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SELM: From Efficient Autonomous Exploration to Long-term Monitoring in Semantic Level

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Lang, Fang Qin, Yongsen Wang, Yinchuan Liu, Jin Wang, Chaoqun Song, Wei Zhu, Qiuguo Song, Rui Zhejiang University Polytechnic Institute Hangzhou310015 China Shandong University School of Control Science and Engineering Jinan China Zhejiang University Robotics Institute Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

Maintaining up-to-date environmental models from initial deployment through long-term autonomy in service is critical for applications like navigation and task planning. To address the challenges of persistent monitoring in unknown environments, we introduce a Two-Stage monitoring strategy, termed the Semantic-level autonomous Exploration and Long-term environment Monitoring (SELM) framework. In the first stage, we introduce a novel semantic exploration method to adapt to new environments quickly. Leveraging the semantic information within the incrementally constructed 3D Scene Graph (3DSG), we combine the Next-Best-View (NBV) selection with room semantics, introducing a more efficient and comprehensive approach for multi-room indoor environment exploration. In addition, the exploration provides patrol routes, the room distance-connectivity graph, and complete environment initial states for subsequential monitoring. The monitoring stage aims to persistently patrol to update the world model in the presence of dynamic changes, including changes in objects' positions. We formulate the long-term monitoring problem as the Partially Observable Markov Decision Process (POMDP) to cope with the environmental uncertainty. To solve the POMDP, we propose the Graph Attention Bidirectional long short-term memory Proximal Policy Optimization (GABPPO) algorithm for the optimal patrol strategy. The feasibility and effectiveness of the proposed SELM framework are verified through extensive experiments. © 2016 IEEE.

关键词： Environmental monitoring

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Quaternary sulfo-halides perovskite solar cell: In depth electrical and impedance spectroscopy analysis

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Journal of Materials Research 2025年 1-18页

作者： Sadek, Samiul Nowrin, Sabrina Sobayel, K. Rashel, Masud Rana Alrashoud, Mubarak Rashed Abdullah-Al-Wadud, M. Solar Energy Research Institute (SERI) The National University of Malaysia (UKM) Bangi Malaysia Department of Avionics Engineering Aviation and Aerospace University Bangladesh Dhaka Bangladesh Instrumentation and Control Lab Department of Mechatronics Engineering University of Évora Évora Portugal Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

Inorganic–organic hybrid lead halide perovskites are promising materials for photovoltaic applications but face challenges related to toxicity and stability. To address these issues, research has focused on “perovskite-inspired” alternatives that retain the advantages of lead-based perovskites. Bismuth (Bi3⁺)-based materials, with electronic structures resembling lead (Pb2⁺), have emerged as potential light absorbers, despite suboptimal efficiencies. This study explores CsBiSI₂ as an absorber layer in quaternary chalcohalide perovskite solar cells using SCAPS-1D simulations. Results highlight the critical influence of donor doping density on efficiency and impedance variations, surpassing the impact of acceptor density. Interface defect density significantly affects charge carrier dynamics, particularly at the electron transport layer and perovskite interface. Electrical and impedance analyses underscore these dynamics, offering insights into optimizing material interfaces for stability and efficiency. This research advances the development of environmentally friendly, stable, and efficient inorganic solar cells, paving the way for improved photovoltaic technologies.

关键词： Electric resistance

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 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

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehiclesin multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. © 2025 IEEE.

关键词： Network function virtualization

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CAST: Contrastive Analysis of Spatial and Temporal Features for QIM-Based VoIP Steganalysis

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IEEE Signal Processing Letters 2025年 32卷 1935-1939页

作者： Cheng Zhang Yue Yan Shujuan Jiang Zhong Chen School of Computer Science and Technology the Engineering Research Center of Mine Digitalization Ministry of Education China University of Mining and Technology Xuzhou China Swinburne College of Shandong University of Science and Technology Shandong China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

QIM(quantization index modulation)-based VoIP steganography is an information-hiding technology that malicious users could misuse to engage in illegal activities. Its countermeasure, commonly known as the QIM-based VoIP steganalysis, has been one of the research hotspots over the past decades. VoIP speech data is sequential, so most previous studies have focused on the temporal features extracted from VoIP encoding codewords to improve detection accuracy. As a result, spatial features are often ignored or fully investigated. In practice, VoIP speech data has unique spatial characteristics. Spatial features could capture the properties of nearby codewords and frames. Inspired by the success of CLIP (contrastive language–image pre-training), we propose a novel model that could efficiently incorporate spatial and temporal features named CAST (contrastive analysis of spatial and temporal features). CLIP introduces the concept of contrastive language–image learning, which has demonstrated exceptional efficiency and effectiveness in aligning textual and visual representations. However, it is not directly applicable to the field of QIM-based VoIP steganalysis. In CAST, we introduce a way to align spatial and temporal features through contrastive analysis. Experimental results demonstrate that this alignment is resource-efficient and could enhance detection accuracy. Meanwhile, CAST outperforms other state-of-the-art models in most scenarios.

