ASMDF Project Publications

A BibTeX file containing bibliographic information for all publications listed here is also available.

Globally, this publication list is ordered in reverse chronological order. Within a given year, the ordering is alphabetical (by author name).

The primary sponsor of the DSPCAD Group's work in this research area is the US Air Force Office of Scientific Research under the Dynamic Data Driven Applications Systems (DDDAS) Program.

We are also grateful to the following sponsors, who have supported the research in Maryland DSPCAD Research Group at large: Agilent Technologies; Angeles Design Systems, Inc.; the Austrian Marshall Plan Foundation; CoolCAD Electronics; the Defense Advanced Research Projects Agency; the Department of Homeland Security; the Fulbright Specialists Program of the Council for International Exchange of Scholars; the Laboratory for Physical Sciences; the Laboratory for Telecommunication Sciences; Management Communications and Control, Inc.; the Maryland Industrial Partnerships (MIPS) Program; National Instruments; the National Institute of Standards and Technology; the National Radio Astronomy Observatory; Northrop Grumman Corp.; the Semiconductor Research Corporation; Techno-Sciences, Inc.; Texas Instruments, Inc.; Trident Systems, Inc.; the University of Maryland Graduate School; the US Air Force Office of Scientific Research under the Dynamic Data Driven Applications Systems (DDDAS) Program; the US Air Force Research Laboratory; the US Army Research Laboratory; the US Army Research Office; and the US National Science Foundation.

DSPCAD Group Website.

[lee2017x1]

K. Lee, B. S. Riggan, and S. S. Bhattacharyya. An accumulative fusion architecture for discriminating people and vehicles using acoustic and seismic signals. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pages 2976-2980, New Orleans, Louisiana, March 2017.

[li2017x5]

H. Li, M. J. Hoffman, A. Vodacek, and S. S. Bhattacharyya. Dynamic, data-driven optimization of hyperspectral video processing under resource constraints. In Proceedings of the International Conference on InfoSymbiotics / DDDAS, Cambridge, Massachusetts, August 2017. Abstract of oral presentation.

[li2017x1]

H. Li, K. Sudusinghe, Y. Liu, J. Yoon, M. van der Schaar, E. Blasch, and S. S. Bhattacharyya. Dynamic, data-driven processing of multispectral video streams. IEEE Aerospace & Electronic Systems Magazine, 2017. To appear.

[teki2017x1]

C. Tekin, J. Yoon, and M. van der Schaar. Adaptive ensemble learning with confidence bounds. IEEE Transactions on Signal Processing, 65(4):888-903, 2017.

[bens2016x1]

H. Ben Salem, T. Damarla, K. Sudusinghe, W. Stechele, and S. S. Bhattacharyya. Adaptive tracking of people and vehicles using mobile platforms. EURASIP Journal on Advances in Signal Processing, 2016(65):1-12, 2016. (PDF)

[blas2016x2]

E. Blasch, A. Aved, and S. S. Bhattacharyya. Dynamic data driven application systems (DDDAS) for multimedia content analysis. In Proceedings of the International Conference on InfoSymbiotics / DDDAS, Hartford, Connecticut, August 2016. Abstract of oral presentation.

[canz2016x1]

L. Canzian, U. Demiryurek, and M. van der Schaar. Collision detection by networked sensors. IEEE Transactions on Signal and Information Processing over Networks, 2(1):1-15, 2016.

[chuk2016x1]

I. Chukhman, Y. Jiao, H. Ben Salem, and S. S. Bhattacharyya. Instrumentation-driven validation of dataflow applications. Journal of Signal Processing Systems, 84(3):383-397, 2016.

[lee2016x1]

K. Lee, H. Ben Salem, T. Damarla, W. Stechele, and S. S. Bhattacharyya. Prototyping real-time tracking systems on mobile devices. In Proceedings of the ACM International Conference on Computing Frontiers, pages 301-308, Como, Italy, May 2016. Invited paper. (PDF)

[li2016x6]

H. Li, K. Sudusinghe, Y. Liu, J. Yoon, E. Blasch, M. van der Schaar, and S. S. Bhattacharyya. Design of a dynamic data-driven system for multispectral video processing. In Proceedings of the International Conference on InfoSymbiotics / DDDAS, Hartford, Connecticut, August 2016. Abstract of oral presentation.

[liu2016x1]

Y. Liu, L. Barford, and S. S. Bhattacharyya. Jitter measurement on deep waveforms with constant memory. In Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, pages 1161-1166, Taipei, Taiwan, May 2016.

[meie2016x1]

Y. Meier, J. Xu, O. Atan, and M. van der Schaar. Predicting grades. IEEE Transactions on Signal Processing, 64(4):959-972, 2016.

[yoon2016x1]

J. Yoon, A. Alaa, S. Hu, and M. van der Schaar. ForecastICU: A prognostic decision support system for timely prediction of intensive care unit admission. In Proceedings of the International Conference on Machine Learning, pages 1680-1689, 2016.

