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

共 20446 条记录，以下是121-130 订阅

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JPEG ENCRYPTION WITH DC PREDICTION AND RUN-based RS PAIRS PERMUTATION 49

JPEG ENCRYPTION WITH DC PREDICTION AND RUN-BASED RS PAIRS PE...

引用

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

作者： Wang, Yanyixiao Li, Peiya Jinan Univ Coll Cyber Secur Guangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

JPEG image encryption technology converts original images into noise-like images that do not contain any useful information, ensuring the security and privacy of valuable images. At present, most existing work may not achieve a good balance between file size preservation and security. Therefore, in order to solve this issue, we present a new JPEG image encryption scheme. In our scheme, we first perform predictions on the DC coefficients, and then implement averaging operation on the prediction error, which provides security effects while effectively decreases the encoding length. To further strengthen the diffusion performance, block permutation excluding the DC coefficients is executed. In addition, in order to control the file size growth, AC coefficients are protected by the run-based RS (run/size) pairs permutation. The experimental results show that the proposed scheme can not only effectively preserve the image file size, but also offers satisfactory security.

关键词： image encryption JPEG image DC prediction RS pairs permutation File size preservation

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Multi-angle image Fusion in Ground-based Radar Lunar Imaging based on image Kurtosis 2

Multi-angle Image Fusion in Ground-based Radar Lunar Imaging...

引用

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

作者： Yang, Yuewen Liu, Peiyao Zhu, Kaiwen Wang, Zhen Sun, Tao Ding, Zegang Beijing Institute of Technology School of Information and Electronics Beijing China Ministry of Education Beijing China

ISBN: (纸本)9798331515669

The fusion of multi-angle lunar images can provide more comprehensive lunar topography information than original images, and ground-based (GB) radar has the ability to acquire multi-angle lunar images. In GB radar multi-angle lunar image fusion, proper radar trajectories should be selected, and image fusion algorithm should be used. In this paper, the multi-angle lunar image fusion technique for GB synthetic aperture radar (SAR) based on image kurtosis is proposed. To begin with, the echo signal model considering space-variant backscattering coefficient is established to reflect lunar topography feature. After that, a multi-angle radar trajectory selection method based on K-means algorithm is proposed to acquire multi-angle lunar images. Thereafter a multi-angle lunar image fusion algorithm based on image kurtosis and gradient descent is proposed to acquire the fused image. Finally, computer simulation results validate the effectiveness of the proposed technique. © 2024 ieee.

关键词： image fusion

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MetaCon: Revitalizing internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 ieee.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Automated Corn Leaf Disease Diagnosis using ResNet-based Convolutional Neural Network 2

Automated Corn Leaf Disease Diagnosis using ResNet-based Con...

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2nd international conference on Intelligent Cyber Physical systems and internet of Things (ICoICI)

作者： Kant, Vishnu Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

ISBN: (纸本)9798331540661;9798331540678

Corn leaf diseases, consisting Common Rust, Gray Leaf Spot, and Blight, pose significant threats to worldwide farming by affecting crop harvest and standards. Blight causes rapid defoliation of plants;Common Rust brought on by Puccinia sorghi and Grey Leaf Spot reduces photosynthesis and yields losses. Early and accurate detection is crucial for timely intervention, reducing chemical dependency, and ensuring food security. This study leverages Residual Networks (ResNet) to advance disease detection. ResNet's deep residual learning architecture efficiently handles complex image classification tasks. Trained on maize leaf images, the ResNet model achieved 96% accuracy in classifying leaves into common rust, gray leaf spot, blight, and healthy categories. A model's high precision and recall significantly enhance detection accuracy. This work supports sustainable agriculture, promotes resilient agricultural practices, and contributes to food security by improving disease control and crop management through advanced technology.

关键词： Artificial Intelligence Residual Networks (ResNet) image Classification Deep Learning Crop Yield Sustainable Agriculture Food Security Disease Detection Precision Agriculture Agricultural Sustainability Machine Learning Plant Pathology

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Application of internet of Thing (IoT) for Precision Farming 16

Application of Internet of Thing (IoT) for Precision Farming

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Songruk, Apidat Arunrungrusmi, Somchai Tunlasakun, Khanchai Suwannakhum, Sirimonpak Mungkungline, Narong Poonthong, Wittawat Yuji, Toshifumi King Mongkuts Univ Technol Thonburi Dept Elect technol Educ Bangkok Thailand King Mongkuts Univ Technol Thonburi Fac Ind Educ & Technol Bangkok Thailand

ISBN: (纸本)9781665464956

With the increasing popularity of the internet of Things (IOT) and devices getting smarter, this research has presented an idea to reform the weather information for precision farming. In this study, the information of weather forecast was prepared for air quality control systems using the sensor of network systems to increase the quality of organic vegetable in Thailand under the condition of Thailand weather The prototype model was described by the devices of embedded systems, which is gotten from the temperature and humidity sensor. Therefore, if there is any data from the input sensor, the users are allowed to access and granted to use this system, then, the log data are transmitted by the internet to the designation website by using a Wi-Fi module (ESP8266). As a result, it can be used the machine learning algorithms for implementing of monitor controller, which is applied for analyzing and collecting data to air quality control in green house

关键词： internet of Thing Arduino smart agriculture

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image Dehazing Scheme based on Haze-line and DCP Prior 6

Image Dehazing Scheme Based on Haze-line and DCP Prior

引用

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

作者： Wu, Xinchun Wang, Xiao Huang, Xiaobing Zhang, Xiaojun Ju, Zhaoyang Southwest Jiaotong University School of Information Science and Technology Chengdu610097 China Technology Co. Ltd Chengdu610095 China

