Research Areas | Advanced Signal Processing Laboratory

Radar Signal Processing and Direction-of-Arrival Estimation

We develop signal processing techniques and algorithms for radar imaging, target localization and tracking, motion classification, and clutter suppression, applied in passive radar, over-the-horizon radar, through-the-wall radar, and fall detection for assisted living. Our recent work features sparse reconstruction, Bayesian learning, and sparse array design.

Selected publications:

S. Qin, Y. D. Zhang, and M. G. Amin, “Improved two-dimensional DOA estimation using parallel coprime arrays,” Signal Processing, in press.

G. Qin, M. G. Amin, and Y. D. Zhang, “DOA estimation exploiting sparse array motions,” IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 3013-3027, June 2019.

Z. Zheng, W-Q. Wang, Y. Kong, and Y. D. Zhang, “MISC Array: A new sparse array design achieving increased degrees of freedom and reduced mutual coupling effect,” IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1728-1741, April 2019.

M. Wang, Y. D. Zhang, and G. Cui, “Human motion recognition exploiting radar with stacked recurrent neural network,” Digital Signal Processing, vol. 87, pp. 125-131, April 2019.

G. Qin, Y. D. Zhang, and M. G. Amin, “DOA estimation exploiting moving dilated nested arrays,” IEEE Signal Processing Letters, vol. 26, no. 3, pp. 490-494, March 2019.

C. Zhou, Y. Gu, X. Fan, Z. Shi, G. Mao, and Y. D. Zhang, “Direction-of-arrival estimation for coprime array via virtual array interpolation,” IEEE Transactions on Signal Processing, vol. 66, no. 22, pp. 5956-5971, Nov. 2018.

C. Zhou, Y. Gu, Z. Shi, and Y. D. Zhang, “Off-grid direction-of-arrival estimation using coprime array interpolation,” IEEE Signal Processing Letters, vol. 25, no. 11, pp. 1710-1714, Nov. 2018.

M. Guo, Y. D. Zhang, and T. Chen, “DOA estimation using compressed sparse array,” IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4133-4146, Aug. 2018.

M. Guo, Y. D. Zhang, and T. Chen, “Performance analysis for uniform linear arrays exploiting two coprime frequencies,” IEEE Signal Processing Letters, vol. 25, no. 6, pp. 838-842, June 2018.

K. Liu and Y. D. Zhang, “Coprime array-based DOA estimation in unknown nonuniform noise environment,” Digital Signal Processing, vol. 79, pp. 66-74, April 2018.

I. Djurovic and Y. D. Zhang, “Accurate parameter estimation of over-the-horizon radar signals using RANSAC and MUSIC algorithms,” Progress In Electromagnetics Research M, vol. 67, pp. 85-92, April 2018.

Zhou, Y. Gu, Y. D. Zhang, Z. Shi, T. Jin, and X. Wu, “Compressive sensing based coprime array direction-of-arrival estimation,” IET Communications, vol. 11, no. 11, pp. 1719-1724, Aug. 2017. (2019 IET Communications Premium Award)

Wang, Y. D. Zhang, and W. Wang, “Robust DOA estimation in the presence of miscalibrated sensors,” IEEE Signal Processing Letters, vol. 24, no. 7, pp. 1073-1077, July 2017.

Xi, S. Chen, Y. D. Zhang, and Z. Liu, “Gridless quadrature compressive sampling with interpolated array technique,” Signal Processing, vol. 133, pp. 1-12, April 2017.

Qin, Y. D. Zhang, M. G. Amin, and F. Gini, “Frequency diverse coprime arrays with coprime frequency offsets for multi-target localization,” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Advances in Time/Frequency Modulated Array Signal Processing, vol. 11, no. 2, pp. 321-335, March 2017.

Y. D. Zhang, M. G. Amin, and B. Himed, “Structure-aware sparse reconstruction and applications to passive multi-static radar,” IEEE Aerospace and Electronic Systems Magazine, vol. 32, no. 2, pp. 68-78, Feb. 2017.

S. Qin, Y. D. Zhang, and M. G. Amin, “DOA estimation of mixed coherent and uncorrelated targets exploiting coprime MIMO radar,” Digital Signal Processing, special issue on Coprime Array and Sampling, vol. 61, pp. 26-34, Feb. 2017..

