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StreamingTag: A Scalable Piracy Tracking Solution for Mobile Streaming Services

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14530-14543页

作者： Dang, Fan Jin, Xinqi Fu, Qi-An Li, Lingkun Peng, Guanyan Chen, Xinlei Liu, Kebin Liu, Yunhao Tsinghua Univ Global Innovat Exchange Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Software Beijing 100044 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518057 Guangdong Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Fuzhou Fuyao Inst Adv Study Fuzhou 350109 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SvD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.

关键词： Streaming media Watermarking Fingerprint recognition Copyright protection Servers Mobile computing Accuracy video streaming piracy tracing dynamic time warping video watermarking collusion

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A Domain-Specific Supercomputer for Training Deep Neural Networks

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COMMUNICATIONS OF THE ACM 2020年第7期63卷 67-78页

作者： Jouppi, Norman P. Yoon, Doe Hyun Kurian, George Li, Sheng Patil, Nishant Laudon, James Young, Cliff Patterson, David Google Mountain View CA 94043 USA Google ML Accelerator Optimizat Scale Mountain View CA USA Univ Calif Berkeley Grad Sch Berkeley CA USA RISC V Int Open Source Lab Berkeley CA USA RISC V Int Open Source Lab Shenzhen Peoples R China

Google's TPU supercomputers train deep neural networks 50x faster than general-purpose supercomputers running a high-performance computing benchmark.

关键词： Supercomputers

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Flexible nanogenerators for intelligent robotics: design, manufacturing, and applications

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intERNATIONAL JOURNAL OF EXTREME MANUFACTURING 2025年第2期7卷 022012-022012页

作者： Zhao, Hongfa Ruan, Liguang Wang, Zihan Shu, Mingrui Lyu, Chuqiao Lakshan, Bulathsinghala Rameesh Ishara, Dharmasena Wu, Changsheng Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU England Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore RISC V Int Open Source Lab Beijing 518055 Peoples R China

The embodied artificial intelligence (EAI) is driving a significant transformation in robotics, enhancing their autonomy, efficiency and evolution ability. In this rapidly evolving technological landscape, robots need numerous sensors to realize high levels of perception, precision, safety, adaptability, and intelligence. Triboelectric and piezoelectric sensors address these needs by providing high sensitivity, flexibility, and the capability of self-powered sensing, leveraging the revolutionary nature of nanogenerators to convert mechanical energy into electrical energy on basis of Maxwell's displacement current. These sensors surpass externally powered passive sensors by offering continuous operation, reduced maintenance, and the capability to function in remote or harsh environments. The integration of EAI with advanced nanogenerators sensors could position robotics to perform autonomously, efficiently, and safely, paving the way for innovative applications in various domains such as industrial automation, environmental monitoring, healthcare, and smart homes. In this paper, the fundamental theories, design, manufacturing, and applications of nanogenerators are comprehensively reviewed as a foundation of the advanced sensors for intelligent robotics in the new era, with three major application fields: sensing (including human-robot interaction, exteroceptive sensing and proprioceptive sensing), computing and actuating. Perspectives are addressed for nanogenerators systems in future development. Fundamentals of TENGs and PENGs are *** design of materials and structures for nanogenerators is *** techniques for nanogenerators are *** for robotic sensing, computing, and actuating systems are demonstrated.

关键词： triboelectric/piezoelectric nanogenerators robotics manufacturing

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Range-SLAM: Ultra-Wideband-Based Smoke-Resistant Real-Time Localization and Mapping

arXiv

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arXiv 2024年

作者： Liu, Yi Jian, Zhuozhu Zheng, Shengtao Liu, Houde Wang, Xueqian Chen, Xinlei Liang, Bin Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Pengcheng Lab Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China

This paper presents Range-SLAM, a real-time, lightweight SLAM system designed to address the challenges of localization and mapping in environments with smoke and other harsh conditions using Ultra-Wideband (UWB) signals. While optical sensors like LiDAR and cameras struggle in low-visibility environments, UWB signals provide a robust alternative for real-time positioning. The proposed system uses general UWB devices to achieve accurate mapping and localization without relying on expensive LiDAR or other dedicated hardware. By utilizing only the distance and Received Signal Strength Indicator (RSSI) provided by UWB sensors in relation to anchors, we combine the motion of the tag-carrying agent with raycasting algorithm to construct a 2D occupancy grid map in real time. To enhance localization in challenging conditions, a Weighted Least Squares (WLS) method is employed. Extensive real-world experiments, including smoke-filled environments and simulated scenarios, demonstrate the efficiency and robustness of the proposed system. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Heterogeneous Mean-Field Multi-Agent Reinforcement Learning for Communication Routing Selection in SAGI-Net 96

Heterogeneous Mean-Field Multi-Agent Reinforcement Learning ...

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IEEE 96th vehicular Technology Conference (vTC-Fall)

作者： Zhang, Hengxi Tang, Huaze Hu, Yuanquan Wei, Xiaoli Wu, Chenye Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781665454681

The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAvs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.

