A Gabor-Atom Network(GAN) approach for application in radar target recognition is proposed. The Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of ...
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A Gabor-Atom Network(GAN) approach for application in radar target recognition is proposed. The Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of radar target *** self-learning mechanism is used not only for the network but for the feature *** on the classification of microwave anechoic chamber data of three different scaled airplane models are presented.
In this paper,we investigate the application of Support Vector Machines(SVMs) in Pattern *** is a learning technique developed by *** and his team(AT&T Bell Labs.) that can be seen as a new method for training pol...
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In this paper,we investigate the application of Support Vector Machines(SVMs) in Pattern *** is a learning technique developed by *** and his team(AT&T Bell Labs.) that can be seen as a new method for training polynomial,neural network,or Radial Basis Functions *** decision surfaces are found by solving a linearly constrained quadratic programming *** present experimental results of our implementation of SVM,and demonstrate its advantage on welllog data classification problem.
Shift invariant features—local bispectra of range profiles,are proposed for application of highresolution radar target recognition in this *** local bispectra are selected through the Fisher's class separability ...
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Shift invariant features—local bispectra of range profiles,are proposed for application of highresolution radar target recognition in this *** local bispectra are selected through the Fisher's class separability discriminant measure on the bifrequency *** selected features are evaluated using range profiles of three scaled aircraft *** is shown that they are highly discriminative.
Shift invariant features-local bispectra of range profiles-are proposed for application to high-resolution radar target recognition. The local bispectra are selected through the Fisher's class separability discrim...
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Shift invariant features-local bispectra of range profiles-are proposed for application to high-resolution radar target recognition. The local bispectra are selected through the Fisher's class separability discriminant measure on the bi-frequency plane. The selected features are evaluated using range profiles of three scaled aircraft models. It is shown that they are highly discriminative.
A novel embedded image coding method denoted as cluster growing embedding (CGE) is proposed in this paper. Similar to some of state-of-the-art wavelet image coding algorithms, CGE can encode images efficiently by expl...
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ISBN:
(纸本)0769507506
A novel embedded image coding method denoted as cluster growing embedding (CGE) is proposed in this paper. Similar to some of state-of-the-art wavelet image coding algorithms, CGE can encode images efficiently by exploiting the self-similarity across subbands in the wavelet domain. By utilizing morphological dilation, CGE forms significance clusters in each wavelet subband, and then exploits the parent-child relationship between subbands to encode the significance map information of one image. A salient new feature of CGE is that it tries to first encode the significance bits in the neighborhood of those already formed clusters. Therefore, CGE is more compatible to the functionality of embedded coding. Meanwhile, CGE is shown to be very suitable to the object-oriented coding framework along with the shape adaptive discrete wavelet transform (SA-DWT).
Structured rank-deficient matrices arise in many applications in signal processing. The inverse iteration algorithm was proposed to solve the so-called structured total least squares (STLS) problems. This algorithm, h...
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Structured rank-deficient matrices arise in many applications in signal processing. The inverse iteration algorithm was proposed to solve the so-called structured total least squares (STLS) problems. This algorithm, however, converges to local-minimum under certain conditions. It is well known that genetic algorithms are stochastic optimization techniques that can often outperform classical methods of optimization. Genetic algorithms was utilized here to get the better solution of the STLS problems. Computer simulations show that our method ensures convergence to global minimum.
A Gabor-Atom network (GAN) approach for application in radar target recognition is proposed. The Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of...
详细信息
A Gabor-Atom network (GAN) approach for application in radar target recognition is proposed. The Gabor atoms selected by a multilayer feedforward neural network extract discriminant features among different classes of radar target returns. The self-learning mechanism is used not only for the network but for the feature parameters. Results on the classification of microwave anechoic chamber data of three different scaled airplane models are presented.
The paper presents the impact caused by channel estimation error on the multiuser detection scheme employing adaptive parallel interference cancellation (APIC) in a Rayleigh fading time-multiplexed pilot channel. The ...
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The paper presents the impact caused by channel estimation error on the multiuser detection scheme employing adaptive parallel interference cancellation (APIC) in a Rayleigh fading time-multiplexed pilot channel. The simulation results confirm that a certain level of channel estimation accuracy is required to ensure the performance improvement of the APIC algorithm. Based on the WMSA channel estimation method and the former adaptive PIC structure, an improved algorithm (APIC II) is proposed, in which a better estimate of channel parameters is obtained and more precise data detection is carried out. Its effectiveness is verified by the simulation result.
keyword spotting is one of important topics in speech recognition. The paper presents a model of keyword spotting and the training method of relevant keyword models and anti-words models, then we give a strategy of ke...
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keyword spotting is one of important topics in speech recognition. The paper presents a model of keyword spotting and the training method of relevant keyword models and anti-words models, then we give a strategy of keyword spotting. Experiment indicates the strategy is effective.
Based on the analyses of the limitation of a common genetic algorithm and the model of the structure and development of human society, the paper discusses a modified genetic algorithm. The algorithm applies unidirecti...
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Based on the analyses of the limitation of a common genetic algorithm and the model of the structure and development of human society, the paper discusses a modified genetic algorithm. The algorithm applies unidirectional inheritance and hierarchical structure to solve the optimizing problems. This method can not only reserve the useful genetic information, but also make the succeeding inheritance more goal-oriented. It utilizes the intrinsic genetic knowledge to calculate.
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