S. Qin, Y. D. Zhang, M. G. Amin, and A. Zoubir, “Generalized coprime sampling of Toeplitz matrix for spectrum estimation,” IEEE Transactions on Signal Processing, vol. 65, no. 1, pp. 81-94, Jan. 2017.

Q. Shen, W. Liu, W. Cui, S. Wu, Y. D. Zhang, and M. G. Amin, “Focused compressive sensing for underdetermined wideband DOA estimation exploiting high-order difference co-arrays,” IEEE Signal Processing Letters, vol. 24, no. 1, pp. 86-90, Jan. 2017.

Q. Shen, W. Cui, W. Liu, S. Wu, Y. D. Zhang, and M. G. Amin, “Underdetermined wideband DOA estimation of off-grid sources employing the difference co-array concept,” Signal Processing, vol. 130, pp. 299-304, Jan. 2017.

S. Qin, Y. D. Zhang, M. G. Amin, and B. Himed, “DOA estimation exploiting a uniform linear array with multiple co-prime frequencies,” Signal Processing, vol. 130, pp. 37-46, Jan. 2017.

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, “Group sparsity based multi-target tracking in passive multi-static radar systems using Doppler-only measurements,” IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3619-3634, July 2016.

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Dual-function radar-communications: Information embedding using sidelobe control and waveform diversity,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 2168-2181, April 2016.

Q. Wu, Y. D. Zhang, M. G. Amin, and B. Himed, “Space-time adaptive processing and motion parameter estimation in multi-static passive radar exploiting Bayesian compressive sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 944 – 957, Feb. 2016.

Y. Tang, Y. D. Zhang, M. G. Amin, and W. Sheng, “Design of wideband MIMO radar waveforms with low peak-to-average ratio,” IET Radar, Sonar, and Navigation, vol. 10, no. 2, pp. 325-332, Feb. 2016.

S. Subedi, Y. D. Zhang, M. G. Amin, and B. Himed, “Group sparsity based multi-target tracking in multi-static passive radar systems using Doppler-only measurements,” IEEE International Radar Conference, Arlington, VA, May 2015. (2nd Place of Best Student Paper Award)

C. Liu, F. Xi, S. Chen, Y. D. Zhang, and Z. Liu, “Pulse-Doppler signal processing with quadrature compressive sampling,” IEEE Transactions on Aerospace and Electronic Systems, vol. 51, no. 2, pp. 1217-1230, April 2015.

S. Qin, Y. D. Zhang, and M. G. Amin, “Generalized coprime array configurations for direction-of-arrival estimation,” IEEE Transactions on Signal Processing, vol. 63, no. 6, pp. 1377-1390, March 2015. (2018 IEEE Signal Processing Society Young Author Best Paper Award)

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, “Radar-based fall detection based on Doppler time-frequency signatures for assisted living,” IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 164-172, Feb. 2015.

S. Qin, Y. D. Zhang, and M. G. Amin, “DOA estimation of mixed coherent and uncorrelated signals exploiting a nested MIMO system,” IEEE Benjamin Franklin Symposium on Microwave and Antenna Sub-systems, Philadelphia, PA, Sept. 2014. (First Prize of Student Paper Competition)

Y. D. Zhang, J. J. Zhang, M. G. Amin, and B. Himed, “Instantaneous altitude estimation of maneuvering targets in over-the-horizon radar exploiting multipath Doppler signatures,” EURASIP Journal on Advances in Signal Processing, special issue on Emerging Radar Techniques, vol. 2013, no. 2013:100, May 2013 .

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Wireless Communications and Networking

We develop novel signal processing methods and algorithms for increased communication capacity and quality related to wireless communications, cooperative networking, energy harvesting system that are operated in challenging environments with rich multipath and jammers.

Selected publications:

Y. Gu and Y. D. Zhang, “Compressive sampling optimization for user signal parameter estimation in massive MIMO systems,” Digital Signal Processing, vol. 94, pp. 105-113, Oct. 2019.

Y. Gu and Y. D. Zhang, “Information-theoretic pilot design for downlink channel estimation in FDD massive MIMO systems,” IEEE Transactions on Signal Processing, vol. 67, no. 9, pp. 2334 –2346, May 2019.

