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Spaceborne ISAR Imaging of Space Target With Intrapulse Motion Compensation Based on Modified Phase Difference

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Chen, Ruida Jiang, Yicheng Ni, He Zhang, Yun Harbin Inst Technol Dept Elect Engn Harbin 150001 Peoples R China

In spaceborne inverse synthetic aperture radar (ISAR) imaging of space targets, the high-speed motion of the radar and target can make the intrapulse motion not negligible. It can cause the range dimension matched filtering error, resulting in image defocusing. In traditional ISAR imaging scenarios, existing analysis and methods only focus on the high-speed motion of the target, without considering the radar, which is not suitable for spaceborne ISAR imaging of space targets. In this letter, the intrapulse motion of spaceborne ISAR imaging of space targets is analyzed in detail, and the corresponding compensation method based on modified phase difference (MPD) is proposed. First, the specific expression of the signal's time delay considering the intrapulse motion is derived. Then, the range-dimension matched filtering error is analyzed, and the condition of ignoring the intrapulse motion is also obtained. This condition can be used to judge whether the intrapulse motion can be ignored in practical processing. According to the error analysis, an intrapulse motion compensation method based on MPD is proposed. This method does not need to estimate the motion parameters of the radar and target. The coefficient used to construct the compensation term can be directly estimated from the echo signal. Finally, the corresponding spaceborne ISAR imaging method with intrapulse motion compensation for space targets is proposed. Isolated scatterer and satellite electromagnetic calculation data experiments using the orbital data verify the effectiveness of the proposed method.

关键词： Air quality prediction BiConvLSTM deep learning encoder-decoder spatial-temporal attention mechanism prediction of PM2.5 and PM10

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OF-DFN: Optical flow prediction network for different perspective image fusion

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NEUROCOMPUTING 2024年 591卷

作者： You, Tianshun Liu, Ming Zhao, Yongming Dong, Liquan Beijing Inst Technol Beijing 100081 Peoples R China Yangtze Delta Reg Acad Beijing Inst Technol Jiaxing 314019 Peoples R China China Aerosp Sci & Ind Corp Def Technol Acad 2 Inst 706 Beijing 100854 Peoples R China

Currently, non -decision -level image fusion algorithms require extremely high registration precision of the images to be fused. In the face of different perspective image fusion scenarios, traditional feature registration algorithms and learning -based methods have poor robustness and are unsuitable for large image differences because of the Registration -Fusion separation. In addition, the lack of relevant datasets also hinders the development of different perspective image fusion methods. Given the above problems, we collect 5000 sets of different perspective RGB-MONO datasets in multiple scenes for raw data support. We present an end -toend learned system for fusing two different perspective photographs into a chosen target view. The cascaded feature extraction based on encoder-decoder structure enables learning optical flow at different feature levels systematically. Then the optical flow module enables the image to be continuously registered and optimized during the fusion process, thus avoiding the deviations introduced by non -end -to -end algorithms. Extensive quantitative and qualitative experiments demonstrate that our proposed system can effectively fuse images from different perspectives in our self -built dataset. Compared with non -end -to -end fusion, our method provides superior performance in several fusion evaluation indicators.

关键词： Image fusion Different perspective Optical flow encoder-decoder End-to-end system

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Data-Driven Recommendation Model Based on Meta-Learning for Non-Intrusive Load Monitoring

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3562-3572页

作者： Chu, Xianghua Pang, Yongsheng Ma, Yue Li, Shuxiang Qu, Yuanju Wang, Yangpeng Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Shenzhen 518060 Peoples R China

Smart meters with Non-Intrusive Load Monitoring (NILM) technology is explored as a means of reducing energy consumption and minimizing emissions in residential settings. Various NILM disaggregation algorithms have been proposed, but their effectiveness varies across different scenarios. To address this issue, a method called Data-driven Recommendation Model (DRM) based on meta-learning is proposed in this paper. DRM recommends the appropriate algorithms for various problems using the existing knowledge repository. Besides, a novel meta-feature extractor named Meta-Learning encoder-decoder is designed to efficiently process the deep meta-features from a more significant number of training samples, which can be implemented to various meta-learners. Experimental results indicate that the proposed DRM exhibits strong learning and generalization capabilities in terms of the efficiency of load disaggregation for datasets with different characteristics.

