SenSIP Industry Consortium

SenSIP Info in a Quad Chart

The mission of the SenSIP Industry Consortium (originally established as an NSF I/UCRC 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 by our center-affiliated faculty include information processing, sensor calibration, biomedical systems, defense and security, environmental technologies, speech/audio processing, 6G+ telephony, imaging and vision systems, smart cameras, low-power realizations, real-time implementations, AI-monitored solar energy, radar, quantum AI simulations with real-life data, and vehicular sensing.

SenSIP Consortium Membership Chart

Download SenSIP Consortium brochure

SenSIP Agreement (Small Business Industrial Membership Agreement)

SenSIP (Membership Agreement)

SenSIP Consortium bylaws

Director

Andreas Spanias

Industry Advisory Board (IAB and Project Directors):

• Soleh Dib, Nitesh Shah Raytheon – Surveillance and Machine Learning
• Abhay Dias , NXP – Sensor Fusion
• Esko, Mikkola, Alphacore, Imaging
• Mike Stanley,  Lightsense, Light Sensors, Spectroscopy
• Joe Marvin, Prime Solutions Group, radar and machine learning
• Devarajan Srinivasan, Solar Monitoring, Poundra LLC

Members at Large (Advisors) and Industry/Lab Collaborators

• Diann Dow, On Semi, Machine Learning
• Steve Miller, Aperio DSP (Associate Membership), ML Applications
• Evgeni Gousev, Qualcomm – Computational Imaging Sensors
• Claire Jackoski, Intel
• Ruchir Sehra, Resonea (Associate Membership), Audio Breathing Analytics
• Glen Abousleman, General Dynamics
• Steve Whalley, Worldwide Ventures

The SenSIP industry consortium was established in 2009 as an NSF funded Industry University Cooperative Research Center (I/UCRC) site. Since 2023 the SenSIP consortium now operates as a graduated I/UCRC using the same agreement and bylaws templates as the I/UCRC structure.

Synopsis

• 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

Grants

Patents

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 RepoRtS, 10(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.