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Use FCN to accurately judge the frontal face or side face 4

Use FCN to accurately judge the frontal face or side face

引用

4th IEEE International conference on computer and communications, ICCC 2018

作者： Zhang, Yan Liu, Xianzhong School of Computer Science and Software Engineering East China Normal University Shanghai China School of Computer Science and Software Engineering East China Normal University Software/Hardware Co-design Engineering Research Center MoE Shanghai China

ISBN: (纸本)9781538683392

Face-to-unlock is widely used in the fields of authentication, mobile payment and other information security. In the process of face-to-unlock, the detection for frontal face and side face can greatly avoid people's non-active intention triggering verification function and greatly improve the security of unlocking. Based on this reason, this paper establishes a set of model to judge frontal face and side face, by combining the latest FCN (Fully convolutional Networks) algorithm with the geometric method creatively, to make the recognition rate for frontal and side face reach to 99.82%, far higher than by OpenCV traditional methods under the same data set. At the same time, in model training, this paper has improved training speed and model generalization ability by transfer learning technology. © 2018 IEEE.

关键词： component FCN Heat map Post estimate

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Fall detection based on depth image sequence 21

Fall detection based on depth image sequence

引用

21st World Multi-conference on Systemics, Cybernetics and Informatics, WMSCI 2017

作者： Liao, Liangcan Cao, Guitao Cao, Wenming MOE Research Center for Software/Hardware Co-Design Engineering East China Normal University Shanghai200062 China College of Information Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781941763643

Fall is one of the most significant causes of injury in the elderly. In this paper, we propose two types of feature extraction methods to detect when the fall happens based on the Kinect depth image sequences. One is assuming there exists different position lines in the XYZ 3-dimensional space, it will be active when the moving object touches it. The other is mapping the image sequences to single image by Speed-Time Depth Mapping (STDM), and obtaining the 36-dimensiona features in this image. The results of experimental demonstrate the effectiveness of our method.

关键词： Feature extraction

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Privacy-Preserving Verifiable Asynchronous Federated Learning 3

Privacy-Preserving Verifiable Asynchronous Federated Learnin...

引用

3rd International conference on software engineering and Development, ICSED 2021

作者： Gao, Yuanyuan Wang, Lulu Zhang, Lei Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University China Guangxi Key Laboratory of Cryptography and Information Security China Software Engineering Institute East China Normal University China

ISBN: (纸本)9781450385213

Federated learning (FL) is a recently proposed technique to cope with growing data and break the barriers among datasets, which enables nodes to train machine learning models without sharing their local datasets. However, the data privacy and model performance concerns in asynchronous federated learning hinder its deployment in practical applications, especially in dynamic scenarios. To address these problems, we propose a verifiable asynchronous federated learning with a peer-to-peer network based on local dataset test and cosine value examination to improve the model performance. We also design a privacy-preserving scheme by using the local differential privacy (LDP) to protect data privacy. We evaluate our scheme on the model accuracy and convergence performance. Numerical results show the high accuracy and efficiency of our proposed scheme while protecting privacy. © 2021 ACM.

关键词： Peer to peer networks

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MACEdge: Real-Time Video Analytics Based on Multi-Access collaborative Edge computing

MACEdge: Real-Time Video Analytics Based on Multi-Access Col...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Dian Zhong Minghua Zhu Binbin Huang MOE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load on network bandwidth and introduces poor user experience due to latency. Edge computing offers a solution by offloading a portion of the video analysis tasks to the edge, significantly reducing the pressure on bandwidth and latency. However, the fixed video configurations (resolution, frame rate, etc.) uploaded to the edge may not be optimal for specific situations. Moreover, using a low video configuration makes it difficult for edge video analysis systems to recognize environmental changes, while using a high video configuration poses challenges to latency and bandwidth. Therefore, in this paper, we propose the MACEdge-an edge video analysis framework by leveraging the multi-access capability of the edge gateway to enhance edge perception by accessing heterogeneous sensors. We offer an algorithm based on Q Learning for dynamic adaptive adjustment of sensor threshold and video configurations. In experiments using real-world data, our collaborative solution improved accuracy and reduced high bandwidth costs compared to other video configuration adjustment benchmarks.