关键词： Load modeling Metalearning Task analysis Training Heuristic algorithms Feature extraction Computational modeling Smart meter meta-learning non-intrusive load monitoring recommendation model encoder-decoder

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A Novel Framework for Motion-Induced Artifact Cancellation in sEMG: Evaluation on English Premier League and Ninapro Datasets

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IEEE SENSORS JOURNAL 2024年第14期24卷 22610-22619页

作者： Ergeneci, Mert Bayram, Erkan Binningsley, David Carter, Daryl Kosmas, Panagiotis Kings Coll London Sch Nat & Math Sci London WC2R 2LS England Neurocess Ltd London WC2A 2JR England Univ Illinois Dept Elect & Comp Engn Coordinated Sci Lab Urbana IL 61801 USA Manchester United Football Club Manchester M16 0RA England Leeds United Football Club Elland Rd Leeds LS11 0ES England Kings Coll London Fac Nat Math & Engn Sci NMES Dept Engn London WC2R 2LS England

This article addresses the challenge posed by motion-induced artifact (MIA) in surface electromyography (sEMG) signals, a prevalent issue in professional sports settings due to the movements and collisions of athletes. The shared frequency spectra and nonstationary characteristics of MIA and sEMG, coupled with the unpredictable and impulsive occurrence of MIA, cause substantial challenges to conventional filtering and signal-processing-based denoising methods. This study proposes a framework involving two consecutive models specifically designed to detect MIA zones in the sEMG stream and to denoise MIA. Using two distinct deep learning models for each task proves more effective than using a singular model, enhancing the signal-to-noise ratio (SNR) by 3.12 dB. A bidirectional long short-term memory recurrent neural network (BLSTM RNN)-based approach is proposed for detecting MIA zones, achieving macro F1 scores of 94.8% and 95% for synthetic and real-world datasets, respectively. This study uses the publicly available Ninapro dataset, enriched with synthetic MIA, and a unique dataset collected from English Premier League (EPL) athletes, incorporating real MIA. For the denoising of MIA, a novel convolution block within the U-Net encoder decoder (UED) is introduced, featuring attention-enhanced kernel and channel selection, which achieves an SNR improvement (SNRimp) of 17.20 dB. This approach surpasses the best state-of-the-art model by 7.01 dB and exceeds the average of contemporary models by 12 dB, signifying a substantial advancement in the field.

关键词： Sports Noise reduction Kernel Signal to noise ratio Sensors Convolution Adaptation models Attention deep learning encoder-decoder motion-induced artifact (MIA) noise cancellation spike loss regularization surface electromyography (sEMG) U-Net

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Deep understanding of radiology reports: leveraging dynamic convolution in chest X-ray images

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DATA TECHNOLOGIES AND APPLICATIONS 2024年第3期58卷 427-446页

作者： Jaiswal, Tarun Pandey, Manju Tripathi, Priyanka Natl Inst Technol Raipur Dept Comp Applicat Raipur India

PurposeThe purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder-decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical ***/methodology/approachIn this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017;Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during c

关键词： GRU DyCNN Attention network Chest X-ray caption generation CNN encoder-decoder

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Convolutional Attention-Based Bidirectional Recurrent Neural Network for Human Action Recognition

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COMPUTATIONAL INTELLIGENCE 2025年第2期41卷

作者： Mahamkali, Aditya Gali, Manvitha Jena, Soumya Ranjan Sreenivas, Velagapudi Univ Cumberlands Dept Informat Technol Williamsburg KY 40769 USA NIMS Univ Sch Comp & Artificial Intelligence Jaipur Rajasthan India SRK Inst Technol Dept Comp Sci & Engn Vijayawada Andhra Pradesh India

