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Screening and Artifact Detection of RFI in Sentinel-1A Time-Series Images Combining Change Detection Techniques With Structural Similarity Index

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10864-10881页

作者： Zhang, Zhizheng Shu, Gaofeng Huang, Yabo Wu, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China

As a wideband radar system, spaceborne synthetic aperture radar (SAR) is susceptible from other high-power radiation sources, which can cause radio frequency interference (RFI) artifacts in the acquired images. Given that RFI significantly impacts data processing and image interpretation, screening and artifact detection of RFI have become top priorities for spaceborne SAR systems, which generate massive data daily. However, current image-level methods mostly rely on the acquisition of relevant prior knowledge, which makes efficient screening of large-scale products difficult when the scenario changes. In this article, a novel screening and detection method based on change detection techniques is proposed. Due to the time-varying feature of RFI artifacts in time-series SAR images, local areas affected by RFI can be masked through change detection operator. The screening step is performed by quantifying and analyzing the feature differences between RFI artifacts and ground-truth information in local areas. Therefore, an RFI-free background is constructed based on the results, enabling effective artifacts detection of RFI-containing images. Experimental results of Sentinel-1A Level-2 products verify the effectiveness and robustness of the proposed method. © 2008-2012 IEEE.

关键词： Radio interference

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Non-uniform electron density estimation based on electromagnetic wave attenuation in plasma

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Chinese Physics B 2025年第1期34卷 381-386页

作者： Zhaoying Wang Lixin Guo Maixia Fu Shaoshuai Guo Yinsheng Li Key Laboratory of Grain Information Processing and Control(Henan University of Technology) Ministry of EducationZhengzhou 450001China College of Information Science and Engineering Henan University of TechnologyZhengzhou 450001China School of Physics Xidian UniversityXi’an 710071China

The surface of a high-speed vehicle reentering the atmosphere is surrounded by plasma *** to the influence of the inhomogeneous flow field around the vehicle,understanding the electromagnetic properties of the plasma sheath can be *** the electron density of the plasma sheath is crucial for understanding and achieving plasma stealth of *** this work,the relationship between electromagnetic wave attenuation and electron density is deduced *** attenuation distribution along the propagation path is found to be proportional to the integral of the plasma electron *** result is used to predict the electron density ***,the average electron density is obtained using a back-propagation neural network ***,the spatial distribution of the electron density can be determined from the average electron density and the normalized derivative of attenuation with respect to the propagation *** to traditional probe measurement methods,the proposed approach not only improves efficiency but also preserves the integrity of the plasma environment.

关键词： attenuation electromagnetic propagation plasma electron density

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Robust primary quantization step estimation on resized and double JPEG compressed images

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Multimedia Tools and Applications 2025年第12期84卷 11097-11118页

作者： Zhang, Lei Chen, XuGuang Niu, YaKun Zuo, XianYu Wang, Huaqing School of Computer and Information Engineering Henan Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng Henan Henan Kaifeng475004 China National Internet Emergency Center Henan Zhengzhou450000 China

As one of the most important forensic tasks, reconstruction of the original information in tampered images is a key step for tampering detection and localization. Currently, a number of methods have been designed to estimate the primary quantization steps of double compressed JPEG images. However, the estimation in the presence of resizing operation remains a challenge. In this paper, we propose a robust primary quantization steps estimation method on resized and double JPEG compressed images. Specifically, the distribution of Discrete Cosine Transform (DCT) coefficients is firstly analyzed on the inverse resized image. Then, a maximum likelihood function together with a filtering strategy is designed to obtain the primary quantization step on Alternating Current (AC) bands. In addition, we find the prominent peak in the Discrete Fourier Transform (DFT) spectrum of the distribution of Direct Current (DC) coefficients is nonlinearly related to the step. Based on this observation, a mapping function derived from the geometric fitting is proposed to estimate the step on DC band. Experimental results demonstrate the proposed method provides superior estimation performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image compression

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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Identification of block cipher algorithms using multi-layer perception algorithm

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Soft Computing 2025年 1-12页

作者： Yuan, Ke Yu, Daoming Yang, Wei Du, Zhanfei Shen, Lin Li, Zheng School of Computer and Information Engineering Henan University Kaifeng China Henan Province Engineering Research Center of Spatial Information Processing Henan University Kaifeng China

