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Mask guided attention for fetal standard plane recognition

Mask guided attention for fetal standard plane recognition

引用

2022 International Conference on Computer Application and Information Security, ICCAIS 2022

作者： Li, Ruijie Guo, Caili Xu, Hong Li, Zheng Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Department of Ultrasound Diagnosis First Medical Center PLA General Hospital Beijing China

ISBN: (纸本)9781510663459

At present, ultrasound is the most widely used in prenatal diagnosis. The accurate acquisition of fetal standard plane is very important in prenatal diagnosis. However, the recognition of fetal standard planes (standard versus non-standard) is a challenging task, mainly because of the need to simultaneously recognize several key anatomical structures. In this paper, we propose a novel method termed mask guided attention (MGA) to integrate global and diagnostic region cues to recognize the fetal standard plane, imitating the sonographer's diagnostic mode. In addition, to further mine key anatomical structures, an innovative attention mining (AM) module is designed to improve the MGA. Utilizing the clinical dataset, we demonstrate that MGA significantly improve the performance of fetal standard plane recognition tasks. © 2023 SPIE.

关键词： Computer aided diagnosis

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Pattern-Independent Human Gait Identification with Commodity WiFi

Pattern-Independent Human Gait Identification with Commodity...

引用

IEEE Conference on Wireless Communications and networking

作者： Zhe Xiao Shuang Zhou Xiangming Wen Sida Ling Xuan Yang Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recent years have witnessed the significant advancement of WiFi-based human gait identification. However, existing works require the human subjects to walk with a standard gait pattern, i.e., the normal walking speed and free limb swings. This significantly impedes the wide adoption of these technologies since humans walk with diverse gait patterns in reality. To this end, we present a Pattern-Independent Authentication system (PIAS), the first system that enables human identification across different gait patterns using commodity WiFi. First, we extract Doppler spectrograms as human gait signatures and standardize them to narrow the distribution discrepancies between different gait patterns. Then, we design a Class-level Unsupervised Domain Adaptive network (CUDAN) to extract human identity features in the source and target domains while minimizing the feature domain discrepancies at the class-level to achieve pattern-independent human gait identification. Extensive experiments are conducted on different gait patterns and the results demonstrate that our system can achieve the highest performance compared to the baseline methods, demonstrating its effectiveness in human identification across diverse gait patterns.

关键词： Legged locomotion Time-frequency analysis Authentication Feature extraction Doppler effect Noise measurement Task analysis

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Edge and Terminal Cooperation Enabled LLM Deployment Optimization in Wireless network

Edge and Terminal Cooperation Enabled LLM Deployment Optimiz...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Wentao Zhao Wenpeng Jing Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recent years have witnessed the remarkable emer-gence of large language models (LLMs), demonstrating exceptional capabilities in handling complex tasks such as natural language processing, computer vision, and speech processing. However, the prevalent deployment of LLMs on cloud servers poses challenges in ensuring user data privacy and minimizing computation and transmission delay. Moreover, LLM needs to read all the parameters to generate a token for each inference and then splices the original input with the generated token as a new input to generate the next token. To address these concerns, we propose a novel LLM deployment framework leveraging edge and terminal cooperation. Our proposed framework involves deploying LLMs on both edge server and terminal device, where the LLM model on the terminal generates tokens serially, while the model on the edge generates tokens in parallel. This edge-terminal cooperative and serial and parallel combination framework can effectively reduce the computation delay of token generation. To further optimize the performance of the proposed framework, we formulate an optimization problem aimed at minimizing average delay and energy consumption. Considering its integer programming property, we employ a branch and bound algorithm to efficiently solve this problem. Extensive simulation results provide compelling evidence of the effectiveness of our LLM deployment optimization algorithm. Under diverse channel conditions and terminal device computing capabilities, our proposed scheme consistently outperforms other baseline algorithms in terms of delay and energy consumption. These findings underscore the potential of our approach in facilitating the practical deployment of LLMs on edge and terminal.