Y. Gu, N. A. Goodman, and Y. D. Zhang, “Adaptive beamforming via sparsity-based reconstruction of covariance matrix,” in A. De Maio, Y. C. Eldar, and A. Haimovich (eds.), Compressed Sensing in Radar Signal Processing, Cambridge University Press, 2019.

A. Ahmed, Y. D. Zhang, and Y. Gu, “Dual-function radar-communications using QAM-based sidelobe modulation,” Digital Signal Processing, vol. 82, pp.166-174, Nov. 2018.

Y. D. Zhang, M. G. Amin, and B. Wang, “Mitigation of sparsely sampled nonstationary jammers for multi-antenna GNSS receivers,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Shanghai, China, March 2016.

M. G. Amin and Y. D. Zhang, “Nonstationary jammer excision for GPS receivers using sparse reconstruction techniques,” ION GNSS+, Tampa, FL, Sept. 2014. (Best Presentation Award)

B. Chalise, Y. D. Zhang, and M. G. Amin, “Local CSI based full diversity achieving relay selection for amplify-and-forward cooperative systems,” IEEE Transactions on Signal Processing, vol. 61, no. 21, pp. 5165-5180, Nov. 2013.

B. Chalise, W.-K. Ma, Y. D. Zhang, H. Suraweera, and M. G. Amin, “Optimum performance boundaries of OSTBC based AF-MIMO relay system with energy harvesting receiver,” IEEE Transactions on Signal Processing, vol. 61, no. 17, pp. 4199-4213, Sept. 2013.

Y. D. Zhang and M. G. Amin, “Anti-jamming GPS receiver with reduced phase distortions,” IEEE Signal Processing Letters, vol. 19, no. 10, pp. 635-638, Oct. 2012.

B. K. Chalise, Y. D. Zhang, and M. G. Amin, “Precoder design for OSTBC based AF MIMO relay system with channel uncertainty,” IEEE Signal Processing Letters, vol. 19, no. 8, pp. 515-518, Aug. 2012.

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Spectrum Sharing and Coexistence of Wireless Systems

We have developed a number of novel techniques to provide significant improvements in the efficiency of radio spectrum utilization and protection of passive sensing services. These techniques support spectrum sharing and coexistence of wireless systems for wireless communications, broadcast, radar, and radio astronomy.

Selected publications:

A. Ahmed, S. Zhang, and Y. D. Zhang, “Optimized sensor selection for joint radar-communication systems,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, Spain, May 2020.

A. Ahmed, Y. D. Zhang, A. Hassanien, and B. Himed, “OFDM-based joint radar-communication system: Optimal sub-carrier allocation and power distribution by exploiting mutual information,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2019.

A. Ahmed, Y. D. Zhang, and B. Himed, “Distributed dual-function radar-communication MIMO system with optimized resource allocation,” IEEE Radar Conference, Boston, MA, April 2019.

A. Ahmed, Y. D. Zhang, and Y. Gu, “Dual-function radar-communications using QAM-based sidelobe modulation,” Digital Signal Processing, vol. 82, pp.166-174, Nov. 2018.

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Phase-modulation based dual-function radar-communications,” IET Radar, Sonar, and Navigation, vol. 10, no. 8, pp. 1411-1421, Oct. 2016.

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Signaling strategies for dual-function radar-communications: An overview,” IEEE Aerospace and Electronic Systems Magazine, vol. 31, no. 10, pp. 36-45, Oct. 2016. (2018 IEEE Aerospace and Electronic Systems Society Harry Rowe Mimno Award)

A. Hassanien, M. G. Amin, Y. D. Zhang, and B. Himed, “A dual-function MIMO radar-communications system using PSK modulation,” European Signal Processing Conference, Budapest, Hungary, Sept. 2016.

A. Hassanien, M. G. Amin, Y. D. Zhang, F. Ahmad, and B. Himed, “Non-coherent PSK-based dual-function radar-communication systems,” IEEE Radar Conference, Philadelphia, PA, May 2016.

