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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531-540 订阅

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Low Complexity Physical Layer Security Schemes based on Channel Characteristics of Near-Field XL-MIMO systems: A Survey 17

Low Complexity Physical Layer Security Schemes Based on Chan...

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17th international Congress on image and signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Li, Lin Zhang, Dengyin Hou, Huijun Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things China School of Internet of Things Nanjing University of Posts and Telecommunications China CETC 14th Research Institute Nanjing China

ISBN: (纸本)9798331507398

Extremely large-scale multiple-input and multiple-output (XL-MIMO) is one of the most promising key technologies for the coming sixth generation wireless communications. Considering passive eavesdropping threats, the influence of near-field channel characteristics of uplink multi-user XL-MIMO systems on the physical layer security schemes are analyzed in this paper. Then, a comprehensive survey of physical layer encrypted methods and low complexity detection methods is presented. Conclusively, it is pointed out that the study on low complexity and high reliability modulation encrypted methods based on near field channel characteristics for uplink multi-user XL-MIMO systems has very important research value for the next generation's green security communications. Some corresponding future potential research works for low complexity physical layer security schemes based on channel characteristics of near-field XL-MIMO systems are also presented in this paper. © 2024 ieee.

关键词： image thinning

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A Deep Learning-based image Segmentation Model for Extracting Migratory Birds and Insects Echoes Using Polarimetric Weather Radar Data 2

A Deep Learning-Based Image Segmentation Model for Extractin...

引用

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

作者： Sun, Zhuoran Cui, Kai Wang, Rui Yan, Zujing Ding, Mingming Wu, Dongli Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China China Meteorological Administration Meteorological Observation Center Beijing China

ISBN: (纸本)9798331515669

Weather radars are essential for monitoring airborne animals, including birds and insects. Despite the potential of dual-polarization radar moments for echo classification, extracting migration echoes from birds and insects at pixel-level remains challenging. In this study, six radar moments were rendered into two RGB images, and a deep learning-based image segmentation model was developed to extract bird and insect echoes at the pixel level. The model uses the typical encoder-decoder architecture. The ResNet-50 model was used to extract the features on different spatial scales, which was modified to accept six input channels, matching the six radar moments of polarimetric weather radar data during the encoding stages. The U-Net model was employed for multiscale feature fusion during the decoding stages. Training and validation on a manually labeled dataset demonstrated that the proposed method performs exceptionally well, achieving a mean intersection over union of 98%. The model's performance was further evaluated using two representative cases. © 2024 ieee.

关键词： Birds

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Towards Task-Scalable Semantic Communications: A Preprocessing Enhanced image Transmission Framework 16

Towards Task-Scalable Semantic Communications: A Preprocessi...

引用

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

作者： Lu, Yanpeng Niu, Kai Dai, Jincheng Ministry of Education Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Beijing China

ISBN: (纸本)9798350390643

Recently, more and more images are transmitted to the receiver for the machine analysis tasks instead of purely for the human-vision task. However, most current machine-type semantic communication systems are optimized towards a specific task or a given set of tasks, without considering the scalability to new tasks. In this work, we propose a preprocessing enhanced image semantic communication system to address this challenge. Instead of optimizing directly towards a specific task, our system first optimizes a neural codec for preserving the holistic image information under the trade-off between bandwidth and distortion. Then, we reveal that the performance drop on the primary task can be ascribed to a lack of generalization of the task head to images with overall artifacts caused by compression and noisy channels. Accordingly, by finetuning a preprocessing module based on the pretrained neural codec under the trade-off between task accuracy and distortion, most loss on the primary task accuracy can be recovered with a trivial degradation on distortion, in this way, the task-scalable ability can also be preserved well due to the high quality on the holistic image. Experiments verify the effectiveness of our method in driving the primary task and the scalability to new tasks. © 2024 ieee.

关键词： image communication systems

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Multimodal feature fusion for pedestrian crossing intention prediction based on hybrid attention mechanism 6

Multimodal feature fusion for pedestrian crossing intention ...

引用

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

作者： Guo, Jieru Ding, Yutong Tian, Aoshang Chongqing Normal University School of Computer and Information Science Chongqing China Hebei North University School of English Language Institute Hebei China Wuhan Sports University School of Institute of Intelligent Sports Engineering Wuhan China

ISBN: (纸本)9798350386974

Pedestrian intent prediction is crucial for developing safe self-driving systems. Pedestrian crossing behavior is influenced by various factors, and self-driving cars can use on-board front cameras to monitor and record a pedestrian's historical trajectory and surroundings to predict future crossing behavior. Effectively fusing multivariate information from different modalities is key to accurate predictions. This paper proposes a multimodal information fusion model based on a hybrid attention mechanism. This model employs hybrid attention networks to extract complementary information from both original and semantic images, enhancing focus on image regions relevant to pedestrian crossing. Additionally, an asymmetric bi-directional gated loop BU-GRU module with an optimal fusion strategy is introduced to achieve superior performance in F1 score and accuracy. © 2024 ieee.

