The Experts below are selected from a list of 250815 Experts worldwide ranked by ideXlab platform
W U Yongqiang - One of the best experts on this subject based on the ideXlab platform.
-
Fast Algorithm for data association in dense clutter
Computer Simulation, 2006Co-Authors: W U YongqiangAbstract:Joint probabilistic data association is one of the most effective Algorithms of multiple target tracking in dense clutter.However,with the increment of target tracked and the number of validation measurement,the computational cost of association probability is the choke point in engineering application.A Fast Algorithm was developed for data association based on the principle of joint probabilistic data association.In this new method,surveillance area was divided into independent area according to the intersection area of association gate of targets tracked,after the PDF value of the common measurements of targets and measurements lying in a target association gate were weighted,and the association probability of targets were computed.Without generating all the association events,this Algorithm is more efficient in engineering application.Simulation results demonstrate the tracking performance of this Algorithm in different conditions.
A.n. Willson - One of the best experts on this subject based on the ideXlab platform.
-
ICASSP - A flexible hardware-oriented Fast Algorithm for motion estimation
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: Fengqi Yu, A.n. WillsonAbstract:This paper discusses the design of a Fast Algorithm for motion estimation with emphasis on hardware cost considerations, real-time application, network adaptation, and flexibility. To achieve the best trade-off among hardware cost, computational complexity, and distortion performance, we propose a multi-stage pixel-subsampling motion estimation Algorithm. The Algorithm has a lower hardware cost than Liu's subsampling Algorithm and the three-step hierarchical search Algorithm (3SHS) in terms of data flow control, I/O bandwidth, and regularity. Its computational complexity is close to that of 3SHS and its distortion performance, which is better than that of Liu's Algorithm and 3SHS, is close to that of full search.
Yu Zhang - One of the best experts on this subject based on the ideXlab platform.
-
A Fast Algorithm for reduced-order modeling
ISA Transactions, 1999Co-Authors: Qing-guo Wang, Yu ZhangAbstract:Abstract A Fast Algorithm for second-order plus delay modeling is presented and shown to be effective, efficient, and applicable to a wide range of systems
Xiaojun Wu - One of the best experts on this subject based on the ideXlab platform.
-
ISCAS (2) - Fast Algorithm of adaptive chirplet-based real signal decomposition
ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001Co-Authors: Aigang Feng, Xiaojun WuAbstract:Great attention has been focused on the chirplet-based signal decomposition in the area of signal processing, and several approaches were recently developed for complex analytic signals. In order to apply these approaches to real signals, analytic processing needs to be performed. The conventional analytic methods include the Hilbert transform in time-domain and the window function method in frequency-domain. They both have certain drawbacks. After reviewing two typical analytical methods, a novel Fast Algorithm of adaptive chirplet-based real signal decomposition without analytic processing is presented in this paper. Numerical simulations illustrate the effectiveness of such a novel Fast Algorithm.
Wai-kuen Cham - One of the best experts on this subject based on the ideXlab platform.
-
Fast Algorithm for Walsh Hadamard Transform on Sliding Windows
IEEE transactions on pattern analysis and machine intelligence, 2010Co-Authors: Wanli Ouyang, Wai-kuen ChamAbstract:This paper proposes a Fast Algorithm for Walsh Hadamard Transform on sliding windows which can be used to implement pattern matching most efficiently. The computational requirement of the proposed Algorithm is about 1.5 additions per projection vector per sample, which is the lowest among existing Fast Algorithms for Walsh Hadamard Transform on sliding windows.
