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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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laboratory simulation of CO_(2) immiscible gas flooding and characterization of seepage resistance

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Frontiers of Earth Science 2023年第3期17卷 797-817页

作者： Jie CHI Binshan JU Wenbin CHEN Mengfei ZHANG Rui ZHANG Anqi MIAO Dayan WANG Fengyun CUI School of Big Data and Fundamental Sciences Shandong Institute of Petroleum and Chemical TechnologyDongying 257061China School of Energy Resources China University of Geosciences(Beijing)Beijing 100083China Key Laboratory of Geological Evaluation and Development Engineering of Unconventional Natural Gas Energy Beijing 100083China School of Chemical Engineering Shandong Institute of Petroleum and Chemical TechnologyDongying 257061China

CO_(2) flooding can significantly improve the recovery rate, effectively recover crude oil, and has the advantages of energy saving and emission reduction. At present, most domestic researches on CO_(2) flooding seepage experiments are field tests in actual reservoirs or simulations with reservoir numerical simulators. Although targeted, the promotion is poor. For the characterization of seepage resistance, there are few studies on the variation law of seepage resistance caused by the combined action in the reservoir. To solve this problem, based on the mechanism of CO_(2), a physical simulation experiment device for CO_(2) non-miscible flooding production manner is designed. The device adopts two displacement schemes, gas-displacing water and gas-displacing oil, it mainly studies the immiscible gas flooding mechanism and oil displacement characteristics based on factors such as formation dip angle, gas injection position, and gas injection rate. It can provide a more accurate development simulation for the actual field application. By studying the variation law of crude oil viscosity and start-up pressure gradient, the characterization method of seepage resistance gradient affected by these two factors in the seepage process is proposed. The field test is carried out for the natural core of the S oilfield, and the seepage resistance is described more accurately. The results show that the advancing front of the gas drive is an arc, and the advancing speed of the gas drive oil front is slower than that of gas drive water;the greater the dip angle, the higher the displacement efficiency;the higher the gas injection rate is, the higher the early recovery rate is, and the lower the later recovery rate is;oil displacement efficiency is lower than water displacement efficiency;taking the actual core of S oilfield as an example, the mathematical representation method of core start-up pressure gradient in low permeability reservoir is established.

关键词： laboratory_simulation viscosity starting pressure gradient CO immiscible flooding characterization of seepage resistance

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Joint Routing and Scheduling for In-Vehicle Networks: A Deterministic Perspective

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Wang, Wenxun Wang, Wenyong Yu, Haisheng Wu, Bochun Zou, Sai Ni, Wei Zhang, Ji School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China International Institute of Next Generation Internet Macau University of Science and Technology Macau China State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China College of Big Data and Information Engineering Guizhou University Guiyang China Data61 Business Unit CSIRO Canberra ACT Australia PA Company SC Branch China Telecom Co. LTD Chendu China

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively. IEEE

关键词： Reinforcement learning

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for AIoT Applications

SeP2CNN: A Simple and Efficient Privacy-Preserving CNN for A...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Tao Xiao Zhenyong Zhang Kuan Shao Hao Li State Key Laboratory of Public Big Data School of Computer Science and Engineering Guizhou University Guiyang China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things ( $A$ IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP 2 CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP 2 CNN.

关键词： Adaptation models Privacy Convolution Transforms Polynomials Internet of Things Convolutional neural networks Cryptography Artificial intelligence Homomorphic encryption

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Dynamic Scaling of Unit Tests for Code Reward Modeling

arXiv

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arXiv 2025年

作者： Ma, Zeyao Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current large language models (LLMs) often struggle to produce accurate solutions on the first attempt for code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests serve as reward signals to identify correct solutions. As LLMs always confidently make mistakes, these unit tests are not reliable, thereby diminishing the quality of reward signals. Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality. Our pioneer experiment reveals a positive correlation between the number of unit tests and reward signal quality, with greater benefits observed in more challenging problems. Based on these insights, we propose CodeRM-8B, a lightweight yet effective unit test generator that enables efficient and high-quality unit test scaling. Additionally, we implement a dynamic scaling mechanism that adapts the number of unit tests based on problem difficulty, further improving efficiency. Experimental results show that our approach significantly improves performance across various models on three benchmarks (e.g., with gains of 18.43% for Llama3-8B and 3.42% for GPT-4o-mini on HumanEval Plus). © 2025, CC BY-SA.

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Convolutional Neural Network with Attention Mechanism for Image-Based Smoke Detection 4

Convolutional Neural Network with Attention Mechanism for Im...

