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SAMFNet: Scene-aware sampling and multi-stage fusion for multimodal 3D object detection

Alexandria engineering Journal 2025年 126卷 90-104页

作者： Wang, Baotong Xia, Chenxing Gao, Xiuju Ge, Bin Li, Kuan-Ching Fang, Xianjin Zhang, Yan Yang, Yuan College of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China College of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China Department of Computer Science and Information Engineering Providence University Taichung43301 China The School of Electronics and Information Engineering Anhui University Hefei230039 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

Recently, multimodal 3D object detection (M3OD) that fuses the complementary information from LiDAR data and RGB images has gained significant attention. However, the inherent structural differences between point clouds and images pose fusion challenges, significantly hindering the exploration of correlations within multimodal data. To address this issue, this paper introduces an enhanced multimodal 3D object detection framework (SAMFNet), which leverages virtual point clouds generated from depth completion. Specifically, we design a scene-aware sampling module (SASM) that employs tailored sampling strategies for different bins based on the density distribution of point clouds. This effectively alleviates the detection bias problem while ensuring the key information of virtual points, significantly reducing the computational cost. In addition, we introduce a multi-stage feature fusion module (MSFFM) that embeds point-level and regional-adaptive feature fusion strategies to generate more informative multimodal features by fusing features with different granularities. To further improve the accuracy of model detection, we also introduce a confidence prediction branch unit (CPBU), which improves the detection accuracy by predicting the confidence of feature classification in the intermediate stage. Extensive experiments on the challenging KITTI dataset demonstrate the validity of our model. © 2025 The Authors

关键词： Image fusion

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Carbon Nanofiber/Polyaniline Composite Aerogel with Excellent Electromagnetic Interference Shielding,Low Thermal Conductivity,and Extremely Low Heat Release

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Nano-Micro Letters 2025年第4期17卷 77-95页

作者： Mingyi Chen Jian Zhu Kai Zhang Hongkang Zhou Yufei Gao Jie Fan Rouxi Chen Hsing-Lin Wang School of Textile Science and Engineering Tiangong UniversityTianjin 300387People’s Republic of China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen 518055People’s Republic of China School of Innovation and Entrepreneurship Southern University of Science and TechnologyShenzhen 518055People’s Republic of China Guangdong Provincial Key Laboratory of Energy Materials for Electric Power Southern University of Science and TechnologyShenzhen 518055People’s Republic of China Ministry of Education Key Laboratory of Advanced Textile Composite Materials Tiangong UniversityTianjin 300387People’s Republic of China

The rapid development of communication technology and high-frequency electronic devices has created a need for more advanced electromagnetic interference(EMI)shielding *** response to this demand,a study has been conducted to develop multifunctional carbon nanofibers(CNFs)/polyaniline(PANI)aerogels with excellent electromagnetic interference shielding,flame retardancy,and thermal insulation *** process involved freeze-drying of electrospun CNFs and PANI nanoparticles followed by in situ growth PANI to coat the CNFs,creating the core-shell structured CNFs/PANI composite fiber and its hybrid aerogels(CP-3@PANI).The interaction between PANI and aniline(ANI)provides attachment sites,allowing additional ANI adsorption into the aerogel for in situ *** results in PANI uniformly covering the surface of the CNFs,creating a core-shell composite fiber with a flexible CNF core and PANI *** process enhances the utilization rate of the ANI monomer and increases the PANI content loaded onto the ***,effective connections are established between the CNFs,forming a stable,conductive three-dimensional network *** prepared CP-3@PANI aerogels exhibit excellent EMI shielding efficiency(SE)of 85.4 dB and specific EMI SE(SE d^(-1))of 791.2 dB cm^(3)g^(-1)in the *** to the synergistic flame-retardant effect of CNFs,PANI,and the dopant(phytic acid),the CP-3@PANI aerogels demonstrate outstanding flame-retardant and thermal insulation properties,with a peak heat release rate(PHRR)as low as 7.8 W g^(-1)and a total heat release of only 0.58 kJ g^(-1).This study provides an effective strategy for preparing multifunctional integrated EMI shielding materials.

