On-board data mining

Scientific Data Mining and Knowledge Discovery: Principles and Foundations

Volumn , Issue , 2010, Pages 345-376

  Tanner, Steve ^a   Stein, Cara ^a   Graves, Sara J ^a  

^a UNIVERSITY OF ALABAMA IN HUNTSVILLE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84856477013     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-02788-8_13     Document Type: Chapter

References (69)

1. Organization web site including all standards documents, http://www.opengeospatial.org/ogc
2. SensorML, http://vast.uah.edu/SensorML
3. S. Chien, T. Debban, C. Yen, R. Sherwood, R. Castano, B. Cichy, A. Davies, M. Burl, A. Fukunaga, Revolutionary deep space science missions enabled by onboard autonomy. International Symposium on AI, Robotics, and Automation in Space, 2003
4. D. Tran, S. Chien, R. Sherwood, R. Castano, B. Cichy, A. Davies, G. Rabideau, The autonomous sciencecraft experiment onboard the EO-1 spacecraft. AAMAS, 2004
5. G. Rabideau, D. Tran, S. Chien, B. Cichy, R. Sherwood, Mission operations of earth observing-1 with onboard autonomy. Proceedings 2nd IEEE International Conference on Space Mission Challenges for Information Technology, 2006
6. R. Castano, N. Tang, T. Doggett, S. Chien, D. Mazzoni, R. Greeley, B. Cichy, A. Davies, Onboard classifiers for science event detection on a remote sensing spacecraft. Proc. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 845-851
7. NASA: Earth Observing-1, http://eo1.gsfc.nasa.gov/new/general/index.html. Accessed 1 Feb. 2008
8. R. Castano, T. Estlin, D. Gaines, A. Castano, B. Bornstein, C. Chouinard, R. C. Anderson, M. Judd, Automated target selection for opportunistic rover science. 37th Lunar and Planetary Science Conference, 2006
9. R. Castano, T. Estlin, R. Anderson, D. Gaines, A. Castano, OASIS: onboard autonomous science investigation system for opportunistic rover science. J. field robot. 24(5), 379-397(2007)
10. K. Wagstaff, R. Castano, S. Chien, A. Ivanov, E. Pounders, T. Titus, An onboard data analysis method to track the seasonal polar caps on Mars. International Symposium on AI, Robots, and Automation in Space, 2005
11. R. Castano, K. Wagstaff, S. Chien, T. Stough, B. Tang, On-board analysis of uncalibrated data for a spacecraft at Mars. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, pp. 922-930
12. K. Wagstaff, J. Bandfield, R. Castano, S. Chien, M. Smith, Dust storms and water ice clouds: feature detection for use onboard THEMIS. 37th Lunar and Planetary Science Conference, 2006
13. Y. Cai, Y. Hu, Onboard inverse physics from sensor web. 2nd IEEE International Conference on Space Mission Challenges for Information Technology, 2006
14. T. Meindl, W. Moniaci, D. Gallesio, E. Pasero, Embedded Hardware architecture for statistical rain forecast. Res. Microelectron. Electron. 1, 133-136(2005)
15. J. A. Kalomiros, J. Lygouras, Design and evaluation of a hardware/software FPGA-based system for fast image processing. Microprocess. Microsyst. (2008). doi:10.1016/j.micpro.2007.09.001
16. M. Lorenz, L. Mengibar, E. SanMillan, L. Entrena, Low power data processing system with self-reconfigurable architecture. J. Syst. Arch. 53(9), 568-576(2007)
17. P. Pingree, J.-F. Blavier, G. Toon, D. Bekker, An FPGA/SoC approach to on-board data processing enabling new mars science with smart payloads. IEEE Aerospace Conference, 2007, pp. 1-12
18. Z. Baker, V. Prasanna, An architecture for efficient hardware data mining using reconfigurable computing systems. 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 2006, pp. 67-75
19. Z. Baker, V. Prasanna, Efficient hardware data mining with the apriori algorithm on FPGAs. 13th, Annual IEEE Symposium on Field-Programmable Custom Computing Machines, 2005, pp. 3-12
20. K. Revett, S. T. de Magalhaes, H. Santos, Data mining a keystroke dynamics based biometrics database using rough sets. Portuguese conference on Artificial Intelligence, 2005, pp. 188-191
21. C. Maple, V. Schetinin, Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics. First International Conference on Availability, Reliability, and Security, 2006
22. K. Umamaheswari, S. Sumathi, S. N. Sivanandam, K. K. N. Anburajan, Efficient finger print image classification and recognition using neural network data mining. International Conference on Signal Processing, Communications, and Networking, 2007, pp. 426-432