Human activity recognition (HAR) technology plays a major role in today's world and is used in detecting human actions and poses in real-time. In the past, researchers employed statistical machine learning methods to build and extract attributes of various movements manually. However, typical techniques are becoming increasingly ineffective in the face of exponentially increasing waveform data that lacks unambiguous principles. With the advancement of deep learning technology, manual feature extraction is no longer required, and performance on challenging human activity recognition problems can be improved. However, various deep learning models have problems such as time consumption, inaccuracy, and the vanishing gradient problem. Therefore, to solve these problems, the proposed study used a deep convolutional attention-based bidirectional recurrent neural network to detect human activities in the provided samples. The input images are first pre-processed using an adaptive bilateral filtering approach to improve their quality and remove image noise. Then, the crucial features are recovered using the convolutional neural network (CNN) based encoder-decoder model. Finally, a deep convolutional attention-based bidirectional recurrent neural network is used to identify human activities. The model recognizes human actions with higher effectiveness and lower latency. The human behaviors are identified using the HMDB51 dataset. The proposed model acquired the highest accuracy of 95.46%, which is 10.51% superior to multi-layer perceptron (MLP), 6.99% superior to CNN, 12.76% superior to long short-term memory (LSTM), 5.59% superior to Bidirectional LSTM (BiLSTM), and 4.82% superior to CNN-LSTM, respectively.

关键词： convolutional neural network deep learning encoder-decoder Gaussian blur human activity recognition hybrid models

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Innovative approaches in image processing: enhancing feature extraction and recognition capabilities

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VISUAL COMPUTER 2025年 1-15页

作者： Yang, Zhaozhao Yu, Yuhai Huang, Yongdong Meng, Jiana Huanggang Polytech Coll Sch Elect Informat Huanggang 438002 Peoples R China Dalian Minzu Univ Sch Comp Sci & Engn Dalian 116600 Liaoning Peoples R China Dalian Minzu Univ Sch Sci Dalian 116600 Liaoning Peoples R China

This paper presents novel methods to improve feature extraction and recognition capabilities in handwritten mathematical expression recognition (HMER). By introducing a multi-scale residual (MSR) module within a DenseNet encoder, we effectively capture detailed and global features across different scales, thus overcoming feature loss problems commonly encountered in HMER tasks. In addition, we propose a data augmentation strategy based on spatial transformations to increase feature diversity without additional data. Our methodology is extensively evaluated on the CROHME 2014, 2016, and 2019 datasets, achieving recognition accuracies of 56.75%, 53.79%, and 56.13%, respectively, demonstrating consistent improvements in accuracy and robustness over traditional methods. This approach further optimizes the overall performance of the model, making it well-suited for real-world applications requiring high accuracy in the recognition of handwritten mathematical expressions. All source code and datasets are accessible at https://***/freedompuls/MsMER, facilitating reproducibility. This work advances the state of the art in HMER and provides valuable insights for researchers and practitioners in image processing and pattern recognition.

关键词： Handwritten mathematical expression recognition encoder-decoder Multi-scale residual module Data augmentation

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Rocknet: lightweight network for real-time segmentation of Martian rocks

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2025年第1期22卷 1-11页

作者： Wei, Pengfei Sun, Zezhou Tian, He Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Beijing Inst Spacecraft Syst Engn Beijing 100094 Peoples R China Beijing Key Lab Intelligent Space Robot Syst Techn Beijing 100094 Peoples R China

Rock segmentation on the Martian is particularly critical for rover navigation, obstacle avoidance, and scientific target detection. We propose a lightweight network for real-time semantic segmentation of Martian rocks (RockNet). First, we propose the cross-dimension channel attention (CDCA) model to replace traditional downsample and upsample operation, which gives more weight to the channels with more useful information by adjusting the weight of each channel. Second, we modify the short-term dense concatenate model, we adopt dilated convolution to learn the feature with a larger receptive field, and through the skip connection structure, the degradation of the network can be reduced. Finally, we propose a feature fusion module (FFM) to fully fuse different levels of features. With only 0.86M parameters, our model gets 82.37% mIoU and 105.7 FPS running speed on the dataset of TWMARS.

