Voir la notice de l'article provenant de la source Library of Science
@article{IJAMCS_2016_26_1_a7, author = {Kreczmer, B.}, title = {Connections between object classification criteria using an ultrasonic bi-sonar system}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {123--132}, publisher = {mathdoc}, volume = {26}, number = {1}, year = {2016}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_1_a7/} }
TY - JOUR AU - Kreczmer, B. TI - Connections between object classification criteria using an ultrasonic bi-sonar system JO - International Journal of Applied Mathematics and Computer Science PY - 2016 SP - 123 EP - 132 VL - 26 IS - 1 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_1_a7/ LA - en ID - IJAMCS_2016_26_1_a7 ER -
%0 Journal Article %A Kreczmer, B. %T Connections between object classification criteria using an ultrasonic bi-sonar system %J International Journal of Applied Mathematics and Computer Science %D 2016 %P 123-132 %V 26 %N 1 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_1_a7/ %G en %F IJAMCS_2016_26_1_a7
Kreczmer, B. Connections between object classification criteria using an ultrasonic bi-sonar system. International Journal of Applied Mathematics and Computer Science, Tome 26 (2016) no. 1, pp. 123-132. http://geodesic.mathdoc.fr/item/IJAMCS_2016_26_1_a7/
[1] Adib, F. and Katabi, D. (2013). See through wall with Wi-Fi, ACM SIGCOMM’13, Hong Kong, China, pp. 75–86.
[2] Barshan, B. (1999). Location and curvature estimation of spherical targets using multiple sonar time-of-flight measurements, IEEE Transactions on Instrumentation and Measurement 48(6): 1212–1223.
[3] Barshan, B. and Kuc, R. (1990). Differentiating sonar reflections from corners and planes by employing an intelligent sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(6): 560–569.
[4] Bozma, O. and Kuc, R. (1991). Building a sonar map in a specular environment using a single mobile sensor, IEEE Transactions on Pattern Analysis and Machine Intelligence 13(12): 1260–1269.
[5] Brown, M.K. (1985). Feature extraction techniques for recognizing solid objects with an ultrasonic range sensor, IEEE Journal of Robotics and Automation 1(4): 191–205.
[6] Heale, A. and Kleeman, L. (2001). Fast target classification using sonar, 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, Maui, HI, USA, Vol. 3, pp. 1446–1451.
[7] Jackson, J.C., Summan, R., Dobie, G.I., Whiteley, S.M., Pierce, S.G. and Hayward, G. (2013). Time-of-flight measurement techniques for airborne ultrasonic ranging, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 60(2): 343–355.
[8] Kleeman, L. (2002). On-the-fly classifying sonar with accurate range and bearing estimation, IEEE/RSJ International Conference on Intelligent Robots and Systems, Lausanne, Switzerland, Vol. 1, pp. 178–183.
[9] Kleeman, L. (2004). Advanced sonar with velocity compensation, International Journal of Robotics Research 23(2): 111–126.
[10] Kleeman, L. and Kuc, R. (1994). An optimal sonar array for target localization and classification, IEEE International Conference on Robotics and Automation, San Diego, CA, USA, pp. 3130–3135.
[11] Kleeman, L. and Kuc, R. (1995). Mobile robot sonar for target localization and classification, International Journal of Robotics Research 14(4): 295–318.
[12] Kreczmer, B. (2010). Objects localization and differentiation using ultrasonic sensors, in H. Yussof (Ed.), Robot Localization and Map Building, InTech, Rijeka, pp. 521–543.
[13] Kreczmer, B. (2013). Relations between classification criteria of objects recognizable by ultrasonic systems, 16th IEEE International Conference MMAR 2011, Międzyzdroje, Poland, pp. 806–811.
[14] Kuc, R. and Siegel, M. (1987). Physically based simulation model for acoustic sensor robot navigation, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI- 9(6): 766–778.
[15] Leonard, J.J. and Durrant-Whyte, H.F. (1991). Mobile robot localization by tracking geometric beacons, IEEE Transactions on Robotics and Automation 7(3): 376–382.
[16] Leonard, J.J. and Durrant-Whyte, H.F. (1992). Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic Publishers, Boston, MA/London/Dordrecht.
[17] Möller, M.M. (1995). Autonomous mobility with triaural sonar system, International Symposium on Intelligent Robotic Systems, Pisa, Italy, pp. 25–30.
[18] Nanani, G.K. and Prasad, K.M.V.V. (2013). A study of wi-fi based system for moving object detection through the wall, International Journal of Computer Applications 79(7): 15–18.
[19] Peremans, H., Audenaert, K. and Campenhout, J.M.V. (1993). A high-resolution sensor based on tri-aural perception, IEEE Transactions on Robotics and Automation 9(1): 36–48.
[20] Peremans, H., Campengout, J.V. and Levrouw, L. (1991). Steps towards tri-aural perception, in P.S. Schenker (Ed.), Sensor Fusion IV: Control Paradigms and Data Structures, SPIE, Bellingham, WA, pp. 165–176.
[21] Queirós, R., Corrêa Alegria, F., Silva Girão, P. and Cruz Serra, A. (2010). Cross-correlation and sine-fitting techniques for high-resolution ultrasonic ranging, IEEE Transactions on Instrumentation and Measurement 59(12): 1–10.
[22] Rencken, W.D., Peremans, H. and Möller, M. (1994). Tri-aural versus conventional sonar localisation and map building, JAS-4 Conference, Karlsruhe, Germany, pp. 398–402.