Smart Speakers, Smarter Protection

Whether you’re looking to try a new recipe, dimming the lights in your living room, or curious about the species of bacteria living inside your mouth, Amazon Alexa has got you covered. With a simple voice command, Alexa’s ability to perform various tasks or answer questions has made it widely popular, with over 40 million users in the United States alone. Despite the convenience smart speakers like Alexa offer, these devices have also raised some privacy concerns. 

Amazon has been known to collect data on users which includes their shopping habits, preferences, and even their location for personalized marketing. But that’s not all. When using waking words such as “Hey Alexa” to activate smart speakers, the audio of your voice command is also recorded and stored, becoming Amazon’s property. This means that Amazon owns your voice audio and can do whatever they want with it. 

“Big tech companies are using our personal information. We’re less like customers and more like their product,” says graduate student Brian Testa ’24. “I’ve always been sensitive to that. I don’t use a lot of technology at home for that reason.” 

Using voice data, companies like Amazon and Google have now developed technology that poses even more threats to privacy: AI and machine learning that can determine people’s emotional state or mood from their voice. This patented technology can even pick up on feelings from emotionally neutral phrases like “What’s the weather?” Since there are no laws in place to prevent this, there’s no protection against it. 

“In the US for the last five to 10 years, lots of researchers have been working on how they can use voice to infer emotions, mood or even mental health,” says assistant professor in electrical engineering and computer science, Asif Salekin. “In my own lab, we have previous works on tech that can infer mental disorders like depression, social anxiety, manic disorder, and even suicidal tendencies from one’s voice.” 

While this technology can be useful in certain circumstances, most users, if not all, have not consented to having their emotions detected by smart speakers. These privacy concerns led Testa, Professor Salekin, graduate students Harshit Sharma ’26 and Yi Xiao 26, and undergraduate student Avery Gump ’24 to begin researching ways to protect users’ privacy from smart speakers. 

“Consent is key,” Salekin says. “We’d still like to use smart speakers since they’re quite useful – I have them in my own home. This project was about finding a way to use these devices without giving companies the power to exploit us.” 

Led by Testa, the group conducted extensive research and developed a device that can be attached to a smart speaker or downloaded as software onto a laptop. This device emits a mild noise that only the smart speaker can hear and masks the emotional tone in your voice, providing a new level of privacy protection for concerned users.

“Through the use of a speech emotion recognition (SER) classifier, a smart speaker can analyze how people are feeling based on how they sound. We created a microphone device that listens for the wake word ‘Hey Alexa’”, Testa says. “When the smart speaker activates, our device activates too and begins to emit a noise that disrupts the smart speaker from detecting your emotions. However, only the smart speaker hears this noise.”  

Currently, their device masks your emotional state by presenting it as a completely different emotion. When you speak, the smart speaker may detect from your voice that you’re sad, angry, or frustrated when you’re not feeling any of these emotions. This unpredictability makes it difficult for smart speakers to accurately determine your true emotions or mood and also prevents machine learning from picking up on any patterns and mood correlations. The group hopes to improve the device’s functionality by making it mask your emotions as neutral rather than presenting them as a different emotion. 

“To create the mild noise our device emits, we utilized genetic programming to identify a combination of specific frequencies that disrupt the smart speaker from determining a person’s mood,” Salekin says. “Only the speaker hears this noise, but it can hear your speech commands clearly, so the utility of the smart speaker remains intact.”  

Though the sound is only detected by the smart speaker, the group wanted to see how loud it would be when the device is used. Testa played the sound in the lab when Professor Salekin was having a meeting and Salekin didn’t even realize it was playing, which showed that the noise wasn’t disruptive. Additionally, they also conducted a survey with others to see if the noise was loud enough to be disruptive. 

Testa, Salekin, Sharma, Xiao, and Gump are currently working on patent submissions, form factors, and speaking with companies about commercializing their device. What sets their patent apart from similar concepts is that while past technology focused on determining people’s moods or emotions, their technology is all about protecting them. This unique approach makes their device the first of its kind.

“It was a fun project,” Testa says. “This paper was published by me and as the first listed author, I’m excited about it. I’ve been working towards my Ph.D., and this is another step towards that goal.”  

“Working with the students in real-world applications and research with real results was exciting,” Salekin says. “This research has many components and the collaboration between us was great. We’re excited to see what the future for this tech holds.” 

Electrical Engineering and Computer Science Professor Senem Velipasalar Awarded Patent for Room Occupancy Detection Platform

Remembering to turn the lights off when leaving a room is easy, but letting the furnace that you’re headed out isn’t as simple. About 37% of all energy used by commercial buildings and 40% of energy used in residences go toward heating, ventilation, and air conditioning (HVAC). The costs related to heating and cooling unoccupied spaces in homes and office buildings have been a challenge for decades.

Current occupancy sensors only detect movement, so they can’t tell if someone is stationary. They also have trouble distinguishing between people and large pets, and often require an external power source and data processing. When a room is occupied, not being able to detect occupancy can cause user discomfort. On the other hand, not reliably knowing when a room is empty adds up to massive amounts of unnecessary heating and cooling costs for spaces without any people in them.

A collaboration between Electrical Engineering and Computer Science Professors Senem Velipasalar and Pramod Varshney, Mechanical and Aerospace Engineering Professor Ed Bogucz, Professor Tarek Rakha from Georgia Tech and SRI International, a nonprofit research institute, has developed a new sensor platform, MicroCam, which addresses many of the limitations that current systems face. Their project received funding from the U.S. Department of Energy’s Advanced Research Projects Agency – Energy (ARPA-E) and had to meet certain requirements. The platform had to be highly accurate, low-maintenance, affordable and easily self-commissioned for consumers while still providing more than 30% energy savings.

“It was important to us and ARPA-E that this platform be highly reliable, practical and inexpensive,” says Velipasalar. “This needed to be useful in real-world spaces, and it was designed to be battery-powered.”

The MicroCam is equipped with multi-modal sensors that can process motion, audio and video data. The camera can operate under daylight, low light or even no light conditions and it can be powered for more than a year on just three AA batteries – all the sensor processing is done inside one small unit.

“We do not use cloud computing, everything is captured and processed on this platform,” says Velipasalar. “You are not transferring or saving data, so it alleviates privacy concerns.”

While the MicroCam can detect occupancy, it does not share potentially private information.

“It senses your presence but only sends a 0 or 1 signal to the HVAC system,” says Velipasalar. “That binary occupancy result is the only data shared with the lead platform.”

Industrial and Interactive Design Professor Don Carr and his students worked with Velipasalar and Bogucz to design a prototype case for the MicroCam.

“Eventually we want a peel and stick and ideally you want to install one per room,” says Velipasalar. “If you have one of these in each room, you could monitor the entire space.”

Velipasalar was granted a patent in March 2023 titled “Low Power and Privacy Preserving Sensor Platform for Occupancy Detection.” It is the sixth patent she has been awarded over her career.

“This was a challenging project. We had to meet low cost and high accuracy requirements but it has incredible potential,” says Velipasalar.

The platform may have additional uses in the future including smart home integration and security monitoring. Velipasalar also sees possibilities for the MicroCam to provide activity monitoring and fall detection for families and nursing homes.

