Applied Mechanics and Materials Vols. 479-480

Abstract: This paper takes the mathematical software Maple as the auxiliary tool to study four types of integral problems related to hyperbolic functions. We can obtain the infinite series forms of these four types of integrals by using geometric series and integration term by term theorem. The research methods adopted in this study involved finding solutions through manual calculations and verifying these solutions by using Maple. This type of research method not only allows the discovery of calculation errors, but also helps modify the original directions of thinking from manual and Maple calculations. For this reason, Maple provides insights and guidance regarding problem-solving methods.

Abstract: This paper uses the mathematical software Maple as the auxiliary tool to study the differential problem of four types of rational functions. We can obtain the closed forms of any order derivatives of these rational functions by using binomial theorem. On the other hand, we propose four examples to do calculation practically. The research methods adopted in this study involved finding solutions through manual calculations and verifying these solutions by using Maple. This type of research method not only allows the discovery of calculation errors, but also helps modify the original directions of thinking from manual and Maple calculations. For this reason, Maple provides insights and guidance regarding problem-solving methods.

Abstract: In this paper, an artificial evolutionary based two-phase approach is proposed for solving the nonlinear constrained optimization problems. In the first phase, an immune based algorithm is applied to solve the nonlinear constrained optimization problem approximately. In the second phase, we present a procedure to improve the solutions obtained by the first phase. Numerical results of two benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature.

Abstract: In this paper, we propose a novel unambiguous correlation function with a high and sharp main-peak for binary offset carrier (BOC) signal tracking. First, we construct a correlation function with a low and narrow main-peak using partial correlation functions. Then, we generate an unambiguous correlation function with a high and sharp main-peak via combinations of the correlation function with the partial correlation functions. From numerical results, it is observed that the proposed unambiguous correlation function with a high and sharp main-peak offers a better tracking performance than the conventional correlation functions with a low and flat main-peak in terms of the tracking error standard deviation.

Abstract: Human visual perception is insensitive to certain shades of gray but can distinguish among 20 to 30 shades of gray under a given adaptation level. In this paper, we propose an image fusion pipeline that generates a high vision quality image by fusing the Three-Scale Adaptive Inverse Hyperbolic Tangent (3SAIHT) and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithms to increase detail and edge information. Fusion results are clearer and better with regard to display quality and contrast enhancement.

Abstract: In this paper, we propose a low complexity integer frequency offset estimation scheme based on coherence phase bandwidth for orthogonal frequency division multiplexing (OFDM) systems. The proposed scheme overcomes the effect of the timing offset via correlating the local and received OFDM training symbols in a coherence phase bandwidth block unit. Moreover, by utilizing a threshold to determine an interger frequency offset etimate, the proposed scheme need not calculate correlation values for all possible interger frequency offset candidates. From numerical results, it is demonstrated that the proposed scheme can estimate the integer freqeuncy offset with a reduced complexity while minataining the same level of the estimation performance.

Abstract: This paper presents a novel feature-point bilateral recognition method for recognizing human faces. At first, from either an input face image or a reference face image, a set of distinct feature points is extracted by using a general salient point detection algorithm. Then, based on the detected feature points, a bilateral recognition is performed. Bilateral recognition means there are two ways of recognition, forward recognition and backward recognition. Finally, the forward score and the backward score are summed up into a bilateral score which is used to obtain recognition result. In order to perform recognition in real-time, we also use a GDA algorithm to select the possible candidates, and then use the proposed bilateral recognition operation to make the final recognition decision. Experiments on two famous face databases show that the proposed algorithm get excellence recognition result and is complementary to traditional global-feature-based face recognition methods.

Abstract: In this paper, we study the robust output feedback control for a class of time-varying systems where the observer is used to estimate the system states and a learning scheme is also proposed to learn the time-varying parameters. A regulator control problem is pursued with known time-varying bounds. It is shown that to estimate the system states asymptotically only part of the time-varying function need to be learned, not necessarily all the time-varying parameters. The designed output feedback closed-loop control system guarantees a robust norm bound performance measure and uniformly asymptotic stability based on quadratic stability.

Abstract: This paper presents a new outline contour generation method to track a rigid body in single video stream taken using a varying focal length and moving camera. We assume feature points and background eliminated images are provided, and we get different views of a tracked object when the object is stationary. Using different views of a tracked object, we volume-reconstruct a 3D model body after 3D scene analysis. For computing camera parameters and target object movement for a scene with a moving target object, we use fixed feature background points, and convert as a parameter optimization problem solving. Performance index for parameter optimization is minimizing feature point errors as well as outline contour difference between reconstructed 3D model and background eliminated tracked object. The proposed method is tested using an input image set.

Abstract: In this paper, we focus on the pinwheel task model for a variable voltage processor with d discrete voltage/speed levels. We propose an intra-task DVS algorithm which constructs a minimum energy schedule for k tasks in O(d+ k log k) time. Previous approaches solve this problem by generating a canonical schedule beforehand and adjusting the tasks' speed in O(dn log n) or O(n³) time. However, the length of a canonical schedule depends on the hyperperiod of those task periods and is of exponential length in general. In our approach, the tasks with arbitrary periods are first transformed into harmonic periods and then profile their key features. Afterward, an optimal discrete voltage schedule can be computed directly from those features.