Introduction to the Dynamic Data Driven Applications Systems (DDDAS) Paradigm

Dynamic Data Driven Applications Systems (DDDAS) is a paradigm for systems analysis and design, and a framework that dynamically couples high-dimensional physical and other analysis models and methods, run-time measurements, and computational architectures. Some of the foremost early applications of DDDAS successes range from environmental assessment of adverse weather and natural disasters such as tornadic activity, hurricane formation and trajectory, wildfire monitoring and volcanic plume detection and tracking, to real-time structural health monitoring in aerospace systems and electrical power grids operation, and to medical and societal applications. Monitoring, understanding and predicting behaviors of complex and dynamic systems with DDDAS principles has expanded over the years to demonstrate new and advanced capabilities in other applications that span space situational awareness, unmanned aerial vehicle (UAV) design and operation, and complex systems adaptive management and security applications. Recent efforts reflect the digital age of information management such as multimedia analysis, electrical power grid control, other civilian infrastructures, and biohealth concerns. Underlying DDDAS developments are advances in sensor design, signal processing and filtering, as well as computational architectures and communications. The book highlights for the reader DDDAS-based advances, with more information available in the DDDAS society’s website: www.1dddas.org.

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Introduction to Dynamic Data Driven Applications Systems

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Introduction to Cyber-Physical Systems

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Review on design, development, and implementation of an unmanned aerial vehicle for various applications

Article 04 September 2024

Notes

Darema coined the term DDDAS in 1999, but she conceived the key concepts of the paradigm itself in 1980, when she was working in large nuclear radiation transport modeling for oil exploration through nuclear accelerator neutron and gamma-ray measurements; between 1980 and through the 80’s, in organizational private communications Darema discussed about “DDDAS” ideas under the title “Gedanken Laboratory” and presented it in [3].

