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

共 20493 条记录，以下是971-980 订阅

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A Method for Filling signal in Live TV Key based on image Comparison and Keying technology 6

A Method for Filling Signal in Live TV Key Based on Image Co...

引用

6th international conference on Electronics technology, ICET 2023

作者： Gu, Linhai Deng, Zhi Production Center Guangxi Radio and Television of China Nanning China Guangxi Radio and Television of China Production Department Nanning China

ISBN: (纸本)9798350337693

In order to ensure the safety of key filling signal production and broadcast, studios or broadcast control platforms often use the system design method of main and backup key filling signal generators. Some television stations also use both main and auxiliary personnel to review the content. However, these two methods consider that when the main equipment malfunctions, the backup equipment can continue to design and broadcast subtitles or graphic packaging, Alternatively, relying on manual pre audit methods cannot solve the problem of reducing the error rate of key filling signal content and how to save it. This article proposes a method for broadcasting live TV keyfill signals based on image comparison and keying technology, which only processes the keyfill signals that have already made mistakes and does not affect the original video signal source or other keyfill signals. Practice has proven that the system established using this method can achieve automation of key filling signal broadcasting, greatly reducing the probability of error in key filling signal content, and can shorten the broadcasting time of incorrect key filling signal content by more than 83%. © 2023 ieee.

关键词： Filling

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Computational Experiments of signal Detection Algorithms in VANETs based on Parallel Intelligence 3

Computational Experiments of Signal Detection Algorithms in ...

引用

3rd ieee international conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Li, Yi Han, Shuangshuang Chen, Yueyun Du, Liping University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China

ISBN: (纸本)9798350318470

With the rapid growth of connected vehicles in internet of Vehicle (IoV), ensuring reliable and secure communication is significant. This paper presents a parallel intelligence-based signal detection method for vehicular networks. The proposed method exploits the main idea of parallel intelligent to analyze wireless communication signal in real-time, achieving efficient and accurate algorithm options for difference IoV environment and different requirements. Experimental results demonstrate that the comparison and analysis of the proposed method based on the detection accuracy and computational complexity, while also contributing to enhanced security in vehicular networks. This approach offers a promising solution for timely, reliable and secure communication in the era of connected vehicles. © 2023 ieee.

关键词： signal detection

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Evaluating 10Gbps Wireless Optical Communication Using NRZ Modulation: Analyzing BER, signal Quality, and System Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

引用

7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 ieee.

关键词： Bit error rate

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SEM-FCNET: SEMANTIC FEATURE ENHANCEMENT AND FULLY CONVOLUTIONAL NETWORK MODEL FOR REMOTE SENSING OBJECT DETECTION 30

SEM-FCNET: SEMANTIC FEATURE ENHANCEMENT AND FULLY CONVOLUTIO...

引用

30th ieee international conference on image Processing (ICIP)

作者： Chen, Jing Cheng, Debo Jiang, Jiacheng Yu, Zhongyi Zhang, Shichao Guangxi Normal Univ Coll Comp Sci Engn Guangxi Key Lab Multisource Informat Min Secur Guilin 541004 Peoples R China Univ South Australia UniSA STEM Adelaide SA 5095 Australia

ISBN: (纸本)9781728198354

It is challenging to detect objects in remote sensing images due to there being a large number of objects with few available features and a lot of background noise. Most existing methods ignore a large amount of background noise. In this paper, we propose an end-to-end based network model, Semantic feature Enhancement Model with a Fully Convolutional head prediction Network, referred to as SEM-FCNet, to reduce the effect of background noise in remote sensing images detection. First of all, SEM-FCNet consists of a new semantic feature enhancement module to enhance the semantic features of small objects and reduce noise interference by fusing attention mechanism. Then, SEM-FCNet utilizes a fully convolutional head prediction network to detect multiple objects by extracting their location information. The experiments on two famous remote sensing image datasets, NWPU VHR-10 and HRSC, demonstrate the performance of the proposed SEM-FCNet model over the existing remote sensing image detection methods.

关键词： Remote sensing image Object detection Attention mechanism Convolutional neural network (CNN)

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image-assisted Label Connective Completion for Vessel Segmentation with Insufficient Annotations

Image-assisted Label Connective Completion for Vessel Segmen...

