2 edition of Signal processing, sensor fusion, and target recognition XIII found in the catalog.
Published
2004
by SPIE in Bellingham, Wash
.
Written in English
Edition Notes
Includes bibliographical references and author index.
| Statement | Ivan Kadar, chair/editor ; sponsored and published by SPIE--The International Society for Optical Engineering. |
| Genre | Congresses. |
| Series | SPIE proceedings series ;, v. 5429, Proceedings of SPIE--the International Society for Optical Engineering ;, v. 5429. |
| Contributions | Kadar, Ivan., Society of Photo-optical Instrumentation Engineers. |
| Classifications | |
|---|---|
| LC Classifications | TK5102.5 .S53752 2004 |
| The Physical Object | |
| Pagination | x, 658 p. : |
| Number of Pages | 658 |
| ID Numbers | |
| Open Library | OL3439247M |
| ISBN 10 | 0819453528 |
| LC Control Number | 2005298516 |
| OCLC/WorldCa | 56472929 |
Unscented Kalman Filters for Multiple Target Tracking with Symmetric Measurement Equations, submitted to IEEE Trans. on Automatic Control, Jan. M. Tobias and A.D. Lanterman, Multitarget Tracking using Multiple Bistatic Range Measurements with Probability Hypothesis Densities, Signal Processing, Sensor Fusion, and Target Recognition XIII. [C11] M. Azam and S. Ragi, "Decentralized formation shape control of UAV swarm using dynamic programming," in Proceedings of SPIE , Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, I, Anaheim, CA, April ,
IEEE TRANSACTIONS ON SIGNAL PROCESSING 2 This likelihood, however, requires all the target measurements collected across the network to be filtered together, and, in turn, centralised processing. Distributed alternatives often resort to joint filtering which embodies all the drawbacks of centralised fusion, both in the case of ML [18] and. An efficient scheme of target classification and information fusion in wireless sensor networks Article in Personal and Ubiquitous Computing 13(7) October with 27 Reads.
Signal Processing and Pattern Recognition using Continuous Wavelets Ronak Gandhi, Syracuse University, Fall Introduction Electromyography (EMG) signal is a kind of biology electric motion which was produced by muscles and the neural system. EMG signals are non-stationary and have highly complex time and frequency Size: KB. P. Zhu, J. Isaacs, B. Fu, and S. Ferrari, “Deep Learning Feature Extraction for Target Recognition and Classification in Underwater Sonar Images”, Proc. of the IEEE Conference on Decision and Control (CDC), December
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And target recognition XIII book, "Title of Paper," in Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, edited by Ivan Kadar, Erik P. Blasch, Lynne L. Grewe, Proceedings of SPIE Vol.
(SPIE, Bellingham, WA, ) Seven -digit Article CID Number. ISSN: X ISSN: X (ele ctronic) ISBN: Signal Processing, Sensor Fusion, and Target Recognition: Volume XIX on *FREE* shipping on qualifying offers. Signal Processing, Sensor Fusion, and Target Recognition: Volume XIXFormat: Paperback.
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII Monday - Thursday 16 - 19 April PROCEEDINGS VOLUME Signal Processing, Sensor Fusion, and Target Recognition XIV. Editor(s): Ivan Kadar *This item is only available on the SPIE Digital Library.
Rapid update of odd DCT and DST for real-time signal processing Author(s). Get this from a library. Signal processing, sensor fusion, and target recognition XIII: April,Orlando, Florida, USA. [Ivan Kadar; Society of Photo-optical Instrumentation Engineers.;].
Signal Processing, Sensor Fusion, and Target Recognition XVII (Proceedings of Spie) by Ivan Kadar (Editor) ISBN ISBN Why is ISBN important. ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book.
Signal processing, sensor fusion, and target recognition XIII: AprilOrlando, Florida, USA Author: Ivan Kadar ; Society of Photo-optical Instrumentation Engineers. Signal Processing, Sensor Fusion, And Target Recognition Xiii 12 14 AprilOrlando, Florida, Usa by Ivan Kadar (Contributor) avg rating — 0 ratings — published Proc.