关键词： Feature extraction Steganography Correlation Accuracy Training Image reconstruction Data mining Transformers Convolutional codes Speech coding

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Optical attenuation coefficient based optical coherence tomography angiography

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Optics Communications 2025年 586卷

作者： Xiang, Ben Ding, Ning Jiang, Huiwen Liu, Jian Yu, Yao Luan, Jingmin Zhao, Yuqian Wang, Yi Yang, Yanqiu Wang, Fengwen Ma, Zhenhe School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China College of Information Science and Engineering Northeastern University Shenyang110819 China School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

In this study, we reported an optical coherence tomography angiography (OCTA) method based on optical attenuation coefficients (OAC), called OAC-based decorrelation (OACD), for mapping high-quality microvascular images. OACD utilizes the OAC instead of amplitude or phase to calculate flow signals, so OACD can intrinsically suppress noise arising from phase errors and intensity variations in OCT signals caused by the blood flow in superficial vasculature. The performance of the newly proposed algorithm is tested against previously reported OCTA algorithms (i.e., split-spectrum amplitude and phase-gradient angiography (SSAPGA) and principal component analysis (PCA)). Compared to SSAGPA and PCA, OACD can provide enhanced motion contrast in phantoms and in vivo experiments, facilitating better differentiation between vessels and static tissue. Benefiting from these advantages, the vascular images processed by the OACD method exhibit increased vascular visibility and connectivity, enhancing overall image quality. Our method holds promise for disease diagnosis and monitoring. © 2025 Elsevier B.V.

关键词： Optical coherence tomography

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MKMGCN-DDI: Predicting Drug-Drug Interactions via Magnetic Graph Convolutional Network With Multiple Kernels

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 469-480页

作者： Ming Chen Yunhan Pan Xiujuan Lei Chunyan Ji Yinglong Dai Yi Pan Department of Artificial Intelligence Hunan Normal University Changsha Hunan China School of Computer Science Shaanxi Normal University Xi’an China Computer Science Department BNU-HKBU United International College Zhuhai China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

Polypharmacy is a common means of clinical treatments, but detecting drug-drug interactions (DDIs) behind unexpected effects can be costly and faces clinical limitations. Recently, graph neural networks (GNNs) have demonstrated encouraging performance in predicting DDIs. However, most studies overlook the comprehensive aspects of DDIs, such as the coexistence of types of pharmacological changes and the asymmetric roles of drugs. In this article, we define new prediction tasks, taking into account both enhancive or depressive changes and the roles of drugs, and then establish spectral GNNs to predict comprehensive information of DDIs. First, we formally define several tasks, including joint prediction tasks designed to leverage both types and directions. These tasks deduce to sub-tasks in previous studies. Then, we propose a unified framework, the MKMGCN-DDI, via introducing two Magnetic Laplacian matrices to encode comprehension information within DDIs, defining multiple graph filters, and designing multiple-kernel based Magnetic graph convolutional networks (MKMGCN). Experiments across three datasets show that it not only has good adaptability to multiple tasks but also significantly improves results on simple tasks. Case studies on breast neoplasms and lung neoplasms verify its feasibility, as over half of top-10 items are supported.

关键词： Drugs Predictive models Magnetic separation Laplace equations Bioinformatics Sodium Market research Graph convolutional networks computer science Computational modeling

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A Survey of fMRI to Image Reconstruction

arXiv

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arXiv 2025年

作者： Guo, Weiyu Sun, Guoying He, Jianxiang Shao, Tong Wang, Shaoguang Chen, Ziyang Hong, Meisheng Sun, Ying Xiong, Hui Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Control Science and Engineering Shandong University China

Functional magnetic resonance imaging (fMRI)based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies. © 2025, CC BY.

关键词： Image reconstruction

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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GITANet: Group Interactive Threshold-based Attention Network for Hyperspectral Image Classification

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IEEE Transactions on Multimedia 2025年

作者： Duan, Yule Chen, Chuang Fu, Maixia Gong, Xiuwen Niu, Yingying Luo, Fulin The University of Sydney Faculty of Engineering NSW2006 Australia Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Zhengzhou450001 China Henan University of Technology Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China Chongqing University College of Computer Science Chongqing400044 China

Group convolution networks have shown great potential in hyperspectral image (HSI) classification because of their ability to divide total spectral bands into multiple groups and focus on fine discrimination within different spectral ranges. Most group convolution networks process parallel spectral groups independently;however, they neglect the important relevance of nearby spectral ranges. Moreover, the feature maps from different spectral groups are not considered for recalibration in the existing attention. To address these issues, we propose a novel group interactive threshold attention network (GITANet). In the network, a stratified-split-concatenation strategy, which not only splits all bands into multiple groups for intragroup convolution but also propagates the intergroup information via the stratified concatenation operation between different groups, is designed for bandwise group convolution. Relying on the high dependencies among nearby spectra, the cross-group interactive attention block is designed to encourage significant spectral features. Subsequently, from different spectral ranges, a learnable threshold generation block is built to estimate the information validity of each pixel. On the basis of this threshold, soft threshold spatial attention is developed in the bandwise encoderdecoder architecture, which emphasizes high-value spatial areas during the fusion of group convolutional features. Therefore, complementary and discriminative spectral-spatial features are obtained to improve the performance of HSI classification. The experimental results on three HSI datasets illustrate that GITANet is superior to several state-of-the-art networks. © 1999-2012 IEEE.

关键词： Convolution

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Hierarchical Multimodal Decoupling-Fusion Framework for offline Multiple Appropriate Facial Reaction Generation

Hierarchical Multimodal Decoupling-Fusion Framework for offl...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qincheng Lv Xiaofeng Liu Jie Li Rongrong Ni Pujun Xue Siyang Song College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals’ facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions.

关键词： Three-dimensional displays Interference Signal processing Acoustics Speech processing

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