引用

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

作者： Zheng, Xiaoqi Yang, Baoyao Fang, Xiuwen Yao, Wenfang Ye, Mang School of Computer Science and Technology Guangdong University of Technology China School of Computer Science Wuhan University China School of Nursing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798350368741

Automatic and accurate vessel segmentation is crucial for disease diagnosis. Deep learning methods are widely used, but their promising results rely on accurately annotated data. Due to complex vessel morphology and low-contrast image, accurate vessel delineation poses a practical challenge, resulting in insufficient annotations, which is a prominent form of noisy labels. This paper proposes an image-assisted Label Connective Completion method, which enhances label's vessel information by images under the supervision of connectivity to address insufficient annotation issue. Specifically, we develop an image-guided Vessel Enhancement module, which transmits structural information extracted from images based on label navigation to label space, promoting completion of missing annotated parts in original labels. In addition, a branch completion-connectivity loss is designed and introduced as an auxiliary supervision to prevent vessel branch disconnection during label completion. Experimental results on DRIVE, CHASE_DB1 and DCA1 datasets demonstrate that our method outperforms existing noisy labels learning methods. © 2025 ieee.

关键词： Insufficient annotation Label completion Vessel segmentation

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Traffic Assistance and Control System- An RFID Approach

Traffic Assistance and Control System- An RFID Approach

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Kalaiarasi, A. Raja, L. Velmurugan, S. Shanthi, G. Ariharan, R. Kumareshan, N. Sri Shakthi Institute of Engineering and Technology Department of EEE Coimbatore India Sri Eshwar College of Engineering Department of ECE Coimbatore India Dr.N.G.P Institute of Technology Department of Biomedical Engineering Coimbatore India Sri Krishna College of Technology Department of ECE Coimbatore India Guide House Chennai India

ISBN: (纸本)9798350348217

All intelligent objects are connected through a worldwide network called the internet of Things (IoT).. It serves as the conduit via which all things can communicate with one another. The term internet of vehicles is used when those intelligent items are limited to solely connected vehicles. It uses an electronic-based traffic violation monitoring setup rather than assigning a specific officer to keep an eye on traffic violations. This is a mechanism for electronically filing complaints and collecting fee It uses RFID technology to allow for the direct debiting of fees and complaint booking from the associated prepaid account. This clever technique assists the government and the populace in preventing traffic signal violations and punishing the defaulters. Because of this system, citizens are more likely to be motivated to obey traffic light laws, and the government finds it simpler to automatically monitor and manage the system. The internet of Vehicles (IoV), which is built on an intelligent traffic management system, has high interoperability, is simple to upgrade, and can replace current traffic control systems. The proposed system's low cost can greatly enhance traffic signal monitoring and control. The internet of Vehicles uses a network of wireless sensors, detection technologies, and the internet to autonomously track, manage, monitor, and process traffic objects. The proposed system's fundamental features take care to simplify living. © 2023 ieee.

关键词： internet of things

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Real-Time Semantic Segmentation for Autonomous Scale Cars using Mixed Real and Synthetic Data 10

Real-Time Semantic Segmentation for Autonomous Scale Cars us...

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10th international conference on Mechatronics and Robotics Engineering (ICMRE)

作者： Ehrenfeuchter, Steffen Corlito, Roberto Marchthaler, Reiner Enzweiler, Markus Esslingen Univ Appl Sci Inst Intelligent Syst Esslingen Germany

ISBN: (纸本)9798350394283;9798350394276

This paper presents a real-time semantic segmentation framework for camera-based environment perception of objects and infrastructure elements in autonomous scale cars. It is specifically targeted towards student competitions such as the Carolo Cup or the Bosch Future Mobility Challenge. To reduce pixel-wise manual annotation efforts, our framework involves a mixture of both synthetic and real image data, carefully tuned towards the unique requirements of the given scenario. Real images are acquired from a 1:10 scale vehicle equipped with a single monocular camera and are manually annotated. Synthetic image data with automatic pixel-wise annotation is obtained via a custom Unity-based simulation pipeline. We evaluate various mixed real-synthetic data strategies to train different state-of-the-art deep neural networks with a focus on both segmentation performance and real-time capability using an NVIDIA Jetson AGX Xavier platform as in-vehicle test bed. Our experimental results show a significant improvement in semantic segmentation performance of the mixed real-synthetic data approach at real-time speeds of approximately 60 FPS on the target platform.

