with the rapid development of mobile intemet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), a...with the rapid development of mobile intemet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and nonadaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well.展开更多
This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered ...This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.展开更多
针对目前智能楼宇监测中数据可靠性低、测量点位分散、数据传输实时性不高和误报频繁等问题,提出基于物联网(Internet of Things,IoT)技术的智能楼宇监测系统设计。首先,采用ZigBee技术组建无线传感器网络,实现分散点位传感器数据的收集...针对目前智能楼宇监测中数据可靠性低、测量点位分散、数据传输实时性不高和误报频繁等问题,提出基于物联网(Internet of Things,IoT)技术的智能楼宇监测系统设计。首先,采用ZigBee技术组建无线传感器网络,实现分散点位传感器数据的收集,并将数据通过网关传输到物联网云平台。其次,利用改进的自适应加权算法融合传感器数据,有效提升多传感器检测数据的准确性。系统云平台能够分析和展示传感器数据,而且能够实时查看待测区域的视频图像,预留数据分析接口。应用表明,系统数据测量准确、相对误差较低、稳定性较好。展开更多
针对牛粪好氧发酵过程中交互性差和依赖经验决策的问题,课题组设计了基于数字孪生的发酵过程监控系统。首先,通过Unity引擎构建了发酵仓数字孪生虚拟模型,并设计搭建了场景环境和虚拟界面;其次,设计了物联网(internet of things, IoT)...针对牛粪好氧发酵过程中交互性差和依赖经验决策的问题,课题组设计了基于数字孪生的发酵过程监控系统。首先,通过Unity引擎构建了发酵仓数字孪生虚拟模型,并设计搭建了场景环境和虚拟界面;其次,设计了物联网(internet of things, IoT)网关以及数据库用于传输和存储孪生数据;最后,通过搭建模拟平台对数字孪生系统进行了时序同步和碰撞检测实验。实验结果表明:设计的发酵仓数字孪生监控系统虚拟模型与实物同步性较高,能做到虚实同步,验证了系统的可行性。展开更多
For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some...For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.展开更多
随着技术的不断发展,物联网(Internet of Things,IoT)和车联网的应用越来越广泛。在IoT系统中,自动驾驶技术能够减少交通事故发生、提升车辆行驶安全性,是未来车辆驾驶发展的主要方向。基于此,分析自动驾驶系统应当具备的功能,提出基于...随着技术的不断发展,物联网(Internet of Things,IoT)和车联网的应用越来越广泛。在IoT系统中,自动驾驶技术能够减少交通事故发生、提升车辆行驶安全性,是未来车辆驾驶发展的主要方向。基于此,分析自动驾驶系统应当具备的功能,提出基于IoT的网联自动驾驶车端系统设计,包括硬件、软件、数据处理与融合以及安全与冗余设计等,为今后相关研究提供参考。展开更多
随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。...随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。设计一套完整的光伏电站智能监控系统,通过对光伏发电区、变配电设备区、电站运维区等关键场景实施布控,利用边缘智能硬件装置采集光伏组件、逆变器、并网柜、储能柜及变配电柜等关键设备及各气象元素数据,通过光伏电站智能监控软件平台数据分析、实时告警、智能诊断、远程巡检等功能实时感知光伏电站发电营收及设备运行情况,有效提升光伏电站发电效率,减少电站本地运维人员成本开销。展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant No. 61361166005, the National High Technology Research and Development Program of China under Grant No. 2014AA01A701, the National Basic Research Program of China (973 Program) under Grant No. 2013CB336600, and the State Major Science and Technology Special Projects (Grant No. 2016ZX03001020-006).
文摘with the rapid development of mobile intemet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and nonadaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well.
基金supported by General Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province(2022SJYB0712)Research Development Fund for Young Teachers of Chengxian College of Southeast University(z0037)Special Project of Ideological and Political Education Reform and Research Course(yjgsz2206).