关键词： Performance evaluation Energy consumption Computational modeling Wireless networks Simulation Delays Decoding Servers Speech processing Optimization

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A Multi-Objective Optimization Approach for Roadside Unit Deployment Strategy in IoV

A Multi-Objective Optimization Approach for Roadside Unit De...

引用

IEEE Conference on Wireless Communications and networking

作者： Meihan Lin Jie Huo Luhan Wang Guanyu Yao Yawen Chen Zhaoming Lu Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Roadside unit (RSU) deployment plays a crucial role in enhancing the quality of service (QoS) in Vehicular Ad-hoc networks (VANET). To determine the optimal quantities and locations of deployed RSUs for maximum effectiveness, previous research categorized this as a single-objective optimization problem, considering only the performance indicators for vehicle-to-infrastructure (V2I) communication and solving it using heuristic algorithms or learning-based algorithms. Nonetheless, in the event of information interaction among RSUs as a supplementary component to V2I communication, the process of data transmission and dissemination will achieve a heightened level of comprehensiveness and efficiency within the network. In this paper, we construct a multi-objective roadside unit deployment model (MORD) that takes both V2I communication and data interaction between RSUs into account. The optimization problem of MORD is solved by a multi-objective evolutionary algorithm. We perform a series of simulation evaluations in the ideal grid scenario and the real road network scenario. The results prove that compared to other schemes, the proposed algorithm can better balance the relationship between various objectives in MORD. Moreover, the algorithm can provide decision makers with non-dominated solution sets to choose an appropriate deployment scheme that is more suitable for the actual conditions.

关键词： Wireless communication Heuristic algorithms Vehicle-to-infrastructure Roads Vehicular ad hoc networks Quality of service Transforms

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Scalable and Efficient URLLC Resource Allocation Based on Offline Graph Reinforcement Learning

Scalable and Efficient URLLC Resource Allocation Based on Of...

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ren Jiaqi Wenpeng Jing Xiangming Wen Zhaoming Lu Shuyue Zhao Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

Deep reinforcement learning (RL) has emerged as a transformative technology for addressing intricate radio resource allocation problems in both 5G and 6G networks. Nevertheless, during the training phases of RL-based resource allocation algorithms, online RL methods introduce potential risks as they necessitate continuous interaction with the environment. Motivated by the fact that the historical data of the resource allocation process in the base station (BS) reveals the operation rules of the mobile network dynamics, this paper proposes a novel data-driven framework for resource allocation optimization, termed Offline Graph Reinforcement Learning (OGRL). Different from existing intelligent resource allocation schemes, which acquire policies by interacting either with wireless network simulators or real-world networks, the proposed OGRL framework exclusively leverages historical data from BS to train a high-performance resource allocation policy. Furthermore, by harnessing the capabilities of the graph neural networks (GNNs) at processing intricate data structures, OGRL can adeptly manage dynamic network environments characterized by continuously changing active users. Extensive experimental results substantiate that, following training with historical data, the proposed OGRL attains Quality of Service (QoS) and packet loss rates comparable to those of the best online algorithms. Furthermore, OGRL exhibits excellent scalability and generalization capabilities.

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Video-Based Multi-Stage 3D Human Mesh Reconstruction

Video-Based Multi-Stage 3D Human Mesh Reconstruction

引用

International Conference on Computer and Communications (ICCC)

作者： Zhiwen Qiu Muqing Wu Qihui Su Chengzhang Yin Beijing Key Laboratory of Network System Architecture and Convergence Beijing Laboratory of Advanced Information Networks Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Existing Model-based 3D human reconstruction provides a new perspective for generating realistic full-body meshes for 3D human pose estimation. However, many existing methods for reconstructing human meshes typically rely on neural networks to directly estimate the parameters of the SMPL model, subsequently utilizing the SMPL model to recover the human mesh. Despite significant progress in recent research, these methods still encounter several issues, including shape estimation overfitting, cumulative rotation errors, and rotational semantic ambiguities. Furthermore, these methods have not leveraged recent advancements in 2D and 3D pose estimation. To address these challenges, we propose a framework that decomposes the task of reconstructing 3D human meshes into three components: extracting 2D keypoints, a 2D-to-3D lifting network, and fitting the rotations of the keypoints. We decompose the rotation of the 3D keypoints into twist and swing components. In the 2D-to-3D network, we regress the 3D keypoint locations, shape parameters, and twist of the keypoints, while also introducing an optimization method for fitting the rotations. We conducted experiments on the Human3.6M dataset, achieving significant performance improvements.