23. J. Krikke, B. Alfonsi. "In the news." IEEE Intelligent Systems, vol. 21, issue 3, Jan.-Feb. 2006, pp. 102-104
24. H. Z. Bing, V. P. Shirochin, S. Jun, An intelligent lightweight intrusion detection system (IDS). IEEE Region 10 TENCON, 2005, pp. 1-7
25. R. Iglesias, M. Orozco, J. Valdes, A. El Saddik, Behavioral features for different haptic-based biometric tasks. IEEE International Workshop on Haptic, Audio, and Visual Environments and Games, 2007, pp. 102-106
26. S. Berrani, C. Garcia, On the impact of outliers on high-dimensional data analysis methods for face recognition. Proc. 2nd International Workshop on Computer Vision Meets Databases, 2005, vol. 160, pp. 43-49
27. R. B. Rao, J. Bi, G. Fung, M. Salganicoff, N. Obuchowski, D. Naidich, LungCAD: a clinically approved, machine learning system for lung cancer detection. Proc. 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, pp. 1033-1037
28. K. Chen, L. Hong, User identification based on game-play activity patterns. Proceedings of 6th ACM SIGCOMM Workshop on Network and System Support for Games, 2007, pp. 7-12
29. T. Kwon, H. Moon, Biometric authentication for border control applications. Trans. Knowl. Data Eng. doi:10.1109/TKDE.2007.190716
30. A. Guven, I. Sogukpinar, Understanding users' keystroke patterns for computer access security. Comput. Secur. 22(8), 695-706(2003)
31. Biometrics enter mobile world, Biomet. Technol. Today 13(8), 10-11(2005)
32. U. S. Department of Homeland Security, US-VISIT: How it works. Accessed 22 Feb. 2008
33. U. S. Department of Homeland Security. US-VISIT biometric exit process to improve national security.. Accessed 22 Feb. 2008
34. T. A. Carper, M. D. Gardiner, Biometric Embedded Device. U. S. Patent 20080040615, filed 2007
35. R. Slywczak, Developing autonomous missions through intelligent on-board architectures. Proceedings of IEEE Conference on Networking, Sensing and Control, 2005, pp. 359-364
36. L. Alkalai, Perspectives on dependable computing for solar system exploration. 2002 Pacific Rim International Symposium on Dependable Computing, 2002
37. L. Paal, N. Golshan, F. Fisher, E. Law, W. Veruttipong, M. Stockett' Deep space terminal demonstration. The Telecommunications and Mission Operations Progress Report, TMO PR 42-138, April-June 1999
38. J. E. Reidel, S. Bhaskaran, S. Desai, D. Han, B. Kennedy, T. McElrath, G. W. Null, M. Ryne, S. P. Synnott, T. C. Want, R. A. Werner, Using autonomous navigation for interplanetary missions: The validation of deep space 1 AutoNav. International Conference on Low-Cost Planetary Missions, 2000
39. S. Collins, Deep space 1 flight experience: adventures on an ion drive. 25th Annual AAS Guidance and Control Conference, 2002
40. M. D. Rayman, P. Varghese, D. H. Lehman, L. L. Livesay, Results from the deep space 1 technology validation mission. Acta Astronaut. 47(2-9), 475-487(2000)
41. NASA, Deep Space 1, http://nmp. jpl.nasa.gov/ds1/. Accessed 3 Mar. 2007
42. NASA, Deep Space 1: Quick Facts. Accessed 3 March 2007
43. J. F. Smith III, T. H. Nguyen, Fuzzy decision trees for planning and autonomous control of a coordinated team of UAVs. Proceedings of SPIE 6567, 656708, 2007
44. S. Srinivasan, H. Latchman, J. Shea, T. Wong, J. McNair, Airborne traffic surveillance systems: video surveillance of highway traffic. In Proceedings of the ACM 2nd International Workshop on Video Surveillance & Amp; Sensor Networks, New York, NY, USA, October 15, 2004. VSSN '04 (ACM, New York, 2004), pp. 131-135
45. J. J. Corner, G. B. Lamont, Parallel simulation of UAV swarm scenarios. In Proceedings of the 36th Conference on Winter Simulation, Washington, DC, December 05-08, 2004. Winter Simulation Conference. Winter Simulation Conference, pp. 355-363, 2004
46. T. Grant, 2005. Unifying planning and control using an OODA-based architecture. In Proceedings of the 2005 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries (White River, South Africa, September 20-22, 2005). ACM International Conference Proceeding Series, vol. 150. South African Institute for Computer Scientists and Information Technologists, pp. 159-170
47. J. Jaffe, C. Schurgers, Sensor networks of freely drifting autonomous underwater explorers. In Proceedings of the 1st ACM International Workshop on Underwater Networks, Los Angeles, CA, USA, Sept 25-25, 2006. WUWNet '06. ACM, (New York, NY, 2006), pp. 93-96