Pramod K. Varshney

Degree(s):

  • Ph. D. (Illinois) 1976

Areas of Expertise:

  • Distributed sensor networks and data fusion
  • Statistical inference
  • Wireless communications
  • Signal processing
  • Machine learning
  • Human-machine teaming

My research addresses fundamental questions in statistics-based signal processing, data/information fusion, sensor data processing, data analytics, machine learning and AI. My research has been generously funded for over four decades by Department of Defense, NSF, ARPA-E, EPA and many companies. Starting in the early 1980s, I have pioneered the area of data/information fusion and inference in sensor networks. While a lot of my work has been inspired by Department of Defense applications, I have also applied my research results to a wide variety of non-defense applications including IoT and health-related applications. For example, I have worked on imaging for breast cancer detection, and methods for more accurate Alzheimer disease detection. My current research includes detection and tracking, secure inference in distributed sensing systems, human-machine teaming for inference, and information fusion.

Honors and Awards:

  • ASEE Dow Outstanding Young Faculty Award, 1981
  • IEEE Fellow 1997
  • Third Millennium Medal IEEE 2000
  • President International Society of Information Fusion 2001.
  • Judith A. Resnik Award IEEE 2012
  • Doctor of Engineering honoris causa, Drexel University, 2014
  • Distinguished Alumni Award, ECE Department, Univ. of Illinois, 2015
  • Yaakov Bar-Shalom Award for a Lifetime of Excellence in Information Fusion, 2018
  • Claude Shannon-Harry Nyquist Technical Achievement Award, IEEE Signal Proc. Society, 2021
  • Pioneer Award, IEEE Aerospace and Electronic Society, 2021
  • Chancellor’s Citation for Lifetime Achievement, Syracuse University, 2023

Selected Publications:

Books

  • P.K. Varshney, Distributed Detection and Data Fusion, Springer-Verlag, 1997.
  • G.L. Foresti, C. S. Regazzoni, and P. K. Varshney (eds.), Multisensor Surveillance Systems: The Fusion Perspective, Kluwer Academic Press, 2003.
  • K. Varshney and M. K. Arora (eds.), Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, Springer Verlag, 2004.
  • A. Vempaty, B. Kailkhura and P. K. Varshney, Secure Networked Inference with Unreliable Data Sources,  Springer 2018

Selected Recent Papers

  • Li C., Wang Z., Wang X., and Varshney, P.K, “COMIC: An unsupervised change detection method for heterogeneous remote sensing images based on copula mixtures and Cycle-Consistent Adversarial Networks”, Elsevier Information Fusion, 2023
  • Dwivedi AK, Chaudhari S, Varshney N and Varshney, P. K., “Performance Analysis of LEO Satellite-Based IoT Networks in the Presence of Interference”, IEEE Internet of Things Journal, 2023
  • Li, C., Li, G., Wang, X. and Varshney, P.K., “A Copula-Based Method for Change Detection with Multi-sensor Optical Remote Sensing Images”, IEEE Trans. on Geoscience and Remote Sensing, September, 2023
  • Joseph, G., Zhong, C., Gursoy, M.C., Velipasalar, S. and Varshney, P.K., “Scalable and decentralized algorithms for anomaly detection via learning-based controlled sensing”, IEEE Trans. on Signal and Information Processing over Networks., Early Access, 2023
  • Zhang, G., Yi, W., Varshney, P.K., and Kong, L., “Direct Target Localization With Quantized Measurements in Noncoherent Distributed MIMO Radar Systems”, IEEE Trans. on Geoscience and Remote Sensing, pp. , Apr, 2023
  • Quan, C., Sriranga, N., Yang, H., Han, Y.S., Geng, B., and Varshney, P.K., “Efficient Ordered-Transmission Based Distributed Detection under Data Falsification Attacks”, IEEE Signal Process Letters, pp. 145 – 149, Feb, 2023
  • Hakansson, V.W., Venkategowda, N.K.D., Werner, S., and Varshney, P.K., “Optimal Scheduling of Multiple Spatiotemporally Dependent Observations for Remote Estimation Using Age of Information”, IEEE Internet of Things Journal, pp. 20308 – 20321, Oct, 2022
  • Sun, J., Yi, W., Varshney, P.K., and Kong, L., “Resource Scheduling for Multi-Target Tracking in Multi-Radar Systems With Imperfect Detection”, IEEE Trans. Signal Process, pp. 3878 – 3893, Jul, 2022
  • Lu, R., Chen, B., Sun, J., Chen, W., Wang, P., Chen, Y., Liu, H., and Varshney, P. K., “Heterogeneity-Aware Recurrent Neural Network for Hyperspectral and Multispectral Image Fusion”, IEEE Journal of Selected Topics in Signal Processing, pp. 649 – 665, Jun, 2022
  • Li, Q., Kailkhura, B., Goldhahn, R., Ray, P., and Varshney, P. K., “Robust Decentralized Learning Using ADMM With Unreliable Agents”, IEEE Trans. Signal Process, pp. 2743 – 2757, June, 2022
  • Trezza, A., Bucci, D. J., and Varshney, P. K., “Multi-Sensor Joint Adaptive Birth Sampler for Labeled Random Finite Set Tracking”, IEEE Trans. Signal Process, pp. 1010 – 1025, Feb, 2022
  • Yuan, Y., Yi, W., and Varshney, P. K., “Exponential Mixture Density based Approximation to Posterior Cramér-Rao Lower Bound for Distributed Target Tracking”, IEEE Trans. Signal Process, pp. 862 – 877, Feb, 2022
  • Chen, Q., Geng, B., Han, Y., and Varshney, P. K., “Enhanced Audit Bit Based Distributed Bayesian Detection in the Presence of Strategic Attacks”, IEEE Trans. on Signal and Information Process. over Networks, pp. 49 – 62, Jan, 2022
  • Bulusu, S., Khanduri, P., Kafle, S., Sharma, P., and Varshney, P. K., “Byzantine Resilient Non-Convex SCSG With Distributed Batch Gradient Computations”, IEEE Trans. on Signal and Information Process. over Networks ., pp. 754 – 766, Nov, 2021
  • Cheng, X., Khanduri, P., Chen, B., and Varshney, P. K., “Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network”, IEEE Trans. Signal Process, pp. 5448 – 5462, Sept, 2021
  • Geng, B., Cheng, X., Brahma, S., Kellen, D., and Varshney, P. K., “Collaborative Human Decision Making with Heterogeneous Agents”, IEEE Trans. on Computational Social Systems., pp. 469 – 479, Jul, 2021
  • Li, C., Li, G., and Varshney, P. K., “Communication-Efficient Federated Learning Based on Compressed Sensing”, IEEE Internet of Things Journal., pp. 15531 – 15541, Apr, 2021
  • Geng, B., Li, Q., and Varshney, P. K., “Utility Theory Based Optimal Resource Consumption For Inference In IoT Systems”, IEEE Internet of Things Journal., pp. 12279 – 12288, Mar, 2021
  • Ciuonzo, D., Rossi, P.S., and Varshney, P. K., “Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests”, IEEE Internet of Things Journal., pp. 9059 – 9071, Feb, 2021
  • Joseph, G., Nettasinghe , B., Krishnamurthy, V., and Varshney, P. K., “Controllability of Network Opinion in Erdos-Renyi Graphs Using Sparse Control Inputs”, SIAM Journal on Control and Optimization., pp. 2321-2345, Jan, 2021
  • Joseph, G. and Varshney, P. K., “Measurement Bounds for Compressed Sensing in Sensor Networks With Missing Data”, IEEE Trans. Signal Process., pp. 905-916, Jan, 2021

Senem Velipasalar

Degrees:

  • Ph. D., Electrical Engineering, Princeton University, Princeton, NJ, 2007
  • M.A., Electrical Engineering, Princeton University, Princeton, NJ, 2004
  • M.S., Electrical Sciences and Computer Engineering, Brown University, Providence, RI, 2001
  • B.S., Electrical and Electronics Engineering, Bogazici University, Istanbul, Turkey, 1999

Lab/ Center/ Institute affiliations:

Director of the Smart Vision Systems Laboratory (http://www.vision.syr.edu/)

Faculty Affiliate, Aging Studies Institute

Areas of Expertise:

  • Machine Learning
  • Computer Vision
  • Wireless Smart Camera Networks
  • Mobile camera applications
  • Signal Processing

Prof. Velipasalar’s primary areas of research are machine learning and computer vision. More specifically, her research has focused on human activity classification and fall detection from egocentric cameras, and applications of machine learning to (i) thermal anomaly detection from UAV-mounted infrared cameras, (ii) driver behavior analysis from in-vehicle mounted cameras, (iii) 3D object detection, (iv) person detection from video data, (v) analysis of functional near infrared spectroscopy (fNIRS) data, (vi) dynamic multi-channel access, and (vii) defense against adversarial jamming attacks.

Honors/Awards:

  • NSF CAREER Award, 2011.
  • 2021 IEEE Region 1 Technological Innovation (Academic) Award.
  • 2021 IAAI Deployed Application Award for our paper titled “Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans”.
  • Top 25 most downloaded IEEE Sensors Journal paper in the months of January-September 2017, and June 2018.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Burak Kakillioglu, 2022.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Natalie Sommer, 2021.
  • Graduate School All-University Doctoral Prize, received by my former Ph.D. student Yantao Lu, 2020.
  • 2017 IEEE Green Communications & Computing Technical Committee Best Journal Paper Award for our paper titled “Analysis of Energy Efficiency in Fading Channels under QoS Constraints”.
  • 2nd place Poster Award at the 17th Annual SyracuseCoE Symposium Student Poster Competition for our work titled “Heat Mapping Drones”, October 2017.
  • 2014 Excellence in Graduate Education Faculty Recognition Award.
  • Graduate School All University Doctoral Prize, received by my former Ph.D. student Akhan Almagambetov, 2014.
  • Nunan Research Day Poster Competition EECS Departmental Winner Award, received by Danushka Bandara (co-advised by Dr.Hirshfield), 2014.
  • Intelligent Transportation Society (ITS) of NY Best ITS Student Essay Award, received by my former Ph.D. student Akhan Almagambetov, based on our research on vehicle taillight tracking and alert signal detection, May 2013.
  • The college-wide award for “Applicability of Research to Business and Industry”, received by my former Ph.D. student Akhan Almagambetov, Nunan Lecture and Research Day, April 2013.
  • Third place paper award at the ACM/IEEE International Conference on Distributed Smart Cameras for the paper titled “Energy-efficient Feedback Tracking on Embedded Smart Cameras by Hardware-level Optimization“, 2011
  • EPSCoR First Award, 2009
  • Layman Award as PI, 2007
  • Layman Award as Co-PI, 2009
  • Best Student Paper Award at the IEEE International Conference on Multimedia & Expo (ICME) for the paper titled “Design and Verification of Communication Protocols for Peer-to-Peer Multimedia Systems,” 2006
  • IBM Patent Application Award, 2005
  • Travel Grant, Office of Graduate Affairs, Princeton University, 2005
  • Graduate Fellowship, Princeton University, 2002
  • Graduate Fellowship, Brown University, 1999

Selected Publications:

(Please visit https://ecs.syr.edu/faculty/velipasalar/ for a complete list)

  • J. Chen, B. Kakillioglu and S. Velipasalar, “Background-Aware 3D Point Cloud Segmentation with Dynamic Point Feature Aggregation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, April 2022.
  • F. Altay and S. Velipasalar, “The Use of Thermal Cameras for Pedestrian Detection,” IEEE Sensors Journal, vol. 22, issue:12, 11489 – 11498, May 2022.
  • Y. Chu, D. Mitra, K. Cetin, N. Lajnef, F. Altay, S. Velipasalar, “Development and Testing of a Performance Evaluation Methodology to Assess the Reliability of Occupancy Sensor Systems in Residential Buildings,” Energy and Buildings, vol. 268, pp. 112148, 2022.
  • J. Wang, T. Grant, S. Velipasalar, B. Geng and L. Hirshfield, “Taking a Deeper Look at the Brain: Predicting Visual Perceptual and Working Memory Load from High-Density fNIRS Data,” IEEE Journal of Biomedical and Health Informatics, vol. 26, issue:5, pp. 2308-2319, December 2021.
  • J. Wang, W. Chai, A. Venkatachalapathy, K. L. Tan, A. Haghighat, S. Velipasalar, Y. Adu-Gyamfi, A. Sharma, “A Survey on Driver Behavior Analysis from In-Vehicle Cameras,” accepted to appear in the IEEE Transactions on Intelligent Transportation Systems, early access version available, November 2021.
  • F. Wang; C. Zhong, M. Cenk Gursoy, S. Velipasalar, “Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning,” IEEE Access Journal, vol. 9 , pp. 163188 – 163203, December 2021.
  • N. M. Sommer, B. Kakillioglu, T. Grant, S. Velipasalar and L. Hirshfield, “Classification of fNIRS Finger Tapping Data with Multi-Labeling and Deep Learning,” IEEE Sensors Journal, vol. 21, issue: 21, pp. 24558-24559, doi: 10.1109/JSEN.2021.3115405, Nov. 2021.
  • Y. Zheng, Y. Lu, and S. Velipasalar, “An Effective Adversarial Attack on Person Re-identification in Video Surveillance via Dispersion Reduction,” IEEE Access Journal, vol. 8, pp. 183891 – 183902, Sept. 2020.
  • N. Sommer, S. Velipasalar, L. Hirshfield, Y. Lu and B. Kakillioglu, “Simultaneous and Spatiotemporal Detection of Different Levels of Activity in Multidimensional Data,” IEEE Access Journal, vol. 8, pp. 118205 – 118218, June 2020.
  • D. Bandara, T. Grant, L. Hirshfield and S. Velipasalar, “Identification of Potential Task Shedding Events Using Brain Activity Data,” Augmented Human Research, 5. 10.1007/s41133-020-00034-y, 2020.
  • M. Cornacchia and S. Velipasalar, “Autonomous Selective Parts-Based Tracking,” IEEE Transactions on Image Processing, vol. 29, pp. 4349-4361, January 2020.
  • B. Kakillioglu, A. Ren, Y. Wang and S. Velipasalar, “3D Capsule Networks for Object Classification with Weight Pruning,” IEEE Access Journal, pp. 27393-27405, Febr. 2020.
  • C. Zhong, M. Cenk Gursoy and S. Velipasalar, “Deep Reinforcement Learning-Based Edge Cashing in Wireless Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 6 , issue 1, pp. 48-61, March 2020.
  • Y. Hu, Y. Li, M. C. Gursoy, S. Velipasalar, and A. Schmeink, “Throughput Analysis of Low-Latency IoT Systems with QoS Constraints and Finite Blocklength Codes,” IEEE Transactions on Vehicular Technology, vol. 69, issue 3, pp. 3093-3104, March 2020.
  • C. Zhong, Z. Lu, M. Cenk Gursoy and S. Velipasalar, “A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, issue 4, pp. 1125-1139, Dec. 2019.
  • Y. Lu and S. Velipasalar, “Autonomous Human Activity Classification from Wearable Multi-Modal Sensors,” IEEE Sensors Journal, vol. 19, issue: 23, pp. 11403-11412, Dec. 2019.
  • D. Qiao, M. Cenk Gursoy and S. Velipasalar, “Throughput-Delay Tradeoffs with Finite Blocklength Coding over Multiple Coherence Blocks,” IEEE Transactions on Communications, pp. 5892 – 5904, volume: 67 , Issue: 8 , Aug. 2019.
  • D. Bandara, L. Hirshfield and S. Velipasalar, “Classification of Affect Using Deep Learning on Brain Blood Flow Data,” Journal of Near Infrared Spectroscopy, 27(3), pp. 206-219, doi: 10.1177/0967033519837986, April 2019.
  • C. Ye, M. Cenk Gursoy and S. Velipasalar, “Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning,” IEEE Transactions on Communications, pp. 5629 – 5644, volume: 67 , Issue: 8 , Aug. 2019.
  • M. Cornacchia, B. Kakillioglu, Y. Zheng and S. Velipasalar, “Deep Learning Based Obstacle Detection and Classification with Portable Uncalibrated Patterned Light,” IEEE Sensors Journal, vol. 18, issue: 20, pp. 8416-8425, Oct 2018.
  • Y. Lu and S. Velipasalar, “Autonomous Footstep Counting and Traveled Distance Calculation by Mobile Devices Incorporating Camera and Accelerometer Data,” IEEE Sensors Journal, vol. 17, issue: 21, pp. 7157-7166, Nov. 2017.
  • K. Ozcan, S. Velipasalar and P. Varshney, “Autonomous Fall Detection with Wearable Cameras by using Relative Entropy Distance Measure,” IEEE Transactions on Human-Machine Systems, vol. 47, issue: 1, pp. 31-39, Febr. 2017.
  • M. Cornacchia, K. Ozcan, Y. Zheng and S. Velipasalar, “A Survey on Activity Detection and Classification Using Wearable Sensors,” IEEE Sensors Journal, vol. 17, issue: 2, pp. 386-403, Jan. 2017. Top 25 most downloaded IEEE Sensors Journal paper for nine consecutive months in 2017, and in June 2018 .
  • F. Erden, S. Velipasalar, A. Z. Alkar, A. Enis Cetin, “Sensors in Assisted Living: A Survey of Signal and Image Processing Methods ,” IEEE Signal Processing Magazine, volume:33, issue:2, pp. 36-44, March 2016.
  • K. Ozcan and S. Velipasalar, “Wearable Camera- and Accelerometer-based Fall Detection on Portable Devices ,” IEEE Embedded Systems Letters, volume: 8, issue: 1, pp. 6-9, March 2016.

