In this paper, we propose a dynamic channel assignment method that utilizes location information of mobile terminals to calculate the optimal channel scheme in indoor WLAN positioning systems. Our method could achieve two goals: (a) the optimal channel scheme can guarantee a maximum throughput of overall wireless network. (b) terminals can communicate and be located simultaneously in our system. By taking advantage of positioning system, we can know the location of terminals, and such location information can be used to optimize network capacity through assigning appropriate channels. Assigning different channel to neighbouring APs is not only for optimizing network capacity, but also for improving the positioning accuracy due to that it can immigrate the interference among APs and receive accurate signal strength. To confirm its effectiveness, we evaluate our approach by simulation. We compare our method with the single, random, and static methods and the LCCS method. The results illustrate that the throughput of our channel assignment method is higher than other methods.

Maneuvering autonomous vehicles in constrained environments, such as autonomous vehicle parking, is not a trivial task and has received increasing attention from both the academy and industry. However, the traditional methods divide the problem into parallel parking, perpendicular parking, and echelon parking, then different methods are applied for the parking motion planning. In this paper a Rapidly-exploring Random Tree (RRT) based path planner is implemented for autonomous vehicle parking problem, which treats all the situations in a unified manner. As the RRT method sometimes generates some complicated paths, a smoother is also implemented for smoothing generated paths. The proposed algorithm is verified in simulation and generates applicable solutions for the proposed application scenarios.

This paper proposed a practical path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm is based on particle filter, Bézier curves and support vector machine to provide a safe path among various static and moving obstacles and to satisfy the vehicle's curvature constraints. The algorithm has been implemented and verified on the simulation software. Experimental results demonstrate the effectiveness of the proposed method in complicated conditions with existing of multi objects.

Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of intelligent vehicles. In this paper, we address a graph-based approach for 2D road representation of 3D point clouds with respect to the road topography. We employ the gradient cues of the road geometry to construct a Markov Random Filed (MRF) and implement an efficient belief propagation (BP) algorithm to classify the road environment into four categories, i.e. the reachable region, the drivable region, the obstacle region and the unknown region. The proposed approach can overcome a wide variety of practical challenges, such as sloped terrains, rough road surfaces, rolling/pitching of the host vehicle, etc., and represent the road environment accurately as well as robustly. Experimental results in typical but challenging environments have substantiated that the proposed approach is more sensitive and reliable than the conventional vertical displacements analysis and show superior performance against other local classifiers.

This paper presents a Bézier curve based path planner which enables the anti-collision behavior of an electronic car. The anti-collision system is a fundamental module in the architecture. The path tracking implementation uses pure pursuit algorithm. The anti-collision system based on laser scanner data consists of estimating the trajectories and behavior of surrounding objects, and a Bézier curve based path planner. Experimental results are presented showing the effectiveness of the overall navigation control system.

This paper introduces kernel partial least squares (K-PLS) for the identification of mixture content from terahertz spectra. Kernel partial least squares is a nonlinear extension of the partial least squares (PLS) method, commonly used in chemometrics. K-PLS and PLS are considered superior to peak matching methods for mixture spectra of multiple compounds because it avoids having to address the problem of overlapping peaks explicitly. Terahertz (THz) radiation is capable of transmitting easily through most dielectric materials and is used as a new tool to collect the original spectral readings from transmission, diffusion and reflection. A multi-output kernel partial least squares method is presented to model mixture composition based on pure substance training patterns, under the assumption of linear spectral mixture behavior. Preprocessing consists of a wavelet transform of the THz spectra and an independent component analysis (ICA) transform. Preliminary results show that the ICA+K-PLS approach is able to classify pure spectra accurately and allows for an accurate estimate of the composition from THz mixed spectra even where there are severe overlapped peaks in these spectra.