SPIESignal Processing, Sensor/Information Fusion, and Target Recognition XXIV, Z (21 May ) We describe a model-based classifier that uses 3D models to control all stages of processing, including detection and segmentation. Objects. K.J.S. Agate, Utilizing negative information to track ground vehicles through move-stop-move cycles, in Proceedings of Signal Processing, Sensor Fusion, and Target Recognition XIII, SPIE volOrlando, FL, April Google ScholarAuthor: Wolfgang Koch.
Proc. SPIE.Signal Processing, Sensor Fusion, and Target Recognition XIII. Proc. SPIE.Signal Processing, Sensor Fusion, and Target Recognition XI KEYWORDS: Target detection, Signal to noise ratio, Switching, Detection and tracking algorithms, Data modeling, Personal digital assistants, Motion models, Signal detection, Filtering (signal processing), Fuzzy logic.
Preprint from Proc. SPIESignal Processing, Sensor/Information Fusion, and Target Recognition XXIV, (April ) 2. Target/Clutter Classification The standard model of the visual cortex described by Serre, Wolf, and Poggio () is an implementation of a multi-layered theory of object recognition (Serre, et al ).
Here we use. and other government-sponsored programs on advanced signal-processing and sensor fusion research, has resulted in the development and transition of algorithms that are beginning to effect discrimination, and thus positively impact the false alarm rate.
The models developed under the MURIs have supported the signal-processing research. Nicholson D and Leung V. Managing a Distributed Data Fusion Network.
In: Proceedings of the Signal Processing, Sensor Fusion and Target Recognition, XIII Conference, SPIE Defence and Security Symposium, Volume Orlando, FL. Google ScholarCited by: 1. In Proc.\ SPIE Signal Processing, Sensor Fusion, and Target Recognition, volume The International Society for Optical Engineering, April Jason~L.
Williams, John~W. {Fisher III}, and Alan~S. Willsky. Importance sampling actor-critic algorithms. In. Appendix V SIGNAL-PROCESSING AND SENSOR FUSION METHODS (PAPER II) Paul Gader, University of Florida SUMMARY Signal processing is a necessary, fundamental component of all detection systems and can result in orders of magnitude improve-ment in the probability of detection (PD) versus false alarm rate (FAR) of almost any sensor system.
SIGNAL PROCESSING AND PERFORMANCE EVALUATION ISSUES IN MULTI-SENSOR DATA FUSION by Chuanming Wei Presented to the Graduate and Research Committee of Lehigh University in Candidacy for the Degree of Doctor of Philosophy in Electrical Engineering Lehigh University January Sensor informatics and medical technology (Sensori-informatiikka ja lääketieteellinen tekniikka) research group focuses to sensor informatics, adaptive signal processing, and data fusion systems especially for medical applications.
Other applications include smartphone sensor fusion, robotics, positioning systems, target tracking, and many other indirectly measured. SensingBaltimore, MD, 29 April – 3 May10 pages, published in Signal Processing, Sensor Fusion, and Target Recognition XXII, edited by Ivan Kadar, Proc.
of the SPIE, Vol.paper R, pages R-1 to R, 14 June Simultaneous optimization by simulation of iterative deconvolution and noise removal. Abstract In the first part of this paper, a brief tutorial review of sensor fusion for target recognition applications is presented.
In this context, relevant aspects of system architecture, sensor integration, and data fusion are discussed. Several unresolved issues in the practical implementation of sensor fusion are identified; significant.Fast Sensor Placement Algorithms for Fusion-based Target Detection Zhaohui Yuan1,4,RuiTan1, Guoliang Xing2,ChenyangLu3, Yixin Chen3, and Jianping Wang1 1City University of Hong Kong, HKSAR 2Michigan State University,USA 3Washington Universityin St.
Louis, USA 4Wuhan University,P.R. China Abstract Mission-critical target detection imposes stringent per.While the signal processing and the control computation require 30 ms, the tactile images are transferred from the sensor to the computer every ms.
Consequently, the acquisition of tactile.
