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Effective image registration model using optimized KAZE algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 33959-33984页

作者： Zhang, Sheng Shen, Jie Zheng, Shengnan Tang, Jingjing Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing 211167 Peoples R China

The incompatible problem with velocity and accuracy has been restricting the application of the KAZE algorithm. In order to resolve this shortage, we propose the effective image registration model using the optimized KAZE algorithm. This effective image registration model consist of four stages. First of all, to reduce the input data of image registration, the original registration images are preprocessed by the fusion preprocessing method based on the average and the perceptual hashing algorithms. Second, to extract image features quickly, we utilize the fast algorithm to extract image features instead of the local extremum based on the Hessian matrix and the Taylor principle. Third, in order to accelerate the velocity of image features matching, the compressed sensing principle is used to reduce the dimension of the image feature descriptors. Finally, the two-step strategy is adopted to ensure the accuracy of image registration, the step one is that the hybrid matching method based on the FLANN and the KNN algorithms is used to rough matching, and the step two is that adopt the RANSAC algorithm to further accurate matching. This paper utilizes two groups of the experiments to verify the effective model, the experiment results show that the effective model has velocity advantage compared with other current image registration methods, and also achieves the compatible with velocity and accuracy in the case of the highest matching score. This model provides an effective solution for the application of image registration, and also has great significance for the development of image registration.

关键词： Image registration KAZE algorithm Hashing algorithm fast algorithm RANSAC algorithm

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VCNPU: An algorithm-Hardware Co-Optimized Framework for Accelerating Neural Video Compression

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2024年第4期33卷 1014-1027页

作者： Zhang, Siyu Mao, Wendong Wang, Zhongfeng Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Sun Yat Sen Univ Sch Integrated Circuits Shenzhen 518107 Peoples R China

Video compression is essential for storing and transmitting video content. Real-time decoding is indispensable for delivering a seamless user experience. Neural video compression (NVC) integrates traditional coding techniques with deep learning, resulting in impressive compression efficiency. However, the real-time deployment of advanced NVC models encounters challenges due to their high complexity and extensive off-chip memory access. This article presents a novel NVC accelerator, called video compression neural processing unit (VCNPU), via an algorithm-hardware co-design framework. First, at the algorithmic level, a reparameterizable video compression network (RepVCN) is proposed to aggregate multiscale features and boost video compression quality. RepVCN can be equivalently transformed into a streamlined structure without extra computations after training. Second, a mask-sharing pruning strategy is proposed to compress RepVCN in the fast transform domain. It effectively prevents the destruction of sparse patterns caused by model simplification, maintaining the model capacity. Third, at the hardware level, a reconfigurable sparse computing module is designed to flexibly support sparse fast convolutions and deconvolutions of the compact RepVCN. Besides, a hybrid layer fusion pipeline is advocated to reduce off-chip data communication caused by extensive motion and residual features. Finally, based on the joint optimization of computation and communication, our VCNPU is constructed to realize adaptive adjustments of various decoding qualities and is implemented under TSMC 28-nm CMOS technology. Extensive experiments demonstrate that our RepVCN provides superior coding quality over other video compression baselines. Meanwhile, our VCNPU achieves 6.7 x improvements in throughput, 2.9 x in area efficiency, and 4 x in energy efficiency compared to prior video processors.

关键词： Video compression Computational modeling Transforms Hardware Streaming media Optimization Decoding Real-time systems Memory management Feature extraction fast algorithm hardware accelerator neural video compression (NVC) pruning reparameterization

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A fast Time Two-Mesh algorithm for Allen-Cahn Equation

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BULLETIN OF THE MALAYSIAN MATHEMATICAL SCIENCES SOCIETY 2020年第3期43卷 2417-2441页

作者： Wang, Danxia Du, Qingqing Zhang, Jianwen Jia, Hongen Taiyuan Univ Technol Coll Math Taiyuan 030024 Peoples R China Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Peoples R China

In this article, we consider numerical methods for solving Allen-Cahn equation involving strong nonlinearities. A fast algorithm based on time two-mesh (TT-M) finite element (FE) scheme is proposed to overcome the time-consuming computation of nonlinear terms. The TT-M FE algorithm includes the following three main steps: Firstly, a nonlinear FE scheme is solved on a coarse time mesh tau c. Here, the FE method is used for spatial discretization and the implicit second-order backward difference scheme is used for temporal discretization. Secondly, the Lagrange's interpolation is used to obtain the interpolation result on the fine grid. Finally, a linearized FE system is solved on a fine time mesh tau(tau

关键词： fast algorithm TT-M FE method Allen-Cahn equation Stability Priori error estimate CPU time 35Q30 74S05