关键词： Rock segmentation Deep learning Lightweight network encoder-decoder

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A Framework for Compound Interrupted Sampling Repeater Jamming Parameter Measurement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Lv, Qinzhe Liu, Liyi Wu, Yaojun Peng, Mimi Quan, Yinghui Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Key Lab Collaborat Intelligence SystMinist Edc Xian 710071 Peoples R China Xidian Univ Xian Key Lab Adv Remote Sensing Xian 710071 Peoples R China

Active compound jamming, particularly compound interrupted sampling repeater jamming (ISRJ), possesses excellent flexibility and jamming effectiveness, making it one of the major threats to radar systems. Accurately measuring the key parameters of each ISRJ component within the compound ISRJ can provide critical prior information for subsequent anti-jamming efforts. However, most of the existing ISRJ parameter measurement methods target a single ISRJ and lack in-depth research on the measurement of compound ISRJ parameters. Therefore, we propose a unified framework for compound ISRJ parameter measurement that contains a compound ISRJ separation network based on an encoder-decoder architecture and a parameter regression module for measuring the key parameters of each ISRJ component in the compound ISRJ. Experimental results indicate that the proposed framework achieves parameter measurement accuracies of over 89% and 85% for dual-compound and multicompound ISRJ, respectively, significantly outperforming existing methods.

关键词： Compounds Jamming Training Time-domain analysis Feature extraction Time measurement Analytical models Repeaters Kernel Decoding Channel attention compound interrupted sampling repeater jamming (ISRJ) encoder-decoder jamming separation parameter measurement

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Multiscale Spatio-Temporal Information Cascade Single-Object Visual Tracker

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Ni, Xiaoyu Yuan, Liang Han, Yongming Beijing Univ Chem Technol Coll Informat Sci & Technol Minist Educ Beijing 100029 Peoples R China Hebei Univ Architecture Sch Mech Engn Zhangjiakou 075000 Hebei Peoples R China Shanghai Jiao Tong Univ ICCI Shanghai 200240 Peoples R China Beijing Univ Chem Technol Engn Res Ctr Intelligent PSE Minist Educ China Beijing 100029 Peoples R China

Recently, Transformer has been largely explored in object tracking, and shown state-of-the-art (SOTA) performance compared to convolutional neural networks (CNNs). Especially, single-object trackers based on pure Transformer and "CNN+Transformer" frameworks have achieved great success in terms of accuracy and speed. However, most methods do not fully exploit the temporal and spatial information of targets. Furthermore, the potential of trackers for spatial information interaction and propagation between the search area and templates remains underexplored. These all limit the further improvement in tracking performance. To address these issues, we propose a multiscale cascaded single-object tracking framework based on spatio-temporal information fusion (STIF), which more comprehensively integrates the temporal and spatial information of targets and more deeply interacts the information in the search area and templates. In particular, to establish extensive spatio-temporal feature correlations, the STIF network is introduced, which uses a Transformer-based encoder-decoder structure to cross-fuse the global nonlinear temporal and spatial information of the target search area with static and dynamic templates, effectively performing fusion-based propagation. To focus on rich spatial semantic information, we design a multiscale feature extraction (MFE) network, and a feature cascade aggregation (FCA) module based on the encoder-decoder structure, which can effectively carry out interaction-based propagation. Finally, a bounding box prediction head and an IoU score head are used to predict the exact location of the target and update the dynamic templates, respectively. Extensive experiments demonstrate that our method attains better tracking performance than the baseline method. Meanwhile, the proposed method also obtains comparable results with other SOTA trackers on six challenging benchmarks, including GOT-10k, TrackingNet, LaSOT, UAV123, NFS30, and OTB100, while runni

关键词： Target tracking Transformers Feature extraction Object tracking Visualization Decoding Cameras Convolutional neural networks Robot vision systems Pipelines encoder-decoder single-object tracker spatio-temporal information Transformer visual object tracking

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