关键词： image enhancement

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High Performance Secured image Transmission System Employing OFDM and Cryptography 8

High Performance Secured Image Transmission System Employing...

引用

8th international conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Maheswari, S. Shifani, S. Agnes Simats Engineering Saveetha Institute of Medical and Technical Sciences India

ISBN: (纸本)9798350367904

In the modern world, it is vital to ensure that appropriate encryption and decryption are used while sending data across the internet to avoid unwanted access. based on the visual cryptography system, a novel cryptographic scheme is suggested to secure color images. As inputs are a color image that needs to be protected and a binary image that serves as the encryption and decryption key. Three monochromatic images based on the YCb Cr color space are created from a secret color image that needs to be shared. Following the conversion of these monochromatic images into binary images, the binary images are then encrypted using binary key images, also known as share-l, to produce binary cipher images. Three half-tones of the secret color image and the binary key image are encrypted independently using the Exclusive OR technique. Share-2 is obtained by combining these binary pictures. After the shares have been encrypted, the binary images that have been retrieved are converted to inverted halftones and merged to create a hidden color image. This technique works better over a diffused channel for transmitting natural images. The OFDM technology is the foundation of this multi modal transmission. In wireless communications, OFDM as a multi-carrier transmission mechanism plays vital role. Due to its many benefits, including its fast data rate transmission, excellent reception quality, and capacity to counteract Inter-Symbol Interference (ISI), particularly in fading channels, the adoption of OFDM has grown significantly.A novel approach is put forward for progressive image transmission via Low Density Parity Check Coding (LDPC) on coded Orthogonal Frequency Division Multiplexing (OFDM) systems. In this technique, the Peak to Average Power Ratio (PAPR) of the OFDM signal is reduced and the performance of the OFDM systems is improved through the utilization of trigonometric transformations. It enhances progressive picture transmission's ability for error resilience and transm

关键词： Binary images

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Secure image Communication: Integrating Diffie-Hellman Key Exchange for Enhanced Confidentiality 3

Secure Image Communication: Integrating Diffie-Hellman Key E...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Shifani Francis, M. Dhalia Sweetlin, J. Sandhiya, M. Madras Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350372120

The safety of data or images is a critical step in the vast field of internet of Things (IoT) network communication. image encryption is a well-known technique for protecting the information contained in images in order to maintain their confidentiality, authenticity, and integrity. The goal of the secure communication system described in the work proposed is to make image data exchange easier while preserving confidentiality and integrity. To create a secret key that is shared between communicating parties over an unsecure channel, the system uses the Diffie-Hellman key exchange mechanism. Sensitive image information connected to the sent photos is encrypted and decrypted using this shared key. In order to prevent eavesdropping and unauthorized access, the Diffie-Hellman key exchange algorithm makes it possible to securely generate a shared key without the need for direct key transmission. Encrypted image data guarantees that the sensitive information contained in the images can only be accessed and decoded by the designated recipient, who has the necessary decryption key. Through the integration of image data encryption techniques with the Diffie-Hellman key exchange's strength, the proposed system aims to offer a dependable and secure means of transferring images while protecting the privacy and confidentiality of related sensitive data. This system's efficiency stems from its capacity to securely exchange images over possibly unreliable channels while preserving the data's integrity and confidentiality. This system's efficiency extends beyond secure image exchange, finding potential applications in signal and image processing and enhancing its versatility for broader usage over possibly unreliable channels while preserving data integrity and confidentiality. © 2024 ieee.

关键词： Electronic data interchange

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Real-time image Recognition System based on Ultra-Low-Latency Spiking Neural Networks 2

Real-time Image Recognition System based on Ultra-Low-Latenc...

引用

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

作者： Lan, Bin Zhang, Xi Dong, Heng Xu, Ming Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Innovation Equipment Research Institute Sichuan TianFu New Area Chengdu610299 China Beijing100081 China

ISBN: (纸本)9798331515669

Remote sensing technology plays an important role in many tasks such as natural disaster detection, weather and climate monitoring and military defense. Currently, remote sensing image processing predominantly relies on Convolutional Neural Networks (CNNs) and Transformers, which require a huge amount of multiplication and addition operations, bringing pressure to the deployment of satellite platforms with strict power consumption and computing power constraints. Spiking neural network (SNN), where the signal is encoded as a spike train instead of analog values, significantly reduces energy consumption compared to traditional artificial neural network. This paper proposes an all-input parallel deployment approach that minimizes or eliminates interactions with off-chip memory, thereby accelerating inference speed and reducing power consumption. We deployed LeNet ultra-low latency SNN on the Xilinx VC709 Board. Experimental results demonstrate that the proposed accelerator achieves a processing speed of 1240 frames per second, an accuracy rate of 98.14%, and a power consumption of only 0.745 Watts, making it well-suited for the low power and real-time requirements of remote sensing tasks. © 2024 ieee.

