A fast computational method for minimum square error transform

In this paper, we describe a basic minimum square error transform for point pattern matching and propose a fast computational method for minimum square error transform. The computational analysis revealed that the proposed method is faster than that of Groen et al. (1985).     

A novel deghosting method for exposure fusion

A novel ghost-free exposure fusion method for generating an HDR image of a dynamic scene is presented in this paper. Given a sequence of input images with gradually increased exposures, due to the theory that the luminance is linearly depended on the exposure time (Mertens et al. Comput Graph Forum 28(1):161–171, 2009), each input image is normalized to make it have consistent luminance with a reference image. Then moving objects in the dynamic scene are detected using a modified difference method for further exposure fusion. Experiments and comparisons show that our method has advantage in deghosting when the reference image contains saturated regions and generate high-quality results with natural textures. Furthermore, our method has a largely improved timing performance compared with previous reference-guided methods.     

一种新的基于2DPCA的遥感图像融合方法

作为一种图像融合的重要技术,PCA已在遥感领域得到了较为广泛的应用.但这种方法也有缺点:首先,在对图像进行PCA分析时需将图像转换为一维向量来实现,从而不能有效利用图像的结构信息;其次,融合后的图像空间分辨率改善明显,但光谱信息损失严重.为解决些问题,我们提出了一种基于2DPCA的遥感图像融合方法.与PCA融合方法相比,该方法的主要特点为:首先,2DPCA是直接对图像矩阵进行,而不是对一维向量,这样就可以有效利用图像的结构信息;其次,融合后的图像不仅空间分辨率大大提高,而且保持了良好的光谱信息.本文的研究和实验证明这种新的图像融合方法是有效的.     

A novel Hough transform method for line detection by enhancing accumulator array

In this paper, an improved Hough transform (HT) method is proposed to robustly detect line segments in images with complicated backgrounds. The work focuses on detecting line segments of distinct lengths, totally independent of prior knowledge of the original image. Based on the characteristics of accumulation distribution obtained by conventional HT, a local operator is implemented to enhance the difference between the accumulation peaks caused by line segments and noise. Through analysis of the effect of the operator, a global threshold is obtained in the histogram of the enhanced accumulator to detect peaks. Experimental results are provided to demonstrate the efficiency and robustness of the proposed method.     

A fast adaptive load balancing method for parallel particle-based simulations

Balancing the work load can improve the performance of distributed simulation systems. In this paper we propose a fast adaptive balancing method, in which a binary tree structure is used to partition the simulation region into sub-domains. From a global view to local views, we balance the loads between sub-domains recursively by compressing and stretching sub-domains in group. This method can adjust the sub-domains with heavy loads and decompose their loads very fast. Then we compare the algorithm with two previously proposed algorithms by an artificial case and a real distributed case respectively. In both cases, our method can get a faster convergence speed and a lower communication overhead.     

A novel adaptive quantization method for video coding

In this paper, we propose a novel AQ (Adaptive Quantization) algorithm to improve the subjective coding performance. Firstly, the factors affecting a suitable adaptive quantization method are carefully analyzed. Two important conclusions are drawn to guide designing a suitable AQ method. Secondly, based on the drawn analysis, a novel SATD (Sum of the Transformed Difference) based temporal adaptive quantization method is proposed. The proposed method fully considers the temporal characteristics, which can produce more visual-friendly QP (quantization parameter) offset distribution. Experiments are performed on x264 and HM16.0, respectively. With SSIM (Structure Similarity) as the metric, more than 21.07% BD-Rate (Bjontegaard-Delta Rate) saving can be achieved on x264, and on HM16.0, 8.07% and 7.99% BD-Rate savings can be obtained for LDP (Low-Delay-main-P) and LDB (Low-Delay-main-B) configurations, respectively, which is better than the state-of-the-art methods.     

A fast fusion method for multi-videos with three-dimensional GIS scenes

Multimedia Tools and Applications - Techniques for the fusion of real-world videos with virtual scenes are key to the augmentation of three-dimensional (3-D) virtual geographic scenes, which...     

A high-performance fast Fourier transform algorithm for the Cray-2

Most implementations of a radix-2 fast Fourier transform on large scientific computers use algorithms that involve memory accesses whose strides are powers of two. (The term stride means the memory increment between successive elements stored or fetched.) Such strides are unacceptable for recently developed supercomputers, particularly the Cray-2, because of serious difficulties with memory bank conflicts.This article describes an algorithm for evaluating the fast Fourier transform that avoids this difficulty and thus could provide the basis for implementations that more fully utilize the power of the Cray-2. A Fortran program implementing this algorithm is included, and timing comparisons with the Cray assembly-coded library subroutine are shown.The author is with the Numerical Aerodynamic Simulation Systems Division at NASA Ames Research Center.     