A. Hassanien, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Efficient sidelobe ASK based Dual-Function radar-communications,” SPIE Radar Sensor Technology Conference, Baltimore, MD, April 2016.

A. Hassanien, M. G. Amin, and Y. D. Zhang, “Computationally efficient beampattern synthesis for dual-function radar-communications,” SPIE Radar Sensor Technology Conference, Baltimore, MD, April 2016.

Machine Learning

We have studied various machine learning and deep learning techniques with applications to radar target classification, elderly fall detection, and road crack detection.

Selected publications:

S. Zhang, A. Ahmed, and Y. D. Zhang, “Sparsity-based time-frequency analysis for automatic radar waveform recognition,” IEEE International Radar Conference, Rockville, MD, April-May 2020.

M. Wang, Y. D. Zhang, and G. Cui, “Human motion recognition exploiting radar with stacked recurrent neural network,” Digital Signal Processing, vol. 87, pp. 125-131, April 2019.

M. Wu, X. Dai, Y. D. Zhang, B. Davidson, J. Zhang, and M. G. Amin, “Fall detection based on sequential modeling of radar signal time-frequency features,” IEEE International Conference on Healthcare Informatics, Philadelphia, PA, Sept. 2013.

Q. Wu, Y. D. Zhang, W. Tao, and M. G. Amin, “Radar-based fall detection based on Doppler time-frequency signatures for assisted living,” IET Radar, Sonar & Navigation, vol. 9, no. 2, pp. 164-172, Feb. 2015.

L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, “Road crack detection with deep convolution neural network,” Proceedings of IEEE International Conference on Image Processing, Phoenix, AZ, Sept. 2016.

Compressive Sensing and Sparse Signal Reconstruction

We have developed a number of novel compressive sensing and sparse reconstruction algorithms that consider the group sparsity and signal structures that find significant importance in many radar, sonar, structure health monitoring applications, and time-frequency analysis.

Selected publications:

Y. Gu and Y. D. Zhang, “Compressive sampling optimization for user signal parameter estimation in massive MIMO systems,” Digital Signal Processing, vol. 94, pp. 105-113, Oct. 2019.

M. Guo, Y. D. Zhang, and T. Chen, “DOA estimation using compressed sparse array,” IEEE Transactions on Signal Processing, vol. 66, no. 15, pp. 4133-4146, Aug. 2018.

S. Liu, Y. D. Zhang, T. Shan, and R. Tao, “Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation with missing observations,” IEEE Transactions on Signal Processing, vol. 66, no. 8, pp. 2153-2166, April 2018.

M. G. Amin, X. Wang, Y. D. Zhang, F. Ahmad, and E. Aboutanios, “Sparse array and sampling for interference mitigation and DOA estimation in GNSS,” Proceedings of IEEE, special issue on Vulnerabilities, Threats, and Authentication in Satellite-based Navigation Systems, vol. 104, no. 6, pp. 1302-1317, June 2016.

Q. Wu, Y. D. Zhang, M. G. Amin, and B. Himed, “Structured Bayesian compressive sensing exploiting spatial location dependence,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Brisbane, Australia, April 2015.

Q. Wu, Y. D. Zhang, F. Ahmad, and M. G. Amin, “Compressive sensing based high-resolution polarimetric through-the-wall radar imaging exploiting target characteristics,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 1043-1047, May 2015.

G. Li, P. K. Varshney, and Y. D. Zhang, “Multistatic radar imaging via decentralized and collaborative subspace pursuit,” International Conference on Digital Signal Processing, Hong Kong, China. Aug. 2014.

B. Jokanovic, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Time-frequency kernel design for sparse joint-variable signal representations,” European Signal Processing Conference, Lisbon, Portugal, September 2014.

Q. Wu, Y. D. Zhang, M. G. Amin, A. Golato, F. Ahmad, and S. Santhanam, “Structural health monitoring exploiting MIMO ultrasonic sensing and group sparse Bayesian learning,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2014.

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Time-frequency Analysis

We have developed time-frequency representations for the analysis and exploitation of time-varying nonstationary signals in single- and multi-sensor platforms. We have used spatial time-frequency distributions for high-resolution direction-of-arrival (DOA) estimation, source separation, interference suppression. Sparse reconstruction methods have been developed to handle nonstationary signals and jammers with missing samples.

