Stability analysis of various neural networks have been successfully applied in many fields such as parallel computing and pattern recognition. This paper is concerned with a class of stochastic Markovian jump neural networks. The general mean-square stability of Backward Euler-Maruyama method for stochastic Markovian jump neural networks is discussed. The sufficient conditions to guarantee the general mean-square stability of Backward Euler-Maruyama method are given.
Navigation without GPS and other knowledge of environment have been studied for many decades. Advance technology have made sensors more compact and subtle that can be easily integrated into micro and hand-hold device. Recently researchers found that bee and fruit fly have an effectively and efficiently navigation mechanism through optical flow information and process only with their miniature brain. We present a navigation system inspired by the study of insects through a calibrated camera and other inertial sensors. The system utilizes SLAM theory and can be worked in many GPS denied environment. Simulation and experimental results are presented for validation and quantification.
Through some research on visual navigation mechanisms of flying insects especially honeybees, a novel navigation algorithm integrating entropy flow with Kalman filter has been introduced in this paper. Concepts of entropy image and entropy flow are also introduced, which can characterize topographic features and measure changes of the image respectively. To characterize texture feature and spatial distribution of an image, a new concept of contrast entropy image has been presented in this paper. Applying the contrast entropy image to the navigation algorithm to test its’ performance of navigation and comparing with simulation results of intensity entropy image, a conclusion that contrast entropy image performs better and more robust in navigation has been made.
Optical flow is a critical component of video processing applications, e.g. for tasks such as object tracking, segmentation, and navigation. In this paper, we present a novel concept entropy flow based on the visual mechanism of bee. It not only generates motion fields form real sequences but also speed up the calculation of optical flow. The introduction of entropy image has shown the information distribution for the given image, and the optical flow algorithm is utilized to get the entropy flow. Global motion estimation and an auto-selecting algorithm of assessment threshold are employed to give a motion image and then the accuracy is improved.