Threshold blind signature is playing an important role in cryptography as well as in practical applications such as e-cash and e-voting systems, etc. In this paper, we present an efficient and practical threshold bind...Threshold blind signature is playing an important role in cryptography as well as in practical applications such as e-cash and e-voting systems, etc. In this paper, we present an efficient and practical threshold bind signature from Weil pairing on super-singular elliptic curves or hyper-elliptic curves over finite field and prove that our scheme is provably secure in the random oracle model.展开更多
In this paper,a new likelihood-based method for classifying phase-amplitude-modulated signals in Additive White Gaussian Noise (AWGN) is proposed.The method introduces a new Markov Chain Monte Carlo (MCMC) algorithm,c...In this paper,a new likelihood-based method for classifying phase-amplitude-modulated signals in Additive White Gaussian Noise (AWGN) is proposed.The method introduces a new Markov Chain Monte Carlo (MCMC) algorithm,called the Adaptive Metropolis (AM) algorithm,to directly generate the samples of the target posterior distribution and implement the multidimensional integrals of likelihood function.Modulation classification is achieved along with joint estimation of unknown parameters by running an ergodic Markov Chain.Simulation results show that the proposed method has the advantages of high accuracy and robustness to phase and frequency offset.展开更多
文摘Threshold blind signature is playing an important role in cryptography as well as in practical applications such as e-cash and e-voting systems, etc. In this paper, we present an efficient and practical threshold bind signature from Weil pairing on super-singular elliptic curves or hyper-elliptic curves over finite field and prove that our scheme is provably secure in the random oracle model.
文摘In this paper,a new likelihood-based method for classifying phase-amplitude-modulated signals in Additive White Gaussian Noise (AWGN) is proposed.The method introduces a new Markov Chain Monte Carlo (MCMC) algorithm,called the Adaptive Metropolis (AM) algorithm,to directly generate the samples of the target posterior distribution and implement the multidimensional integrals of likelihood function.Modulation classification is achieved along with joint estimation of unknown parameters by running an ergodic Markov Chain.Simulation results show that the proposed method has the advantages of high accuracy and robustness to phase and frequency offset.
