Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

An online hidden feature extraction algorithm is proposed for unknown and unstructured agricultural environments based on a supervised kernel locally linear embedding (SKLLE) algorithm. Firstly, an online obtaining method for scene training samples is given to obtain original feature data. Secondly, Bayesian estimation of the a posteriori probability of a cluster center is performed. Thirdly, nonlinear kernel mapping function construction is employed to map the original feature data to hyper-high dimensional kernel space. Fourthly, the automatic deter mination of hidden feature dimensions is performed using a local manifold learning algorithm. Then, a low-level manifold computation in hidden space is completed. Finally, long-range scene perception is realized using a 1-NN classifier. Experiments are conducted to show the effectiveness and the influence of parameter selection for the proposed algorithm. The kernel principal component analysis (KPCA), locally linear embedding (LLE), and supervised locally linear embedding (SLLE) methods are compared under the same experimental unstructured agricultural environment scene. Test results show that the proposed algorithm is more suitable for unstructured agricultural environments than other existing methods, and that the computational load is significantly reduced.

A new color image encryption scheme based on chaos synchronization of time-delay Lorenz system

In this paper, a new image encryption scheme is presented based on time-delay chaos synchronization. Compared with existing methods, a new method is pro- posed and a lot of coupled items can be taken as zero items to simplify the whole system. A simple linear controller is introduced to realize time-delay chaos synchronization and image encryption. The positions of the image pixels are firstly shuffled and then be hidden in the cartier image. The address codes of the chaotic sequences are adopted to avoid the disturbances induced by the initial value and computer accuracy error. Simulation results for color image are provided to illustrate the effectiveness of the proposed method. It can be seen clearly that the system can converge quickly and the image can be encrypted rapidly.

A medical image encryption algorithm based on synchronization of time-delay chaotic system

This paper presents a new synchronization method of the time-delay chaotic system and its application in medical image encryption. Compared with the existing techniques, the error system is greatly simplified because many coupled items can be considered zero items. An improved image encryption scheme based on a dynamic block is proposed. This scheme divides the image into dynamic blocks, and the number of blocks is determined by a previous block cipher. Numerical simulations are provided to illustrate the effectiveness of the proposed method.

Adaptive dynamic programming for linear impulse systems

We investigate the optimization of linear impulse systems with the reinforcement learning based adaptive dynamic programming(ADP)method.For linear impulse systems,the optimal objective function is shown to be a quadric form of the pre-impulse states.The ADP method provides solutions that iteratively converge to the optimal objective function.If an initial guess of the pre-impulse objective function is selected as a quadratic form of the pre-impulse states,the objective function iteratively converges to the optimal one through ADP.Though direct use of the quadratic objective function of the states within the ADP method is theoretically possible,the numerical singularity problem may occur due to the matrix inversion therein when the system dimensionality increases.A neural network based ADP method can circumvent this problem.A neural network with polynomial activation functions is selected to approximate the pre-impulse objective function and trained iteratively using the ADP method to achieve optimal control.After a successful training,optimal impulse control can be derived.Simulations are presented for illustrative purposes.