References

  1. A. Aved, E. Blasch, Dynamic Data Driven Applications Systems (DDDAS), (2104) Website, www.1dddas.org.
  2. F. Darema, Grid Computing and Beyond: The Context of Dynamic Data Driven Applications Systems. Proceedings of the IEEE, 93 (3):692–697, (2005) ArticleGoogle Scholar
  3. F. Darema, Parallel Applications and the Gedanken Laboratory, Conference of the Society of Engineering Sciences, (1990) Google Scholar
  4. F. Darema et al., DDDAS: Dynamic Data Driven Applications Systems, US National Science Foundation (2005). https://www.nsf.gov/pubs/2005/nsf05570/nsf05570.htm
  5. F. Darema, The Next Generation Program, (1998). http://www.nsf.gov/pubs/1999/nsf998/nsf998.htm
  6. F. Darema, New software architecture for complex applications development and runtime support, Int. J. High-Performance Computation, Special Issue on Programming Environments, Clusters, and Comp. Grids for Sci. Comp., 14(3), (2000) Google Scholar
  7. Report of the August 2010 Multi-Agency Workshop on Info/Symbiotics/DDDAS: The power of Dynamic Data Driven Applications Systems, AFOSR-NSF (2010), available at https://s3.amazonaws.com/static.1dddas.org/docs/2010_DDDAS-InfoSymbioticsReport.pdf
  8. B. Plale, D. Gannon, D. Reed, S. Graves, K. Droegemeier, B. Wilhelmson, M. Ramamurthy, Towards dynamically adaptive weather analysis and forecasting in LEAD, International Conference Computational Science (2005). Google Scholar
  9. F. Darema, The Next Generation Software Program, International Journal of Parallel Programming 33 (2–3): 73–79, (2005). https://doi.org/10.1007/s10766-005-4785-6. ArticleGoogle Scholar
  10. G. Allen, Building a Dynamic Data Driven Application System for Hurricane Forecasting, International Conf. on Computational Science, 1034–1041 (2007) Google Scholar
  11. G. Allen, P. Bogden, R.A. Luettich, Jr, E. Seidel, R. Twilley, Designing a Dynamic Data Driven Application System for Coastal and Environmental Modeling, Grid-Based Problem Solving Environments, 275–293 (2007) Google Scholar
  12. D.S. Bernstein, A. Ridley, J. Cutler, A. Cohn, Transformative Advances in DDDAS with Application to Space Weather Monitoring, Project Report, Univ. Michigan (2015) Google Scholar
  13. C. Yang, M. Bakich, et al., Pose Angular-Aiding for Maneuvering Target Tracking, Int. Conf. on Info Fusion (2005) Google Scholar
  14. J. Dunık, O. Straka, et al., Random-Point-Based Filters: Analysis and Comparison in Target Tracking, IEEE Tr. on Aerospace and Elec. Sys., 51(2): 1403–1421, (2015) ArticleGoogle Scholar
  15. E.P. Blasch, E. Bosse, D.A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA (2012) Google Scholar
  16. US National Science Foundation, Cyber-Physical Systems (CPS) Program Solicitation, https://www.nsf.gov/pubs/2010/nsf10515/nsf10515.htm
  17. N. Celik, S. Lee, K. Vasudevan, Y.-J. Son, DDDAS-based multi-fidelity simulation framework for supply chain systems, IIE Transactions, 42(5):325–341 (2010). https://doi.org/10.1080/07408170903394306ArticleGoogle Scholar
  18. F. Darema, The Next Generation Software Workshop – IPDPS’07, IEEE Int’l Parallel and Distributed Processing Symposium (IPDPS), (2007) Google Scholar
  19. F. Darema, Cyberinfrastructures of Cyber-applications-systems, Procedia Computer Science, 1 (1): 1287–1296 (2010). https://doi.org/10.1016/j.procs.2010.04.143. ArticleGoogle Scholar
  20. A.R. Chaturvedi, Society of simulation approach to dynamic integration of simulations, IEEE Winter Simulation Conference, 2125–2131 (2006) Google Scholar
  21. R. Fujimoto, J. Barjis, et al., Dynamic Data Driven Application Systems: Research Challenges and Opportunities, Winter Simulation Conference, 664–678 (2018) Google Scholar
  22. S. Sarkar, P. Chattopdhyay, A. Ray, S. Phoha, M. Levi, Alphabet size selection for symbolization of dynamic data-driven systems: An information-theoretic approach, American Control Conference (ACC), 5194–5199 (2015) Google Scholar
  23. V. Maroulas, K. Kang, I.D. Schizas, M.W. Berry, A learning drift homotopy particle filter, International Conference on Information Fusion, 1930–1937 (2015) Google Scholar