文摘This study aims to reduce the interference of ambient noise in mobile communication,improve the accuracy and authenticity of information transmitted by sound,and guarantee the accuracy of voice information deliv-ered by mobile communication.First,the principles and techniques of speech enhancement are analyzed,and a fast lateral recursive least square method(FLRLS method)is adopted to process sound data.Then,the convolutional neural networks(CNNs)-based noise recognition CNN(NR-CNN)algorithm and speech enhancement model are proposed.Finally,related experiments are designed to verify the performance of the proposed algorithm and model.The experimental results show that the noise classification accuracy of the NR-CNN noise recognition algorithm is higher than 99.82%,and the recall rate and F1 value are also higher than 99.92.The proposed sound enhance-ment model can effectively enhance the original sound in the case of noise interference.After the CNN is incorporated,the average value of all noisy sound perception quality evaluation system values is improved by over 21%compared with that of the traditional noise reduction method.The proposed algorithm can adapt to a variety of voice environments and can simultaneously enhance and reduce noise processing on a variety of different types of voice signals,and the processing effect is better than that of traditional sound enhancement models.In addition,the sound distortion index of the proposed speech enhancement model is inferior to that of the control group,indicating that the addition of the CNN neural network is less likely to cause sound signal distortion in various sound environments and shows superior robustness.In summary,the proposed CNN-based speech enhancement model shows significant sound enhancement effects,stable performance,and strong adapt-ability.This study provides a reference and basis for research applying neural networks in speech enhancement.
文摘针对目前智能楼宇监测中数据可靠性低、测量点位分散、数据传输实时性不高和误报频繁等问题,提出基于物联网(Internet of Things,IoT)技术的智能楼宇监测系统设计。首先,采用ZigBee技术组建无线传感器网络,实现分散点位传感器数据的收集,并将数据通过网关传输到物联网云平台。其次,利用改进的自适应加权算法融合传感器数据,有效提升多传感器检测数据的准确性。系统云平台能够分析和展示传感器数据,而且能够实时查看待测区域的视频图像,预留数据分析接口。应用表明,系统数据测量准确、相对误差较低、稳定性较好。
文摘针对牛粪好氧发酵过程中交互性差和依赖经验决策的问题,课题组设计了基于数字孪生的发酵过程监控系统。首先,通过Unity引擎构建了发酵仓数字孪生虚拟模型,并设计搭建了场景环境和虚拟界面;其次,设计了物联网(internet of things, IoT)网关以及数据库用于传输和存储孪生数据;最后,通过搭建模拟平台对数字孪生系统进行了时序同步和碰撞检测实验。实验结果表明:设计的发酵仓数字孪生监控系统虚拟模型与实物同步性较高,能做到虚实同步,验证了系统的可行性。
文摘For years,foot ulcers linked with diabetes mellitus and neuropathy have significantly impacted diabetic patients’ health-related quality of life(HRQoL). Diabetes foot ulcers impact15% of all diabetic patients at some point in their lives. The facilities and resources used for DFU detection and treatment are only available at hospitals and clinics,which results in the unavailability of feasible and timely detection at an early stage. This necessitates the development of an at-home DFU detection system that enables timely predictions and seamless communication with users,thereby preventing amputations due to neglect and severity. This paper proposes a feasible system consisting of three major modules:an IoT device that works to sense foot nodes to send vibrations onto a foot sole,a machine learning model based on supervised learning which predicts the level of severity of the DFU using four different classification techniques including XGBoost,K-SVM,Random Forest,and Decision tree,and a mobile application that acts as an interface between the sensors and the patient. Based on the severity levels,necessary steps for prevention,treatment,and medications are recommended via the application.
文摘随着技术的不断发展,物联网(Internet of Things,IoT)和车联网的应用越来越广泛。在IoT系统中,自动驾驶技术能够减少交通事故发生、提升车辆行驶安全性,是未来车辆驾驶发展的主要方向。基于此,分析自动驾驶系统应当具备的功能,提出基于IoT的网联自动驾驶车端系统设计,包括硬件、软件、数据处理与融合以及安全与冗余设计等,为今后相关研究提供参考。
文摘随着国家层面“碳达峰、碳中和”目标的确立,我国对清洁能源的需求与日俱增。利用物联网(Internet of Things,IoT)、大数据、人工智能(Artificial Intelligence,AI)等当今先进技术提升光伏(Photovoltaic,PV)电站运营效率得到广泛关注。设计一套完整的光伏电站智能监控系统,通过对光伏发电区、变配电设备区、电站运维区等关键场景实施布控,利用边缘智能硬件装置采集光伏组件、逆变器、并网柜、储能柜及变配电柜等关键设备及各气象元素数据,通过光伏电站智能监控软件平台数据分析、实时告警、智能诊断、远程巡检等功能实时感知光伏电站发电营收及设备运行情况,有效提升光伏电站发电效率,减少电站本地运维人员成本开销。