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Joint computing Offloading and Resource Allocation in MEC-Enabled IoT: A Diffusion-Based Reinforcement Learning Approach

Joint Computing Offloading and Resource Allocation in MEC-En...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Huimin Cao Bo Xiao MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling user equipments(UE) to migrate the computation of tasks onto edge servers. This paper focuses on optimizing the performance of MEC-enabled IoT system by formulating a joint computing offloading and resource allocation problem. The objective is to minimize the total delay of the system consisting of multiple servers and multiple users. The denoising network of a diffusion model with capabilities of generation can be trained to obtain optimal solution given the changed environment conditions. Therefore, we propose the diffusion-based deep deterministic policy gradient (DiffDDPG) algorithm which utilizes a diffusion model as the policy to learn optimal decisions jointly. Simulation results exhibits the superior performance of the DiffDDPG algorithm.

关键词： Heuristic algorithms Simulation Training data Reinforcement learning Diffusion models Delays Resource management Servers Internet of Things Optimization

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QueryEdge: Real-Time Muti-Video Query in Edge-Cloud collaborative System

QueryEdge: Real-Time Muti-Video Query in Edge-Cloud Collabor...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Jihua Zhong Yannian Niu Minghua Zhu MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditional cloud-based intelligent video processing faces significant challenges in terms of latency and bandwidth, while the pure edge computing approach is deficient in query accuracy due to its lack of computational power. Existing edge cloud collaboration approaches, such as SurveilEdge, focus on real-time target queries within a single video stream and do not show promising results during target queries across multiple video streams. For this reason, this paper proposes Query Edge, an edge-cloud collaborative real-time query system for multiple video streams. Specifically, we design a real-time query system based on an edge-cloud collaboration framework to achieve highly accurate and low-latency target query services in multiple video streams. In addition, we introduce a prioritization mechanism and a load-balancing strategy in the query task scheduling process to further improve query efficiency. The evaluation proves that QueryEdge has a significant improvement in query latency and bandwidth consumption compared with pure cloud computing, pure edge computing, and SurveilEdge.

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Articulated pose estimation method research based on deep learning 21

Articulated pose estimation method research based on deep le...

引用

21st World Multi-conference on Systemics, Cybernetics and Informatics, WMSCI 2017

作者： Chen, Kai Cao, Guitao Meng, Dan Cao, Wenming MOE Research Center for Software Hardware Co-Design Engineering East China Normal University Shanghai200062 China College of Information Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781941763599

This paper proposes an articulated pose estimation method based on DeepPose, improving it's estimation accuracy. Firstly, it analyzes and compares the advantages and disadvantages of Faster Rcnn and DeepPose in the field of articulated pose estimation, proposes the R-Optimization algorithm to reajust joints' bounding box properly. Secondly, through combining DeepPose and Faster Rcnn, an improved articulated pose estimation method is proposed, which is better than using DeepPose or Faster Rcnn separately. © by the International Institute of Informatics and Systemics.

关键词： Articulated pose estimation Caffe convolution neural network Deep learning

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Slim-MY: A Lightweight Network for Power Line Defect Detection with FPGA Acceleration

Slim-MY: A Lightweight Network for Power Line Defect Detecti...

