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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20484 条记录，以下是331-340 订阅

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Hypernetwork Design for Self-Supervised Domain Adaptation in Compressive Sensing MRI Reconstruction 6

Hypernetwork Design for Self-Supervised Domain Adaptation in...

引用

6th ieee international conference on image Processing, Applications and systems, IPAS 2025

作者： Thomas, Libya Zacharias, Joseph Trivandrum Affiliated to: A.P.J Abdul Kalam Technical University College of Engineering Kerala India

ISBN: (纸本)9798331506520

This paper presents a novel hypernetwork design for self-supervised domain adaptation in compressed sensing MRI (CS-MRI) reconstruction. Unlike conventional models, which often fail to generalize across distribution shifts in MRI data, our hypernetwork generates task-specific parameters for a primary reconstruction network based on learned regularization weights, dynamically adjusting to new test data. Leveraging test-time training (TTT), our hypernetwork significantly improves reconstruction accuracy on previously unseen data distributions. Evaluations using the fastMRI dataset demonstrate a notable gain in Peak signal-to-Noise Ratio (PSNR) by 3.6 dB and Structural Similarity Index (SSIM) by 0.05 over baseline models, such as U-Net and VarNet, especially in out-of-distribution settings. © 2025 ieee.

关键词： signal to noise ratio

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A deep learning-based crop pest and disease recognition system for farmland in Xinjiang 6

A deep learning-based crop pest and disease recognition syst...

引用

6th international conference on internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Sun, Rui Xinjiang University Urumqi China

ISBN: (纸本)9798350386974

With the rapid development of modern agriculture, the timely detection and treatment of pests and diseases is very important to ensure the healthy growth of crops and improve production. This paper presents a deep learning-based system for identifying crop diseases and pests in Xinjiang, utilizing Convolutional Neural Networks (CNNs). The system achieves over 95% accuracy, outperforming traditional methods. It offers real-time pest control suggestions, enhancing both detection efficiency and accuracy. © 2024 ieee.

关键词： Agricultural science and technology Convolution neural network (CNN) Crop health deep learning image processing Plant diseases and insect pests recognition ResNet - 50

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Multimodal Slot Vision Transformer for SAR image Classification 2

Multimodal Slot Vision Transformer for SAR Image Classificat...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Yu, Tianyi Han, Fangzhou Zhang, Lamei Zou, Bin Harbin Institute of Technology Department of Information Engineering Harbin150001 China

ISBN: (纸本)9798331515669

In recent years, deep learning network models, particularly Vision Transformer (ViT), have achieved remarkable progress in the domain of computer vision. Radar remote sensing has garnered attention due to its all-weather operational capabilities;However, due to limitations in imaging mechanisms, radar data is susceptible to noise interference, affecting data quality and interpretation accuracy. Hyperspectral data, on the other hand, is known for its extensive spectral coverage. To effectively utilize the modal features of both and promote information exchange between different modalities, addressing the shortcomings of single-modal data and solving the issue of feature indistinguishability in multimodal networks, a ViT network based on slot attention encoder, referred to as SLViT, is proposed. To better achieve feature decoupling, parallel branches are employed to handle remote sensing image blocks from various modalities, while position-based weight sharing is applied, providing a new means to simultaneously leverage spatial information and specific modal information. Meanwhile, unlike traditional multimodal fusion methods, attention mechanisms are employed for feature fusion, and a depthwise separable convolution module is introduced for processing remote sensing image blocks. © 2024 ieee.

关键词： Radar imaging

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Coherent Integration with Multi-pass Synthetic Aperture Radar images 2

Coherent Integration with Multi-pass Synthetic Aperture Rada...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Hu, Zihan Li, Gen Wang, Yifan Sun, Chuanxv Zhai, Yiming Dong, Zehua Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

Synthetic aperture radar (SAR) can be used to acquire high-resolution images of huge areas and has been widely used in planetary exploration. However, in earth-based SAR imaging of the moon, the effect of additive noise is not ignorable and the restricted temporal window for single observation limits the potential for image denoising through integration methods. To address this issue, a method to achieve coherent integration with multi-pass SAR images for additive noise removal is proposed. The proposed method achieves envelope alignment through sub-pixel level image registration and phase alignment by compensating the flat earth phase and topographic phase, thus satisfying the requirement of coherent integration. The signal-to-noise (SNR) gain resulting from this method is given by a function of the correlation. The effectiveness of the method and the correctness of the function are verified by the simulation experiments. © 2024 ieee.

关键词： image denoising

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SOLUTION AND ANALYSIS FOR 3-D LOCALIZATION IN CLOSED-FORM INTEGRATING SA AND TDOA MEASUREMENTS 49

SOLUTION AND ANALYSIS FOR 3-D LOCALIZATION IN CLOSED-FORM IN...

引用

49th ieee international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Xing, Tianyi Sun, Yimao Ni, Lihua Peng, Xiangyu Zhang, Kehao Wan, Qun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Sichuan Univ Coll Comp Sci Chengdu Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Linear array-based three-dimensional (3-D) localization is a recently proposed technology. It uses a set of newly defined one-dimensional (1-D) angles, called space angle (SA), to locate the source. Integrating SA with time difference of arrival (TDOA) promises higher accuracy, and more attractive, provides the formulation leading to closedform solutions. Currently, studies of hybrid SA-TDOA localization leverage iteration to refine the performance to the Cramer-Rao lower bound (CRLB), which suffers from the possible convergence issues. This paper advances this topic by proposing a two-stage weighted least squares (TSWLS) closed-form estimator, which avoids the risk of divergence and can attain the CRLB if the noise is mild. Analytical and numerical results show the performance ascendancy of the proposed method.

