To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >

Frontend feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational costs and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tom... More >

Presents information on the new ICCK Transactions on Intelligent Systematics. More >