A novel adaptive control for reaching movements of an anthropomorphic arm driven by pneumatic artificial muscles

Pneumatic artificial muscle is widely used since it has the advantage including simple structure, lightweight, force controllable, compliance and so on. In this paper, a two-link anthropomorphic arm is constructed by pneumatic artificial muscles for the upper-limb rehabilitation training. In order to assist impaired upper-limb patients to achieve various training, the anthropomorphic arm should realize flexible human reaching movements. Due to frictions and model uncertainties of the anthropomorphic arm system, an adaptive fuzzy backstepping control is proposed to ensure the stability and the adaptivity during the motion. The control method is proved by Lyapunov asymptotic stability and is verified by numerical simulations. Furthermore, experiments are performed and results demonstrate that the proposed control method is efficient and robust.     

A fast SVM-based wrapper feature selection method driven by a fuzzy complementary criterion

The selection of informative and non-redundant features has become a prominent step in pattern classification. However, despite the intensive research, it is still an open issue to identify valuable feature subsets, especially in highly dimensional feature spaces. This paper proposes a wrapper feature selection method, in the context of support vector machines (SVMs), named Wr-SVM-FuzCoC. Our method combines effectively the advantages of the wrapper and filter approaches, achieving three goals simultaneously: classification performance, dimensionality reduction, and computational efficiency. In the filter part, a forward feature search methodology is developed, driven by a fuzzy complementary criterion, whereby at each iteration a feature is selected that exhibits the maximum additional contribution in regard to the previously selected subset. The quality of single features or feature subsets is assessed via a fuzzy local evaluation criterion with respect to patterns. This is achieved by the so-called fuzzy partition vector (FPV), comprising the fuzzy membership grades of every pattern in their target classes. Derivation of the feature FPVs is accomplished by incorporating a fuzzy output kernel-based support vector machine. The proposed method is favorably compared with existing SVM-based wrapper methods, in terms of performance capability and computational speed. Experimental investigation is carried out using a diverse pool of real datasets, including moderate and high-dimensional feature spaces.     

Application of the fast fourier transform method for compression of spectral data obtained from a photodiode array spectrophotometer

Data compression using the Fourier transform method was applied to data generated from an ultraviolet-visible photodiode array spectrophotometer. The spectroscopic data were converted into the Fourier domain by using the fast Fourier transform technique. Four different methods were utilized to reduce the number of Fourier coefficients required for accurate reproductions of the original spectra. The performance of these methods in data compression was evaluated by using synthetic spectra as well as experimental absorption spectra. It was found that the storage space of the spectral data can be reduced by more than 90% by using three of the suggested methods.     

A multiscale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform

A novel methodology based on multiscale spectral and spatial information fusion using wavelet transform is proposed in order to classify very high resolution (VHR) satellite imagery. Conventional wavelet‐based feature extraction methods employ single windows of a fixed size, which are not satisfactory as the VHR imagery contains complex and multiscale objects. In this paper, spectral and spatial features are extracted based on a set of concentric windows around a central pixel in order to integrate the information across different windows/scales. The proposed method is made up of three blocks: (1) the conventional wavelet‐based feature extraction methods are extended from single band processing to multispectral bands, and from single window to multi‐windows, (2) two multiscale fusion algorithms are proposed to exploit the multiscale spectral and spatial information and (3) a support vector machine (SVM), a relatively new method of machine learning, is used to classify the multiscale spectral–spatial feature sets. The proposed classification method is evaluated on two VHR datasets and the results show that the multiscale approach can improve the classification accuracy in homogeneous areas while simultaneously preserving accuracy in edge regions.     

Application of an adaptive neuro-fuzzy inference system for classification of Behcet disease using the fast Fourier transform method

Abstract: In this study, ophthalmic arterial Doppler signals were obtained from 200 subjects, 100 of whom suffered from ocular Behcet disease while the rest were healthy subjects. An adaptive neuro-fuzzy inference system (ANFIS) was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by the fast Fourier transform method for determining the ANFIS inputs. The ANFIS was trained with a training set and tested with a testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the ANFIS. The correct classification rate was 94% for healthy subjects and 90% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the ANFIS was effective at detecting ophthalmic arterial Doppler signals from subjects with Behcet disease.     

A novel solution procedure for fuzzy fractional heat equations by homotopy analysis transform method

In this paper, we designed a reliable recipe of homotopy analysis method and Laplace decomposition method namely homotopy analysis transform method to solve fuzzy fractional heat and wave equations. This method overcomes the difficulties arise in other analytical method and removes the restrictive condition of nonlinearity and assumptions of small and large parameters.     