关键词： CNFs/PANI hybrid aerogels EMI shielding Flame retardancy Thermal insulation

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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LooP: A Low-Overhead Path Reconstruction for Large-Scale IoTs

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1623-1634页

作者： Fu, Luwei Zhao, Zhiwei Liu, Zhuoliu Min, Geyong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Internet of Things (IoT) plays a vital role in various smart applications due to its cost-efficiency and good scalability. For safety and management of a large-scale IoT with many gateways, packet-level path reconstruction which exactly reveals the transmission path of each packet is desired. However, the existing path reconstruction schemes rely on correlation between diverse packets, which results in either considerable overhead or poor accuracy. It is particularly difficult to reconstruct the paths by affordable overhead under the intensive deployment, constrained resource, and weak correlation of IoT devices. To this end, we propose a Low-overhead Path reconstruction (LooP) for large-scale IoT. The proposed scheme exploits the nature of incremental update that each node only identifies the changes of its one-hop topology for diverse gateways. The gateway nodes then iteratively recover the complete path of each received packet according to the reported changes and historical information. It removes much redundant overhead (e.g., the common paths information) while guaranteeing the accurate reconstruction in a large scale and dynamic topology network. We analyze the information entropy of LooP and several existing schemes to theoretically prove the superiority of our proposal. The experimental results also demonstrate LooP can achieve 98% reconstruction accuracy on average and at least 3x gain-cost-ratio of the sub-optimal rival. © 2013 IEEE.

关键词： Gateways (computer networks)

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Improvised Spectral Efficiency and Channel Estimation Parameters in Visible Light Vehicular Communication by Integrating Simulation of Urban Mobility Data

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IEEE Access 2025年 13卷 70828-70848页

作者： Wheeb, Ali H. Maurya, Sudhanshu Karimullah, Shaik Shaik, Fahimuddin Chowdhury, Subrata Siva Prasad, B.V.V. Sinha, Anurag Sood, Adamya Hussain, Syed Abid University of Baghdad College of Engineering Department of Aeronautical Engineering Baghdad10071 Iraq Department of Computer Science and Engineering Pune412115 India Annamacharya University Department of ECE Rajampet516126 India Sreenivasa Institute of Technology and Management Studies Department of Computer Science and Engineering Chittoor517127 India Anurag University School of Engineering Department of Computer Science and Engineering Hyderabad500088 India ICFAI University Tech School Computer Science Department Ranchi835222 India Amity University Amity School of Engineering and Technology Computer Science Department Noida201313 India Bakhtar University Department of Computer Science and Engineering Karta-e-Chahar Kabul1001 Afghanistan Lovely Professional University Division of Research and Development Phagwara144001 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India

Visible Light Communication (VLC) has emerged as a promising technology for vehicular communication due to its high data rates, low latency, and immunity to electromagnetic interference. However, optimizing spectral efficiency and channel estimation in dynamic vehicular environments remains a key challenge. This paper proposes an OFDM-QAM-based VLC system to enhance spectral efficiency and improve channel estimation accuracy in vehicular networks. To achieve this, we leverage Simulation of Urban Mobility (SUMO) data, which provides detailed insights into vehicular movement and traffic patterns. By integrating SUMO with VLC system simulations, we optimize modulation schemes, multiple access techniques, and spatial reuse strategies to adapt to varying traffic conditions. Additionally, a mathematical framework is developed to evaluate the impact of vehicular mobility on spectral efficiency and bit error rate (BER) performance across different QAM modulation schemes. Simulation results demonstrate that the proposed VLC system significantly improves spectral efficiency, achieving values up to 1.4 bits/s/Hz, while maintaining low BER across varying Signal to Noise Ratio (SNR) levels. These findings confirm the effectiveness of the proposed methodology in enhancing Visible Light Communication (VLC) performance for vehicular applications, making it a viable solution for next-generation intelligent transportation systems. © 2013 IEEE.