Cryptographic algorithm identification is the process of distinguishing or identifying the cryptographic algorithm by analysing the potential information of various features in the ciphertext under the condition of known ciphertext, which is the basis of cryptanalysis work. As the complexity of ciphertext increases, the interference between ciphertext data and encryption algorithms increases, and the identification effect of single-layer cryptographic algorithms in encryption algorithm identification work is poor. In order to solve the problems of low recognition accuracy and poor stability of the block cipher algorithm in the single-layer identification scheme, we propose a cryptographic algorithm identification scheme of block cipher algorithm using deep learning algorithm Multi-Layer Perception (MLP) in this paper. In this scheme, 15 NIST randomness test methods are used to extract ciphertext feature, and 10 meaningful features are selected as input to MLP classifier to make predictions. In the ciphertext-only scenario, five block cipher algorithms, AES, 3DES, Blowfish, CAST and RC2 are selected for the study of cryptographic algorithm identification, then binary classification and multiclassification identification are carried out. The experimental results demonstrate that, when the size of the ciphertext files and other experimental conditions are the same as for the five conventional machine learning models, the proposed scheme has superior accuracy and stability. Among them, the average identification accuracy of binary classification is 76.5% for ciphertext files of 1KB to 512KB size, which is 35.3%, 37.5%, 34.2%, 38.5%, and 41.6% higher than that of the traditional classical machine learning models SVM, GNB, KNN, RF, and LR respectively. The average identification accuracy of the multiclassification is 36.2%, which is significantly higher than the other five classical machine learning algorithms. When the ciphertext file sizes are different, among the ident

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Co-regulation effect of solvation and interface of pyridine derivative enabling highly reversible zinc anode

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材料科学技术（英文版） 2025年第1期204卷 1-9页

作者： Binrui Xu Guangbin Wang Yong Liu Quanan Li Fengzhang Ren Jianmin Ma School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China School of Materials Science and Engineering Provincial and Ministerial Coconstruction of Collaborative Innovation Center for Non-ferrous Metal New Materials and Advanced Processing TechnologyHenan University of Science and TechnologyLuoyang 471023China Longmen laboratory Luoyang 471000China School of Chemistry Tiangong UniversityTianjin 300387China

The poor reversibility and stability of Zn anodes greatly restrict the practical application of aqueous Zn-ion batteries(AZIBs),resulting from the uncontrollable dendrite growth and H2O-induced side reactions during *** additive modification is considered one of the most effective and simplest methods for solving the aforementioned ***,the pyridine derivatives(PD)including 2,4-dihydroxypyridine(2,4-DHP),2,3-dihydroxypyridine(2,3-DHP),and 2-hydroxypyrdine(2-DHP),were em-ployed as novel electrolyte additives in ZnSO4 *** density functional theory calculation and experimental findings demonstrated that the incorporation of PD additives into the electrolyte effectively modulates the solvation structure of hydrated Zn ions,thereby suppressing side reactions in ***-ditionally,the adsorption of PD molecules on the zinc anode surface contributed to uniform Zn deposi-tion and dendrite growth ***,a 2,4-DHP-modified Zn/Zn symmetrical cell achieved an extremely long cyclic stability up to 5650 h at 1 mA ***,the Zn/NH4V4O10 full cell with 2,4-DHP-containing electrolyte exhibited an outstanding initial capacity of 204 mAh g-1,with a no-table capacity retention of 79％after 1000 cycles at 5 A ***,this study expands the selection of electrolyte additives for AZIBs,and the working mechanism of PD additives provides new insights for electrolyte modification enabling highly reversible zinc anode.

关键词： Zn anode Pyridine derivative Electrolyte additive Solvation regulation Interface modification

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NGMO: A Novel Geometric Mean Optimizer for Intrusion Detection in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 296-308页

作者： Yao, Yunhang Zhang, Zhiyong Zhao, Kejing Wang, Peng Wu, Ruirui Henan International Joint Laboratory of Cyberspace Security Applications College of Information Engineering Luoyang471023 China Henan University of Science and Technology Henan Intelligent Manufacturing Big Data Development Innovation Laboratory Luoyang471023 China China Nonferrous Metals Processing Technology Company Ltd. Luoyang471039 China

Industrial cyber-physical systems (CPS) are experiencing various malicious attacks and encountering increasing security challenges. Although machine learning-based intrusion detection systems can help users quickly detect attacks in industrial CPS, feature redundancy and the tuning of model hyperparameters hinder further detection performance. In this study, a Novel Geometric Mean Optimizer (NGMO) is designed to filter redundant industrial features while optimizing the hyperparameters of model. The proposed NGMO incorporates good point sets and dynamic opposition learning strategies during the population initialization and generation hopping phases to enhance the search capabilities of algorithm. Furthermore, the NGMO is combined with three gradient boosting decision tree models for intrusion detection in industrial CPS. Finally, four datasets from industrial scenarios and a real-world case are used to evaluate the effectiveness of NGMO. The experimental results show that NGMO can reduce time consumption while improving model detection accuracy. Therefore, the proposed NGMO can effectively enhance the security of industrial CPS. © 2023 IEEE.

