检索结果-内蒙古大学图书馆

Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Wang, Yihao Li, Lingxiao Tang, Yifan Zhang, Ru Liu, Jianyi Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger public safety and cause social losses. Therefore, this paper proposes a watermarking that can verify the integrity of copyright in response to the needs of ITS, termed ITSmark. ITSmark focuses on functions such as extracting watermarks, verifying permission, and tracing tampered locations. The scheme uses the copyright information to build the multi-bit space and divides this space into multiple segments. These segments will be assigned to tokens. Thus, the next token is determined by its segment which contains the copyright. In this way, the obtained data contains the custom watermark. To ensure the authorization, key parameters are encrypted during copyright embedding to obtain cipher data. Only by possessing the correct cipher data and private key, can the user entirely extract the watermark. Experiments show that ITSmark surpasses baseline performances in data quality, extraction accuracy, and unforgeability. It also shows unique capabilities of permission verification and tampered location tracing, which ensures the security of extraction and the reliability of copyright verification. Furthermore, ITSmark can also customize the watermark embedding position and proportion according to user needs, making embedding more flexible.

关键词： Watermarking Data mining Security Ciphers Telecommunications Data integrity Encryption Copyright protection Vehicular ad hoc networks Transportation Intelligent transportation systems copyright watermark large language models GenAI

来源：评论

学校读者我要写书评

暂无评论

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

引用

China Communications 2023年第6期20卷 100-115页

作者： Zhen Zhang Yuxiang Zhang Jianhua Zhang Feifei Gao State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Institute for Artificial Intelligence Tsinghua UniversityBeijing 100084China

In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real *** capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

关键词： channel prediction time-varying channel conditional generative adversarial network multipath channel deep learning

来源：评论

学校读者我要写书评

暂无评论

Simultaneous Snow Mask Prediction and Single Image Desnowing with a Bidirectional Attention Transformer Network 7th

Simultaneous Snow Mask Prediction and Single Image Desnowing...

引用

7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Zhang, Yonghong Yan, Danfeng BUPT State Key Lab Networking & Switching Technol Beijing Peoples R China BUPT Sch Comp Beijing Peoples R China

ISBN: (纸本)9789819786848;9789819786855

Snow's diverse shapes and sizes pose a significant challenge for single-image desnowing. Current methods typically rely on simple networks to predict the snow masks and subsequently remove snow from images accordingly. However, inaccurate predictions often result in incomplete snow removal. This paper introduces BAT-Net, a novel solution equipped with an image transformer encoder and dual transformer decoders. This architecture enables precise snow mask prediction and comprehensive single image desnowing simultaneously. The image encoder enhances and consolidates features from various layers using Scale Conversion Modules (SCM) and a Feature Aggregation Module (FAM). To ensure accurate snow mask prediction, the snow decoder mirrors the complete transformer structure of the background decoder. Within the background decoder, multiple Bidirectional Attention Modules (BAM) with integrated forward and reverse attention branches effectively leverage snow features and reverse snow features from the snow decoder, facilitating complete snow removal. Moreover, real-world desnowing datasets often contain fallen snow, complicating model training and validation. To address this challenge, we introduce the FallingSnow dataset, exclusively featuring scenes of falling snow. Experimental results across five diverse synthetic and real-world snow removal datasets demonstrate BAT-Net's significant advancements in addressing the challenges of snow mask prediction and single-image desnowing.

关键词： Single image desnowing Snow mask prediction Bidirectional attention

来源：评论

学校读者我要写书评

暂无评论

Part-Aware Framework for Robust Object Tracking

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 750-763页

作者： Li, Shengjie Zhao, Shuai Cheng, Bo Chen, Junliang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Guangxi Key Lab Cryptog & Informat Secur Guilin 541004 Peoples R China

The local parts of the target are vitally important for robust object tracking. Nevertheless, existing excellent context regression methods involving siamese networks and discrimination correlation filters mostly represent the target appearance from the holistic model, showing high sensitivity in scenarios with partial occlusion and drastic appearance changes. In this paper, we address this issue by proposing a novel part-aware framework based on context regression, which simultaneously considers the global and local parts of the target and fully exploits their relationship to be collaboratively aware of the target state online. To this end, the spatial-temporal measure among context regressors corresponding to multiple parts is designed to evaluate the tracking quality of each part regressor by solving the imbalance among global and local parts. The coarse target locations provided by part regressors are further aggregated by treating their measures as weights to refine the final target location. Furthermore, the divergence of multiple part regressors in each frame reveals the interference degree of background noise, which is quantified to control the proposed combination window functions in part regressors to adaptively filter redundant noise. Besides, the spatial-temporal information among part regressors is also leveraged to assist in accurately estimating the target scale. Extensive evaluations demonstrate that the proposed framework help many context regression trackers achieve performance improvements and perform favorably against state-of-the-art methods on the popular benchmarks: OTB, TC128, UAV, UAVDT, VOT, TrackingNet, GOT-10k, LaSOT.

