ABSTRACT
In this paper, a novel chaos-based cryptosystem is proposed to ensure the communication security of video/audio streaming in the network environment. Firstly, by the proposed synchronization controller for the master and slave chaotic systems, respectively, embedded in the transmitter and receiver, the cryptosystem can generate the synchronized and dynamic chaotic random numbers at the transmitter and receiver simultaneously. Then integrating the chaotic random numbers with SHA3-256 (Secure hash algorithm 3), the design of synchronized dynamic key generators (SDKGs) is completed. Continuously, we can apply the SDKGs to encrypt/decrypt streaming audio/video data. In our design, we introduce the AES CFB (Advanced encryption standard cipher feedback) encryption algorithm with SDKGs to encrypt the video/audio streaming. Then the cipher-text is transmitted to the receiver via the network public channel and it can be fully decrypted with the dynamic random keys synchronously generated at the receiver. A duplex audio/video cryptosystem is realized to illustrate the performance and feasibility of this proposed research. Finally, many tests and comparisons are performed to stress the quality of random sequences generated by proposed SDKGs.
ABSTRACT
The global exponential stability is investigated for neural networks with interval time-varying delays. Based on the Leibniz-Newton formula and linear matrix inequality technique, delay-dependent stability criteria are proposed to guarantee the exponential stability of neural networks with interval time-varying delays. Some numerical examples and comparisons are provided to show that the proposed results significantly improve the allowable upper and lower bounds of delays over some existing ones in the literature.
Subject(s)
Biological Clocks/physiology , Models, Neurological , Nerve Net/physiology , Neural Networks, Computer , Synaptic Transmission/physiology , Computer Simulation , Feedback/physiology , Nonlinear Dynamics , Time FactorsABSTRACT
This correspondence investigates the global exponential stability problem of Takagi-Sugeno fuzzy cellular neural networks with time-varying delays (TSFDCNNs). Based on the Lyapunov-Krasovskii functional theory and linear matrix inequality technique, a less conservative delay-dependent stability criterion is derived to guarantee the exponential stability of TSFDCNNs. By constructing a Lyapunov-Krasovskii functional, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is released in the proposed delay-dependent stability criterion. Two illustrative examples are provided to verify the effectiveness of the proposed results.
Subject(s)
Algorithms , Models, Statistical , Neural Networks, Computer , Nonlinear Dynamics , Pattern Recognition, Automated/methods , Computer Simulation , Markov Chains , Stochastic ProcessesABSTRACT
In this paper we investigate the synchronization problem of drive-response chaotic systems with a scalar coupling signal. By using the scalar transmitted signal from the drive chaotic system, an observer-based response chaotic system with dead-zone nonlinear input is designed. An output feedback control technique is derived to achieve generalized projective synchronization between the drive system and the response system. Furthermore, an adaptive control law is established that guarantees generalized projective synchronization without the knowledge of system nonlinearity, and/or system parameters as well as that of parameters in dead-zone input nonlinearity. Two illustrative examples are given to demonstrate the effectiveness of the proposed synchronization scheme.
Subject(s)
Nonlinear Dynamics , Physics/methods , Algorithms , Models, Statistical , Models, Theoretical , Oscillometry , Systems Theory , Time FactorsABSTRACT
Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria.
Subject(s)
Algorithms , Models, Theoretical , Neural Networks, Computer , Computer Simulation , Time FactorsABSTRACT
This paper aims to present a synchronization scheme for a class of delayed neural networks, which covers the Hopfield neural networks and cellular neural networks with time-varying delays. A feedback control gain matrix is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory, and its exponential synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalues on the imaginary axis. This condition can avoid solving an algebraic Riccati equation. Both the cellular neural networks and Hopfield neural networks with time-varying delays are given as examples for illustration.