ISBN: (纸本)9798350386974

In visual research, researchers strive to understand how optical sensors capture image information and how the brain interprets and processes this information. However, natural atmospheric haze introduces serious visual challenges in images. Haze causes loss of image details, color distortion, and blurring of depth information, posing a significant challenge for many intelligent device applications. This paper combines the strengths of the Haze-line and DCP algorithms, complementing their weaknesses, to develop a method that accurately restores detailed object information in hazy images without introducing distortion or artifacts. By constraining atmospheric light, the method aims to approximate the true atmospheric light value for each pixel in the image. It involves solving the transmission twice and imposing constraints to maximize the restoration of transmission values, which often deviate significantly from real transmission rates due to differences in depth of field. Peak signal-to-noise ratio and structural similarity are used to evaluate the dehazing performance. Experimental results demonstrate that compared to other algorithms, this approach achieves effective dehazing with restored detail while minimizing distortion in the resulting hazefree images. © 2024 ieee.

关键词： Restoration

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Robust image watermarking scheme based on orientation of local image texture 16

Robust image watermarking scheme based on orientation of loc...

引用

16th international conference on signal Processing systems, ICSPS 2024

作者： Li, Qiutong Zhang, Qiyuan Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

With the rapid development of big data and internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust watermarking technology can effectively resist various attacks and protect image copyright. Most of the existing robust watermarking schemes generally adopt the global embedding method and often ignore the texture orientation of the cover image itself leading to poor imperceptibility and robustness. In this paper, we propose an innovative digital image robust watermarking scheme. Firstly, we conduct the Oriented FAST and Rotated BRIEF (ORB) and Speeded Up Robust Features (SURF) feature point detection algorithms, combined with the texture complexity of the cover image to select the most complex texture region as the local embedding location of the watermark. Secondly, to further improve the robustness of the watermarked image, we conduct the Discrete Cosine Transform (DCT) adaptive inter-block correlation algorithm. We embed the watermark into the cover image by adjusting the difference between the image embedding block and the adjacent block. We have tested a dataset containing multiple images, in which various common attacks are simulated and multiple cover images are tested. By comparing the experimental results, our scheme performs better regarding robustness and imperceptibility. © 2025 SPIE.

关键词： Cosine transforms

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Real time eye-blinks detection from dry and portable EEG headset using machine learning 16

Real time eye-blinks detection from dry and portable EEG hea...

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Alexandre, Houze Slephane, Binczak Univ Burgundy Dijon France

ISBN: (纸本)9781665464956

Brain computer interfaces (BCI) are applications that allows user to interact with their environment through their brain waves patterns, in this context the detection of eye blinks seems a good way to perform basic interactions. Electroencephalography (EEG) are methods that allows recording of brain waves through invasives or non invasives methods, in the cases of BCI application the lesser the invasiveness the higher the applicability. As this work will focus on pattern recognition within brain waves we developped a machine learning based approach to detect and classify different actions. Additionnaly the differentiation for each eye allows better interactivity for BCI applications.

关键词： Electroencephalography Wearable sensors signal detection signal processing Machine learning

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EqGAN: Reformation-based Feature Equalization Fusion for Few-shot image Generation

EqGAN: Reformation-based Feature Equalization Fusion for Few...

引用

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

作者： Zhou, Yingbo Yue, Zhihao Ye, Yutong Zhang, Pengyu Wei, Xian Chen, Mingsong MoE Eng. Research Center of SW/HW Co-Design Tech. and App. East China Normal University China

ISBN: (纸本)9798350368741

Due to the absence or mismatch of semantic information, existing few-shot image generation methods suffer from unsatisfactory generation quality and diversity, which have minimal benefits as data augmentation for downstream classification tasks. Reformatting the contextual and textural information of features at different scales, we propose a novel Feature Equalization Fusion Generative Adversarial Network (EqGAN) for few-shot image generation. Specifically, we first decompose the encoded features into textual and structural components to mitigate the influence of irrelevant and redundant information. based on feature correlation learning and attention mechanism, we then obtain fused features by refining different contents (i.e., textures and structures) with a more fine-grained semantic alignment. Moreover, an attention-based reconstruction loss and a consistency-based equalization loss are devised to provide better training stability and generation performance. Comprehensive experiments on three public datasets demonstrate that EqGAN not only significantly improves the FID scores (by up to 14.10%) and LPIPS scores (by up to 3.17%) of generated images, but also outperforms the state-of-the-art in terms of accuracy (by up to 3.89%) for downstream classification. © 2025 ieee.

关键词： Equalization Fusion Few-shot image Generation Generative Adversarial Network

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FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-scale Fusion Network for Aneurysm image Segmentation

FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-sca...

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

作者： Pang, Shanchen Zhao, Xue Zhang, Yulin Zhao, Yawu Ding, Hengtao Zhao, Zhiyuan Qiao, Sibo Qingdao266580 China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao266590 China The College of Software Tiangong University Tianjin300387 China

ISBN: (纸本)9798350368741

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov-Arnold-based (FKAN) Group Multi-scale Fusion Network, termed FKAN-GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced-FKAN block. Furthermore, we develop the Attention Group Multi-scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high- and low-scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN-GMFNet significantly outperforms seven state-of-the-art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS-L dataset are 88.82% and 80.09%, respectively. Code is available at https://***/zx123868/FKAN-GMFNet. © 2025 ieee.

关键词： Fourier Kolmogorov-Arnold Intracranial aneurysms Medical image Segmentation

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