关键词： SAGI-Net heterogeneous mean field MARL communication routing selection computational complexity

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Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in vehicular Networks

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IEEE intERNET OF THINGS JOURNAL 2023年第3期10卷 2667-2679页

作者： Zhang, Hengxi Lu, Chengyue Tang, Huaze Wei, Xiaoli Liang, Le Cheng, Ling Ding, Wenbo Han, Zhu Tsinghua Univ Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity O(n(N)) with n denoting the dimension of action space and N denoting the number of v2X vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to O(n(2)) while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

关键词： Joint resource allocation mean-field game (MFG) theory multiagent reinforcement learning (MARL) vehicular networks

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Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover

arXiv

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arXiv 2024年

作者： Yu, Ran Yu, Haixin Li, Shoujie Huang, Yan Song, Ziwu Ding, Wenbo Shenzhen Ubiquitous Data Enabling Key Lab Shenzhen International Graduate School Tsinghua University Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China

Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complicate depth estimation. For assistant robots, however, accurately perceiving hand-held transparent objects is critical to effective human-robot interaction. This paper presents a Hand-Aware Depth Restoration (HADR) method based on creating an implicit neural representation function from a single RGB-D image. The proposed method utilizes hand posture as an important guidance to leverage semantic and geometric information of hand-object interaction. To train and evaluate the proposed method, we create a high-fidelity synthetic dataset named TransHand-14K with a real-to-sim data generation scheme. Experiments show that our method has better performance and generalization ability compared with existing methods. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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A triboelectric gait sensor system for human activity recognition and user identification

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NANO ENERGY 2023年 112卷

作者： Li, Jiarong Xie, Zixuan Wang, Zihan Lin, Zenan Lu, Chengyue Zhao, Zihao Jin, Yuchao Yin, Jihong Mu, Shilong Zhang, Chaobo Gui, Weihua Liang, Xiaojun Wang, Jiyu Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Cent S Univ Sch Automat Changsha 410083 Peoples R China

Floor-based sensing systems to monitor human activities and identify users are essential for smart homes and intelligent buildings. A low-cost, easy-to-fabricate, and flexible gait sensor system based on triboelectric nanogenerator (TENG) is presented in this paper, which can transform gait movements, even in the low-frequency form, into electrical impulses without an external power source. To realize this, a TENG-based gait sensor unit with an optimized structure design is proposed to enhance the sensing sensitivity. A sensing insole module is formed by arranging the sensor units according to the foot pressure distribution. The sensor distribution is then explored and improved by comparative studies of gait recognition performance, which increases the recognition efficiency and the possible application in edge computing scenarios. Furthermore, a deep learning network is developed based on long short-term memory (LSTM) and residual units to extract deep features from multichannel time-series gait data to boost recognition performance. Experimental results demonstrate that the proposed gait sensor system can be utilized for human activity recognition and user identification with accuracies of 97.9 % and 99.4 %, respectively. Finally, a gait-sensing-based fitness exercise monitoring system is constructed that can estimate calorie expenditure and distinguish between standard and non-standard fitness activities with an accuracy of 97.2 %. This work can be extended to various application scenarios such as security surveillance, health monitoring, and intelligent control, which provides a new ubiquitous self-powered sensing solution for the internet of Things (IoT).

关键词： Triboelectric nanogenerator Self-powered sensor Gait recognition system Deep learning network Activity monitoring User identification

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HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control 21

HTPad: Hexagon-fractal TENG Pad for Scalable Touch Control

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ACM international Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) / ACM international Symposium on Wearable Computers (ISWC)

作者： Yang, Xu Yin, Jihong Wang, Zihan Song, Ziwu Song, Jian Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Beijing Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst RISC Int Open Source Lab Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781450384612

The development of the human-machine interface (HMI) is endeavored to find effective approaches to interact with machines by applying emerging technologies. Triboelectric nanogenerator (TENG) can convert mechanical stimuli to electricity, which not only shows great potential in sensing but also is widely used in various HMI applications. This paper proposed a TENG-based hexagon-fractal touchpad (HTPad) system using two channels to realize 18 sliding patterns from 3 different modes and a signal recognition module. A one-dimensional convolution neural network (1D CNN) model is proposed for the recognition of the sliding direction signal with 96.5% accuracy, and handwriting digit signals collected by the touchpad can be recognized with a modified model with 99% accuracy. The proposed TENG-based hexagon-fractal touchpad is easy to fabricate, scalable, and with high sensitivity. Furthermore, the recognition model can serve as a unified platform for different *** tasks with little computational cost, which reveals great potential in HMI applications.

关键词： Triboelectric sensor deep learning human machine interface

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Growing from Exploration:A Self-Exploring Framework for Robots Based on Foundation Models

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CAAI Artificial intelligence Research 2024年第1期3卷 187-201页

作者： Shoujie Li Ran Yu Tong Wu Junwen Zhong Xiao-Ping Zhang Wenbo Ding Shenzhen Ubiquitous Data Enabling Key Lab Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China Department of Physics and Chemistry Faculty of Science and TechnologyUniversity of MacaoMacao 999078China RISC-V International Open Source Laboratory Tsinghua-Berkeley Shenzhen InstituteShenzhen 518055China

intelligent robot is the ultimate goal in the robotics *** works leverage learning-based or optimization-based methods to accomplish human-defined ***,the challenge of enabling robots to explore various environments autonomously remains *** this work,we propose a framework named GExp,which endows robots with the capability of exploring and learning autonomously without human *** achieve this goal,we devise modules including self-exploration,knowledgebase-building,and close-loop feedback based on foundation *** by the way that infants interact with the world,GExp encourages robots to understand and explore the environment with a series of self-generated *** the process of exploration,the robot will acquire skills from experiences that are useful in the *** provides robots with the ability to solve complex tasks through *** work is independent of prior interactive knowledge and human intervention,allowing it to adapt directly to different scenarios,unlike previous studies that provided in-context examples as few-shot *** addition,we propose a workflow of deploying the real-world robot system with self-learned skills as an embodied *** website:***.

关键词： intelligent robot foundation models self-exploring framework

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