关键词： Source localization TDOA SA closed-form solution

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SynTraC: A Synthetic Dataset for Traffic signal Control from Traffic Monitoring Cameras 27

SynTraC: A Synthetic Dataset for Traffic Signal Control from...

引用

27th ieee international conference on Intelligent Transportation systems, ITSC 2024

作者： Chen, Tiejin Shirke, Prithvi Chakravarthi, Bharatesh Vaghela, Arpitsinh Da, Longchao Lu, Duo Yang, Yezhou Wei, Hua School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Rider University Department of Computer Science and Physics LawrencevilleNJ United States

ISBN: (纸本)9798331505929

This paper introduces SynTraC, the first public image-based traffic signal control dataset, aimed at bridging the gap between simulated environments and real-world traffic management challenges. Unlike traditional datasets for traffic signal control which aim to provide simplified feature vectors like vehicle counts from traffic simulators, SynTraC provides real-style images from the CARLA simulator with annotated features, along with traffic signal states. This image-based dataset comes with diverse real-world scenarios, including varying weather and times of day. Additionally, SynTraC also provides different reward values for advanced traffic signal control algorithms like reinforcement learning. Experiments with SynTraC demonstrate that it is still an open challenge to image-based traffic signal control methods compared with feature-based control methods, indicating our dataset can further guide the development of future algorithms. The code for this paper can be found in https://***/DaRL-Libsignal/SynTraC. © 2024 ieee.

关键词： Traffic signals

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Multi-Target Respiration Sensing based on Cell-Free Millimeter-Wave System

Multi-Target Respiration Sensing Based on Cell-Free Millimet...

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2024 ieee international conference on Unmanned systems, ICUS 2024

作者： Sun, Xiaoxu Jiang, Qingji Wang, Dongming Hou, Huazhou Zhang, Qiankun Southeast University Nanjing China Purple Mountain Laboratories Nanjing China China Information Technology Designing & Consulting Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350384185

In recent years, respiration sensing has flourished. However, most existing solutions are based on Wi-Fi signals, suitable for single-target scenarios, and are limited in sensing distance due to noise interference. Compared to Wi-Fi systems, cell-free millimeter-wave systems have advantages such as large bandwidth, better security, and stronger stability, enabling finer-grained respiration sensing. Therefore, we proposed a multi-target respiration sensing method based on cell-free millimeter-wave system. First, we use the Extensive Cancellation Algorithm (ECA) to extract static signals. By leveraging the time-invariant property of static signals, we compensate for the asynchronous clocks of the transceivers without compromising the linear property of the received signal. For multi-target scenarios, we decompose the received signal in the subcarrier domain using Independent Component Analysis (ICA) and demonstrate the feasibility of this approach. Simulation and experimental results show that the proposed method achieves a wider sensing coverage when only one pair of transceiver nodes is used, up to a maximum sensing distance of 20 meters with a relative error limit of 10%. When limited to the same absolute error constraint (0.5 bpm), the proposed scheme achieves about 40% improvement in sensing range compared to other sensing schemes. © 2024 ieee.

关键词： image segmentation

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Global Information Edge Augmentation Network for Remote Sensing image Continuous Super-Resolution 2

Global Information Edge Augmentation Network for Remote Sens...

引用

2nd ieee international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Gao, Shize Lei, Yong Zhang, Jingya Wang, Guoqing Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques. © 2024 ieee.

关键词： image enhancement

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RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Network for Unsupervised Deformable Medical image Registration

RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Ne...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： He, Zixuan Tang, Jing Zhao, Zitong Gong, Zeyu School of Future Technology Huazhong University of Science and Technology Hubei Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368741

Medical image registration is essential for integrating information from diverse imaging modalities for clinical diagnosis and treatment planning. Despite significant advancements, achieving efficient and precise deformable image registration remains a formidable challenge. In this study, we propose a novel medical image registration model, RWKVMatch, which employs global attention and cross-fusion mechanism based on the Vision-RWKV module to address complex deformations in medical images effectively. Additionally, the elastic transformation from data augmentation techniques is integrated into the model architecture to enhance its capability to handle multi-scale features and improve robustness to geometric variations in image registration. Experimental evaluations on two medical image registration datasets indicate the effectiveness of our approach, surpassing existing state-of-the-art methods in terms of registration accuracy and computational efficiency. These findings underscore the potential of RWKVMatch as a highly effective tool for medical image registration. © 2025 ieee.

关键词： deformable image registration elastic transformation multi-scale feature fusion Vison-RWKV

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Comparative Experimental Analysis of Different Wireless Communication technology for Various UAV Application with Alerting System 4

Comparative Experimental Analysis of Different Wireless Comm...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Mandal, Priti Roy, Lakshi Prosad Das, Santos Kumar Nit Rourkela Dept. of Ece Rourkela India

ISBN: (纸本)9798350343670

The deployment of sensor-based systems is growing rapidly because of its capabilities and intelligence in real-time data transmission and reception. In order to access remote places where there is a risk to human life, Unmanned Aerial Vehicles (UAVs) are employed to build up the aerial internet of Things (IoT). Various wireless communication technologies, such as Zigbee, Wi-Fi, and LoRa, are used in this work to convey data, comprising messages and acoustic signals, to the gateway. Various factors, including packet loss, packet delay, and Received signal Strength Indicator (RSSI), are taken into account while evaluating the performance in an experimental setting. For analysis of the parameters, both stationary and flying gateway are considered. These analyses are done in order to examine the performance of the wireless communication technology for various UAV applications. Additionally, an android application is developed for the notification, in order to alert the end users and take the necessary action accordingly. © 2024 ieee.

关键词： internet of Things (IoT) Unmanned Aerial Vehicle (UAV) Wireless Communication technology

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