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fast feature selection algorithm for neighborhood rough set model based on Bucket and Trie structures

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GRANULAR COMPUTING 2020年第3期5卷 329-347页

作者： Benouini, Rachid Batioua, Imad Ezghari, Soufiane Zenkouar, Khalid Zahi, Azeddine Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol Lab Intelligent Syst & Applicat LSIA Fes Morocco

Feature selection is viewed as the problem of finding the minimal number of features from an original set with the minimum information loss. Due to its high importance in the fields of pattern recognition and data mining, it is necessary to investigate fast and effective search algorithms. In this paper, we introduce a novel fast feature selection algorithm for neighborhood rough set model based on Bucket and Trie structures. This proposed algorithm can guarantee to find the optimal minimal reduct by adopting a global search strategy. In addition, the dependence degree is subsequently used to evaluate the relevance of the attribute subset. Consequently, the proposed algorithm is tested on several standard data sets from UCI repository and compared with the most recent related approaches. The obtained theoretical and experimental results reveal that the present algorithm is very effective and convenient for the problem of feature selection, indicating that it could be useful for many pattern recognition and data mining systems.

关键词： Feature selection Rough set theory Neighborhood rough set fast algorithm Trie structure Bucket structure

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Super Fourier analysis: A highly efficient framework for multivariate signal processing

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SIGNAL PROCESSING 2025年 231卷

作者： Li, Tianqi Chen, Nan Peng, Zhike He, Qingbo Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Ningxia Univ Sch Mech Engn Yinchuan 750021 Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore

The escalating integration of multi-sensor systems across diverse high-dimensional monitoring areas generates a large amount of large-scale multivariate signals. However, the theoretical foundation of efficient multivariate signal processing methods is currently lacking, making it challenging to fully exploit the value of multivariate signals under the requirement of rapid response. Here, we introduce super Fourier analysis (SFA), which innovates traditional Fourier analysis with the principle of multivariate statistics for highly efficient processing of multivariate signals. By integrating multi-channel information and reducing the data dimensionality, SFA can inherently handle the correlation across channels and has low time complexity. In the framework of SFA, we deduce and define the super Fourier series, super Fourier transform, and discrete super Fourier transform. Mode alignment property and noise resilience property of the SFA are analyzed. As an example, variational mode decomposition, a classic univariate signal processing method, is extended to multivariate context based on SFA. Our demonstrations include simulated signals, multi-channel electroencephalography, global sea surface temperature, and motion microscopy, highlighting SFA's potential in rapid and large-scale multivariate signal processing. SFA's efficiency and effectiveness promise its applications in various areas with a large number of sensors or channels, making the processing of multivariate signals as simple as univariate signals.

关键词： Multivariate signals Fourier analysis Multivariate statistics fast algorithm

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fast Second-Order Evaluation for Variable-Order Caputo Fractional Derivative with Applications to Fractional Sub-Diffusion Equations

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Numerical Mathematics(Theory,Methods and Applications) 2022年第1期15卷 200-226页

作者： Jia-Li Zhang Zhi-Wei Fang Hai-Wei Sun Department of Mathematics University of MacaoMacaoSARChina

In this paper,we propose a fast second-order approximation to the variable-order(VO)Caputo fractional derivative,which is developed based on L2-1σformula and the exponential-sum-approximation *** fast evaluation method can achieve the second-order accuracy and further reduce the computational cost and the acting memory for the VO Caputo fractional *** fast algorithm is applied to construct a relevant fast temporal second-order and spatial fourth-order scheme(F L2-1σscheme)for the multi-dimensional VO time-fractional sub-diffusion ***,F L2-1σscheme is proved to fulfill the similar properties of the coefficients as those of the well-studied L2-1σ***,F L2-1σscheme is strictly proved to be unconditionally stable and convergent.A sharp decrease in the computational cost and the acting memory is shown in the numerical examples to demonstrate the efficiency of the proposed method.

关键词： Variable-order Caputo fractional derivative exponential-sum-approximation method fast algorithm time-fractional sub-diffusion equation stability and convergence

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A POD based reduced-order local RBF collocation approach for time-dependent nonlocal diffusion problems

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APPLIED MATHEMATICS LETTERS 2025年 160卷

作者： Lu, Jiashu Zhang, Lei Guo, Xuncheng Qi, Qiong Xian Modern Control Technol Res Inst Natl Key Lab Land & Air Based Informat Percept & C Xian 710065 Shaanxi Peoples R China

A fast algorithm based on reduced-order model (ROM) is proposed for unsteady nonlocal diffusion models. It combines proper orthogonal decomposition (POD) approach and collocation method with local radial basis functions (RBFs), which makes it possible for using ROM to solve nonlocal models. Several numerical experiments showed that this approach significantly reduce the computational cost of nonlocal models while keep the similar convergent behavior compared with the RBF collocation methods.