[atan2015x1]

O. Atan, Y. Andreopoulos, C. Tekin, and M. van der Schaar. Bandit framework for systematic learning in wireless video-based face recognition. IEEE Journal on Selected Topics in Signal Processing, 9(1):180-194, 2015.

[bens2015x1]

H. Ben Salem. Adaptive tracking of people and vehicles on mobile platforms. Master's thesis, Department of Electrical and Computer Engineering, Technical University of Munich, Germany, May 2015.

[blat2015x1]

T. Blattner, W. Keyrouz, M. Halem, M. Brady, and S. S. Bhattacharyya. A hybrid task graph scheduler for high performance image processing workflows. In Proceedings of the IEEE Global Conference on Signal and Information Processing, pages 634-637, Orlando, Florida, December 2015.

[canz2015x4]

L. Canzian and M. Van Der Schaar. Real-time stream mining: online knowledge extraction using classifier networks. In IEEE Network, pages 10-16, 2015.

[canz2015x1]

L. Canzian and M. van der Schaar. Timely event detection by networked learners. IEEE Transactions on Signal Processing, 63(5):1282-1296, 2015.

[canz2015x2]

L. Canzian, U. Demiryurek, and M. Van der Schaar. Collision detection by networked sensors. IEEE Transactions on Signal and Information Processing over Networks, (99), 2015. DOI: 10.1109/TSIPN.2015.2504721.

[canz2015x3]

L. Canzian, Y. Zhang, and M. van der Schaar. Ensemble of distributed learners for online classification of dynamic data streams. IEEE Transactions on Signal and Information Processing over Networks, 1(3):180-194, 2015.

[chuk2015x1]

I. Chukhman. Profile- and Instrumentation-Driven Methods for Embedded Signal Processing. PhD thesis, Department of Electrical and Computer Engineering, University of Maryland, College Park, 2015. (PDF)

[kano2015x1]

K. Kanoun and Mihaela van der Schaar. Big-data streaming applications scheduling with online learning and concept drift detection. In Proceedings of the Design, Automation and Test in Europe Conference and Exhibition, pages 1547-1550, 2015.

[sudu2015x2]

K. Sudusinghe. Design Tools for Dynamic, Data-Driven, Stream Mining Systems. PhD thesis, Department of Electrical and Computer Engineering, University of Maryland, College Park, 2015. (PDF)

[sudu2015x1]

K. Sudusinghe, Y. Jiao, H. Ben Salem, M. van der Schaar, and S. S. Bhattacharyya. Multiobjective design optimization in the lightweight dataflow for DDDAS environment (LiD4E). In Proceedings of the International Conference on Computational Science, pages 2563-2572, Reykjavik, Iceland, June 2015.

[teki2015x2]

C. Tekin and M. van der Schaar. Contextual online learning for multimedia content aggregation. IEEE Transactions on Multimedia, 17(4):549-561, 2015.

[teki2015x1]

C. Tekin and M. van der Schaar. RELEAF: An algorithm for learning and exploiting relevance. IEEE Journal on Selected Topics in Signal Processing, 9(4):716-727, 2015.

[teki2015x3]

C. Tekin and M. van der Schaar. Active learning in context-driven stream mining with an application to image mining. IEEE Transactions on Image Processing, 24(11):3666-3679, 2015.

[teki2015x4]

C. Tekin and M. van der Schaar. Distributed online learning via cooperative contextual bandits. IEEE Transactions on Signal Processing, 63(14):3700-3714, 2015.

[teki2015x5]

C. Tekin, O. Atan, and M. van der Schaar. Discover the expert: Context-adaptive expert selection for medical diagnosis. IEEE Transactions on Emerging Topics in Computing, 3(2):220-234, 2015.

[wolf2015x1]

M. Wolf, M. van der Schaar, H. Kim, and J. Xu. Caring analytics for adults with special needs. IEEE Design & Test, 32(5):35-44, 2015.

[xu2015x3]

J. Xu, D. Deng, U. Demiryurek, C. Shahabi, and M. van der Schaar. Mining the situation: Spatiotemporal traffic prediction with big data. IEEE Journal on Selected Topics in Signal Processing, 9(4):702-715, 2015.

[xu2015x2]

J. Xu, C. Tekin, S. Zhang, and M. van der Schaar. Distributed multi-agent online learning based on global feedback. IEEE Transactions on Signal Processing, 63(9):2225-2238, 2015.

[xu2015x1]

J. Xu, M. van der Schaar, J. Liu, and H. Li. Forecasting popularity of videos using social media. IEEE Journal on Selected Topics in Signal Processing, 9(2):330-343, 2015.

[atan2014x1]

O. Atan, Y. Andreopoulos, C. Tekin, and M. van der Schaar. Bandit framework for systematic learning in wireless video-based face recognition. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 2014.