Amit K. Sanyal

Degrees:

  • Ph.D. (Aerospace Engineering, U of Michigan)
  • MS (Mathematics, U of Michigan)
  • MS (Aerospace Engineering, Texas A&M)
  • B. Tech. (Indian Institute of Technology, Kanpur)

Lab/Center Affiliation:

  • Autonomous Unmanned Systems Laboratory (AUSL) at the Syracuse Center of Excellence

Areas of Expertise:

  • Geometric mechanics and its applications to robotics and control 
  • Geometric control of nonlinear systems 
  • Geometric observer design for nonlinear systems 
  • Guidance, navigation and control of aerospace vehicles 
  • Dynamics and control of autonomous vehicles 

Current Research:

My research develops technologies that increase the safety and reliability of autonomous vehicles and robots working alongside humans. As the roles and uses of robots and autonomous vehicles are growing and expected to grow over the next several years, we need to ensure that they are safe and reliable when deployed for tasks benefitting human society. This is accomplished through design of nonlinearly stable and robust onboard guidance, navigation and control schemes that can be implemented onboard resource-constrained robotic platforms, using commercially available sensors and onboard processors. Research investigations undertaken in my lab include: (1) motion and relative motion estimation of autonomous unmanned aerial and ground vehicles, (2) data-enabled robust and stable control of single and multiple autonomous unmanned vehicles, and (3) spacecraft guidance, navigation and control for Earth-orbiting and deep space missions. 

Courses Taught:

Courses taught at NMSU from fall 2013 till spring 2015 are:

  • AE 362 (Orbital Mechanics)
  • ME 452 (Control System Design)
  • AE 561/ME 405 (Spacecraft Dynamics and Control)
  • AE/ME 527 (Control of Mechanical Systems)
  • AE/ME 529 (Nonlinear and Optimal Control)
  • ME 580 (Numerical Analysis II)

Courses taught at Syracuse University from fall 2015 are:

  • AEE 577 (Introduction to Space Flight)
  • MEE 725 (Advanced Engineering Dynamics)
  • MAE 312 (Engineering Analysis)
  • MAE 675 (Methods of Analysis)
  • MAE 728 (Geometric and Optimal Control)
  • AEE 630 (Spacecraft Dynamics and Control)
  • MAE 628 (Linear Systems)

Honors and Awards: 

  • 2001 Distinguished Graduate Student Masters Research Award, Texas A & M University.  
  • 2002 College of Engineering Fellowship, University of Michigan. 
  • 2003 Engineering Academic Scholar Certificate, College of Engineering, University of Michigan. 
  • 2012 Summer Faculty Fellow, Air Force Research Laboratory. 
  • 2015 Senior Member, AIAA and IEEE. 
  • 2021 Associate Fellow, AIAA. 
  • 2024 Visiting Faculty Research Fellow, Air Force Research Laboratory. 

Recent Research Awards:

  • CPS: Small: NSF-DST: Autonomous Operations of Multi-UAV Uncrewed Aerial Systems using Onboard Sensing to Monitor and Track Natural Disaster Events, NSF, 3/1/2024 to 2/28/2027, PI, $453,372.
  • Collaborative Research: NRI: Integration of Autonomous UAS in Wildland Fire Management, NSF (with Ohio State), 1/1/2022 to 12/31/2025, PI at Syracuse University, $536,983.
  • A Platform-Independent Flight Management Unit for Small UAS, Akrobotix LLC (flow through from NSF SBIR Phase 1), 2/1/2020 to 04/30/2021, PI, $31,981.
  • Reliable Perception and Control for UAV Navigation in 3D Space, Semiconductor Research Corporation, 2/1/2019 to 1/31/2022, Co-PI, $299,638.
  • Enabling Multimodal Sensing, Real-time Onboard Detection and Adaptive Control for Fully Autonomous Unmanned Aerial Systems, NSF Cyber-Physical Systems, 8/15/2017 to 8/14/2020, Co-PI, $600,000.