Selected publications:

S. Zhang and Y. D. Zhang, “Robust time-frequency analysis of multiple FM signals with burst missing samples,” IEEE Signal Processing Letters, vol. 26, no. 8, pp. 1172-1176, Aug. 2019.

V. S. Amin, Y. D. Zhang, and B. Himed, “Sparsity-based time-frequency representation of FM signals with burst missing samples,” Signal Processing, vol. 155, pp. 25-43, Feb. 2019.

M. G. Amin, D. Borio, Y. D. Zhang, and L. Galleani, “Time-frequency analysis for GNSS: From interference mitigation to system monitoring,” IEEE Signal Processing Magazine, vol. 34, no. 5, pp. 85-95, Sept. 2017.

Y. D. Zhang, “Resilient quadratic time-frequency distribution for FM signals with gapped missing data,” IEEE Radar Conference, Seattle, WA, May 2017.

M. G. Amin, B. Jakonovic, Y. D. Zhang, and F. Ahmad, “A sparsity-perspective to quadratic time-frequency distributions,” Digital Signal Processing, vol. 46, pp. 175-190, Nov. 2015.

Y. D. Zhang, L. Guo, Q. Wu, and M. G. Amin, “Multi-sensor kernel design for time-frequency analysis of sparsely sampled non-stationary signals,” IEEE International Radar Conference, Arlington, VA, May 2015.

L. Stankovic, S. Stankovic, I. Orovic, and Y. D. Zhang, “Time-frequency analysis of micro-Doppler signals based on compressive sensing,” in M. Amin (ed.), Compressive Sensing for Urban Radars, CRC Press, 2014.

S. Liu, T. Shan, R. Tao, Y. D. Zhang, G. Zhang, F. Zhang, and Y. Wang, “Sparse discrete fractional Fourier transform and its applications,” IEEE Transactions on Signal Processing, vol. 62, no. 24, pp. 6582-6595, Dec. 2014.

A. Belouchrani, M. G. Amin, N. Thirion-Moreau, and Y. D. Zhang, “Source separation and localization using time-frequency distributions,” IEEE Signal Processing Magazine, special issue on Time-Frequency Analysis and Applications, vol. 30, no. 6, pp. 97-107, Nov. 2013.

Y. Zhang, W. Mu, and M. G. Amin, “Subspace analysis of spatial time-frequency distribution matrices,” IEEE Transactions on Signal Processing, vol. 49, no. 4, April 2001.

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Elderly Assisted Living

Assisted living is an emerging area that addresses the challenges of self-dependence living within homes or residences for the elderly population. Among these challenges, elderly falls are a major public health concern as they often result in disability and the main cause of accidental death in the U.S. senior citizens. Because immediate assistance provided after a fall can significantly reduce complications of fall risks, it is critical to detect elderly falls in a timely and accurate manner so that immediate response and proper care can be rendered. Among different available methods for this purpose, radar is an excellent sensing modality because it offers non-intrusive, clutter suppressed and noise tolerant sensing capabilities for moving human objects. Radar systems avoid direct contact (unlike wearable devices or accelerometers) and, in contrast to optical systems, can operate in all types of environments, can penetrate walls and fabrics, preserve privacy, and are insensitive to lighting conditions. We perform experimental studies and analyses for motion classification and fall detection using different radar systems.

Selected publications:

M. G. Amin, Y. D. Zhang, F. Ahmad, and K. C. Ho, “Radar signal processing for elderly fall detection,” IEEE Signal Processing Magazine, special issue on Signal Processing for Assisted Living, vol. 33, no. 2, pp. 71-80, March 2016.

B. Jokanovic, M. G. Amin, Y. D. Zhang, and F. Ahmad, “Multi-window time-frequency signature reconstruction from undersampled continuous wave radar measurements for fall detection,” IET Radar, Sonar & Navigation, special issue on Application of Radar to Remote Patient Monitoring and Eldercare, vol. 9, no. 2, pp. 173-183, Feb. 2015.

L. Ramirez Rivera, E. Ulmer, Y. D. Zhang, W. Tao, and M. G. Amin, “Radar-based fall detection exploiting time-frequency features,” IEEE China Summit and International Conference on Signal and Information Processing, Xi’an, China, July 2014.