[bhat2014x2]

S. S. Bhattacharyya, M. van der Schaar, O. Atan, C. Tekin, and K. Sudusinghe. Data-driven stream mining systems for computer vision. In B. Kisacanin and M. Gelautz, editors, Advances in Embedded Computer Vision, Advances in Computer Vision and Pattern Recognition, pages 249-264. Springer, 2014.

[cho2014x1]

I. Cho. Hardware and Software Architectures for Energy- and Resource-Efficient Signal Processing Systems. PhD thesis, Department of Electrical and Computer Engineering, University of Maryland, College Park, 2014.

[chuk2014x1]

I. Chukhman and S. S. Bhattacharyya. Instrumentation-driven framework for validation of dataflow applications. In Proceedings of the IEEE Workshop on Signal Processing Systems, pages 1-6, Belfast, UK, October 2014.

[lee2014x1]

C.-S. Lee, W.-C. Chen, S. S. Bhattacharyya, and T.-S. Lee. Dynamic, data-driven spectrum management in cognitive small cell networks. In Proceedings of the International Conference on Signal Processing and Communication Systems, pages 1-5, Gold Coast, Australia, December 2014.

[sudu2014x1]

K. Sudusinghe, I. Cho, M. van der Schaar, and S. S. Bhattacharyya. Model based design environment for data-driven embedded signal processing systems. In Proceedings of the International Conference on Computational Science, pages 1193-1202, Cairns, Australia, June 2014. (PDF)

[teki2014x2]

C. Tekin and M. van der Schaar. Discovering, learning and exploiting relevance. In Advances in Neural Information Processing Systems, pages 1233-1241, 2014.

[teki2014x1]

C. Tekin, L. Canzian, and M. van der Schaar. Context-adaptive big data stream mining. In Proceedings of the Allerton Conference on Communication, Control, and Computing, pages 483-490, 2014.

[bhat2013x2]

S. S. Bhattacharyya, E. F. Deprettere, and B. Theelen. Dynamic dataflow graphs. In S. S. Bhattacharyya, E. F. Deprettere, R. Leupers, and J. Takala, editors, Handbook of Signal Processing Systems, pages 905-944. Springer, second edition, 2013.

[cho2013x2]

I. Cho, K. Sudusinghe, C. Shen, J. McGee, and S. S. Bhattacharyya. A system-level design approach for dynamic resource coordination and energy optimization in sensor network platforms. In Proceedings of the IEEE Asilomar Conference on Signals, Systems, and Computers, pages 1436-1441, Pacific Grove, California, November 2013. Invited paper.

[duca2013x1]

R. Ducasse and M. van der Schaar. Finding it now: Construction and configuration of networked classifiers in real-time stream mining systems. In S. S. Bhattacharyya, E. F. Deprettere, R. Leupers, and J. Takala, editors, Handbook of Signal Processing Systems, pages 97-134. Springer, second edition, 2013.

[ren2013x1]

S. Ren and M. van der Schaar. Efficient resource provisioning and rate selection for stream mining in a community cloud. IEEE Transactions on Multimedia, 15(4):723-734, June 2013.

[ren2013x2]

S. Ren, C. Lan, and M. van der Schaar. Energy-efficient design of real-time stream mining systems. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, 2013.

[sudu2013x1]

K. Sudusinghe, S. Won, M. van der Schaar, and S. S. Bhattacharyya. A novel framework for design and implementation of adaptive stream mining systems. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, San Jose, California, July 2013.

[teki2013x1]

C. Tekin and M. van der Schaar. Distributed online big data classification using context information. In Proceedings of the Allerton Conference on Communication, Control, and Computing, pages 1435-1442, October 2013.

[won2013x1]

S. Won, I. Cho, K. Sudusinghe, J. Xu, Y. Zhang, M. van der Schaar, and S. S. Bhattacharyya. A design methodology for distributed adaptive stream mining systems. In Proceedings of the International Conference on Computational Science, pages 2482-2491, Barcelona, Spain, June 2013.

[xu2013x1]

J. Xu, C. Tekin, and M. van der Schaar. Learning optimal classifier chains for realtime big data mining. In Proceedings of the Allerton Conference on Communication, Control, and Computing, pages 512-519, October 2013.

[zhan2013x1]

Y. Zhang, D. Sow, D. Turaga, and M. van der Schaar. A fast online learning algorithm for distributed mining of big data. In Proceedings of the Big Data Analytics workshop at SIGMETRICS, 2013.

[zhu2013x1]

X. Zhu, C. Lan, and M. van der Schaar. Low-complexity reinforcement learning for delay-sensitive compression in networked video stream mining. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, July 2013.

[ren2012x1]

S. Ren. Strategic Pricing and Resource Allocation: Framework and Applications. PhD thesis, Department of Electrical Engineering, University of California at Los Angeles, 2012.

[won2012x2]

S. Won. A networked dataflow simulation environment for signal processing and data mining applications. Master's thesis, Department of Electrical and Computer Engineering, University of Maryland, College Park, 2012. (PDF)