关键词： Object tracking siamese network discrimina-tion correlation filter global and local parts

来源：评论

学校读者我要写书评

暂无评论

Dual Class Token Vision Transformer for Direction of Arrival Estimation in Low SNR

引用

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 76-80页

作者： Guo, Yu Zhang, Zhi Huang, Yuzhen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Acad Mil Sci PLA Beijing 100091 Peoples R China

In this letter, we propose a deep learning-based method for the direction of arrival (DOA) estimation in the low signal-to-noise ratio (SNR) scenario. Specifically, the DOA estimation is modeled as a multi-label classification task, and a novel dual class token Vision Transformer (DCT-ViT) is designed to fit it. Different from the classical ViT architecture with a single class token, the DCT-ViT includes two class tokens which are located at the beginning and end of the latent vector sequence, respectively. This architecture enables enhanced information mining and feature extraction from the array signal data in order to improve the accuracy of DOA estimation. Furthermore, a single DCT-ViT model can accommodate different source numbers by leveraging a training dataset with different numbers of sources. Simulation results illustrate that our proposed method outperforms existing methods in the low SNR scenario, including classical model-based and other deep learning-based methods.

关键词： Direction of arrival (DOA) estimation Vision Transformer (ViT) low signal-to-noise ratio (SNR) deep learning

来源：评论

学校读者我要写书评

暂无评论

A Concurrent Multibeam Passive Radar for Respiratory Detection From Multiple Subjects

引用

IEEE TRANSACTIONS ON MICROWAVE THEORY AND techNIQUES 2025年

作者： Huang, Shuaiming Zhao, Heng Hong, Hong Guo, Fengxian Peng, Mugen Zhu, Xiaohua Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

The proliferation of the Internet of Things (IoT) technology has increased wireless interconnectivity, enhancing smart living environments. The millimeter-wave radar is prevalent for indoor positioning, and it consumes spectrum resources and risk interference in multiradar scenarios. In contrast, passive sensing leverages ambient electromagnetic signals, detecting targets through channel variations without occupying the spectrum. However, existing systems are constrained by hardware designs, thus resulting in limited environmental adaptability and poor sensing capabilities. In this article, a concurrent multibeam passive radar is proposed for localization and respiration detection of multiple human targets. Utilizing 16 independent digital receivers with a variable frequency local oscillator (LO), the radar adaptively senses ambient radio frequency signals and selects suitable ones for target detection. Upon identifying the positions of radiation sources and targets, the system generates multiple independent beams to capture motion, mitigating multipath echo interference from wide-beam antennas and enhancing environmental adaptability. Performance evaluation of the passive radar involved various experiments with simulated radiation sources, sliding platforms, and real human targets. The proposed passive radar system accurately localizes and distinguishes external illuminator from human targets, achieving an angular estimation error of sub-2(degrees) in 3 m. It also detects respiration signals through orthogonal baseband processing, with a maximum error of 0.2 beats per minute (BPM) and a median error below 0.03 BPM, ensuring high-precision respiratory monitoring.

关键词： Passive radar Sensors Radar Baseband Radar antennas Monitoring Interference Radar detection Wireless sensor networks Wireless fidelity Digital receiver multibeam passive radar noncontact vital sign detection

来源：评论

学校读者我要写书评

暂无评论

An Approximate Wave-Number Domain Expression for Near-Field XL-Array Channel

引用

IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2025年第5期74卷 8267-8272页

作者： Xing, Hongbo Zhang, Yuxiang Zhang, Jianhua Xu, Huixin Liu, Guangyi Wang, Qixing Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Future Res Lab Beijing 100053 Peoples R China

As Extremely large-scale array (XL-array) technology advances and carrier frequency rises, the near-field effects in communication are intensifying. In near-field conditions, channels exhibit a diffusion phenomenon in the angular domain, existing research indicates that this phenomenon can be leveraged for efficient parameter estimation and beam training. However, the channel model in angular domain lacks closed-form analysis, making the time complexity of the corresponding algorithm high. To address this issue, this paper analyzes the near-field diffusion effect in the wave-number domain, where the wave-number domain can be viewed as the continuous form of the angular domain. A closed-form approximate wave-number domain expression is proposed, based on the Principle of Stationary Phase. Subsequently, we derive a simplified expression for the case where the user's distance is much larger than the array aperture, which is more concise. Subsequently, we verify the accuracy of the proposed approximate expression through simulations and demonstrate its effectiveness using a beam training example. Results indicate that the beam training scheme, improved by the wave-number domain approximation model, can effectively estimate near-field user parameters and perform beam training using far-field DFT codebooks. Moreover, its performance surpasses that of existing DFT codebook-based beam training methods.