关键词： Network intrusion

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Wind turbine detection based on high spatial resolution four-band reflectance images 6

Wind turbine detection based on high spatial resolution four...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Deng, Fengying Qiao, Baojun Li, Kaitao Zhao, Limin Chen, Xingfeng Li, Jiaguo Liu, Jun Sun, Yuan Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Aerospace Information Research Institute Chinese Academy of Science Beijing China School of Space Information Space Engineering University Beijing China

ISBN: (纸本)9781510688018

Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detection accuracy of conventional methods is low, especially when dealing with multi-scale wind turbine targets. In addition, the underutilization of the near-infrared (NIR) band limits the capability of optical remote sensing sensors in wind turbine detection. To address the above problems, this paper proposes a wind turbine detection algorithm YOLOv8m-BD based on high spatial resolution four-band reflectance images using YOLOv8m as the baseline model. Firstly, the four-band image is input through channel adaptation modification. Next, the CSPDarknet53 to 2-stage FPN (C2f) module is enhanced by introducing deformable convolution (DCN) to expand the receptive field. Finally, the effectiveness of our design was validated through extensive experiments on a self-built wind turbine dataset. Compared to the YOLOv8m baseline model that only inputs RGB three bands, the improved YOLOv8m-BD achieves an accuracy of 96.4% and a false alarm rate of 2.0%, an increase of 3.4% in accuracy and a decrease of 2.5% in false alarm rate. © 2025 SPIE.

关键词： Proximity sensors

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On the Performance of Active RIS-Assisted Mixed RF-THz Relaying Systems

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IEEE Internet of Things Journal 2025年第10期12卷 14938-14951页

作者： Yin, Yiyang Yang, Liang Li, Xingwang Liu, Hongwu Guo, Kefeng Li, Yingsong Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Space Engineering University School of Space Information Beijing101407 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

We investigate the performance of an active reconfigurable intelligent surface (RIS)-assisted mixed radio frequency (RF)-terahertz (THz) relaying system, where the RF signal reaches the relay through the active RIS and is then transmitted to the user via the THz channel. Under this scenario, we analyze the system performance with the relay employing amplify-and-forward (AF) and decode-and-forward (DF) protocols. More specifically, we derive the exact expressions for the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) for both relaying protocols. Based on this, we obtain the exact expressions for the outage probability, average bit error rate (ABER), and average channel capacity (ACC). Furthermore, to gain deeper insights, we derive the asymptotic expressions at high SNRs and obtain diversity order (DO) of the system. Moreover, we extend the analysis to the variable gain relaying scheme. The findings reveal that the DOs for both relaying protocols are determined by the THz channel parameters, and the DO under the AF relaying protocol is twice that of the DF relaying protocol. Finally, we validate that active RIS (A-RIS) can more effectively assist the performance of the mixed RF-THz relaying system compared to passive RIS. © 2014 IEEE.

关键词： Distribution functions

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Hand Gesture Recognition Method based on mmWave Radar with MobileViT and Knowledge Distillation 16

Hand Gesture Recognition Method based on mmWave Radar with M...

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16th International Conference on Signal processing Systems, ICSPS 2024

作者： Zhang, Xiangqun Ge, Zhizhou Lu, Kai Du, Genyuan Shen, Jiawen Gao, Xiangqian School of Information Engineering Xuchang University Xuchang461000 China Henan International Joint Laboratory of Polarization Sensing and Intelligent Signal Processing Xuchang461000 China Henan Shengshi Hengxin Technology Co. Ltd Xuchang461111 China

ISBN: (纸本)9781510689251

Human-hand gesture recognition using millimetre wave radar is attractive in human-computer interfaces, industrial Internet of Things, and smart home. However, the existing CNN or RNN model is so complex and large that it is hard to apply to mobile vision tasks and embedded devices. This paper proposes dual lightweight convolutional neural networks, MobileViT and knowledge distillation, to solve this problem. Firstly, we acquire the original radar echoes of frequency-modulated continuous wave (FWCW) signal and reshape the three-dimensional matrix by Chirps * Samples * Frames. Then, we perform the signal processing method to eliminate the noise and static background. Secondly, we employ the Fast Fourier Transform to extract the hand gestures feature map of distance and Doppler information. Finally, the feature map is input into the modified MobileViT to recognize dynamic hand gestures, and knowledge distillation models is used to simplify the model structure. The experimental results show that the parameter space complexity of the constructed model is reduced to 0.018 M, and the computational complexity is 0.082 GFLOPs after knowledge distillation *** method is verified in 12 complex gesture. It has the potential to be applied in mobile tasks and embedded devices. © 2025 SPIE.

关键词： Fast Fourier transforms

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