A novel motion-based online temporal calibration method for multi-rate sensors fusion

Accurate and robust calibration is an essential prerequisite for multi-rate sensors fusion. However, most existing calibration methods ignore the temporal calibration and assumed the timestamps of the multi-rate sensors are precisely aligned; more importantly, many approaches are designed for offline calibration. For these reasons, this paper develops a novel online temporal calibration method for multi-rate sensors fusion based on the motion constrains of the sensors. In this new calibration framework, the high update rate inertial measurement unit (IMU) is utilized as the unified calibrating references, while other moderate or low-frequency target sensors can be estimated based on the reference IMU. As a result, the targetless, online, and high-precision temporal self-calibration can be achieved. During the calibration, an improved multi-state constraint Kalman filter (I-MSCKF) algorithm is proposed for both position and temporal states estimation of the multi-rate sensors to establish a multi-constraint filter and correct the temporal offset error in a real-time manner. Furthermore, the motion constraints models in the two-dimensional (2D) planar and three-dimensional (3D) space are developed from per-sensor ego-motion to enhance the robust and reliable abilities of the proposed temporal self-calibration method. Experimental results demonstrate that the proposed method can accurately and online estimate the temporal offset error and transformation parameters, which significantly improves the performance of moving trajectory estimation for robots equipped with the multi-rate sensors.     

一种基于融合指标的小波去云参数选取方法

遥感图像普遍存在着薄云噪声,对其使用造成影响,因此需要针对薄云的特性来进行去云处理.一种利用频率特点将各层小波系数分开处理的小波去云方法有较好的去云效果,但其中一些参数的正确选取对结果有很大影响,尚未完全找到有效的方法来选择.根据遥感图像的特点,选择评价指标来构造融合指标,利用该融合指标寻找到最佳的分界层和分解层数.通过使用该方法求得的两个参数能够实现较好的去云目的,证明方法的高效性.     

A global adaptive link position-tracking controller for robot manipulators driven by induction motors

We present an adaptive, partial-state feedback, link position tracking controller for robot manipulators driven by induction motors. The proposed controller compensates for parametric uncertainty in the mechanical subsystem while yielding global asymptotic link position tracking. The proposed controller does not require measurement of rotor flux; furthermore, the controller does not exhibit any singularities. Preliminary experimental and simulation results are provided to illustrate the effectiveness of the proposed controller.     

A novel trajectory planning-based adaptive control method for 3-D overhead cranes

A three-dimensional (3-D) overhead crane is a complicated nonlinear underactuated mechanical system, for which high-speed positioning and anti-sway control are the kernel objective. Existing trajectory-based methods for 3-D overhead cranes focus on combining efficient and smooth trajectories with anti-sway tracking controllers without regard for payload motion; moreover, the exact value of plant parameters is required for accurate compensation during the control process. Motivated by these facts, we present a two-step design tracking strategy which consists of a trajectory planning stage and an adaptive tracking control design stage for 3-D overhead cranes. As shown by Lyapunov techniques and Barbalat's Lemma, the proposed controller guarantees asymptotic swing elimination and trolley positioning result in the presence of system uncertainties including unknown parameters and external disturbances. Simulation results also showed the applicability of the proposed method with good robustness against parameter uncertainties and external disturbances.     

A novel method for adaptive knowledge map construction in the aircraft development

Aircraft is a typical transportation facility and its development need to refer to the existing knowledge. With the rapid increase of knowledge, a knowledge map may deliver excess knowledge to users that they cannot manage at once, thereby causing the problem of knowledge overload. Hence, a novel method for adaptive knowledge map construction was proposed to solve this problem. First, the knowledge was semantically annotated and stored with the domain ontology and a knowledge model that integrates context. Then, user requirement was described by the context of product design, and knowledge nodes that met users’ requirement could be extracted from the knowledge retrieval technology on the basis of context similarity. Finally, the connection between knowledge nodes was constructed with a composite connection model, and the knowledge map was visualized using a hierarchical approach. To verify the effectiveness of the proposed method, the constructed knowledge map was applied in an airplane wing design to assist users in browsing the knowledge base. Results indicate that the proposed method can change the displayed contents according to user requirement and identify the displayed knowledge nodes at a highly acceptable level, the constructed knowledge map can guide users efficiently, and the knowledge overload can be reduced significantly.

     

A novel analytical solution of a fractional diffusion problem by homotopy analysis transform method

In this study, we present the homotopy analysis transform method for finding solution of fractional diffusion-type equations. We can attain these equations by substituting a first-order time derivative by a fractional-order derivative in regular diffusion equation. We add some examples in order to illustrate the usefulness and efficiency of our novel proposed technique for fractional diffusion equations.