关键词： Training Discrete Fourier transforms Antennas Channel models Vectors Closed-form solutions Channel estimation Apertures Solid modeling Phased arrays Near-field extremely large-scale array (XL-array) wave-number domain the principle of stationary phase

来源：评论

学校读者我要写书评

暂无评论

Multi-Domain Image-to-Image Translation with Cross-Granularity Contrastive Learning

引用

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第7期20卷 1-21页

作者： Fu, Huiyuan Liu, Jin Yu, Ting Wang, Xin Ma, Huadong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Key Lab Intelligent Telecommun Software & Beijing Beijing Peoples R China SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY USA

The objective of multi-domain image-to-image translation is to learn the mapping from a source domain to a target domain in multiple image domains while preserving the content representation of the source domain. Despite the importance and recent efforts, most previous studies disregard the large style discrepancy between images and instances in various domains, or fail to capture instance details and boundaries properly, resulting in poor translation results for rich scenes. To address these problems, we present an effective architecture for multi-domain image-to-image translation that only requires one generator. Specifically, we provide detailed procedures for capturing the features of instances throughout the learning process, as well as learning the relationship between the style of the global image and that of a local instance in the image by enforcing the cross-granularity consistency. In order to capture local details within the content space, we employ a dual contrastive learning strategy that operates at both the instance and patch levels. Extensive studies on different multi-domain image-to-image translation datasets reveal that our proposed method outperforms state-of-the-art approaches.

关键词： Image-to-image translation GAN cross-granularity contrastive learning multi-domain

来源：评论

学校读者我要写书评

暂无评论

Efficient, Scalable, and Sustainable DNN Training on SoC-Clustered Edge Servers

引用

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14344-14360页

作者： Xu, Mengwei Xu, Daliang Lou, Chiheng Zhang, Li Huang, Gang Jin, Xin Liu, Xuanzhe Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peking Univ Sch Comp Sci Key Lab High Confidence Software Technol Minist Educ Beijing 100871 Peoples R China

In the realm of industrial edge computing, a novel server architecture known as SoC-Cluster, characterized by its aggregation of numerous mobile systems-on-chips (SoCs), has emerged as a promising solution owing to its enhanced energy efficiency and seamless integration with prevalent mobile applications. Despite its advantages, the utilization of SoC-Cluster servers remains unsatisfactory, primarily attributed to the tidal patterns of user-initiated workloads. To address such inefficiency, we introduce SoCFlow+, a pioneering framework designed to facilitate the co-location of deep learning training tasks on SoC-Cluster servers, thereby optimizing resource utilization. SoCFlow+ incorporates three novel techniques tailored to mitigate the inherent limitations of commercial SoC-Cluster servers. First, it employs group-wise parallelism complemented by delayed aggregation, a strategy engineered to enhance the training efficiency and scalability of deep learning models, effectively circumventing network bottlenecks. Second, it integrates a data-parallel mixed-precision training algorithm, optimized to exploit the heterogeneous processing capabilities inherent to mobile SoCs fully. Third, SoCFlow+ employs an underclocking-aware workload re-balanacing mechanism to tackle the training performance degradation caused by the thermal control of mobile SoCs. Through rigorous experimental validation, SoCFlow+ achieves a convergence speedup ranging from 1.6x to 740x across 32 SoCs, compared to conventional benchmarks. Furthermore, when juxtaposed with commodity GPU servers (e.g., NVIDIA V100) under identical power constraints, SoCFlow+ not only exhibits comparable training speed but also achieves a remarkable reduction in energy consumption by a factor of 2.31x to 10.23x, all while preserving convergence accuracy.

关键词： Training Servers Task analysis Accuracy Topology Network topology Convergence Edge computing distributed training

来源：评论

学校读者我要写书评

暂无评论

DPU-Enhanced Multi-Agent Actor-Critic Algorithm for Cross-Domain Resource Scheduling in Computing Power Network

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第6期21卷 6008-6025页

作者： Wang, Shuaichao Guo, Shaoyong Hao, Jiakai Ren, Yinlin Qi, Feng Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Grid Beijing Elect Power Co State Grid Beijing Informat & Telecommun Co Beijing 100051 Peoples R China

The distribution of computing resources in the Computing Power Network (CPN) is uneven, leading to an imbalance in resource supply and demand within domains, necessitating cross-domain resource scheduling. To address the cross-domain resource scheduling challenge in CPN, this paper presents an Improved Multi-Agent Actor-Critic (IMAAC) resource scheduling approach leveraging Data Processing Unit (DPU) offloading. Initially, we introduce a cross-domain resource scheduling architecture tailored for CPN by leveraging DPU offloading. Specifically, we delegate certain functionalities of the Multi-Agent Deep Reinforcement Learning (MADRL) Agent to DPUs, aiming to mitigate communication costs incurred during the generation of cross-domain scheduling decisions. Second, we introduce the parallel experience ensemble and multi-head attention mechanism in the Multi-Agent Actor-Critic (MAAC) framework to compress the state-space dimensionality of agent association across domains. Finally, we introduce the parallelized dual-policy network structure to mitigate training instability and convergence challenges within the actor and critic networks. Experimental results showcase that IMAAC achieves noteworthy reductions of 5.98%similar to 13.56%, 23.54%similar to 33.55%, and 41.17%similar to 58.88% in total system delay, energy consumption, and the number of discarded tasks, respectively, compared to benchmark experiments.

关键词： DPU computing power network resource scheduling multi-agent actor-critic

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：