Applied Mechanics and Materials Vol. 392

Abstract: A de-noising and simplification approach based on spatial connectivity is proposed which is applied to deal with the boundary points of point cloud. First, grid method is used to represent the spatial topology relationship of the scattered point cloud and calculate the k-nearest neighbors for each data point. Then boundary points are extracted according to uniform distribution of point cloud. And next, an algorithm for boundary points simplification of point cloud is presented to further simplify boundary points. Consequently, not only the details characteristics are reserved well, but also the boundary points are simplified. The experimental result shows that the proposed approach can not only reserve characteristics of both details and boundaries but also realize de-noising and simplification of point cloud.

Abstract: Squaring is an important operation in many algorithms. In this paper, an efficient design approach for the combined squarer, capable of operating on either unsigned or twos complement numbers based on a mode selection signal, is presented. By simulations, it is shown that the proposed combined squarers lead to up to 17% reduction in area, 10% reduction in propagation delay and 9% reduction in power consumption compared with the previous combined squarers.

Abstract: In this paper, we propose a method to solve the problem of parting pages of comics and rear-ranging the consequential partial pictures. It is enable portable devices with small screens to display large page format comics. After being divided into single pictures segmentations, large pages are rearranged in an appropriate way for watching in small screen. The key work in segmentation process is to segment large format comic pages by unicom domain detection. And then the method examined by examples of common comics, proving that the algorithms are effective for comics with integral frames and nearly rectangular partial pictures. We also discuss the picture sequence-restructuring problem with empirical prediction method. Experiments are analyzed by this method and the results of which validates the feasibility of the proposed method.

Abstract: This proposed path planning method combines cellular neural network (CNN) with artificial potential field approach. The fundamental operation based on CNN gray scale image processing and artificial potential is the additional approach for global path-planning. Every point of the environment has a potential value with respect to start and destination position. In the trajectory planning process, a minimum search of potential value of every surrounding neighbor points around a point is done and the neighbor point with the least minimum value is selected as the next location. This procedure is repeated until the goal point is reached. The advantage of using CNN based image processing with artificial potential field function in a vision system is its effectiveness in robot localization while the use of minimum potential value gives a simple yet efficient path planning method. Their feedback criterion is similar to a procedure in filtering the image and it frequently updates the information about obstacles and free path. The parallel processing properties of CNN makes the proposed method robust for real time application.

Abstract: Traditional K-means clustering methods have great attachment to the selection of the initial value and easily get into the local extreme value. This paper proposes a synthetic clustering algorithm of rough set and K-means based on Ant colony algorithm. While the rough set theory presents processing method of uncertain boundary objects, Ant colony algorithm is a bionic optimization algorithm, which has strong robustness, easily with other method unifies, solving efficiency higher characteristic.. Therefore, the K-means algorithm based on Ant colony algorithm in this paper combines rough set theory with simulated annealing algorithm and K-means, in which K means cluster number and initial cluster centers can be obtained dynamically with the principle of maximum minimum, and processing boundary objects with upper and lower approximation of rough set theory. Finally, the UCIs Iris set is used to test the algorithm. The experimental results show that the algorithm has higher accuracy rate, faster execution time and more stable performance.

Abstract: The inhomogeneous equilibrium point of a delayed complex network is investigated. In this letter, a novel local adaptive approach is used to make the delayed network achieve an inhomogeneous equilibrium point, where the whole nodes are divided into several groups; the nodes in the same group achieve one equilibrium point. The coupling strength between nodes varies with the information of the related nodes. By constructing a Lyapunov function, a sufficient condition about the stability of the inhomogeneous equilibrium point is obtained. When the isolated node is chaotic Lorenz system, simulations verify the effectiveness of the strategy.

Abstract: Recently, compressive sampling (CS) based detector schemes for impulse ultra-wideband (I-UWB) systems have been proposed to bypass the difficulty of Nyquist sampling. These schemes suffer from either low energy capture or implementation complexity. In this paper, we propose a new compressive detector for I-UWB, where the measurement matrix is redesigned to boost the energy capture and meanwhile reduce the implementation complexity. Simulation results show that satisfactory performance can be achieved by using the newly proposed scheme.

Abstract: We investigate ultra-wideband (UWB) channel estimation based on compressive sampling (CS), where the orthogonal matching pursuit (OMP) algorithm is employed to recover the channel waveform from noisy measurements. In order to boost the robustness of OMP in the presence of additive Gaussian noise (AWGN), we propose a weighted OMP (WOMP) algorithm. For a given sparse dictionary, weighting factors are assigned to the atoms and a weighted matching process is performed by WOMP. Simulation results show that the proposed WOMP is more robust than the original OMP and can be used to gain better channel estimation precision.

Abstract: This paper studies the problem of QoS graded optimization in the next generation electric power communication network and proposes an energy-efficient QoS graded optimization method based on artificial fish swarm algorithm. Firstly, this paper discusses the networks model of the QoS graded optimization and describes the networks model using delay and packet loss ratio in time-variant network. Secondly, we set up the model of networks throughput and energy consumption using the characteristics of delay and packet loss ratio in time-variant network. Finally, simulation results show that our approach is effective.

Abstract: As one of the core algorithms in most public key cryptography, modular exponentiation has a flaw of its efficiency, which often uses the Montgomerys algorithm to realize the fast operation. But the Montgomerys algorithm has the issue of side channel leakage from the final conditional subtraction. Aiming at this problem, this paper presents an improved fast Montgomery window algorithm. The new algorithm generates the remainder table with odd power to reduce the amount of pre-computation, and combines with the improved Montgomerys algorithm to realize modular exponentiation, which can accelerate the speed and reduce the side channel leakage. The new algorithm cant only thwart side channel attacks, but also improve the efficiency.