  24. E. Blasch, Enhanced air operations using JView for an air-ground fused situation awareness UDOP, IEEE/AIAA Digital Avionics Systems Conference (DASC) (2013). https://doi.org/10.1109/DASC.2013.6712597
  25. F. Darema, et al., Panel on Unifying Directions for Systems Engineering, ASME/IEEEInternational Conf. on Mechatronic and Embedded Sys. and App. (2011) Google Scholar
  26. F. Darema, Y.-J. Son, A. Patra, AFOSR Panel: Dynamic Data Driven Application Systems (DDDAS) in the Age of Big Compute and Big Data, ASME/IDETC International Design Engineering Technical Conferences (2014) Google Scholar
  27. J. Michopoulos, Ddema: A data driven environment for multiphysics applications, International Conference Computational Science (2003) Google Scholar
  28. G. Carmichael, D.N. Daescu, A. Sandu, T. Chai, Computational aspects of chemical data assimilation into atmosphere models, International Conference Computational Science (2003) Google Scholar
  29. C. Evangelinos, R. Chang, P.F.J. Lermusiaux, N.M. Patrikalakis, Rapid real-time interdisciplinary ocean forecasting using adaptive sampling and adaptive modeling and legacy codes: Component ecapsulation using xml, International Conference Computational Science (2003) Google Scholar
  30. J. Mandel, J. D. Beezley, L. Cobb, A. Krishnamurthy, Data Driven Computing by the Morphing Fast Fourier Transform Ensemble Kalman Filter in Epidemic Spread Simulations, DDDAS/ICCS Workshop, Procedia Computer Sci., 1, 1221–1229 (2010) ArticleGoogle Scholar
  31. M. Parashar, V. Matossian, W. Bangerth, H. Klie, B. Rutt, T. Kurc, U. Catalyurek, J. Saltz, M.F. Wheeler, Towards dynamic data-driven optimization of oil well placement, International Conference Computational Science, (2005) Google Scholar
  32. T.B. Trafalis, I. Adrianto, M.B. Richman, Active learning with support vector machines for tornado prediction, International Conference Computational Science (2007) Google Scholar
  33. L. Ramakrishnan, Y. Simmhan, B. Plale, Realization of dynamically adaptive weather analysis and forecasting in LEAD: Four years down the road, International Conference Computational Science (2007) Google Scholar
  34. L. Zhang, A. Sandu, Data assimilation in multiscale chemical transport models, International Conference Computational Science (2007) Google Scholar
  35. N. Roy, H.-L. Choi, D. Gombos, J. Hansen, J. How, S. Park, Adaptive observation strategies for forecast error minimization, International Conference Computational Science (2007) Google Scholar
  36. S. Ravela, Quantifying uncertainty for coherent structures, Procedia Computer Science, 9, 1187–1196 (2012) ArticleGoogle Scholar
  37. J. Michopoulos, P. Tsompanopoulou, E. Houstis, A. Joshi, Agent-based simulation of data-driven fire propagation dynamics, International Conference Computational Science (2004) Google Scholar
  38. J. Mandel, J.D. Beezley, L.S. Bennethum, S. Chakraborty, J.L. Coen, C.C. Douglas, J. Hatcher, M. Kim, A. Vodacek, A dynamic data driven wildland fire model, International Conference Computational Science (2007) Google Scholar
  39. J.D. Beezley, S. Chakraborty, J.L. Coen, C.C. Douglas, J. Mandel, A. Vodacek, Z. Wang, Real-time data driven wildland fire modeling, International Conference Computational Science (2008) Google Scholar
  40. R. Rodriguez-Aseretto, M.D. Leo, A. Cortés, J.S. Miguel-Ayanz, A data-driven model for big forest fires behavior prediction in Europe, Procedia Computer Science, 18, 186–1870 (2013) ArticleGoogle Scholar
  41. L. Wang, D. Chen, W. Liu, Y. Ma, Y. Wu, Z. Deng, DDDAS-Based Parallel Simulation of Threat Management for Urban Water Distribution Systems, Computing in Science & Engineering 16(1): 8–17 (2014). https://doi.org/10.1109/MCSE.2012.89ArticleGoogle Scholar
  42. A.K. Patra, M.I. Bursik, J. Dehn, M. Jones, M. Pavolonis, E.B. Pitman, T. Singh, P. Singla, E.R. Stefanescu, S. Pouget, P. Webley, Challenges in developing DDDAS based methodology for volcanic ash hazard analysis - effect of numerical weather prediction variability and parameter estimation, Procedia Computer Science 18, 1871–1880 (2013) ArticleGoogle Scholar
