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BEDiff: denoising diffusion probabilistic models for building extraction

Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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A Semantic Communication Scheme for Distributed Holographic-Type Communications With Multi-View Images

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IEEE Communications Letters 2025年第5期29卷 878-882页

作者： Fu, Yuechun Zhao, Zhongyuan Yan, Ji Quek, Tony Q. S. State Key Laboratory of Networking and Switching Technology The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Singapore University of Technology and Design Department of Information Systems Technology and Design Tampines487372 Singapore

Holographic-type communication (HTC) is emerging as an important application scenario for the next-generation communication systems. In particular, to provide immersive user experiences, HTC requires to implement 3D visual reconstruction of a physical object by collecting multi-view images from multiple viewpoints. However, it is a dilemma to balance the efficiency and the visual quality of this distributed HTC scenario. To tackle this problem, a distributed multi-view image semantic communication (DMISC) scheme is proposed in this letter. First, a framework consisting of multiple individual semantic encoders and a joint multi-view semantic decoder is proposed to achieve high-quality image recovery by leveraging the correlations among multi-views. Second, a convolutional neural network-based DMISC model is designed to achieve high communication efficiency and visual quality for 3D reconstruction, by combining joint source-channel coding with joint multi-view decoding. Finally, the simulation results are presented to show that our proposed DMISC scheme can achieve better performance than those existing schemes. © 2024 IEEE.

关键词： Decoding

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Bistatic ISAR Imaging for Maneuvering Target With Complex Motion Based on Phase Retrieval-Assisted ICBA

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IEEE Sensors Journal 2025年第10期25卷 17434-17446页

作者： Shi, Hongyin Yang, Zixin Yang, Ting Liu, Hui Zhao, Tianchi Beijing University of Civil Engineering and Architecture School of Intelligence Science and Technology Beijing Key Laboratory of Super Intelligent Technology for Urban Architecture Beijing100044 China Linyi University School of Information Science and Engineering Linyi276000 China

Compared with monostatic inverse synthetic aperture radar (ISAR), bistatic ISAR (Bi-ISAR) avoids the problem of a limited version for geometric imaging and can obtain more target motion information. However, the complex motion of maneuvering target makes the imaging projection plane (IPP) change continuously, causing 2-D spatial-variant phase errors. To tackle this issue, we propose a bistatic ISAR imaging algorithm based on phase retrieval-assisted image contrast-based focusing algorithm (ICBA). First, this article establishes the signal model of the bistatic 3-D non-uniformly rotating target and analyzes each phase error term. Then, the ICBA algorithm performs phase coarse compensation by estimating velocity and acceleration. Finally, without parameter estimation and signal modeling, the phase retrieval (PR) algorithm can achieve the compensation of 2-D spatial-variant phase errors. The aperture function is utilized to sample the spectrum traversal, recording the low-resolution intensity information in the spatial domain. Alternating iterations with the mapping relation in the frequency domain are used to perform the spectrum reconstruction. Simulation results are conducted with scattering point data and electromagnetic data, and the superiority and robustness of the algorithm are presented in the experimental results. © 2001-2012 IEEE.

关键词： Inverse synthetic aperture radar

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of Computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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Robust Predictive Bipartite Bumpless Control for Markov Jump MASs Under Switching Topology: Application to Rotary-Wing Air Vehicles

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7020-7032页

作者： Wang, Jian Dong, Jiuxiang The Liaoning Province Key Laboratory of Safe Operation Technique for Autonomous Unmanned Systems College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation of Process Industries Shenyang110819 China

This article addresses the issue of bipartite consensus for Markov jump multi-agent systems subjected to bounded disturbance and input constraints under mode-dependent switching topology. To surmount the challenge of handling disturbance, hard constraints and bump suppression, which are not considered and difficult to deal with in existing literature on solutions to algebraic Riccati equation, a novel bipartite robust predictive bumpless control algorithm is proposed with integrating H∞ performance. To be specific, the offline designing process of bipartite controller parameters is firstly performed by investigating terminal constraint set and robust invariant set. In this sense, H∞ type cost function and consensus error state bump cost are constructed for achieving disturbance attenuation and suppressing bump in error state, respectively. This further avoids possible counterproductive results caused by setting constraints on bump with a small gain. Next, based on the optimization prediction dynamics, perturbation state of controller is optimized online for each agent, which efficiently reduces the computational burden compared with conventional online and efficient model predictive control. Furthermore, the recursive feasibility of the proposed method and stochastic stability of error dynamics are theoretically guaranteed. Finally, an application to rotary-wing air vehicles is utilized to verify the potential of theoretic results. © 1967-2012 IEEE.

关键词： Cost functions

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Three-dimensional interconnected carbon nanoreactors with CoP nanocrystals to enhance performance in solar cells

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CrystEngComm 2025年第20期27卷 3257-3266页

