Machine Learning  (active)
Program with Qualcomm, Imaging (active)
Program with PSG, Radar and Machine Learning (active)
Program with Raytheon – Computer Vision; Target tracking (active)
Program with NXP  –  Machine Learning / Sensors Calibration  (active)
Program with Resonea, COVID-19 Cough Analytics (active)
Program with CI Labs, Quantum Machine Learning (active)
Program with NCSS, Covid-19 hotspot estimation (active)
Program with Intel Corporation On Architecture Design Tools for IoT (ended)
Program with Lockheed Martin;   Extraction of Advanced Geospatial Intelligence (AGI)  (ended)
Program with LG;  Sensor Internetworks for Time  Critical Applications (ended)
Program with Sprint – Sensor Localization Sequential WSN (ended)
Program with IFS;  Hemoflow Sensors (SBIR)  (ended)
Program with ViaSOL and ACT; Sensors for Solar Monitoring   (ended)
Program with Acoustic Acoustic Technologies, Non Linear Echo Cancellers (ended)
Program with  National Instruments,  LabView Programming for DSP and Sensors.  (ended)


The objective of the NSF Sensor Signal and Information Processing (SenSIP) Industry Consortium(NSF I/UCRC Phase 2 Site) is to develop a research and education partnership with local and national industry. This partnership will focus on the development of new signal/information processing algorithms, tools, and software for integrated sensor systems. The primary objective will be the development of new methods for extracting, parameterizing , transmitting, and classifying information from heterogeneous sensors and sensor networks.The faculty involved in SenSIP have a track record of funded programs in areas associated with the theoretical aspects of the SenSIP mission and are actively establishing industry collaborations that promote cutting-edge application-driven research in these areas. The SenSIP Industry Consortium is a site of the NSF Net Centric Industry University Collaborative Research Center (I/UCRC) and collaborates with the University of North Texas, SMU, and the University of Texas at Dallas. The creation of intellectual property and trained workforce from this program will contribute to several important state initiatives including biomedical, sustainability, defense, and border control. Some specific application-driven problems to be examined include

  • sensors and machine learning
  • quantum machine learning
  • detection and tracking algorithms for sensors
  • source localization with microphone arrays
  • motion detection with camera array sensors
  • algorithms for waveform design for radar and sonar sensors
  • sensor information processing for intrusion and border security
  • signal processing for biological and chemical sensors
  • information and decision networks for sensor arrays
  • acoustic scene characterization

Presentations by the consortium director, Dr. Andreas Spanias and his colleagues and students

  • Proposed project on Flexible sensors by the SenSIP Center, UTD, Richardson (Dallas), April 2017
  • SenSIP Solar Power Research, KIOS center, Cyprus, Feb. 2017
  • The SenSIP REU Site, Prairie View A&M University (HBCU), Dec. 2016.
  • The SenSIP Consortium, Intel Vietnam, Ho Chi Minh City, Vietnam, Nov. 2016.
  • SenSIP Research in Sensor Data Security,  Global Software (a Hitachi subsidiary in Vietnam), Ho Chi Minh City, Vietnam, Nov. 2016.
  • The SenSIP Partnership in International Partnership in Research and Education, Ho Chi Minh University of Technology, Nov. 2016.
  • SenSIP Tutorial on Machine Learning, SensMACH 2016, Hilton Scottsdale, Nov 2016.  (audience 51)
  • SenSIP Consortium Projects in Machine Learning, SensMACH 2016, Hilton Scottsdale, Nov 2016.  (audience 51)
  • The SenSIP Solar Array Facility, University of Cyprus, Nicosia, June 29, 2016  (audience 35).
  • SenSIP Research in Audio Processing,  Toyota Institute, University of Chicago, April 2016 (audience 40)
  • SenSIP Adaptive Signal Processing Tutorial, Invited Tutorial, IISA, July 14, 2016 (audience 23).
  • SenSIP Research and I/UCRC, Signal GeneriX, Limassol, Cyprus, Feb 2016 (audience 10)
  • SenSIP Activities in Machine Learning Algorithms, Imperial College, Nov 2015 (audience 30)
  • The SenSIP Center and NSF I/UCRC, UOP, Athens, Feb 2014  (audience 25)
  • SenSIP Speech Processing Algorithms, Cirrus Logic, June 2013. (audience 20)
  • SenSIP Research on Loudness Estimation, Qualcomm, Feb. 2013
  • Mobile Sensor Research at SenSIP , LG Communications, San Diego, May 2012 (audience 9)
  • Qualcomm, “The SenSIP I/UCRC – Imaging Sensors”, Santa Barbara, Oct 15, 2019, (15 – more by telco)
  • ON SEMI, Spring 2019, “The SenSIP I/UCRC Machine Learning Efforts (20)
  • SenSIP NSF I//UCRC meeting, Machine Learning for Power converters, Tempe, Oct 2019 (30)
  • SenSIP Summer Meeting – Project Status, July 2, 2019, Status of SenSIP Center (36)
  • NSF CPS PI Meeting, The Solar CPS Project, Alexandria, Nov 2019 (200)
  • SenSIP /NCSS I/UCRC presentation, Alphacore, August 2019
  • SenSIP /NCSS I/UCRC presentation, PSG, August 2018
  • DELFT University, April 2019, SenSIP Signal Processing project for Solar Systems – The SenSIP I/UCRC, (25)
  • SenSIP /NCSS I/UCRC presentation, Virtual,  June 2020  (40)
  • SensMACH 2020, Workforce Programs, Oct. 2021  (65)
  • SenSIP /NCSS I/UCRC, Virtual, Covid Cough Audio Analytics, June 2021  (40)