引用

International conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaoyang Zhang Wenjie Chen Zihao Zhou Zhuowen Duan MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the safe operation of the power system. Traditional manual inspections and image processing methods are time-consuming, labor-intensive, and have lower accuracy. In light of these, we propose a lightweight network called Slim-MY. It utilizes lightweight feature extraction and fusion modules, significantly reducing computational and parameter overhead while ensuring defect detection accuracy. Furthermore, to accelerate the post-processing process, we propose the FP-NMS algorithm. It parallelizes the sorting and overlapping box removal parts of the NMS algorithm, making it more suitable for deployment on devices that support parallel computing, such as FPGA. By testing on our dataset of power line defects, the Slim-MY network achieves a detection accuracy and computation of 92.7% and 1.1 GFLOPs respectively, which outperforms most other networks. By accelerating on the KV260 FPGA, the Slim-MY network achieves an inference speed of 94 fps, and the FP-NMS algorithm’s execution speed is 14.8 times that of the GTX 1080Ti. It indicates the feasibility and efficiency of the method we propose in power line defect detection tasks.

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An MQTT-based Scalable Remote control Framework with NodeMCU and Cloud platfrom

An MQTT-based Scalable Remote Control Framework with NodeMCU...

引用

International conference on Mechanical, control and computer engineering (ICMCCE)

作者： Xiaotong Chi Minghua Zhu Jing Yang Wenjie Wang MoE Engineering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China

Wi-Fi(IEEE802.11x) has an unshakable position in the field of wireless communication technology due to its low-cost, high-speed characteristics with wide coverage and large data transmission volume. In the Internet of Things(IoT) fields such as smart home, smart medical care and smart transportation, applications for remote control via easy-peasy Wi-Fi modules are in abundance. The development of cloud platform enables devices scattered in different areas to access the Internet for centralized control and breaks geographical restrictions. In the communication process, choosing an appropriate, reliable and lightweight application layer protocol with less load can better guarantee the quality of data transmission and so improve efficiency. In this paper, we proposed an scalable remote control framework based on MQTT application protocol. We utilize the simple-to-deploy, easy-to-develop NodeMCU board as edge wireless Wi-Fi nodes called SA, LK and SS NMN. combined with a single-chip microcomputer deployed with MQTT application layer protocol through TCP transparent transmission, the control system will be connected into cloud platform with proper burned SDK. In this way, people can control devices remotely, and cooperate with RocketMQ messages for message flow. We deployed a relatively complete experimental system, and verified the feasibility and reliability of the framework by testing the message delivery time and efficiency of pub/sub scenarios. The experimental results show that the speed of uplink and downline of this framework is within and above reason, and the efficiency is still high in the QoS2 state.

关键词： Wireless communication Cloud computing Protocols Microcomputers Reliability Data communication Uplink

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LWSA: A Learning-Based Workflow Scheduling Algorithm for Energy-Efficient UAV Delivery System

LWSA: A Learning-Based Workflow Scheduling Algorithm for Ene...

引用

International conference on Parallel and Distributed Systems (ICPADS)

作者： Jiali Wang Yutong Ye Ting Wang Mingsong Chen MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in delivery networks face significant challenges due to limited battery life and computational capabilities, particularly for tasks that entail intensive computing workflows. In this context, Multi-access Edge computing (MEC), which provides computing resources in close proximity to mobile terminal devices, has emerged as a promising solution. UAVs can offload computing tasks to MEC resources across diverse Internet of Things (IoT) environments. Although task offloading can enhance their task processing capability, it simultaneously brings additional costs, encompassing data transmission time and energy consumption. To address these issues, this paper proposes a novel workflow scheduling method based on the Proximal Policy Optimization (PPO) algorithm, aimed at optimizing UAV energy consumption within MEC environments. The proposed approach establishes a learning-based workflow scheduling strategy harnessing the adaptability of the PPO algorithm to manage dynamic and intricate scenarios, which facilitates efficient task allocation to optimal computational resources while accounting for flight time constraints. Extensive experiments conducted on various well-known scientific workflow benchmarks in real-world UAV delivery networks validate the effectiveness of our method. compared with state-of-the-art methods, our approach significantly reduces UAV energy consumption and task completion time, simultaneously increasing UAV’s effective payload capacity.

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