作者： Wang, Wen Li, Dan Li, Weidong Fang, Yuanjie Wang, Xiuying Xu, Mingkun Ye, Song Li, Guang School of Electronic Engineering Chaohu University Chaohu238024 China Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou511370 China School of Materials Science and Engineering School of Physics and Optoelectronic Engineering Key Laboratory of Structure and Functional Regulation of Hybrid Materials Ministry of Education Anhui Key Laboratory of Information Materials and Devices Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Failure to fully utilize the catalytic activity of electrode materials is a crucial bottleneck under the harsh operating conditions of a high concentration of iodine. Herein, we design an optimized counter electrode (CE) material with a large number of highly dispersed CoP ultrafine nanoparticles (∼10 nm in size) throughout P-doped multi-aperture honeycomb carbon as three-dimensional interconnected nanoreactors. The established 3D porous conductive honeycomb architecture offers a "highway" for accelerating charge and mass transfer, which facilitates the homogeneous distribution of infiltrated iodine and the close interaction between infiltrated iodine and CoP nanoparticles. This constructed structure not only provides a high surface-to-volume ratio to increase the number of exposed catalytic sites but also prevents nanoparticles from aggregating during cycling owing to the pore spatial confinement effect, ensuring the uniform dispersion of CoP, maximum catalytic effect and enhanced iodide ion diffusion. Additionally, the synergistic coupling effects between rich defect interfaces and chemical bonding derived from heteroatom-doping increase the catalytic activity. As a result, the developed CoP@PC catalyst presents superior power conversion efficiency (8.88%), outperforming all the known noble-metal Pt materials (7.82%). Such an unconventional material design offers an attractive and instructive model of high-performance nanomaterial engineering for application in various fields. © 2025 The Royal Society of Chemistry.

关键词： Covalent bonds

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Tree comprehensive diversity-based pruned deep forest

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International Journal of Machine Learning and Cybernetics 2025年 1-20页

作者： Ding, Jiaman Jiang, Xiangyu Jia, Lianyin Fu, Xiaodong Jiang, Ying Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Artificial Intelligence Key Laboratory of Yunnan Province Kunming University of Science and Technology Yunnan Kunming650500 China

Deep Forest (DF) is proposed as a deep learning model based on non-differentiable modules, which extends the deep learning paradigm by incorporating non-neural network structures and has demonstrated remarkable performance in several fields. DF utilizes a cascade structure where each layer contains one or more sets of random forests. However, some of these forests may include decision trees with high redundancy and suboptimal performance. Additionally, the feature representations in DF are composed of prediction class probability vectors, and these prediction-based representations require the retention of a large amount of information about the forest models for subsequent testing. These two characteristics of DF make it prone to issues related to high time, memory, and storage costs, and may even lead to performance degradation. To address these challenges, we design a novel pruning strategy aimed at optimizing random forests in cascade layers and propose a new deep forest model called Tree Comprehensive Diversity-based Pruned Deep Forest (TCDPDF). Unlike most methods that rely on model prediction results to measure diversity, we explore a diversity measure based on the morphological structure of decision trees. Furthermore, we propose a tree comprehensive diversity measure for the pruning strategy by balancing both structural diversity and prediction accuracy of decision trees. We conduct experiments on 12 public datasets, and the results demonstrate that TCDPDF achieves competitive performance, significantly reduces the model’s high computational complexity, and further enhances its generalization ability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning

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MCFF-Net: Multi-Scale Contextual Feature Fusion Network for Polyp Segmentation

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Ma, Lei Yan, Jiangkai Shao, Dangguo Li, Jingtao Wang, Jiawei Yan, Yukun Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Provincial Key Laboratory of Artificial Intelligence Kunming650500 China

In clinical practice, technologies for automatically segmenting polyps might greatly improve the speed and accuracy of medical diagnosis, significantly reducing the chances of patients developing colorectal cancer. Nevertheless, the segmentation of polyps presents a considerable challenge due to their low contrast, presence of occlusion, and intricate background disturbances. In this paper, we propose an innovative approach for segmenting polyps, called multi-scale contextual feature fusion network for polyp segmentation (MCFF-Net). Specifically, MCFF-Net includes three modules: feature enhancement module (FEM), global-local feature fusion module (GLFF), and cross-scale feature fusion module (CFFM). Among them, FEM is used to improve the features at each scale. Building on this, the CFFM enables comprehensive interaction among the information across various scales, enhancing the diversity of the feature scales extracted. Moreover, the GLFF fuses global-local features to generate a rough object localization map. This map further modulates the fusion features from the CFFM, facilitating more precise segmentation. On five publicly available datasets, our approach not only segments various polyp types but also surpasses existing methods in terms of accuracy and generalizability. © Shanghai Jiao Tong University 2024.

关键词： Diseases

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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State Estimation of a Spatial 2-D Linear Diffusion Process With Mobile Sensors

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IEEE Transactions on Automation science and engineering 2025年 22卷 12024-12032页

作者： Zhang, Xiao-Wei Chen, Kui Wu, Huai-Ning Wang, Jin-Liang Wang, Zi-Peng Beijing University of Technology School of Information Science and Technology Beijing100124 China Tiangong University School of Control Science and Engineering Tianjin300387 China Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tianjin300387 China Beijing University of Technology School of Information Science and Technology Beijing Institute of Artificial Intelligence Beijing100124 China

This paper studies the state observer design of a spatial two-dimensional (2-D) linear diffusion process described by a linear parabolic partial differential equation (PDE) under mobile sensors. Firstly, we analyze the well-posedness of the PDE system and give the structure form of the state observer with mobile sensors. Subsequently, according to the number of mobile sensors, the 2-D space domain is divided into multiple sub-domains, and the mobile sensors are guided by the projection operator method, which can guarantee that the mobile sensors can only move in their respective 2-D sub-domains. Then, in the light of Lyapunov theory, Poincaré-Wirtinger inequality and Barbalat lemma, we propose an observation-plus-guidance design method to ensure the asymptotic stability of the state estimation error system. In the designed mobile strategy, the actual guidance of mobile sensors is essentially a physical synthesis of two direction guidance laws, where two dimensional guidance laws are designed separately. Moreover, the existence condition of the observer is given by linear matrix inequalities. At last, a numerical example is provided to demonstrate the efficacy of the proposed design scheme. © 2004-2012 IEEE.

关键词： Asymptotic stability

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