Faculty Products and Projects



Recent Publications

  1. S. Rao, G. Muniraju, C.  Tepedelenlioglu, D.  Srinivasan, G. Tamizhmani and A. Spanias, “Dropout and Pruned Neural Networks for Fault Classification in Photovoltaic Arrays,  IEEE Access, 2021.
  2. Jaskie, J. Martin, and A. Spanias, “PV Fault Detection using Positive Unlabeled Learning,” Applied Sciences, vol. 11, Jun. 2021.
  3. G. Muniraju, G. Kailkhura, J. Thiagarajan, Jayaraman J.; Bremer, Peer-Timo; Tepedelenlioglu, Cihan; Spanias, Andreas, “Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization” IEEE Trans. NNLS-2019-P-11125.R1, 2020.
  4. Thiagarajan, J. J., Rajan, D., Katoch, S., & Spanias, A. (2020). DDxNet: a deep learning model for automatic interpretation of electronic health records, electrocardiograms and electroencephalograms. Scientific RepoRtS10(1), 1-11.
  5. G. Muniraju, C. Tepedelenlioglu, and A. Spanias, “Analysis and design of robust max consensus for wireless sensor networks,” IEEE Transactions on Signal and Information Processing over Networks, pp. 779-791, Vol. 5, Dec. 2019.
  6. G. Muniraju, C. Tepedelenlioglu, and A. Spanias, “Consensus Based Distributed Spectral Radius Estimation,” in Proceeding of IEEE Signal Processing Letters, pp. 1–5, June 2020.
  7. U. Shanthamallu, J. J. Thiagarajan, H. Song, A. Spanias, “GrAMME: Semi-Supervised Learning using Multi-layered Graph Attention Models,” IEEE Trans. on Neural Networks and Learning Systems, Volume: 31, pp. 3977 – 3988, Oct. 2020.
  8. H. Braun, S. Katoch, P. Turaga, A. Spanias, and C. Tepedelenlioglu, “A MACH filter based reconstruction-free Target Detector and Tracker for Compressive Sensing Cameras”, International Journal of Smart Security Technologies (IJSST), pp. 1-21, ,  Vol. 7, Issue 2,  DOI: 10.4018/IJSST.2020070101, 2020.
  9. J. Zuniga-Mejia1, R. Villalpando-Hernandez, C. Vargas-Rosales1, A. Spanias, “A Linear Systems Perspective on Intrusion Detection for Routing in Reconfigurable Wireless Networks”,  IEEE Access, Vol. 7, 1, pp. 60486-60500, Dec. 2019.
  10. V. Berisha, A. Wisler, A. Hero, A. Spanias, “Data-driven estimation of density functionals using a polynomial basis” IEEE Transactions on Signal Processing, pp. 558-572,  Vol. 66, January 2018.
  11. M. Shah, M. Tu, V. Berisha, C. Chakrabarti, A. Spanias, “Articulation Constrained Learning with Application to Speech Emotion Recognition,” Computer Speech and Language, Elsevier,  2019.
  12. S. Ranganath, J. Thiagarajan, D. Rajan, M. Banavar, A. Spanias, J. Fan, K. Jaskie and C. Tepedelenlioglu,”Interactive Signal Processing Education Applications for the Android Platform,” ASEE Computers in Education Journal, Volume 10, Issue 2 June 2019.
  13. X. Zhang, C. Tepedelenlioglu, M. Banavar, A. Spanias, G. Munariju, “Location estimation and detection in wireless sensor networks in the presence of fading,”  Physical Communication, Elsevier, Vol. 32, pp. 62-74, Feb. 2019.
  14. H. Song, J. Thiagarajan, P. Sattigeri, A. Spanias, “Optimizing Kernel Machines using Deep Learning”  IEEE Transactions on Neural Networks and Learning Systems,  NLS-2017-P-8053.R1, pp.  5528–5540, Feb. 2018.
  15. S. Zhang, C. Tepedelenlioglu, M.K. Banavar and A. Spanias, “Distributed Node Counting in Wireless Sensor Networks in the Presence of Communication Noise,” IEEE Sensors Journal, pp. 1175 – 1186, Vol. 17, Feb. 2017.
  16. S. Zhang, C. Tepedelenlioğlu, A. Spanias,  “Distributed Network Center and Size Estimation,” IEEE Sensors Journal, Volume: 18 , Issue: 14, pp. 6033 – 6045, 2018.
  17. Thiagarajan, J.J., Narayanaswamy, V., Rajan, D.,Liang, J., Chaudhri, A., Spanias, A., (2021). Designing Counterfactual Generators using Deep Model Inversion. Neurips 2021
  18. Shanthamallu, U. S., Thiagarajan, J. J., & Spanias, A. (2021, May). Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 11, pp. 9524-9532).