关键词： Convolutional neural networks

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Time-frequency Analysis and Convolutional Neural Network based Fuze Jamming signal Recognition 25

Time-frequency Analysis and Convolutional Neural Network Bas...

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25th international conference on Advanced Communication technology (ICACT) - New Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic

作者： Yang, Jikai Bai, Zhiquan Hu, Jiacheng Yang, Yingchao Xian, Zhaoxia Hao, Xinhong Kwak, KyungSup Shandong Univ Sch Informat Sci & Engn Shandong Prov Key Lab Wireless Commun Technol Qingdao 266237 Shandong Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Inha Univ Grad Sch Informat Technol & Telecommun Incheon 22212 South Korea

ISBN: (纸本)9791188428106

Fuze jamming signal recognition plays a critical role in the battlefield environment. To improve the performance of fuze jamming signals detection, we propose a fuze jamming signal detector based on time-frequency analysis (TFA) and convolutional neural network (CNN), called TFA-CNN, in this paper. The detailed recognition process of the proposed TFACNN detector is provided, where the short-time Fourier transform (STFT) is first employed to convert the original jammed fuze signals into the time-frequency images and then the TFACNN detector is built to train the recognition model. Simulation results verify that the TFA-CNN detector outperforms the typical existing recognition detectors, such as LeNet, time-frequency images and convolutional neural network (TFI-CNN) and deep neural network (DNN), in the detection performance with a slightly higher time complexity. Specially, the average recognition accuracy of the proposed detector achieves 99.8% even at a low signal-to-interference-plus-noise ratio (SINR).

关键词： fuze CNN STFT image accuracy

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Retinex-based Self-Conditioned Diffusion Model for Low-Light image Enhancement

Retinex-Based Self-Conditioned Diffusion Model for Low-Light...

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

作者： Zhang, Jiawei Li, Ziwen Zhang, Jinpu Wang, Yuehuan Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional priors to guide the enhancement process, often resulting in unsatisfactory results. To address the issue, we propose Retinex-based Self-Conditioned Diffusion Models, dubbed RSCDM, which utilizes self-conditioned illumination representation learning and representation guidance enhancement to generate high-quality image. To be specific, in the first stage, we pretrain a retinex decomposed model (RDM) to capture illumination representation and devise a illumination-representation restoration model (IRM) to accurately reconstruct the representation from noisy images. Moreover, we further design dynamic resblock (DRB) and dynamic simplified attention gated block (DSAGB) as basic units of IRM for better fine-grained restoration. In the second stage, we employ a self-conditioned diffusion model (SDM) to generate realistic results conditioned on the illumination representation. Extensive experiments demonstrates our method outperforms the existing SOTA methods both quantitatively and qualitatively. The codes will be publicly available. © 2025 ieee.

关键词： Diffusion Models(DMs) Low Light image Enhancement Retinex-based Model

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Performance Evaluation of Multimodal image Generation Algorithm based on Deep Learning 5

Performance Evaluation of Multimodal Image Generation Algori...

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5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Chen, Zexiang Guizhou University College of Computer Science and Technology Guiyang550025 China

ISBN: (纸本)9798331530334

With the rapid development of deep learning technology, multimodal image generation (MMIG) has become an important research direction in the field of computer vision. In practical applications, the quality of generated images and the consistency between modalities play a crucial role in the effectiveness of the generated model. The problems of poor structural similarity index and peak signal-to-noise ratio in traditional MMIG methods limit their application in complex scenes. This article aims to propose an improved MMIG algorithm based on Generative Adversarial Networks (GANs) to enhance the quality of generated images and consistency between modalities. This study adopts the framework of generative adversarial networks and designs a multimodal generator and discriminator to achieve joint modeling of text and image data. The generator integrates information from different modalities and uses a new loss function to strengthen the relationships between modalities, thereby generating more realistic and diverse images. The experimental results showed that the structural similarity index and peak signal-to-noise ratio of the generated images were significantly improved, with a maximum value of 0.8 for the former and an average value of 41.37 dB for the latter. This study provides an effective solution for MMIG. © 2024 ieee.

关键词： Generative adversarial networks

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