Global exponential stabilization of a quadrotor by hybrid control

The main purpose of this paper is to introduce a hybrid controller for global attitude tracking of a quadrotor. This controller globally exponentially stabilizes the desired attitude, a task that is impossible to accomplish with memoryless discontinuous or continuous state feedback owing to topological obstruction. Thereafter, this paper presents a new centrally synergistic potential function to construct hybrid feedback that defeats the topological obstruction. This function induces a gradient vector field to globally asymptotically stabilize the reference attitude and produces the synergy gap to generate a switching control law. The proposed control structure is consisting of two major parts. In the first part, a synergetic controller is designed to cooperate with the hybrid controller, whereas it exponentially stabilizes the origin of the error dynamics. In the second part, a hybrid controller is introduced to globally stabilize the attitude of the quadrotor, where an average dwell constraint is considered with the switching control law to guarantee the exponential stability of the switched system. Finally, the effectiveness and superiority of the proposed control technique are validated by a comparative analysis in simulations.

Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images

This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance.

Edge-based Color Image Segmentation using Particle Motion on Vector Fields Derived from Local Color Distance Images

This paper presents an Edge-based color image segmentation derived from the method of Particle Motion in a Vector Image Fields (PMVIF) that could previously be applied only to monochrome images. Instead of using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields, directly computed from a color image, one parallel and another orthogonal to the edges, were used in the model to force a particle to move along the object edges. The normal compressive vector field is derived from the center-to-centroid vectors of local color distance images. Next, the edge vector field is derived by taking the normal compressive vector field, multiplied by differences of auxiliary image pixels to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012) and the Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison with those of the traditional PMVIF, Watershed, SLIC, K-means, Mean shift, and JSEG. The proposed method yields better RI, GCE, NVI, BDE, Dice coefficients, faster computation time, and noise resistance.

A novel sketch-based 3D model retrieval approach based on skeleton

<span>Since the skeleton represents the topology structure of the query sketch and 2D views of 3D model, this paper proposes a novel sketch-based 3D model retrieval algorithm which utilizes skeleton characteristics as the features to describe the object shape. Firstly, we propose advanced skeleton strength map (ASSM) algorithm to create the skeleton which computes the skeleton strength map by isotropic diffusion on the gradient vector field, selects critical points from the skeleton strength map and connects them by Kruskal's algorithm. Then, we propose histogram feature comparison algorithm which adopts the radii of the disks at skeleton points and the lengths of skeleton branches to extract the histogram feature, and compare the similarity between two skeletons using the histogram feature matrix of skeleton endpoints. Experiment results demonstrate that our approach which combines these two algorithms significantly outperforms several leading sketch-based retrieval approaches.</span>

Discrete Morse Theory Based Dynamic P Systems

This paper proposes a class of dynamic P systems with constraint of discrete Morse function (DMDP systems). Membrane structure is extended on complex. Rules control activities of membranes. New classes of rules and mechanism to change types of rules by discrete gradient vector field are provided as well.DMDP system extends P systems both in structures and rules. Solving air quality evaluation problem in linear time verifies the effectiveness ofDMDP systems. Since air quality evaluation problem has significance in many areas. The new P systems provide an alternative for traditional membrane computing.