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4th International Conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Chen, Hanyang Ji, Qingge Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Guangzhou510006 China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Smoke detection based on computer vision plays a key role in fire prevention. With the development of deep learning, convolutional neural network frameworks are increasingly used in this field. However, due to the large span of smoke's scale and the variety of its color as well as its texture, the features are even more complex. In addition, the sampling of features by the existing neural networks is limited to the size of the convolution kernel. It is difficult to extract the global features. So, detecting smoke from pictures is still a problem. In this work, we proposed a feature-fusion method based on local and global attention mechanisms. It is able to better fuse the low-level and high-level features. In order to solve the scale problem, we proposed a mechanism for selection and offset of the receptive field. In addition, we combine the transformer based on self-attention mechanism and convolution operation to make the model aware of global information. Finally, we optimize the sampling of feature maps by the detection head of the one-stage network based on the smoke color features. Experiments show that the proposed model outperforms state-of-the-art relevant competitors by 4.6% on AP.5:.95. © 2022 IEEE.

关键词： Smoke

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Ultrasound-triggered biomimetic ultrashort peptide nanofiber hydrogels promote bone regeneration by modulating macrophage and the osteogenic immune microenvironment

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Bioactive Materials 2024年第1期31卷 231-246页

作者： Fan Zhang Mingchen Lv Siyuan Wang Mengyao Li Yu Wang Congjiao Hu Wei Hu Xuekui Wang Xiaogang Wang Zhiduo Liu Zhen Fan Jianzhong Du Yao Sun Department of Oral Implantology Stomatological Hospital and Dental School of Tongji UniversityShanghai Engineering Research Center of Tooth Restoration and Regeneration200072China Department of Gynaecology and Obstetrics Shanghai Fourth People’s HospitalSchool of MedicineTongji UniversityShanghai200434China Department of Polymeric Materials School of Materials Science and EngineeringTongji University201804China Department of Immunology and Microbiology The Minister of Education Key Laboratory of Cell Death and DifferentiationShanghai Institute of ImmunologyShanghai Jiao Tong University School of MedicineChina Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityChina

The immune microenvironment plays a vital role in bone defect *** create an immune microenvironment that promotes osteogenesis,researchers are exploring ways to enhance the differentiation of M2-type *** peptides have been discovered to effectively improve this process,but they are limited by low efficiency and rapid degradation in *** overcome these issues,peptide with both M2 regulatory and self-assembly modules was designed as a building block to construct an ultrasound-responsive nanofiber *** nanofibers can be released from hydrogel in a time-dependent manner upon ultrasound stimulation,activating mitochondrial glycolytic metabolism and the tricarboxylic acid cycle,inhibiting reactive oxygen species production and enhancing M2 macrophage *** hydrogel exhibits advanced therapeutic potential for bone regeneration by triggering M2 macrophages to secrete BMP-2 and IGF-I,accelerating the differentiation of bone marrow mesenchymal stem cells(BMSCs)into ***,modularly designed biomimetic ultrashort peptide nanofiber hydrogels provide a novel strategy to rebuild osteogenic immune microenvironments for bone repair.

关键词： Peptide Nanofiber hydrogel Immune microenvironment Bone tissue regeneration

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Multi-objective Optimization of Multi-video Synopsis Based on NSGA-III 8

Multi-objective Optimization of Multi-video Synopsis Based o...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Chen, Xiaoxiao Xie, Yujia Wang, Xing Nanjing University of Finance and Economics College of Information Engineering Jiangsu Key Laboratory of Grain Big-data Mining Nanjing China Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Multi-video synopsis can display all information of surveillance video and show all sports solution of moving objects in the sight of multi-cameras. However, most of the existing multi-video synopsis can't achieve these functions. This is because these approaches do not comprehensively consider these factors: video target display timing, maximum target display number, target display consistency, maximum moving target display density, and compression ratio. To address the comprehensive optimization problem of multiple elements for multi-video synopsis, a multi-objective optimization algorithm for multi-video synopsis is proposed. The proposed approach differs significantly from the existing methods and has several appealing properties. First, we propose five objective functions, which are consistent in time sequence, coherent in cross-camera pedestrian display, largest in number of moving objects displayed, smallest in density of moving objects, and smallest in compression ratio, then, establish a multi-objective optimization model based on the five objective functions. Second, we build a model for multi-video synopsis. Third, we use the non-dominated sorting genetic algorithm NSGA-III to optimize the multi-object model of the multi-video synopsis. Through such a solution, the poor performance of multi-video synopsis can be avoided by comprehensively considering the main influencing factors of multi-video synopsis. Besides, we present extensive experiments that demonstrate the effectiveness and efficiency of the proposed approach. © 2022 IEEE.

关键词： Multiobjective optimization

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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

Suppress Content Shift: Better Diffusion Features via Off-th...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at this url. © 2024 Neural information processing systems foundation. All rights reserved.

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