关键词： Mobile Robotics Autonomous systems Robot Intelligence and Learning signal and image Processing

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Experimental Evaluation of Transmission Control Method based on Received signal Strength for Spatio-Temporal Data Retention 47

Experimental Evaluation of Transmission Control Method based...

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47th ieee-Computer-Society Annual international conference on Computers, Software, and Applications (COMPSAC)

作者： Akashi, Renju Nohayashi, Daiki Tsukarnoto, Kazuya Ikenaga, Takeshi Lee, Myung Kyushu Inst Technol Fukuoka Japan CUNY City Coll New York NY USA

ISBN: (纸本)9798350326970

With the development and spread of IoT technology, the number of devices connected to the internet is increasing. Some data generated by IoT devices include spatio-temporal data (STD) that depends on the location and time of data generation. Therefore, we have proposed the STD retention system (STD-RS) using vehicles as a network infrastructure for local production and consumption of STD. This paper proposes a transmission control method that can mitigate the fluctuation of RSS due to the influence of obstacles in the real environment and evaluates its effectiveness through experiments on actual devices.

关键词： Cyber-Physical System Spatio-Temporal Data Retention Ad hoc networks Local Production and Consumption of Data

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Spotting Deep Fakes: Exploring Detection Techniques for image Recognition

Spotting Deep Fakes: Exploring Detection Techniques for Imag...

引用

2024 ieee international conference on signal Processing and Advance Research in Computing, SPARC 2024

作者： Gupta, Niharikaa Tripathi, Om Kaur, Damandeep Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350385199

The birth of the Deep Fake technology has got into the system like a parasite in a new world of digital manipulation, and this poses profound challenges to the integrity of multimedia content. The following paper undertakes a comprehensive examination of the insights on principles and methodologies employed in recognizing Deep Fakes from authentic media assets. At the heart of the detection challenge lies the inherent differences in the generation processes of Deep Fakes compared to genuine content, necessitating the development of specialized detection techniques. Multiple approaches have been proposed, enclosing both traditional and cutting - edge methodologies. As deep learning has taken its place at the forefront of technology, there is an upgrade in deep fake detection. The rapid advancement in artificial intelligence has given rise to Deep Fake technology, capable of producing highly realistic but fabricated multimedia content. This technology poses significant threats to the authenticity and integrity of digital media, making the development of effective detection mechanisms crucial. This paper provides a comprehensive analysis of various detection techniques, with a focus on deep learning-based models that enhance the accuracy and reliability of identifying manipulated content. Many potent tools like CNNs(Convolutional neural networks, RNNs(recurrent neural networks), and GNNs (Generative Adversarial Networks) have arisen to help distinguish the features or patterns of the Deep fakes. © 2024 ieee.

关键词： Generative adversarial networks

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Fusion image Design System based on Machine Learning Algorithm 1

Fusion Image Design System based on Machine Learning Algorit...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Sun, Xin Yang, Ning Changchun Humanities and Sciences College College of Art and Design Jilin Changchun China

ISBN: (纸本)9798331529246

In the field of multi-modal image fusion, the fusion of unregistered images has always been a difficult problem, especially when the images cannot be accurately aligned due to factors such as scale inconsistency and parallax. This paper studies a fusion algorithm based on Generative Adversarial Networks (GAN) and Orthogonal Sequence Fusion (OSF) to address these challenges. First, use the OSF algorithm to preprocess the image to ensure the consistency of the image in resolution, contrast and brightness. Next, GAN is used for deep feature extraction, and the features are fused through the least squares decomposition method to enhance orthogonality and effectively extract the unique information of the image. Finally, the classifier generated by the GAN algorithm is used to conduct adversarial training on the images, evaluate the quality of the fused images, and provide feedback to optimize the results. Experimental results show that the method proposed in this article has advantages in peak signal-to-noise ratio, structural similarity index and visual information fidelity. The SSIM values of resolution, contrast and color scale differences are 0.97, 0.96 and 0.96 respectively. This proves that this method can handle image scale differences and parallax problems more effectively, and significantly improve the quality and information fidelity of the fused image. Multi-modal image fusion methods based on GAN and OSF can significantly improve the quality and information fidelity of fused images when dealing with scale differences and parallax issues. © 2025 ieee.

关键词： Generative adversarial networks

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