48. M. Wzorek, P. Doherty, Reconfigurable path planning for an autonomous unmanned aerial vehicle. Hybrid Information Technology, 2006. ICHIT'06. Vol 2.
49. International Conference on. Volume 2, Nov. 2006, pp. 242-249
50. B.-J. Yoon, M.-W. Park, J.-H. Kim, UGV (unmanned ground vehicle) navigation method using GPS and compass. SICE-ICASE, 2006. International Joint Conference, Oct. 2006, pp. 3621-3625
51. B. Ludington, J. Reimann, G. Vachtsevanos, I. Barlas, Target tracking with unmanned aerial vehicles: From single to swarm vehicle autonomy and intelligence. Control and automation, 2006. MED '06. 14th Mediterranean Conference, June 2006, pp. 1-6
52. B. Sundaram, M. Palaniswami, S. Reddy, M. Sinickas, Radar localization with multiple unmanned aerial vehicles using support vector regression. Intelligent sensing and information processing, 2005. ICISIP 2005. Third International Conference, 14-17 Dec. 2005, pp. 232-237
53. K. Jerosch, A. Ldtke, M. Schlter, G. T. Ioannidis, Automatic content-based analysis of georeferenced image data: Detection of Beggiatoa mats in seafloor video mosaics from the Hkon Mosby Mud Volcano. Comput. Geosci. 33(2), 202-218(2007)
54. P.-T. Cheng, L.-M. Tsai, L.-W. Lu, D.-L. Yang, The design of PDA-based biomedical data processing and analysis for intelligent wearable health monitoring systems. Computer and Information Technology, 2004. CIT '04. The Fourth International Conference, 14-16 Sept 2004, pp. 879-884
55. J. Crowe, B. Hayes-Gill, M. Sumner, C. Barratt, B. Palethorpe, C. Greenhalgh, O. Storz, A. Friday, J. Humble, C. Setchell, C. Randell, H. L. Muller, Modular sensor architecture for unobtrusive routine clinical diagnosis. Distributed Computing Systems Workshops, 2004. Proceedings of 24th International Conference, 2004, pp. 451-454
56. P. Kulkarni, Y. ztrk, Requirements and design spaces of mobile medical care. SIGMOBILE Mob. Comput. Commun. Rev. 11(3), 12-30(2007)
57. A. Jaimes, Sit straight (and tell me what I did today): A human posture alarm and activity summarization system. In Proceedings of the 2nd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences (Hilton, Singapore, November 11-11, 2005). CARPE '05. ACM, New York, NY, pp. 23-34
58. A. Milenkovic, C. Otto, E. Jovanov, Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer Communications, Special issue: Wireless Sensor Networks: Performance, Reliability, Security, and Beyond (Elsevier, Amsterdam, 2006)
59. M. Sung, C. Marci, A. Pentland, Wearable feedback systems for rehabilitation. J. NeuroEng. Rehabil. 2, 17(2005)
60. T. Hester, R. Hughes, D. M. Sherrill, B. Knorr, M. Akay, J. Stein, P. Bonato, Using wearable sensors to measure motor abilities following stroke. Wearable and implantable body sensor networks, 2006. BSN 2006. International Workshop, 3-5 April 2006
61. E. Jovanov, A. Milenkovic, C. Otto, P. C. de Groen, A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J. NeuroEng. Rehabil. 2, 6(2005). Available at: http://www.jneuroengrehab.com/content/2/1/6
62. L. Meador (Panel Chairman), Leveraging basic research into MASINT capabilities (A fiveyear plan), Response to Conference Report to H. R. 4546, the National Defense Authorization Act for Fiscal 2003, March 2003
63. Research issues in data stream association rule mining N. Jiang, L. Gruenwald, ACM SIGMOD RECORD 35, pp. 14-19(2006)
64. S. Ivengar, S. Sastry, N. Balakrishnan, Foundations of data fusion for automation. IEEE Instrum. Meas. Mag. 6(14), 35-41(2003)
65. L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, R. Motwani, Streaming data algorithms for high quality clustering. Proceedings of the 18th International Conference on Data Engineering, pp. 685-694, 2002
66. L. Weixian, L. Yilong, J. Fu, Data fusion of multiradar system by using genetic algorithm, IEEE Trans. Aerosp. Electron. Syst. 38(2), 601-612(2002)
67. M. Halatchev, L. Gruenwald, Estimating missing data in related sensor data streams. International Conference on Management of Data, Jan 2005, pp. 83-94
68. G. W. Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, M. Welsh, Deploying a wireless sensor network on an active volcano, In IEEE Internet Computing, Special issue on data-driven applications in sensor networks, March/April 2006
69. Z. K. Baker, V. K. Prasanna, Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs, fccm, pp. 3-12, 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'05), 2005