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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261-270 订阅

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Unsupervised Real-Time Flow Data Drift Detection based on Model Logits for internet of Things Network Traffic Classification 17

Unsupervised Real-Time Flow Data Drift Detection Based on Mo...

引用

ieee Congress on Cybermatics: 17th ieee international conference on internet of Things, iThings 2024, 20th ieee international conference on Green Computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Wang, Pan Liu, Minyao Li, Zeyi Wang, Zixuan Chen, Xuejiao Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing Vocational College of Information Technology School of Communications Nanjing China

ISBN: (纸本)9798350351637

In recent years, numerous internet of Things (IoT) traffic classification techniques based on deep learning have been proposed, demonstrating impressive classification performance. With ubiquitous access to massive heterogeneous IoT endpoints, networks exhibit high levels of dynamism, heterogeneity, and complexity, leading to potential data distribution drift (DDD), which will significantly degrade the performance of IoT network traffic classification. Although numerous scholars have explored various drift detection methods, most studies require real-time stream data labeling. Meanwhile, some unsupervised methods incur high resource consumption and latency when applied to network traffic. To address this issue, we propose an unsupervised real-time drift detection method based on Model Logits (ML-UDD). based on the Logit scores from the deep learning model's traffic classification/inference process, the model's Maximum Logit Score (MLS) will be calculated as the key metric for detection DDD. This method does not need to rely on the data labels, thus it can explore in an unsupervised manner, which is superior to many other supervised methods in terms of expert experience and labor. Furthermore, an adaptive weighted CUSUM-type rapid detection approach is proposed to detect the DDD based on a sliding time window during the traffic flow proceeding. In addition, this method can further locate the 'Drift Flow' where DDD happened, namely Flow Drift Location. This offers a real-time and efficient method to monitor and identify DDD with minimal resource consumption. The experiments have been conducted on two public and private network traffic datasets. The comparative experiments with other SOTA DDD detection methods have demonstrated that the proposed ML-UDD method can surpass these methods in terms of different performance metrics. © 2024 ieee.

关键词： image thinning

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FINE-TUNING TEXT-TO-image DIFFUSION MODELS FOR CLASS-WISE SPURIOUS FEATURE GENERATION 31

FINE-TUNING TEXT-TO-IMAGE DIFFUSION MODELS FOR CLASS-WISE SP...

引用

2024 international conference on image Processing

作者： MaungMaung, AprilPyone Nguyen, Huy H. Kiya, Hitoshi Echizen, Isao Natl Inst Informat Tokyo Japan Tokyo Metropolitan Univ Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9798350349405;9798350349399

We propose a method for generating spurious features by leveraging large-scale text-to-image diffusion models. Although the previous work detects spurious features in a large-scale dataset like imageNet and introduces Spurious imageNet, we found that not all spurious images are spurious across different classifiers because images that contain spurious features were filtered by a limited number of classifiers. Although spurious images help measure the reliance of a classifier, filtering many images from the internet to find more spurious features is time-consuming. To this end, we utilize an existing approach of personalizing large-scale text-to-image diffusion models with available discovered spurious images and propose a new spurious feature similarity loss based on neural features of an adversarially robust model. Precisely, we fine-tune Stable Diffusion with several reference images from Spurious imageNet with a modified objective incorporating the proposed spurious-feature similarity loss. Experiment results show that our method can generate spurious images that are consistently spurious across different classifiers. Moreover, the generated spurious images are visually similar to reference images from Spurious imageNet.

关键词： Spurious features stable diffusion fine-tuning

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LEGIT: TEXT LEGIBILITY FOR USER-GENERATED MEDIA 31

LEGIT: TEXT LEGIBILITY FOR USER-GENERATED MEDIA

引用

2024 international conference on image Processing

作者： Mandal, Maniratnam Birkbeck, Neil Adsumilli, Balu Bovik, Alan C. Univ Texas Austin Austin TX 78712 USA YouTube Mountain View CA USA Google Inc Mountain View CA USA

ISBN: (纸本)9798350349405;9798350349399

User-generated content (UGC) is ubiquitous across the internet as a result of billions of videos and images being uploaded each day. All kinds of UGC media are affected by natural distortions, occurring both during and after capture, which are inherently diverse and commingled. These distortions have different perceptual effects based on the media content. Given recent dramatic increases in the consumption of short-form content, the analysis and control of their perceptual quality has become an important problem. Regardless of the content, many UGC videos have overlaid and embedded texts in them, which are visually salient. Hence text quality has a significant impact on the global perception of video or image quality and needs to be studied. One of the most important factors in perceptual text quality in user-generated media is legibility, which has been studied very little in the context of computer vision. Predicting text legibility can also help in text recognition applications such as image search or document identification. This work aims at modeling text legibility using computer vision techniques and thus studying the relationship between text quality and legibility. We propose a modified dataset variant of COCO-Text [1] and a model for predicting text legibility for both handwritten and machine-generated texts. We also demonstrate how models trained to predict text legibility can help in the prediction of text (perceptual) quality. The dataset and models can be accessed here https://***/research/Quality/***.

关键词： image Quality Assessment Text Quality Text Legibility User-generated Content

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Semantic Segmentation-based Low-Rate image Communication With Diffusion Models 16

Semantic Segmentation-Based Low-Rate Image Communication Wit...

