Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerpr...Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerprint features match his stored template. To resist being tampered on public channel, the user＇s message and the signed message are encrypted by the signer＇s public key and the user＇s public key, respectively. In the other signature system, the keys are generated by combining the signer＇s fingerprint features, check bits, and a rememberable key, and there are no matching process and keys stored on the smart card. Additionally, there is generally more than one public key in this system, that is, there exist some pseudo public keys except a real one.展开更多
A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spe...A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparamet- ric kernel density estimator is built using the probability his- togram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is ac- curate, fast, and robust. It meets the needs of dim-small target tracking adequately.展开更多
Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,...Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,and strong antiinterference.However,non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition,since it not only is sensitive to noise but also has non-linear,non-Gaussian,and non-stability characteristics,which make it difficult to guarantee the classification in the original signal space.Some existing classifiers,such as the sparse representation classifier(SRC),generally use an individual representation rather than all the samples to classify the test data,which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples.To address these problems,we propose a novel classifier,called the kernel joint representation classifier(KJRC),for FH transmitter fingerprint feature recognition,by integrating kernel projection,collaborative feature representation,and classifier learning into a joint framework.Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.展开更多
基金This project was supported by the National Science Foundation of China (60763009)China Postdoctoral Science Foundation (2005038041)Hainan Natural Science Foundation (80528).
文摘Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerprint features match his stored template. To resist being tampered on public channel, the user＇s message and the signed message are encrypted by the signer＇s public key and the user＇s public key, respectively. In the other signature system, the keys are generated by combining the signer＇s fingerprint features, check bits, and a rememberable key, and there are no matching process and keys stored on the smart card. Additionally, there is generally more than one public key in this system, that is, there exist some pseudo public keys except a real one.
文摘A precise method for accurately tracking dim- small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparamet- ric kernel density estimator is built using the probability his- togram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is ac- curate, fast, and robust. It meets the needs of dim-small target tracking adequately.
基金Project supported by the National Natural Science Foundation of China(No.61601500)
文摘Frequency-hopping(FH)is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception,good confidentiality,and strong antiinterference.However,non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition,since it not only is sensitive to noise but also has non-linear,non-Gaussian,and non-stability characteristics,which make it difficult to guarantee the classification in the original signal space.Some existing classifiers,such as the sparse representation classifier(SRC),generally use an individual representation rather than all the samples to classify the test data,which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples.To address these problems,we propose a novel classifier,called the kernel joint representation classifier(KJRC),for FH transmitter fingerprint feature recognition,by integrating kernel projection,collaborative feature representation,and classifier learning into a joint framework.Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.