关键词： Nonlocal models Radial basis functions Model reduction fast algorithm

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Acceleration of the frequency-shift demodulation in phase-sensitive OTDR

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第2期35卷 025101-025101页

作者： Pu, Zhengyu He, Haijun Zhou, Yin Jiang, Lin Pan, Wei Yan, Lianshan Southwest Jiaotong Univ Sch Informat Sci & Technol Ctr Informat Photon & Commun Chengdu 611756 Sichuan Peoples R China

The frequency-shift demodulation is a primary demodulation method in phase-sensitive optical time domain reflectometry (phi-OTDR) with intrinsic resistance to interference fading. So far, the least mean squares (LMS) estimation method has the optimal demodulation accuracy and robustness. However, it takes much processing time due to the step-by-step sliding operation. In this work, we propose a fast LMS estimation method based on cross-correlation calculation to accelerate the demodulation while maintaining accuracy. Experiments are performed along a 9 km sensing fiber with a 4 m spatial resolution. The performance of the fast LMS, LMS, and cross-correlation methods are compared by using the same parameters. Compared with the LMS method, the fast LMS achieves a 12-time improvement in processing speed while remaining the same demodulation accuracy. Although the proposed fast LMS method takes slightly more time than the cross-correlation method (1.6 times), it improves the demodulation accuracy similar to 6 dB for the vibration signal and similar to 2.1 dB for the overall demodulation accuracy.

关键词： distributed optical fiber sensor phase-sensitive optical time domain reflectometry frequency-shift demodulation fast algorithm high-resolution measurement

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Temporal second-order fully discrete two-grid methods for nonlinear time-fractional variable coefficient diffusion-wave equations

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APPLIED MATHEMATICS AND COMPUTATION 2024年 466卷

作者： Tan, Zhijun Zeng, Yunhua Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Computat Sci Guangzhou 510275 Peoples R China

This paper proposes a temporal second-order fully discrete two-grid finite element method (FEM) for solving nonlinear time-fractional variable coefficient diffusion-wave equations. The method involves introducing an auxiliary variable and utilizing a reducing order technique for the fractional derivative. This reduces the original fractional wave equations to a coupled system consisting of two equations with lower order derivative in time. The time-fractional derivative is discretized using the L2-1(sigma) formula, while a second-order scheme is used for discretizing the first-order time derivative. The spatial direction is discretized using an efficient two-grid Galerkin FEM. The paper demonstrates that the optimal error estimates in L-2-norm and H-1-norm of the two-grid methods can achieve optimal convergence order when the mesh size satisfies H = h(1/2) and H = h(r/2r+2), respectively. To handle the initial singularity of the solution and the historical memory of the fractional derivative term, the paper presents the nonuniform and fast two-grid algorithms. The numerical results validate the theoretical analysis and demonstrate the effectiveness of the proposed two-grid methods.

关键词： Nonlinear TFDW equations Variable coefficient Two-grid method L2-1 sigma scheme Nonuniform meshes fast algorithm Stability Error estimate

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fast L2-1σ Galerkin FEMs for generalized nonlinear coupled Schrodinger equations with Caputo derivatives

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APPLIED MATHEMATICS AND COMPUTATION 2022年 416卷 126734-126734页

作者： Li, Meng Wei, Yifan Niu, Binqian Zhao, Yong-Liang Zhengzhou Univ Sch Math & Stat Zhengzhou 001 Henan Peoples R China Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Sichuan Normal Univ Sch Math Sci Chengdu 611731 Sichuan Peoples R China

The paper is concerned with the unconditional stability and optimal error estimates of Galerkin finite element methods (FEMs) for a class of generalized nonlinear coupled Schrodinger equations with Caputo-type derivatives. We improve the results in [1] to a higher order temporal scheme by using a type of new Gronwall inequality. By introducing a time-discrete system, the error is separated into two parts: the temporal error and the spatial error. As the result of tau-independent of the spatial error, we obtain the L-infinity-norm boundedness of the fully discrete solutions without any restrictions on the grid ratio. The unconditionally optimal L-2-norm error estimate is then obtained naturally. Furthermore, in order to numerically solve the system with nonsmooth solutions, we construct another Galerkin FEM with nonuniform temporal meshes, and corresponding fast algorithm by using sum-of-exponentials technique is also built. Finally, numerical results are reported to show the accuracy and efficiency of the proposed FEMs and the corresponding fast algorithms. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Generalized nonlinear coupled Schrodinger equations L2-1 sigma formula FEM Unconditional convergence fast algorithm

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