  43. A.K. Patra, E.R. Stefanescu, R.M. Madankan, M.I. Bursik, E.B. Pitman, P. Singla, T. Singh, P. Webley, Fast construction of surrogates for UQ central to DDDAS application to volcanic ash transport, Procedia Computer Science 29: 1227–1235 (2014) ArticleGoogle Scholar
  44. V.H.V.S. Rao, A. Sandu, A posteriori error estimates for DDDAS inference problems, Procedia Computer Science 29, 1256–1265 (2014) ArticleGoogle Scholar
  45. D. Metaxas, S. Venkataraman, C. Vogler, Image-based stress recognition using a model-based dynamic face tracking system, International Conference Computational Science (2004) Google Scholar
  46. D. Metaxas, G. Tsechpenakis, Z. Li, Y. Huang, A. Kanaujia, Dynamically adaptive tracking of gestures and facial expressions, International Conference Computational Science (2006) Google Scholar
  47. A. Majumdar, A. Birnbaum, D. Choi, A. Trivedi, S.K. Warfield, K. Baldridge, P. Krysl, A dynamic data driven grid system for intra-operative image guided neurosurgery, International Conference Computational Science (2005) Google Scholar
  48. J.T. Oden, K.R. Diller, C. Bajaj, J.C. Browne, J. Hazle, I. Babuska, J. Bass, L. Demkowicz, Y. Feng, D. Fuentes, S. Prudhomme, M.N. Rylander, R.J. Stafford, Y. Zhang, Development of a computational paradigm for laser treatment of cancer, International Conference Computational Science (2006) Google Scholar
  49. C. Bajaj, J.T. Oden, K.R. Diller, J.C. Browne, J. Hazle, I. Babuska, J. Bass, L. Bidaut, L. Demkowicz, A. Elliott, Y. Feng, D. Fuentes, B. Kwon, S. Prudhomme, R.J. Staord, Y. Zhang, Using cyber-infrastructure for dynamic data driven laser treatment of cancer, International Conference Computational Science (2007) Google Scholar
  50. I.S. Kim, J. Chandrasekar, A. Ridley, D.S. Bernstein, Data assimilation using the global ionosphere-thermosphere model, International Conference Computational Science, (2006) Google Scholar
  51. S. Ravela, J. Marshall, C. Hill, A. Wong, S. Stransky, Real-time observatory for laboratory simulation of planetary circulation, International Conference Computational Science (2007) Google Scholar
  52. A.V. Morozov, A.J. Ridley, D.S. Bernstein, N. Collins, T.J. Hoar, J.L. Anderson, Data assimilation and driver estimation for the Global Ionosphere–Thermosphere Model using the Ensemble Adjustment Kalman Filter, Journal of Atmospheric and Solar-Terrestrial Physics 104, 126–136 (2013) ArticleGoogle Scholar
  53. A.G. Burrell, A. Goel, A.J. Ridley, D.S. Bernstein, Correction of the Photoelectron Heating Efficiency Within the Global Ionosphere-Thermosphere Model Using Retrospective Cost Model Refinement, Journal of Atmospheric and Solar-Terrestrial Physics, 124, 30–38 (2015). ArticleGoogle Scholar
  54. C. Farhat, J.G. Michopoulos, F.K. Chang, L.J. Guibas, A.J. Lew, Towards a dynamic data driven system for structural and material health monitoring, International Conference Computational Science (2006) Google Scholar
  55. J. Cortial, C. Farhat, L.J. Guibas, M. Rajashekhar, Time-parallel exploitation of reduced-order modeling and sensor data reduction for structural and material health monitoring DDDAS, International Conference Computational Science (2007) Google Scholar
  56. E.E. Prudencio, P.T. Bauman, D. Faghihi, J.T. Oden, K. Ravi-Chandar, S.V. Williams, A dynamic data driven application system for real-time monitoring of stochastic damage, Procedia Computer Science 18, 2056–2065 (2013) ArticleGoogle Scholar
  57. E.E. Prudencio, P.T. Bauman, D. Faghihi, K. Ravi-Chandar, J.T. Oden, A Computational Framework for Dynamic Data Driven Material Damage Control, Based on Bayesian Inference and Model Selection, International Journal for Numerical Methods in Engineering 102 (3-4): 379–403 (April 2015). https://doi.org/10.1002/nme.4669ArticleMathSciNetMATHGoogle Scholar
  58. D. Allaire, J. Chambers, R. Cowlagi, D. Kordonowy, M. Lecerf, L. Mainini, F. Ulker, K. Willcox, A baseline offine/online DDDAS capability for self-aware aerospace vehicles, Procedia Computer Science, 18, 1959–1968 (2013) ArticleGoogle Scholar