Selected Publications:

  1. N. Wang, R. Hamrah, A. K. Sanyal and M. Glauser, “Geometric Extended State Observer on TSE(3) with Fast Finite-Time Stability: Theory and Validation on a Rotorcraft Aerial Vehicle,” under revision for Aerospace Engineering Science and Technology.  
  2. N. Wang, R. Hamrah and A. K. Sanyal, “Robust and H¨older-continuous finite-time stabilization of rigid body attitude dynamics using rotation matrices,” American Control Conference, Toronto, Canada, July 2024.  
  3. A. Dongare, R. Hamrah, and A. K. Sanyal, “Finite-time Stable Pose Estimation on SE(3) using Onboard Optical Sensors,” AIAA SCITECH 2024 Forum, Orlando, FL, Jan 2024.  
  4. M. Bhatt, A. Sanyal, and S. Sukumar, “Asymptotically Stable Optimal Multi-rate Rigid Body Attitude Estimation based on Lagrange-d’Alembert Principle,” Journal of Geometric Mechanics, vol. 15(1), pp. 73-97, 2023.  
  5. H. Eslamiat, N. Wang, R. Hamrah, and A. K. Sanyal, “Geometric Integral Attitude Control on SO(3),” Electronics, vol. 11(18), pn. 2821, 2022.  
  6. P. Cruz, P. Batista, and A. Sanyal, “Design and analysis of attitude observers based on the Lagrange-d’Alembert principle applied to constrained three-vehicle formations,” Advances in Space Research, vol. 69 (11), pp. 4001-4012, 2022.  
  7. M. Bhatt, S. Sukumar, and A. K. Sanyal, “Discrete-Time Rigid Body Pose Estimation Based on Lagrange–d’Alembert Principle,” Journal of Nonlinear Science, vol. 32, pn. 86, 2022.  
  8. A. K. Sanyal, “Data-Driven Discrete-time Control with H¨older-Continuous Real-time Learning,” International Journal of Control, vol. 95(8), pp. 2175-2187, 2022, doi: 10.1080/00207179.2021.1901993; arXiv version available at: https://arxiv.org/abs/2006.05288.  
  9. R. Hamrah and A. K. Sanyal, “Finite-time stable tracking control for an underactuated system in SE(3) in discrete time,” International Journal of Control, vol. 95 (4), pp. 1106-1121, 2022, doi: 10.1080/00207179.2020.1841299.  
  10. R. Hamrah, R. R. Warier, and A. K. Sanyal, “Finite-time stable estimator for attitude motion in the presence of bias in angular velocity measurements,” Automatica, vol. 132(10), 2021, doi: 10.1016/j.automatica.2021.109815.  
  11. X. Li, R. R. Warier, A. K. Sanyal, and D. Qiao, “Trajectory Tracking Near Small Bodies Using Only Attitude Control and Orbit-Attitude Coupling,” AIAA Journal of Guidance, Control and Dynamics, vol. 42(1), 2019, doi: 10.2514/1.G003653.  
  12. S. P. Viswanathan and A. K. Sanyal, “Adaptive Singularity-Free Control Moment Gyroscopes,” AIAA Journal of Guidance, Control and Dynamics, vol. 41(11), 2018, doi: 10.2514/1.G003545.  
  13. S. P. Viswanathan, A. K. Sanyal and E. Samiei, “Integrated Guidance and Feedback Control of Underactuated Robotics System in SE(3),” Journal of Intelligent & Robotic Systems, vol. 89, pp. 251-263, 2018, doi: 10.1007/s10846-017-0547-0.  
  14. A. K. Sanyal and M. Izadi, “Stable Estimation of Rigid Body Motion Based on the Lagrange-d’Alembert Principle,” in Multisensor Attitude Estimation: Fundamental Concepts and Applications, pp. 57-76, 2016, ed.: H. Fourati, CRC Press (Taylor and Francis), FL.  
  15. M. Izadi and A. K. Sanyal, “Rigid Body Pose Estimation based on the Lagrange-d’Alembert Principle,” Automatica, vol. 71(9), pp. 78-88, 2016, doi: 10.1016/j.automatica.2016.04.028. 
  16. S. P. Viswanathan, A. K. Sanyal, F. Leve and N. H. McClamroch, “Dynamics and Control of Spacecraft with a Generalized Model of Variable Speed Control Moment Gyroscopes,” ASME Journal of Dynamic Systems, Measurement and Control, vol. 137(7), paper 071003, 2015, doi: 10.1115/1.4029626.  
  17. A. K. Sanyal and J. Bohn, “Finite Time Stabilization of Simple Mechanical Systems using Continuous Feedback,” International Journal of Control, vol. 88(4), pp. 783-791, 2015.  
  18. D. Lee, A. Sanyal, E. Butcher and D. Scheeres, “Almost Global Asymptotic Tracking Control for Spacecraft Body-Fixed Hovering near an Asteroid,” Aerospace Science and Technology, vol. 38, pp. 105-115, 2014.  
  19. M. Izadi and A. K. Sanyal, “Rigid Body Attitude Estimation Based on the Lagrange-d’Alembert Principle,” Automatica, vol. 50(10), pp. 2570-2577, 2014.  
  20. A. K. Sanyal and A. Goswami, “Dynamics and Balance Control of the Reaction Mass Pendulum (RMP): A 3D Multibody Pendulum with Variable Body Inertia,” ASME Journal of Dynamic Systems, Measurement and Control, vol. 136(2), paper 021002, 2014.  
  21. A. K. Sanyal and N. Nordkvist, “Attitude State Estimation with Multi-Rate Measurements for Almost Global Attitude Feedback Tracking,” AIAA Journal of Guidance, Control and Dynamics, vol. 35(3), pp. 868-880, 2012.  
  22. A. M. Bloch, P. E. Crouch, N. Nordkvist and A. K. Sanyal, “Embedded geodesic problems and optimal control for matrix Lie groups,” Journal of Geometric Mechanics, vol. 3(2), pp. 197-223, 2011.  
  23. N. A. Chaturvedi, A. K. Sanyal, and N. H. McClamroch, “Rigid Body Attitude Control: Using rotation matrices for continuous, singularity-free control laws,” IEEE Control Systems Magazine, vol. 31(3), pp. 30-51, 2011.  
  24. A. K. Sanyal, N. Nordkvist and M. Chyba, “An Almost Global Tracking Control Scheme for Maneuverable Autonomous Vehicles and its Discretization,” IEEE Transactions on Automatic Control, vol. 56(2), pp. 457-462, 2011.  
  25. A. K. Sanyal, A. M. Bloch, P. E. Crouch, and J. E. Marsden, “Optimal Control and Geodesics on Quadratic Matrix Lie Groups,” Foundations of Computational Mathematics, vol 8(4), pp. 469-500, 2008. 

Qinru Qiu

Degree(s):

  • Ph.D.

Areas of expertise:

  • Green computing
  • Neuromorphic computing
  • Machine learning
  • Distributed systems
  • Explainable AI

Research Interests:

  • Dynamic power and thermal management for computer systems
  • Power and performance optimization of energy harvesting real-time embedded systems
  • Neuromorphic computing and high performance computing for cognitive applications

Current Research:

Dr. Qinru Qiu received her PhD in Electrical Engineering from the University of Southern California. She is currently a Distinguished Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. Her research interests include neuromorphic computing, machine learning, and energy efficient computing. She served/serves as an associate editor for several IEEE/ACM journals including IEEE TNNLS, IEEE CAS Magazine, IEEE TCDS, IEEE TCAD, IEEE TC-CS, and Frontier on Neuroscience on Neuromorphic Engineering. She also served/serves on the organization committee and technical program committee of many IEEE/ACM conferences. Dr. Qiu is a recipient of the NSF CAREER award in 2009, IEEE Region 1 Technological Innovation award in 2020, ACM Distinguished Member in 2022, and Distinguished Lecturer of the IEEE CEDA Society (2023-2024). She is a Fellow of IEEE.

