The image denoising is a very basic but important issue in the field of image procession. Most of the existing methods addressing this issue only show desirable performance when the image complies with their underlyin...
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The image denoising is a very basic but important issue in the field of image procession. Most of the existing methods addressing this issue only show desirable performance when the image complies with their underlying assumptions. Especially, when there is more than one kind of noises, most of the existing methods may fail to dispose the corresponding image. To address this problem, we propose a two-step image denoising method motivated by the statistical learning theory. Under the proposed framework, the type and variance of noise are estimated with support vector machine (SVM) first, and then this information is employed in the proposed denoising algorithm to further improve its denoising performance. Finally, comparative study is constructed to demonstrate the advantages and effectiveness of the proposed method.
Heading errors of atomic magnetometers refer to the dependence of measurement results on the sensor orientation with respect to the external magnetic field. There are three main sources of such errors: the light shift...
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Establishing reliable correspondences is crucial for all registration tasks, including 2D image registration, 3D point cloud registration, and 2D-3D image-to-point cloud registration. However, these tasks are often co...
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Establishing reliable correspondences is crucial for all registration tasks, including 2D image registration, 3D point cloud registration, and 2D-3D image-to-point cloud registration. However, these tasks are often complicated by challenges such as scale inconsistencies, symmetry, and large deformations, which can lead to ambiguous matches. Previous feature-based and correspondence-based methods typically rely on geometric or semantic features to generate or polish initial potential correspondences. Some methods typically leverage specific geometric priors, such as topological preservation, to devise diverse and innovative strategies tailored to a given enhancement goal, which cannot be exhaustively enumerated. Additionally, many previous approaches rely on a single-step prediction head, which can struggle with local minima in complex matching scenarios. To address these challenges, we introduce an innovative paradigm that leverages a diffusion model in matrix space for robust matching matrix estimation. Our model treats correspondence estimation as a denoising diffusion process in the matching matrix space, gradually refining the intermediate matching matrix to the optimal one. Specifically, we apply the diffusion model in the doubly stochastic matrix space for 3D-3D and 2D-3D registration tasks. In the 2D image registration task, we deploy the diffusion model in a matrix sub-space, where dual-softmax projection regularization is applied. For all three registration tasks, we provide adaptive matching matrix embedding implementations tailored to the specific characteristics of each task while maintaining a consistent"match-to-warp" encoding pattern. Furthermore, we adopt a lightweight design for the denoising module. In inference, once points or image features are extracted and fixed, this module performs multi-step denoising predictions through reverse sampling. Evaluations on both 2D and 3D registration tasks demonstrate the effectiveness of our approach. Copyrigh
In this paper, we propose a dynamic hybrid active-passive reconfigurable intelligent surface (HRIS) to enhance multiple-input-single-output (MISO) communications, leveraging the property of dynamically placing active ...
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作者:
ZENG JieDOU LihuaXIN BinSchool of Automation
State Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology
This paper proposes a solution for the problem of cooperative salvo attack of multiple cruise missiles against targets in a group. Synchronization of the arrival time of missiles to hit their common target, minimizing...
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This paper proposes a solution for the problem of cooperative salvo attack of multiple cruise missiles against targets in a group. Synchronization of the arrival time of missiles to hit their common target, minimizing the time consumption of attack and maximizing the expected damage to group targets are taken into consideration simultaneously. These operational objectives result in a hierarchical mixed-variable optimization problem which includes two types of subproblems, namely the multi-objective missile-target assignment(MOMTA) problem at the upper level and the time-optimal coordinated path planning(TOCPP) problems at the lower level. In order to solve the challenging problem, a recently proposed coordinated path planning method is employed to solve the TOCPP problems to achieve the soonest salvo attack against each target. With the aim of finding a more competent solver for MOMTA, three state-of-the-art multi-objective optimization methods(MOMs),namely NSGA-II, MOEA/D and DMOEA-εC, are adopted. Finally, a typical example is used to demonstrate the advantage of the proposed method. A simple rule-based method is also employed for comparison. Comparative results show that DMOEA-εC is the best choice among the three MOMs for solving the MOMTA problem. The combination of DMOEA-εC for MOMTA and the coordinated path planning method for TOCPP can generate obviously better salvo attack schemes than the rule-based method.
This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communicat...
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Integrated sensing and communication (ISAC) emerges as a promising technology for 6G, particularly in the millimeter-wave (mmWave) band. However, the widely utilized hybrid architecture in mmWave systems compromises m...
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Auto navigation and vehicle distance estimation are of the critical importance in intelligent *** paper proposes a novel approach to solve these two problems simultaneously by using a monocular ***,we introduce an inc...
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Auto navigation and vehicle distance estimation are of the critical importance in intelligent *** paper proposes a novel approach to solve these two problems simultaneously by using a monocular ***,we introduce an incremental depth parameterized model to overcome the scale ambiguity problem in monocular vision,which differs from other parameterized models with reduced state-dimension and consistent *** addition,the view-field of camera is divided into static and dynamic parts so that the egomotion and vehicle-space can be calculated *** experiments are implemented to validate the effectiveness of the proposed approach.
This study investigates the consensus problem of second-order multi-agent systems subject to time- varying interval-like delays. The notion of consensus is extended to networks containing antagonistic interactions mod...
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This study investigates the consensus problem of second-order multi-agent systems subject to time- varying interval-like delays. The notion of consensus is extended to networks containing antagonistic interactions modeled by negative weights on the communication graph. A unified framework is established to address both the stationary and dynamic consensus issues in sampled-data settings. Using the reciprocally convex approach, a sufficient condition for consensus is derived in terms of matrix inequalities. Numerical examples are provided to illustrate the effectiveness of the proposed result.
Three-dimensional (3D)-printed fish analogs are gradually nearing the ability to mimic real fish meat in response to personal demand, supply pressure, food safety, and environmental concerns. However, the use of 3D fo...
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