  59. D. Allaire, D. Kordonowy, M. Lecerf, L. Mainini, K. Willcox, Multi-fidelity DDDAS methods with application to a self-aware aerospace vehicle, Procedia Computer Science 29, 1182–1192 (2014) ArticleGoogle Scholar
  60. L. Peng, K. Mohseni, Sensor driven feedback for puff estimation using unmanned aerial vehicles, International Conference on Unmanned Aircraft Systems (ICUAS), 562–569, (2014). https://doi.org/10.1109/ICUAS.2014.6842298.
  61. E. Blasch, P. Paces, P. Kostek, K. Kramer, Summary of Avionics Technologies, IEEE Aerospace and Electronics Systems Magazine 30(9): 6–11, (Sept. 2015) ArticleGoogle Scholar
  62. W. Silva, E. W. Frew, W. Shaw-Cortez, Implementing path planning and guidance layers for dynamic soaring and persistence missions, International Conference on Unmanned Aircraft Systems (ICUAS), 92–101, (2015). https://doi.org/10.1109/ICUAS.2015.7152279
  63. S. Imai, E. Blasch, A. Galli, F. Lee, C.A. Varela, Airplane Flight Safety Using Error-Tolerant Data Stream Processing, IEEE Aerospace and Electronics Systems Magazine, 32(4): 4–17 (April 2017) ArticleGoogle Scholar
  64. A. Sandu, W. Liao, G.R. Carmichael, D. Henze, J.H. Seinfeld, T. Chai, D. Daescu, Computational aspects of data assimilation for aerosol dynamics, International Conference Computational Science (2004) Google Scholar
  65. S. Ravela, Amplitude-position formulation of data assimilation, International Conference Computational Science (2006) Google Scholar
  66. B. Jia, K.D. Pham, E. Blasch, D. Shen, Z. Wang, G. Chen, Cooperative Space Object Tracking using Space-based Optical Sensors via Consensus-based Filters, IEEE Tr. on Aerospace and Electronics Systems, 52(3): 1908–1936 (2016) ArticleGoogle Scholar
  67. S. Ravela, Two extensions of data assimilation by field alignment, International Conference Computational Science (2007) Google Scholar
  68. P. Tagade, S. Ravela, On a quadratic information measure for data assimilation, American Control Conf., 598–603 (2014) Google Scholar
  69. T.C. Henderson, N. Boonsirisumpun, The impact of parameter estimation on model accuracy assessment, Procedia Computer Science 18, 1969–1978 (2013) ArticleGoogle Scholar
  70. P. Tagade, H. Seybold, S. Ravela, Mixture ensembles for data assimilation in dynamic data-driven environmental systems, Procedia Computer Science 29: 1266–1276 (2014) ArticleGoogle Scholar
  71. E.P. Blasch, Dynamic data driven applications system concept for information fusion,” Procedia Computer Science 18, 1999–2007 (2013) ArticleGoogle Scholar
  72. N. Virani, S. Marcks, S. Sarkar, K. Mukherjee, A. Ray, S. Phoha, Dynamic data driven sensor array fusion for target detection and classification, Procedia Computer Science, 18, 2046–2055 (2013) ArticleGoogle Scholar
  73. E. Blasch, G. Seetharaman, F. Darema, Dynamic Data Driven Applications Systems (DDDAS) modeling for Automatic Target Recognition, Proc. SPIE 8744 (2013) Google Scholar
  74. B. Smith, P. Chattopadhyay, A. Ray, T.R. Damarla, Performance robustness of feature extraction for target detection & classification, IEEE American Control Conference, (2014) Google Scholar
  75. T. Chin, Jr., K. Xiong, E. Blasch, CRAMStrack: Enhanced Nonlinear RSSI Tracking Using Circular Multi-Sectors for Threat Detection, Journal of Signal Processing Systems, June (2020) Google Scholar
  76. B. Uzkent, M.J. Hoffman, A. Vodacek, J.P. Kerekes, B. Chen, Feature matching and adaptive prediction models in an object tracking DDDAS, Procedia Computer Science 18, 1939–1948 (2013) ArticleGoogle Scholar
  77. R. Fujimoto, A. Guin, M. Hunter, H. Park, R. Kannan, G. Kanitkar, M. Milholen, S. Neal, P. Pecher, A dynamic data driven application system for vehicle tracking, Procedia Computer Science 29, 1203–1215 (2014) ArticleGoogle Scholar
  78. B. Uzkent, M.J. Hoffman, A. Vodacek, Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(9): 4325–4333, (2016). https://doi.org/10.1109/JSTARS.2016.2560220ArticleGoogle Scholar