Radio Frequency Identification (RFID)

Radio-frequency identification (RFID) uses wireless signals to transfer data for the purposes of automatic identification and and tracking tags attached to objects. We develop technologies for RFID reader and tag localization, propagation characterization, and collision avoidance algorithms.

Selected publications:

S. Subedi, E. Pauls, and Y. D. Zhang, “Accurate localization and tracking of a passive RFID reader based on RSSI measurements,” IEEE Journal of Radio Frequency Identification, vol. 1, no. 2, pp. 144-154, June 2017.

E. Pauls and Y. D. Zhang, “Experimental studies of high-accuracy RFID localization with channel impairments,” SPIE Mobile Multimedia/Image Processing, Security, and Applications, Baltimore, MD, April 2015.

S. Subedi, Y. D. Zhang, and M. G. Amin, “Precise RFID localization in impaired environment through sparse signal recovery,” SPIE Wireless Sensing, Localization, and Processing Conference, Baltimore, MD, April-May 2013.

Y. Zhang, X. Li, and M. G. Amin, “Principles and techniques of RFID positioning,” in M. Bolic, D. Simplot-Ryl, and I. Stojmenovic (eds.), RFID Systems, Research Trends and Challenges, John Wiley, 2010.

Y. Zhang, K. Yemelyanov, X. Li, and M. G. Amin, “Effect of metallic objects and liquid supplies on RFID links,” IEEE AP-S International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, Charleston, SC, June 2009.

X. Li, Y. Zhang, and M. G. Amin, “Multifrequency-based range estimation of RFID tags,” IEEE International Conference on RFID, Orlando, FL, April 2009.

Y. Zhang, M. G. Amin, and S. Kaushik, “Localization and tracking of passive RFID tags based on direction estimation,” International Journal of Antennas and Propagation, vol. 2007, Article ID 17426, doi:10.1155/2007/17426, 9 pages, Dec. 2007.

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Ultrasound Signal Processing for Nondesctructive Testing and Structure Health Monitoring

Mechanical and aerospace structures can develop debilitating defects, such as cracks, during their service lifetime. Nondestructive testing and structural health monitoring represent a collection of strategies and techniques for the timely detection of flaws. Our research focus lies in the development multi-input multi-output (MIMO) based processing and sparse reconstruction algorithms for high-resolution flaw imaging that enables effective detection and classifications.

Selected publications:

R. Demirli, M. G. Amin, X. Shen, and Y. D. Zhang, “Ultrasonic flaw detection and imaging through reverberant layer via subspace analysis and projection,” Advances in Acoustics and Vibration, special issue on Advances in Acoustic Sensing, Imaging and Signal Processing, vol. 2012, doi:10.1155/2012/957379, June 2012.

Y. D. Zhang, X. Shen, R. Demirli and M. G. Amin, “Ultrasonic flaw imaging via multipath exploitation,” Advances in Acoustics and Vibration, special issue on Advances in Acoustic Sensing, Imaging and Signal Processing, vol. 2012, doi:10.1155/2012/874081, April 2012.

R. Demerli, X. Rivenq, Y. D. Zhang, C. Ioana, and M. G. Amin, “MIMO imaging for ultrasonic nondestructive testing,” SPIE Conference on Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security V, San Diego, CA, March 2011.

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Image Processing & Watermarking

We perform image segmentation, digital watermarking, and image-based time-frequency analysis for nonstationary signal classification.

Selected publications:

Y. Zhang, B. Mobasseri, B. M. Dogahe, and M. G. Amin, “Image-adaptive watermarking using 2-D chirps,” Signal Image and Video Processing, vol. 4, no. 1, pp. 105-121, March 2010.

W. Tao, H. Jin, Y. Zhang, L. Liu, and D. Wang, “Image thresholding using graph cuts,” IEEE Transactions on Systems, Man and Cybernetics, Part A, vol. 38, no. 5, pp. 1181-1195, Sept. 2008.

W. Tao, H. Jin, and Y. Zhang, “Color image segmentation based on mean shift and graph cuts,” IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 37, no. 5, pp. 1382-1389, Oct. 2007.

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