Courses Taught:

  • VLSI Design
  • Computer architecture

Honors:

  • Distinguished Professor
  • IEEE fellow
  • CEDA Distinguished Lecturer (2023 – 2024)
  • IEEE CAS Magazine Best Associate Editors (2023)
  • Chancellor’s Citation for Faculty Excellence and Scholarly Distinction (2023)
  • IEEE Circuit and System Magazine Best Associate Editors 2022
  • ACM Distinguished Member (2022)
  • IEEE Region 1 Technological Innovation (Academic) Award (2020)
  • ACM Recognition of Service Award (2019)
  • ACM SIGDA Distinguished Service Award (2011)
  • NSF Career Award (2009)
  • American Society for Engineering Education (ASEE) Summer Research Faculty Fellowship (2007)

Selected Publications:

1. B. Wang, Y. Ma, and Q. Qiu, “Prompt-based Domain Incremental Learning with Modular Classification Layer,” European Conference on Artificial Intelligence (ECAI), 2024.

2. N. Lin, J. Chen, R. Zhao, Y. He, K. Wong, Q. Qiu, Z. Wang, J J. Yang, “In-memory and in-sensor reservoir computing with memristive devices,” APL Machine Learning, 2024.

3. J. Liu, Y. Bu, and Q. Qiu, “Improved Efficiency Based on Learned Saccade and Continuous Scene Reconstruction From Foveated Visual Sampling,” International Conference on Learning Representations (ICLR), 2024.

4. Z. Zhang, J. Jing, and Q. Qiu, “SOLSA: Neuromorphic Spatiotemporal Online Learning for Synaptic Adaptation,” to appear on 29th Asia and South Pacific Design Automation Conference (ASP-DAC), 2024.

5. Y. Bu, J. Liu, and Q. Qiu, “Predictive Temporal Attention on Event-based Video Stream for Energy-efficient Situation Awareness,” International Green and Sustainable Computing (IGSC), 2023.

6. Q. Huang, C. Luo, S. Khan, A. B. Wu, H. Li, and Q. Qiu, “Multi-agent Cooperative Games Using Belief Map Assisted Training,” European Conference on Artificial Intelligence (ECAI), 2023.

Young B. Moon

Degree(s):

  • Ph.D., Purdue University
  • M.S., Stanford University
  • B.S., Seoul National University

Areas of Expertise:

  • Cyber-Manufacturing Systems
  • Cyber-Manufacturing Security
  • Systems Modeling and Simulation
  • Application of Machine Learning and Artificial Intelligence
  • Sustainable Product Realization Processes and Systems

Professor Moon teaches courses and conducts research in the areas of Cyber-Manufacturing Systems, Cyber-Manufacturing Security, Sustainable Manufacturing, Product Realization Processes and Systems, Enterprise Resource Planning (ERP) Systems, Systems Modeling and Simulation, Computer Integrated Manufacturing (CIM), Product Lifecycle Management (PLM), and Application of Artificial Intelligence and Machine Learning. He has had extensive interactions with industry and has published over 130 journal and conference publications. Dr. Moon is a licensed P.E. (Professional Engineer) registered in the State of New York, a CFPIM (Certified Fellow in Production and Inventory Management), and a CMfgE (Certified Manufacturing Engineer). He is active in numerous professional organizations such as INCOSE, ABET, SME, ASME, ASEE, and IFIP. He has served as a Commissioner for ABET’s Engineering Accreditation Commission and is serving on ABET’s Board of Delegates and Engineering Area Delegation. A Fulbright Scholar, he has also held visiting positions in various organizations across the globe.

Honors and Awards:

  • Outstanding Service Award from INCOSE (International Council on Systems Engineering)

Selected Publications:

Prasad R., Seyed, S.A.Z. and Y.B. Moon, “Recovery systems architecture for cyber-manufacturing systems against cyber-manufacturing attacks,” Manufacturing Letters, Vol. 31, pp. 851–860, The 51st SME North American Manufacturing Research Conference (NAMRC 51), New Brunswick, NJ, June 12–16, 2023.

Espinoza-Zelaya, C. and Y.B. Moon, “Framework for enhancing the operational resilience of cyber-manufacturing systems against cyber-attacks,” Manufacturing Letters, Vol. 31, pp. 843–850, The 51st SME North American Manufacturing Research Conference (NAMRC 51), New Brunswick, NJ, June 12–16, 2023.

Prasad R. and Y.B. Moon, “Comprehensive Analysis of Cyber-Manufacturing Attacks using a Cyber-Manufacturing Testbed,” Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Columbus, OH, October 30–November 3, 2022.

Espinoza-Zelaya, C. and Y.B. Moon, “Assessing Severity of Cyber-Attack Threats against Cyber-Manufacturing Systems,” Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Columbus, OH, October 30–November 3, 2022.

Espinoza-Zelaya, C. and Y.B. Moon, “Resilience Enhancing Mechanisms for Cyber-Manufacturing Systems against Cyber-Attacks,” The 10th IFAC Triennial Conference on Manufacturing Modeling, Management and Control (MIM 2022), Nantes, France, June 22–24, 2022.

Prasad, R. and Y.B. Moon, “Architecture for Preventing and Detecting Cyber-Attacks in Cyber-Manufacturing Systems,” The 10th IFAC Triennial Conference on Manufacturing Modeling, Management and Control (MIM 2022), Nantes, France, June 22–24, 2022.

Song, J., Wang, J. and Y.B. Moon, “Blockchain Applications in Manufacturing Systems: A Survey,” Proceedings of the ASME International Mechanical Engineering Congress and Exposition, Virtual, November 1–4, 2021.

Wu, M., Song, J., Sharma, S., Di, J., He, B., Wang, Z., Zhang, J., Lin, L., Greaney, E., and Y.B. Moon, “Development of Testbed for Cyber-Manufacturing Security Issues,” International Journal of Computer Integrated Manufacturing, vol. 33, no. 3, pp. 302–320, 2020.

Wu, M. and Y.B. Moon, “Alert Correlation for Detecting Cyber-Manufacturing Attacks and Intrusions,” Journal of Computing and Information Science in Engineering, Transactions of the ASME, vol. 20, no. 1, pp. 011004-1–011004-12, 2020.

Wu, M., Song, Z., and Y.B. Moon, “Detecting Cyber-Physical Attacks in CyberManufacturing Systems with Machine Learning Methods,” Journal of Intelligent Manufacturing, vol. 30, no 3, pp. 1111–1123, 2019.

Wu, M. and Y.B. Moon, “Intrusion Detection for Cyber-Manufacturing System,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 141, no. 3, pp. 031007-1–031007-9, 2019.

Song, Z. and Y.B. Moon, “Sustainability Metrics for Assessing Manufacturing Systems: A Distance-to-Target Methodology,” Environment, Development and Sustainability, vol. 21, no. 6, pp. 2811–2834, 2019.

Wu, M. and Y.B. Moon, “DACDI (Define, Audit, Correlate, Disclose, and Improve) Framework to Address Cyber-Manufacturing Attacks and Intrusions,” Special Issue on Industry 4.0 and Smart Manufacturing, Manufacturing Letters, vol. 15, Part B, pp. 155–159, 2018.

Moon, Y.B., “Simulation Modeling for Sustainability: A Review of the Literature,” International Journal of Sustainable Engineering, vol. 10, no. 1, pp. 2–19, 2017.

Moon, Y.B., “Enterprise Resource Planning (ERP): A Review of the Literature,” International Journal of Management and Enterprise Development, vol. 4, no. 3, pp. 235–264, 2007.