  79. N. Nguyen, M.H.H. Khan, Context Aware Data Acquisition Framework for Dynamic Data Driven Applications Systems (DDDAS), IEEE Military Communications Conf., 334–341 (2013). https://doi.org/10.1109/MILCOM.2013.65
  80. N. Virani, J-W. Lee, S. Phoha, A. Ray, Learning context-aware measurement models,” American Control Conference (ACC), 4491–4496 (2015). https://doi.org/10.1109/ACC.2015.7172036
  81. L. Snidaro, J. Garcia Herrero, J. Llinas, E. Blasch, Context-Enhanced Information Fusion: Boosting Real-World Performance with Domain Knowledge, Springer (2016) BookGoogle Scholar
  82. A. Chaturvedi, J. Chi, S. Mehta, D. Dolk, SAMAS: Scalable architecture for multi-resolution agent-based simulation, International Conference Computational Science, (2004) Google Scholar
  83. N. Koyuncu, S. Lee, K.K. Vasudevan, Y-J. Son, P. Sarfare, DDDAS-basedmulti-fidelitysimulation for onlinepreventivemaintenancescheduling in semiconductorsupply chain, Winter Simulation Conference, 1915–1923, (2007) https://doi.org/10.1109/WSC.2007.4419819
  84. A. Boukerche, F.M. Iwasaki, R.B. Araujo, E.B. Pizzolato, Web-Based Distributed Simulations Visualization and Control with HLA and Web Services, IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, 17–23, (2008). https://doi.org/10.1109/DS-RT.2008.30
  85. A.J. Aved, E. Blasch, Multi-INT Query Language for DDDAS Designs, Procedia Computer Science 51, 2518–2523 (2015) ArticleGoogle Scholar
  86. E. Blasch, S. Phoha, Special Issue: Dynamic Data-Driven Applications Systems (DDDAS) concepts in Signal Processing, J. Signal Processing Systems (2017) https://doi.org/10.1007/s11265-017-1253-7
  87. E.H. Abed, N.S. Namachchivaya, T.J. Overbye, M.A. Pai, P.W. Sauer, A. Sussman, Data driven power system operations, International Conference Computational Science, (2006) Google Scholar
  88. N. Celik, A.E. Thanos, J.P. Saenz, DDDAMS-based dispatch control in power networks, Procedia Computer Science 18, 1899–1908 (2013) ArticleGoogle Scholar
  89. E. Frew, B. Argrow, A. Houston, C. Weiss, J. Elston, An energy-aware airborne dynamic data-driven application system for persistent sampling and surveillance, Procedia Computer Science 18, 2008–2017 (2013) ArticleGoogle Scholar
  90. S. Neal, R. Fujimoto, M. Hunter, Energy consumption of Data Driven traffic simulations, Winter Simulation Conference (WSC), 1119–1130 (2016). https://doi.org/10.1109/WSC.2016.7822170
  91. G. R. Madey, A.-L. Barabsi, N.V. Chawla, M. Gonzalez, D. Hachen, B. Lantz, A. Pawling, T. Schoenharl, G. Szabo, P. Wang, P. Yan, Enhanced situational awareness: Application of DDDAS concepts to emergency and disaster management, International Conference Computational Science (2007) Google Scholar
  92. R.M. Fujimoto, N. Celik, H. Damgacioglu, M. Hunter, D. Jin, Y-J. Son, J. Xu, Dynamic data driven application systems for smart cities and urban infrastructures, Winter Simulation Conference, 1143–1157, (2016). https://doi.org/10.1109/WSC.2016.7822172
  93. K. Sudusinghe, I. Cho, M. Van der Schaar, S.S. Bhattacharyya, Model based design environment for data-driven embedded signal processing systems, Procedia Computer Science 29, 1193–1202 (2014). ArticleGoogle Scholar
  94. S. Chakravarthy, A. Aved, S. Shirvani, M. Annappa, E. Blasch, Adapting Stream Processing Framework for Video Analysis, Procedia Computer Science, 51, 2648–2657, (2015) ArticleGoogle Scholar
  95. H. Li, K. Sudusinghe, Y. Liu, J. Yoon, M. Van Der Schaar, E. Blasch, S.S. Bhattacharyya, Dynamic, Data-Driven Processing of Multispectral Video Streams, IEEE Aerospace and Electronics Systems Magazine, 32 (4): 50–57 (June 2017) ArticleGoogle Scholar
  96. P. Chew, N. Chrisochoides, S. Gopalsamy, G. Heber, T. Ingraffea, E. Luke, J. Neto, K. Pingali, A. Shih, B. Soni, P. Stodghill, D. Thompson, S. Vavasis, P. Wawrzynek, Computational science simulations based on web services, International Conference Computational Science (2003) Google Scholar
  97. O. Onolaja, R. Bahsoon, G. Theodoropoulos, Conceptual framework for dynamic trust monitoring and prediction, Procedia Computer Science, 1, 1241–1250 (2010) ArticleGoogle Scholar