关键词： Spectrum efficiency

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Wide field of view large aperture meta-doublet eyepiece

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Light(science & Applications) 2025年第1期14卷 167-176页

作者： Anna Wirth-Singh Johannes E.Fröch Fan Yang Louis Martin Hanyu Zheng Hualiang Zhang Quentin T.Tanguy Zhihao Zhou Luocheng Huang Demis DJohn Biljana Stamenic Juejun Hu Tian Gu Arka Majumdar Department of Physics University of WashingtonSeattleWAUSA Department of Electrical and Computer Engineering University of WashingtonSeattleWAUSA Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMAUSA Department of Electrical and Computer Engineering University of MassachusettsLowellMAUSA Department of Electrical and Computer Engineering University of CaliforniaSanta BarbaraCAUSA

Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.

关键词： optics refractive illustrate

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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computer Modeling in engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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A cellulose-based lithium-ion battery separator with regulated ionic transport and high thermal stability for extreme environments

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Nano Research 2025年第1期18卷 315-322页

作者： Kun-Peng Yang Song Xie Zi-Meng Han Hao-Cheng Liu Chong-Han Yin Wen-Bin Sun Zhang-Chi Ling Huai-Bin Yang De-Han Li Qing-Fang Guan Shu-Hong Yu Department of Chemistry New Cornerstone Science LaboratoryInstitute of Biomimetic Materials&ChemistryAnhui Engineering Laboratory of Biomimetic MaterialsDivision of Nanomaterials&ChemistryHefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Institute of Innovative Materials Department of Materials Science and EngineeringDepartment of ChemistrySouthern University of Science and TechnologyShenzhen 518055China

Separators play a critical role in lithium-ion ***,the restrictions of thermal stability and inferior electrical performance in commercial polyolefin separators significantly limit their applications under harsh ***,we report a cellulose-assisted self-assembly strategy to construct a cellulose-based separator massively and *** an ultrahigh ionic conductivity in electrolytes of 3.7 mS·cm^(-1) and the ability to regulate ion transport,the obtained separator is a promising alternative for high-performance lithium-ion *** addition,integrated with high thermal stability,the cellulose-based separator endows batteries with high safety at high temperatures,greatly expanding the application scenarios of energy storage devices in extreme environments.

关键词： lithium-ion batteries bio-inspired materials battery separators cellulose nanofiber ultrahigh ionic conductivity high safety

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Do Not Skip over the Offline: Verifiable Silent Preprocessing from Small Security Hardware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4860-4873页

作者： Dong, Wentao Xu, Lei Zheng, Leqian Duan, Huayi Wang, Cong Wang, Qian Nanjing University of Science and Technology School of Mathematics and Statistics China ETH Zurich Department of Computer Science Switzerland City University of Hong Kong Department of Computer Science Hong Kong SAR Hong Kong Wuhan University School of Cyber Science and Engineering China

Multi-party computation (MPC) has garnered growing research and industry attention, with many protocols adhering to the preprocessing model to prioritize online performance via offline-generated, data-independent correlated randomness (or correlation for short). However, existing studies have predominantly focused on the online phase, and the equally critical offline phase remains overlooked. This oversight can lead to significant yet unaddressed challenges in both security and efficiency. To address these challenges, we start from the pseudorandom correlation generator (PCG) notion by Boyle et al. (CRYPTO'19, FOCS'20) and propose a programmable, verifiable, and efficient PCG construction using dedicated, small security hardware. Our technique, termed HPCG, bridges cryptography and hardware worlds for both strong security and high performance. It enables parties to generate virtually unbounded target correlations and verify their well-formedness at a low cost. Compared to existing cryptographic PCGs, HPCG restrainedly relies on minimal hardware trust, while advancing: 1) support for versatile correlations;2) well-formedness enforcement for any correlation regardless of its form;and 3) reduced offline costs further. In contrast to prior hardware-based MPC solutions, HPCG 1) minimizes the trusted computing base;2) eliminates single-vendor trust assumptions;and 3) avoids hosting sensitive user inputs directly on security chips. Furthermore, although HPCG operates solely in the offline phase and requires no direct access to user inputs, it enables performant online MPC protocols by efficiently generating some new, highly customized correlations. Our results demonstrate that HPCG outperforms prior preprocessing techniques in performance, security, simplicity, or a combination thereof. © 2025 IEEE.

关键词： Digital storage

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