  19. Thiagarajan, Jayaraman J., Vivek Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, and Andreas Spanias. “Accurate and Robust Feature Importance Estimation under Distribution Shifts.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 9, pp. 7891-7898. 2021.
  20. M. Esposito, S. Rao, V. Narayanaswamy, A. Spanias,  “COVID-19 Detection using Audio Spectral Features and Machine Learning,” Asilomar Conference on Circuits, Systems and Computers, Monterey, Oct. 2021.
  21. G. Uehara, A. Spanias, W. Clark, “Quantum Information Processing Algorithms with Emphasis on Machine Learning,”  Proc. IEEE IISA 2021, July 2021.
  22. S. Rao, M. Esposito, V. Narayananswami,  J. Thiagarajan, A. Spanias,”   Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection, Proc. IEEE IISA 2021, July 2021.
  23. M. Malu, G. Dasarathy, A. Spanias,” Bayesian Optimization in High-Dimensional Spaces: A Brief Survey,”   Proc. IEEE IISA 2021, July 2021.
  24. Glen Uehara, Sunil Rao, Mathew Dobson, Cihan Tepedelenlioglu and Andreas Spanias, “Quantum Neural Network Parameter Estimation for Photovoltaic Fault,”  Proc. IEEE IISA 2021, July 2021
  25. V. S. Narayanaswamy, J. J. Thiagarajan and A. Spanias,“On the Design of Deep Priors for Unsupervised Audio Restoration,” Interspeech 2021, Brno, Czech Republic, 2021.
  26. Odrika Iqbal, Saquib Siddiqui, Joshua Martin, Sameeksha Katoch, Andreas Spanias, Daniel Bliss, Suren Jayasuriya, ‘Design And Fpga Implementation Of An Adaptive Video Subsampling Algorithm For Energy-Efficient Single Object Tracking, IEEE ICIP 2020,  UAB, Oct. 2020.
  27. V. Narayanaswamy, J. J. Thiagarajan, R. Anirudh and A. Spanias,  “Unsupervised Audio Source Separation using Generative Priors,”   Proc.  Interspeech 2020, Shanghai, Oct. 2020..
  28. J. Booth, A. Alkhateeb, A. Ewaisha, A. Spanias, “Deep Learning Based MIMO Channel Prediction: An Initial Proof of Concept Prototype,”  IEEE Asilomar Conference, Nov 2020
  29. Shanthamallu, Uday, JayaramanThiagarajan, and Andreas Spanias. “A Regularized Attention Mechanism for Graph Attention Networks.” ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, May 2020.
  30. J. Fan, S. Rao, G. Muniraju, C. Tepedelenlioglu, and A. Spanias, “Fault Classification in Photovoltaic Arrays Using Graph Signal Processing,” in IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Tampere, June, 2020.
  31. A. Spanias, Machine Learning Workforce Development Programs on Health and COVID-19, Proc. IEEE IISA 2020, Piraeus, July 2020.
  32. J. J. Thiagarajan, D.Rajan, S. Katoch and A. Spanias, “Accurate Abnormal EEG Detection using Multi-scale Densenets,” Artificial Intelligence in Medicine, AIME 2019, Submitted Jan. 2019, Poznan, Poland, June 2019.
  33. Jaskie and A. Spanias, “Positive and Unlabeled Learning Algorithms and Applications: A Survey,” Proc. IEEE IISA 2019, Patras, July 2019
  34. J. Fan, C. Tepedelenlioglu, A. Spanias,  “Global Optimization of Graph Filters with Multiple Shift Matrices,” IEEE Asilomar Conference on Signals, Systems and Computers,  Monterrey, Nov. 2019
  35. K. Jaskie, C. Elkan, A.Spanias, A Modified Logistic Regression For Positive and Unlabeled Learning, IEEE Asilomar Conference on Signals, Systems and Computers, Monterrey, Nov. 2019
  36. D. Mohan, S. Katoch, S. Jayasuriya, P. Turaga, A. Spanias,  Adaptive Video Subsampling For Energy-Efficient Object DetectioN,” IEEE Asilomar Conference on Signals, Systems and Computers, Monterrey, Nov. 2019
  37. Vivek Narayanaswamy, Jayaraman Thiagarajan, Andreas Spanias,  “Designing An Effective Metric Learning Pipeline for Speaker Diarization,” IEEE ICASSP 2019, Brighton, UK,  May 2019.
  38. J. Fan, Cihan Tepedelenlioglu, A. Spanias,  ” Graph Filtering With Multiple Shift Matrices,” IEEE ICASSP 2019, Brighton, UK,  May 2019.
  39. R. Ramakrishna, A. Scaglione, A. Spanias, C. Tepedelenlioglu,   ” Distributed Bayesian Estimation With Low-Rank Data: Application To Solar Array Processing,” IEEE ICASSP 2019, Brighton, UK,  May 2019.

Sponsored in part by NSF I/UCRC Awards 0934418 and 1035086. NSF Phase 2 I/UCRC Award 1540040.