  98. L. Pournajaf, L. Xiong, V. Sunderam, Dynamic data driven crowd sensing task assignment, Procedia Computer Science, 29: 1314–1323 (2014) ArticleGoogle Scholar
  99. E. Blasch, Y. Al-Nashif, S. Hariri, Static versus dynamic data information fusion analysis using DDDAS for cyber trust, Procedia Computer Science, 29, 1299–1313, 2014. ArticleGoogle Scholar
  100. Y. Badr, S. Hariri, Y. Al-Nashif, E. Blasch, “Resilient and Trustworthy Dynamic Data-Driven Application Systems (DDDAS) Services for Crisis Management Environments,” Procedia Computer Science, 51, 2623–2637 (2015) ArticleGoogle Scholar
  101. T. Chen, R. Bahsoon, G. Theodoropoulos, Dynamic qos optimization architecture for cloud-based DDDAS, Procedia Computer Science, 18, 1881–1890 (2013) ArticleGoogle Scholar
  102. R. Wu, B. Liu, Y. Chen, E. Blasch, H. Ling, G. Chen, A Container-based Elastic Cloud Architecture for Pseudo Real-time Exploitation of Wide Area Motion Imagery (WAMI) Stream, The Journal of Signal Processing Systems, 1–13 (Nov. 2016). https://doi.org/10.1007/s11265-016-1206-6.
  103. S. Shekar, Dynamic Data Driven Cloud Systems for Cloud-Hosted CPS, IEEE International Conference on Cloud Engineering Workshop (IC2EW),195–197(2016). https://doi.org/10.1109/IC2EW.2016.38
  104. C.-S. Li, F. Darema, V. Chang, Distributed behavior model orchestration in cognitive internet of things solution, Enterprise Information Systems, 12, 414–434 (2017). https://doi.org/10.1080/17517575.2017.1355984ArticleGoogle Scholar
  105. G. Seetharaman, A. Lakhotia, et al., Unmanned Vehicles Come of Age: The DARPA Grand Challenge, IEEE Computer Society Magazine, 39(12): 26–29 (2006) ArticleGoogle Scholar
  106. E. Blasch, D. Shen, B. Jia, Z. Wang, G. Chen, Y. Chen, K. Pham, Autonomy in use for space situation awareness, Proc. SPIE, 11017 (2019) Google Scholar
  107. E. Blasch, B. Pokines, Analytical Science for Autonomy Evaluation, IEEE National Aerospace and Electronics Systems Conference (2019) Google Scholar
  108. T. El-Ghazawi, V. Solker, V, Narayana, et al., Dynamically Adaptive Hybrid Nanoplasmonic Networks on Chips (NoCs), AD1096804, Technical Report (2019) Google Scholar
  109. Y. Zheng. E. Blasch, Z. Liu, Multispectral Image Fusion and Colorization, SPIE, Bellingham, Washington (2018) Google Scholar
  110. T. Mukherjee, P. Kumar, D. Pati, et al., LoSI: Large Scale Location Inference through FM Signal Integration and Estimation, IEEE Big Data Mining and Analytics, 2(4): 319–348 (Dec 2019). https://doi.org/10.26599/BDMA.2019.9020013. ArticleGoogle Scholar
  111. U. Majumder, E. Blasch, D. Garren, Deep Learning for Radar and Communications Automatic Target Recognition, Artech House (2020). Google Scholar
  112. R. Xu, Yu Chen, et al., An Exploration of Blockchain-Enabled Decentralized Capability-based Access Control Strategy for Space Situation Awareness, Optical Engineering, 58(4), 041609 (2019). https://doi.org/10.1117/1.OE.58.4.014609ArticleGoogle Scholar
  113. E. Blasch, J. S. Tiley, D. Sparkman, S. Donegan, M. Cherry, Data fusion methods for materials awareness, Proc SPIE 11423, (2020) Google Scholar
  114. F. Darema, E. Blasch, DDDAS Solutions for Border Patrol and Emergency Response Environments, IEEE Future Networks: Enabling 5G and Beyond (Oct. 2020) Google Scholar
  115. E. Blasch, R. Bohn, J. Gato, et al., Future Direction of DDDAS/InfoSymbiotics and Collaborations with Related Initiatives, Int’l., Conf. on DDDAS, (2020) Google Scholar

Acknowledgements

Work presented in this book was supported in part by the DDDAS Program of the Air Force Office of Scientific Research (AFOSR) as well as other funding agencies. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government, or any other funding entities.

Author information

Authors and Affiliations

  1. Air Force Office of Scientific Research, Arlington, VA, USA Erik P. Blasch
  2. InfoSymbiotics Systems Society, Boston, MA, USA Frederica Darema
  3. Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, USA Dennis Bernstein