Endadul Hoque

Degree:

  • Ph.D., Computer Science, Purdue University, 2015
  • M.S., Computer Science, Marquette University, 2010
  • B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology, 2008

Lab/ Center/ Institute affiliation

Research interests:

  • Security of computer networks and systems
  • IoT systems security
  • Program analysis, software testing and verification
  • Vulnerability detection

Current Research:

His research focuses on the security of computer networks and systems. The software of computer networks and systems continues to have exploitable vulnerabilities, which are lucrative targets for adversaries. Within this broad domain, his particular emphasis is on automated detection of vulnerabilities as well as creating resilient protocols and systems. His research primarily builds on and expands program analysis, software engineering, and formal verification. His interests span several domains of computing, including network communication protocols, operating systems, distributed systems, internet-of-things (IoT) systems and embedded devices.

Honors and Awards:

  • NSF CAREER Award, 2024
  • Google Research Scholar Award, 2022
  • Distinguished Paper Award at NDSS (Network and Distributed System Security Symposium) 2018
  • Bilsland Dissertation Fellowship Award from the Graduate School at Purdue University, 2015
  • Graduate Teaching Fellowship Award from Dept. of Computer Science at Purdue University, 2014

Selected Publications:

  • A. J. Nafis, O. Chowdhury, and E. Hoque, “VetIoT: On Vetting IoT Defenses Enforcing Policies at Runtime,” Proc. of IEEE Conference on Communications and Network Security (CNS) pp. 1-9, 2023.
  • M. H. Mazhar, L. Li, E. Hoque, and O. Chowdhury, “MAVERICK: An App-independent and Platform-agnostic Approach to Enforce Policies in IoT Systems at Runtime,” Proc. of ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec ’23), 2023.
  • M. Yahyazadeh, S. Y. Chau, L. Li, M. H. Hue, J. Debnath, S. C. Ip, C. N. Li, E. Hoque, and O. Chowdhury, “Morpheus: Bringing The (PKCS) One To Meet the Oracle,” Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS ’21) (CCS ’21), Association for Computing Machinery, New York, NY, USA, pp. 2474–2496, 2021.
  • M. H. Hue, J. Debnath, K. M. Leung, L. Li, M. Minaei, M. H. Mazhar, K. Xian, E. Hoque, O. Chowdhury, and S. Y. Chau, “All Your Credentials Are Belong to Us: On Insecure WPA2-Enterprise Configurations,” Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security (CCS ’21), Association for Computing Machinery, New York, NY, USA, pp. 1100–1117, 2021.

M. Cenk Gursoy

Degree(s):

  • Ph.D. , Princeton University, 2004.
  • B.S., Bogazici University, Istanbul, Turkey, 1999.

Lab/ Center/ Institute Affiliations:

Director, Wireless Communication and Networking Lab.

Senior Research Associate and Core Faculty Member, Autonomous Systems Policy Institute

Areas of Expertise:

Wireless Networking

Signal Processing

Communication/Information Theory

Machine Learning

Decision Making Theory

Optimization

Unmanned Systems

Dr. Gursoy has broad research expertise in the general areas of wireless communications and networking, signal processing, information theory, optimization, and machine learning. In particular, he has conducted research in detection and estimation, hypothesis testing, anomaly detection, optimal resource allocation, wireless performance evaluation, cognitive radio networks, dynamic spectrum access, energy efficiency analysis, multiple-antenna communication, millimeter wave communications, low-latency communications, physical-layer security, radio access networks, scheduling, edge computing, content caching, and 4G/5G/beyond-5G wireless network design. His expertise in information theory includes the analysis of wireless channel capacity and optimal signaling and coding schemes. He further has expertise in machine learning through the design, implementation and application of deep learning, reinforcement learning, and federated learning algorithms. Moreover, he has studied sequential optimization and decision-making in highly dynamic scenarios (involving autonomous and unmanned systems), and security and privacy in distributed learning.

Honors and Awards:

  • 2020 IEEE Region 1 Technological Innovation (Academic) Award
  • 2019 The 38th AIAA/IEEE Digital Avionics Systems Conference Best of Session    Award.
  • 2017 IEEE Green Communications & Computing Technical Committee Best Journal Paper Award.
  • 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) Best Paper Award
  • 2008 EURASIP Journal of Wireless Communications and Networking Best Paper Award
  • NSF CAREER Award

Selected Publications:

  • G. Joseph, C. Zhong, M. C. Gursoy, S. Velipasalar, and P. K. Varshney, “Anomaly Detection via Learning-Based Sequential Controlled Sensing,” IEEE Sensors Journal, vol. 24, no. 13, pp. 21025-21037, July 2024.
  • F. Wang, M. C. Gursoy, and S. Velipasalar, “Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 823-840, 2024.
  • M. H. Sulieman, M. Liu, F. Kong, and M. C. Gursoy, “Path Planning for UAVs Under GPS Permanent Faults,” ACM Transactions on Cyber-Physical Systems, 2024.
  • X. Li, Z. Tian, W. He, G. Chen, M. C. Gursoy, S. Mumtaz, and A. Nallanathan, “Covert Communication of STAR-RIS Aided NOMA Networks,” IEEE Transactions on Vehicular Technology, vol. 73, no. 6, pp. 9055-9060, June 2024.
  • Y. Yang, Y. Hu, and M. C. Gursoy, “Energy Efficiency of RIS-Assisted NOMA-Based MEC Networks in the Finite Blocklength Regime,” IEEE Transactions on Communications, vol. 72, no. 4, pp. 2275-2291, April 2024.
  • F. Wang, M. C. Gursoy, and S. Velipasalar, “Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 49-63, 2024.
  • Y. Yang and M. C. Gursoy, “Joint Trajectory Design and Resource Optimization in UAV-assisted Caching-Enabled Networks with Finite Blocklength Transmissions,” Drones, 2024; 8(1):12.
  • Z. Lu and M. C. Gursoy, “Resource Allocation for Multi-target Radar Tracking via Constrained Deep Reinforcement Learning,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1677-1690, Dec. 2023.
  • G. Joseph, C. Zhong, M. C. Gursoy, S. Velipasalar, and P. K. Varshney, “Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing,” IEEE Transactions on Signal and Information Processing over Networks, vol. 9, pp. 640-654, 2023.
  • X. Wang and M. C. Gursoy, “Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks,” in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 2766-2779, 2023.
  • M. Guo and M. C. Gursoy, “Joint Activity Detection and Channel Estimation for Intelligent-Reflecting-Surface-Assisted Wireless IoT Networks,” in IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10207-10221, June, 2023.
  • Y. Zhu, X. Yuan, Y. Hu, T. Wang, M. C. Gursoy and A. Schmeink, “Low-Latency Hybrid NOMA-TDMA: QoS-Driven Design Framework,” in IEEE Transactions on Wireless Communications, vol. 22, no. 5, pp. 3006-3021, May 2023.
  • Y. Zhu, Y. Hu, X. Yuan, M. C. Gursoy, H. V. Poor and A. Schmeink, “Joint Convexity of Error Probability in Blocklength and Transmit Power in the Finite Blocklength Regime,” in IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2409-2423, April 2023.
  • Y. Shi, Y. E. Sagduyu, T. Erpek and M. C. Gursoy, “How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning,” IEEE Open Journal of Vehicular Technology, vol. 4, pp. 181-192, 2023.
  • D. Deng, X. Li, S. Dang, M. C. Gursoy and A. Nallanathan, “Covert Communications in Intelligent Reflecting Surface-Assisted Two-Way Relaying Networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12380-12385, Nov. 2022
  • X. Wang, M. C. Gursoy, T. Erpek, and Y. E. Sagduyu, “Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 16663-16676, Sep. 2022.
  • X. Wang and M. C. Gursoy, “Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints,” IEEE Transactions on Wireless Communications, vol. 21, no. 6, pp. 4350-4363, Jun. 2022.
  • Z. Lu, C. Zhong, and M. C. Gursoy, “Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1588-1601, Feb. 2022.
  • Z. Xu, J. Tang, C. Yin, Y. Wang, G. Xue, J. Wang, M. C. Gursoy, “ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning,” EEE Transactions on Mobile Computing, vol. 21, no. 7, pp. 2533-2545, Jul. 2022
  • M. Guo and M. C. Gursoy, “Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity,” IEEE Transactions on Communications, vol. 70, no. 1, pp. 317-331, Jan. 2022.
  • H. Huang, D. Qiao and M. C. Gursoy, “Age-Energy Tradeoff Optimization for Packet Delivery in Fading Channels,” IEEE Transactions on Wireless Communications, vol. 21, no. 1, pp. 179-190, Jan. 2022.
  • F. Wang, C. Zhong, M. C. Gursoy, and S. Velipasalar, “Resilient Dynamic Channel Access via Robust Deep Reinforcement Learning,” IEEE Access, vol. 9, pp. 163188-163203, 2021.
  • P. Sinha, I. Guvenc, and M. C. Gursoy, “Fundamental Limits on Detection of UAVs by Existing Terrestrial RF Networks,” IEEE Open Journal of the Communications Society, vol. 2, pp. 2111-2130, 2021
  • X. Wang and M. C. Gursoy, “Uplink Coverage in Heterogeneous mmWave Cellular Networks with Clustered Users,” IEEE Access, vol. 9, 2021.
  • M. Guo and M. C. Gursoy, “Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems,” IEEE Transactions on Green Communications and Networking, vol. 5, no. 1, pp. 471-483, March 2021