关键词： Solid modeling Three-dimensional displays Shape Pose estimation Pipelines Neural networks Fitting Semantics Bones Overfitting

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Sum-Rate Maximization for Active RIS-Aided Downlink RSMA system

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA Sys...

引用

2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Li, Xinhao Wang, Tao Tong, Haonan Yang, Zhaohui Mao, Yijie Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781665494274

In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption. © 2023 IEEE.

关键词： Iterative methods

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Two-Stage DoA Estimation for ISAC system Based on a MIMO OFDM Radar

Two-Stage DoA Estimation for ISAC System Based on a MIMO OFD...

引用

Radio Frequency and Antenna Technologies (iWRF&AT), IEEE International Workshop on

作者： Zhengyu Huang Danpu Liu Zhilong Zhang Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications P.R. China Beijing China

ISBN: (数字)9798350384765

ISBN: (纸本)9798350384772

In the next generation of communication networks, mobile networks and widely deployed based stations will achieve statisfactory integrated sensing and communication (ISAC) functions under the standard multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) architecture and waveform. In this paper, we focuse on an ISAC system based on a MIMO OFDM radar and propose a Two-Stage mutiple signal Classification (MUSIC) direction of arrival (DoA) estimation algorithm. In order to achieve a precise estimation while maintaining low computational cost, an estimate on the direction of the user is roughly obtained in the first stage, and then refined in the second stage. Compared with traditional DoA estimation algorithms including MUSIC and discrete fourier transform-compressed sensing, the root mean square error (RMSE) for multi-target DoA estimation can be reduced by two orders of mangnitude under the same computational cost.

关键词： Direction-of-arrival estimation Transmitters OFDM Estimation Pattern classification Radar Integrated sensing and communication

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Sparsely Deployed Bluetooth and Magnetic Fusion Positioning for Large Underground Parking Lots

Sparsely Deployed Bluetooth and Magnetic Fusion Positioning ...

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IEEE Conference on Wireless Communications and networking

作者： Ziwen Luo Yihong Zheng Shuya Zhou Shumin Wang Xinghe Chu Luhan Wang Zhaoming Lu Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m 2 ), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices.

关键词： Global navigation satellite system Bluetooth Accuracy Costs Heuristic algorithms Magnetic fields Received signal strength indicator Vehicle dynamics Low latency communication Smart phones

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Latency-Aware Server Deployment in Internet of Vehicles Based on Multi-Agent Reinforcement Learning

Latency-Aware Server Deployment in Internet of Vehicles Base...

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IEEE Conference on Wireless Communications and networking

作者： Xinran Liu Yaqiong Liu Junsheng Mu Guochu Shout Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The performance of the servers directly affects the efficiency of the whole intelligent Internet of Vehicles (lo V) system. A reasonable server deployment strategy can effectively reduce network latency and improve the efficiency of ***, it is necessary to study the problem of server deployment in Io *** this paper, we formulate a latency-aware server deployment problem based on Multi-Agent Reinforcement Learning (MARL) and utilize an Actor-Critic network based on Long Short-Term Memory (LSTM) encoding network to solve it. Besides, four deployment strategies (DQN, DRQN, Random and No Movement) are used as the baseline methods for performance comparison with our proposed method on two real-world datasets. The simulation results show our solution has excellent performance in reducing network latency under different scenarios.

关键词： Training Simulation Transportation Reinforcement learning Encoding Stability analysis Servers

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