引用

16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Huang, Jun Liu, Chang Liu, Dong Moe Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China

ISBN: (纸本)9798350390643

Semantic communication aims to transmit the underlying semantic information of a signal from the sender to the receiver, where the key requirement is to ensure that the receiver reconstructs a signal semantically (almost) equivalent to the source. Conventional image communication methods lack effective mechanisms for preserving both semantic coherence and reconstruction quality, especially for low-rate scenarios. In this paper, we propose an alternative method, namely Instance-Consistent image Communication (ICIC), for semantic segmentation-based low-rate image communication. Our method leverages segmented instances and detected captions as intermediate vehicles to transmit semantic information from the sender to the receiver. Once transmitted, the receiver is capable of utilizing the vehicles to regenerate images with off-the-shelf diffusion models. Compared to state-of-the-art methods, experimental results on the COCO dataset illustrate that our method obtains higher semantic accuracy and higher image quality at extremely low rates. © 2024 ieee.

关键词： Semantic Segmentation

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Toward Reliable Mobile CrowdSensing Data Collection: image Splicing Localization Overview 19

Toward Reliable Mobile CrowdSensing Data Collection: Image S...

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19th ieee international Wireless Communications and Mobile Computing (ieee IWCMC)

作者： Abou-Zbiba, Wahiba Benbrahim, Houda El Bakkali, Hanan El Gadi, Hajar Maqour, Zaina Benhaddou, Driss Mohammed V Univ IRDA ENSIAS Rabat Morocco Mohammed V Univ SSL ENSIAS Rabat Morocco Univ Houston Engn Technol Dept Houston TX 77204 USA Alfaisal Univ Dept Elect Engn Coll Engn Riyadh Saudi Arabia

ISBN: (纸本)9798350333398

With the advancement of technology, the collection of data used in Intelligent Transportation systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.

关键词： Intelligent Transportation systems Mobile CrowdSensing image Forgery image Splicing Localization

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A Semantic Communication based Wireless image Transmission for internet of Things Devices

A Semantic Communication Based Wireless Image Transmission f...

引用

international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Zhu, Rangang Huang, Shengxian He, Chenguang Su, Shaojing Chen, Hao College of Electronic Engineering National University of Defense Technology Hunan China Communications Research Center Harbin Institute of Technology Harbin China College of Intelligence Science and Technology National University of Defense Technology Hunan China

ISBN: (纸本)9789819975396

In recent years, wildfires occur frequently as global warming. People set a large number of sensors to monitor the wild land. However, the poor network in remote area can hardly afford the big data transmission while low delay. In this paper, we firstly propose a semantic communication framework, which consists of the GAN-based semantic extraction and LDPC code. To make it affordable for internet of Things (IoT) devices, we then compression the pre-trained model by parameters pruning and clustering with the acceptable price of inference performance. based on our analysis, the proposed semantic communication system can significantly reduce the volume of transmission data by extracting the semantic information of images and preform robustness in fading channel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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internet of Things Enabled Blockage Detection in Micro-Irrigation System by Using Drones 5

Internet of Things Enabled Blockage Detection in Micro-Irrig...

引用

5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Vijayakumar, R. Divyapriya, S. Kumar, R. Satheesh Ramya, C. Sharmila, S. Sns College of Technology Department of Eee Coimbatore India Karpagam Academy of Higher Education Department of Eee Coimbatore India

ISBN: (纸本)9798350379945

Drip irrigation systems, while efficient in water usage, often suffer from clogged emitters, leading to crop loss. To address this challenge, a drone-based solution is proposed. Equipped with image processing capabilities and controlled by loT, the drone can identify clogged emitters and potentially clear obstructions. Additionally, the system can collect salty water from the irrigation system, contributing to overall water management. This integrated approach aims to optimize water utilization, enhance crop yield, and mitigate the impacts of water scarcity in agriculture. © 2024 ieee.

关键词： Drones

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IoT-based Smart Home Care systems with Location Detection

IoT-based Smart Home Care Systems with Location Detection

引用

2023 international conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cheng, Chia-Hsin Huang, Kuo-Ting Chen, Jun-Jie Chiu, Tzu-Yuan National Formosa University Department of Electrical Engineering Yunlin Huwei Taiwan

ISBN: (纸本)9798350324174

In this paper, a home care system with location information is designed. From this we can learn from the physiological information and location information of the elders. The physiological information part includes the measurement of blood oxygen and pulse value and the medication information. The medication information part is realized through image Recognition technology, by using Yolov3 for drug identification. The location information part uses Wi-Fi wireless transmission signals to do indoor positioning (Indoor Positioning), measures the received signal strength (RSSI) of the distance between the elder and the wireless signal sender, and judges the elder's location through the measured results. © 2023 ieee.

关键词： internet of things

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2024 16th international conference on Wireless Communications and signal Processing Physical Layer Authentication based on CNN-GAN 16

2024 16th International Conference on Wireless Communication...

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16th international conference on Wireless Communications and signal Processing, WCSP 2024

作者： Xie, Wei Wang, Hongjun Feng, Zimo Ma, Chunlai College of Electronic Engineering National University of Defense Technology Hefei China

ISBN: (纸本)9798350390643

The rapid growth of the internet of Things (loT) has created significant opportunities for the future of telecommunications. Research on physical layer authentication with channel features holds promise to improve wireless network security for loT devices. In this paper, we exploit Channel State Information (CSI) phase extracted from the receiver with multiple antennas to enhance the channel spatial diversity. Then Convolutional Neural Network and Generative Adversarial Network (CNN-GAN) model is designed for physical layer authentication. Outlier score from model is introduced to authenticate device identity. Finally, we carry out extensive experimental evaluations about the authentication performance and robustness of the proposed scheme by data collected from Wi-Fi signal based on ieee 802.11n protocol. The results have successfully demonstrated that the proposed scheme exhibits outstanding performance. © 2024 ieee.

关键词： Physical layer

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A Grouping Strategy-based Progressive Fusion Network for Hyperspectral image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 ieee.

关键词： group convolution Hyperspectral image image fusion super-resolution

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