This article focuses on designing a novel stochastic data-based denial-of-service (DoS) attack strategy that can intercept measurements and reduce the quality of remote state estimation in interval type-2 (IT2) T–S fuzzy systems. The new proposed DoS attack strategy has two main characteristics. First different from most of the existing DoS models, it is data-based, which mainly attacks the packets playing important roles in the system performance. Therefore, compared with the existing indiscriminate DoS attack approaches, the data-based DoS attack is more intelligent and can cause larger disruptions. Second, the proposed attack strategy is random, which has better concealment than the periodic or consecutive DoS attack models. Then, by using a stochastic method, the relationship among attack effect, attack probability, and attack parameter are analyzed and shown in Theorem1. Furthermore, if the attacks are energy-constrained, the relation between the attack rate threshold and the upper bound of the attack parameter is expressed analytically, which is shown in Theorem2. Simulation results demonstrate that, compared with some existing DoS models, the proposed stochastic data-based DoS attack strategy brings about greater destructiveness to the estimation quality of the systems, which demonstrates the effectiveness of the proposed attack strategy from the attacker’s perspective.

Compact, worn computers with projected, on-skin touch interfaces have been a long-standing yet elusive goal, largely written off as science fiction. Such devices offer the potential to mitigate the significant human input/output bottleneck inherent in worn devices with small screens. In this work, we present the first fully functional and self-contained projection smartwatch implementation, containing the requisite compute, power, projection and touch-sensing capabilities. Our watch offers roughly 40 sq. cm of interactive surface area -- more than five times that of a typical smartwatch display. We demonstrate continuous 2D finger tracking with interactive, rectified graphics, transforming the arm into a touchscreen. We discuss our hardware and software implementation, as well as evaluation results regarding touch accuracy and projection visibility.

Stereo cameras are widely used in autonomous vehicles as environmental perception sensors because of their availability and low cost. However, efficiently utilizing the obtained disparity images to generate a desirable local path for the vehicle still remains a challenging problem. In this paper, we present a novel navigation framework for autonomous vehicles equipped with stereo cameras, featuring finishing all of the perception and path planning tasks directly within the disparity space. Comparing with the popular 3-D counterpart, disparity is a projected geometric space that contains more primitive information directly computed from the stereo images. Furthermore, disparity image is a more compact representation for large field of 3-D Cartesian space, which makes perception and path finding in longer distance possible. The proposed framework is composed of three modules, namely, local disparity map building, slope analysis and obstacle detection, and path planning. Two important properties concerning the motion and slope in disparity space are presented for the first time, i.e., the motion model and the slope model in disparity space. With the motion model, the framework first fuses consecutive disparity maps to construct a more reliable and complete local map. Then, a novel slope analysis method called V-Intercept is developed based on the slope model. It can efficiently analyze slopes and obstacles, generating a reasonable cost map directly from the disparity image. Finally, the obstacles in the cost map are expanded properly, and a customized A* search algorithm is performed to find a reasonable path in disparity space. The experimental results show that our framework works well under various kinds of environments. The resulting system can efficiently perceive and plan on a much larger range and react to obstacles further beyond the traditional Cartesian-based method.

Robust vehicle detection is one of the key task for autonomous vehicle under the complex urban environment. Using 3D Lidar, the difficulty of the task lies in that the appearance of a vehicle in the sparse range data changes greatly with the distance, the angle of view, as well as occlusions. In this paper we present a new algorithm to detect vehicles using the finely segmented 3D object points. In segmentation, RGLOS (Ring Gradient based Local Optimal Segmentation) algorithm is proposed. Instead of processing in the grid map, the point-wise segmentation method keeps the connection between points and is able to extract object points in far distance. Using the local optimal ground height, it produces much more correct object points with less wrong ground points. In feature extraction stage, three novel features: position-related shape, object height along length and reflective intensity histogram are proposed. Finally the kernel based SVM is used to finish the classification task. Experiments are carried out using the real data acquired from urban environment. The results demonstrate the superior performance comparing with previous methods, thanks to the improved segmentation and new features.