Media platforms and devices that allow an input from a user's finger/hand touch are becoming more ubiquitous, such as Microsoft Surface and DiamondTouch, as well as numerous experimental systems in research labs. Currently the definition of touch styles is application-specific and each device/application has its own set of available touch types to be recognized as input. In this paper we attempt a comprehensive understanding of all possible touch types for touch-sensitive devices by constructing a design model for touch interaction and clarifying their characteristics. The model is composed of three structural levels (action level, motivation level and computing level) and the relationships between them (mapping). In action level, we construct a unified definition and description of all possible touch gestures, first by analyzing how a finger/hand touch on a surface can cause a particular event that can be recognized as a legitimate action, and then using this analysis we define all possible touch gestures, resulting in touch gesture taxonomy. In motivation level, we analyze and describe all the direct interactive motivation according to applications. Then we define the general principles for mapping between the action and motivation levels. In computing level, we realize the motivation and response to gestural inputs using computer languages. The model is then used to illustrate how it can be interpreted in the context of a photo management application based on DiamondTouch and iPod Touch. It allows to reuse touch types in different platforms and applications in a more systematic and generic manner than how touch has been designed so far.

Near-midair collisions (NMACs) between aircraft have long been a primary safety concern and have incessantly motivated the development of ingenious onboard collision avoidance (CA) systems to reduce collision risk. The Traffic Alert and Collision Avoidance System (TCAS) acts as a proverbially accepted last-resort means to resolve encounters, while it also has been proved to potentially induce a collision in the hectic and congested traffic. This paper aims to improve the TCAS collision avoidance performance by enriching traffic alert information, which strictly fits with present TCAS technological requirements and extends the threat detection considering induced collisions and probabilistic pilot response. The proposed model is specified in coloured Petri net (CPN) formalism, to generate by simulation all the future possible downstream reachable states to enhance the follow-up decision making of pilots via synthesising relevant information related to collision states. With the complete state space, the potential collision scenarios can be identified together with those manoeuvres that may transform a conflict into a collision. The causal TCAS model is demonstrated to work effectively for complex multiaircraft scenarios and to identify the feasible manoeuvres that contribute to reduce the nonzero TCAS-induced collision risk.

Flocking model has been widely used in robotic swarm control. However, the traditional model still has some problems such as manually adjusted parameters, poor stability and low adaptability when dealing with autonomous navigation tasks in large-scale groups and complex environments. Therefore, it is an important and meaningful research problem to automatically generate Optimized Flocking model (O-flocking) with better performance and portability. To solve this problem, we design Comprehensive Flocking (C-flocking) model which can meet the requirements of formation keeping, collision avoidance of convex and non-convex obstacles and directional movement. At the same time, Genetic Optimization Framework for Flocking Model (GF) is proposed. The important parameters of C-flocking model are extracted as seeds to initialize the population, and the offspring are generated through operations such as crossover and mutation. The offspring model is input into the experimental scene of autonomous navigation for robotic swarms, and the comprehensive fitness function value is obtained. The model with smallest value is selected as the new seed to continue evolution repeatedly, which finally generates the O-flocking model. The extended simulation experiments are carried out in more complex scenes, and the O-flocking and C-flocking are compared. Simulation results show that the O-flocking model can be migrated and applied to large-scale and complex scenes, and its performance is better than that of C-flocking model in most aspects.

This paper presents a conflict resolution (CR) model in colored Petri net (CPN) formalism for 4D (three-dimensional + time) planned trajectories of cooperating unmanned aerial vehicles (UAVs). The CR model is integrated with the conflict detection algorithm using spatial data structure to detect time-based separation infringements among UAVs and to generate an intuitionistic representation of 4D conflict information. The main contribution of this paper is the proposed CR model, which is based on the logic of cause-and-effect analysis. This model not only chooses a preferred trajectory according to the priority for solving the current conflict but also considers the follow-up influence (domino effect) to update segments and conflicts. The novel causal model exploits the state space to achieve the solution using CPN. The model is validated with the experimental results of a scenario involving multiple UAVs (composed of clusters) cruising in a bounded region and exhibits the main advantages of scalability, efficiency, and short execution time.