Bing Dong

Degrees:

  • Ph.D. in Building Performance and Diagnostics, Carnegie Mellon University
  • M.S. in Building Science, National University of Singapore
  • B.E. in Electrical and Mechanical Engineering, Nanjing University of Technology

Lab/Center Affiliations:

  • Built Environment Science and Technology (BEST) Lab
  • Syracuse Center of Excellence in Energy and Environmental Systems

Research interests:

  • Modeling occupant behavior in buildings
  • Intelligent building operation
  • Fault detection and diagnostics
  • Buildings-to-grid integration
  • Grid-interactive Efficient Buildings
  • Urban mobility
  • Urban building energy modeling
  • Modeling and optimization of urban energy system
  • Human performance

Current Research:

Prof. Dong’s current research goal is to explore how smart buildings play an active role in urban scale cyber-physical energy system considering human behavior, renewable energy, energy storage, smart grid, health and resilience through physics-based modeling, optimization and controls, heterogeneous sensing and data-driven models. Current major research topics are: (1) Human-Building-Interactions including Detecting, Modeling and Simulating Occupant Behavior in Buildings and Behavior-driven Control and Optimization for Energy Systems and (2) System-level Modeling, Optimization and Control for Urban Built Environment including Buildings-to-Grid Integration Control and Optimization Framework, Modeling of Occupancy Behavior at a Community Level and Connect with other Urban Infrastructures and Community energy planning and management.

Major ongoing research projects are (1) NSF CAREER: Holistic Assessment of the Impacts of Connected Buildings and People on Community Energy Planning and Management, (2) Department of Energy – Argonne National Lab: Spatial-temporal data-driven weather and energy forecasting for improved implementation of advanced building controls, and (3) ARPA-E: Quantification of HVAC Energy Savings for Occupancy Sensing in Buildings through An Innovative Testing Methodology.

Teaching Interests:

  • HVAC design
  • Building performance modeling and diagnostics

Honors and Awards:

  • 2023 IBPSA World Fellow
  • 2023 ASHRAE Distinguished Service Award (DSA)
  • 2023 Best Paper Awards (Journals of Building and Environment, Building Simulations)
  • 2019 NSF CAREER Award
  • 2018 IBPSA-USA Emerging Contributor Award

Select Publications:

Jiang, Z. and Dong, B., 2024. Modularized neural network incorporating physical priors for future building energy modeling. Patterns.

Wang, X. and Dong, B., 2024. Long-term experimental evaluation and comparison of advanced controls for HVAC systems. Applied Energy, 371, p.123706.

Liu, Y. and Dong, B., 2024, January. Modeling urban scale human mobility through big data analysis and machine learning. In Building Simulation (Vol. 17, No. 1, pp. 3-21). Beijing: Tsinghua University Press.

Liu, Y., Dong, B., Hong, T., Olesen, B., Lawrence, T. and O’Neill, Z., 2023. ASHRAE URP-1883: Development and Analysis of the ASHRAE Global Occupant Behavior Database. Science and Technology for the Built Environment, 29(8), pp.749-781.

Deng, Z., Wang, X. and Dong, B., 2023. Quantum computing for future real-time building HVAC controls. Applied Energy, 334, p.120621.

Wang, X., Dong, B. and Zhang, J.J., 2023, February. Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season. In Building Simulation (Vol. 16, No. 2, pp. 205-223). Beijing: Tsinghua University Press.

Wu, W., Dong, B., Wang, Q.R., Kong, M., Yan, D., An, J. and Liu, Y., 2020. A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption. Applied Energy, 278, p.115656.

Wagner, A., O’Brien, W. and Dong, B. eds., 2018. Exploring Occupant Behavior in Buildings: Methods and Challenges. Springer.

Dong, B., Li, Z.*, Taha, A. and Gatsis, N., 2018. Occupancy-based buildings-to-grid integration framework for smart and connected communities. Applied Energy, 219, pp.123-137.(IF: 7.182)

Biao Chen

Degree(s):

  • Ph. D., University of Connecticut

Lab/Center Affiliation:

  • Communication Laboratory

Areas of Expertise:

  • Information Theory
  • Signal Processing
  • Statistical Learning Theory

Chen’s area of research interest mainly focuses on information theory, signal processing, and foundational theory to machine learning, with applications to wireless communications and sensor networks. On the applied side, he has worked extensively on software radio system design, including leading two student teams to compete as finalist in the DARPA Spectrum Challenge and DARPA Spectrum Collaboration Challenge. His most recent endeavors include the development of passive RF sensing theory and systems for a variety of indoor situational awareness missions.

Honors and Awards:

IEEE Fellow (2015)

NSF CAREER Award (2006)

Selected Publications:

  • Y. Liu, T. Wang, Y. Jiang and B. Chen, “Harvesting Ambient RF for Presence Detection Through Deep Learning” , IEEE Trans. Neural Networks and Learning Syst., vol. 33, no. 4, pp. 1571-1583, April 2022, doi: 10.1109/TNNLS.2020.3042908.
  • S. Zhu, B. Chen, Z. Chen and P. Yang, “Asymptotically Optimal One- and Two-Sample Testing With Kernels,” in IEEE Transactions on Information Theory, vol. 67, no. 4, pp. 2074-2092, April 2021, doi: 10.1109/TIT.2021.3059267.
  • G. Xu, W. Liu and B. Chen, “A Lossy Source Coding Interpretation of Wyner’s Common Information,” in IEEE Transactions on Information Theory, vol. 62, no. 2, pp. 754-768, Feb. 2016, doi: 10.1109/TIT.2015.2506560.
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