With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems without overlapping parts is inaccurate. Furthermore, the potential of feature matching points and their spatial extent in calculating the extrinsic parameters of multi-camera systems has not yet been fully realized. To this end, we propose a multi-camera calibration algorithm to solve the problem of the high-precision calibration of multi-camera systems without overlapping parts. The calibration of multi-camera systems is simplified to the problem of solving the transformation relationship of extrinsic parameters using a map constructed by multiple cameras. Firstly, the calibration environment map is constructed by running the SLAM algorithm separately for each camera in the multi-camera system in closed-loop motion. Secondly, uniformly distributed matching points are selected among the similar feature points between the maps. Then, these matching points are used to solve the transformation relationship between the multi-camera external parameters. Finally, the reprojection error is minimized to optimize the extrinsic parameter transformation relationship. We conduct comprehensive experiments in multiple scenarios and provide results of the extrinsic parameters for multiple cameras. The results demonstrate that the proposed method accurately calibrates the extrinsic parameters for multiple cameras, even under conditions where the main camera and auxiliary cameras rotate 180°.

A Diffie-Hellman public-key cryptography based on chaotic attractors of neural networks is described in the paper. There is a one-way function between chaotic attractors and initial states in an overstoraged Hopfield neural network (OHNN). If the synaptic matrix of OHNN is changed, each attractor and its corresponding domain of initial state attraction is changed. Then, we regard the neural synaptic matrix as a trap door, and change it with commutative random permutation matrix. A new Diffie-Hellman public-key cryptosystem can be implemented, namely keeping the random permutation operation of the neural synaptic matrix as the secret key, and the neural synaptic matrix after permutation as public-key. In order to explain the practicability of the encryption scheme, security and encryption efficient of the scheme are discussed. The scheme of application for Internet secure communications is implemented by using Java program. The experimental results show that the proposed cryptography is feasible, and has a good performance of encryption and decryption speed to ensure the real time of IPng secure communications

Since Rijndael algorithm was selected as the AES (advanced encryption system) to substitute the DES (data encryption standard), the foremost concern for people has been its security. The result of new attack methods shows that there may be some lacuna in the design of S-box and key schedule with AES algorithm. The problem is the weakness of linearity existing in the S-box and key schedule. In order to keep from the new attacks and implement the AES for PDA secure communication in Java, we analyze in detail on the AES algorithm and give out a new implementation scheme for increasing complexity of nonlinear transformation in design of S-box. The experimental results show our implementation scheme with Java is feasible for the PDA application and is of an acceptable speed for data encryption and decryption.