As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT network...As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.展开更多
The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 t...The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 test-day records about 155 893 Chinese Holstein dairy cows collected by the Henan Dairy Herd Improvement Center from January 2008 to April 2016, the dynamics of test times during a complete lactation, test interval during a complete lactation, days in milk （DIM） of first test-day record, daughter descendant number and herd number of bull, age at first calving and pedigree integrity rate among different years and different herd sizes were analyzed by MEANS order of SAS 9.4. In addition, the data that were applicable to genetic evaluation were screened by SQL program. The results showed that during 2008-2015, the number of cow individuals participating in DHI in Henan Province increased from 7 379 to 93 706; the test-day milk yield increased from 19.91 to 24.05 kg; the somatic cell count reduced from 411.09×10^3 to 277.08×10^3 cells/ml; the percentage of cows with DIM ranging from 5-305 d reached 70.92%; the average test times increased from 3.20 to 6.31 times; the test interval decreased from 70.22 to 33.83 d; the dairy cows with age at first calving of 25 months were dominant, accounting for 12.57%; the bulls whose daughter descendant number was 20 or more and the daughters were distributed in 10 or more farms accounted for 6.05%; the one-generation pedigree integrity rate was 82.54%; the percentage of data that could be used for genetic evaluation was screened as 20.67%, which was lower than the results of other similar studies.展开更多
Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and ...Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.展开更多
Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and ...Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.展开更多
The aim is to solve the problem that how to share dispersive and heterogeneous data inside business information system or some other information source. On the basis of Web service, this paper adopts the notion of Dat...The aim is to solve the problem that how to share dispersive and heterogeneous data inside business information system or some other information source. On the basis of Web service, this paper adopts the notion of Data As ,Service to build service-oriented data integration architecture. According to this architecture, we develop data collection system which effectively integrates data from heterogeneous informa tion source and present a uniform data view to end users by implementing sharing data from heterogeneous systems and information source . At last, this paper gives an example of a compositive information collection platform system.展开更多
---Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integri...---Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integrity （SI） simulation flow for the DDR3 interface, two system-level SI simulation methodologies, which are board-level S-parameter extraction in the frequency-domain and system-level simulation assumptions in the time domain, are introduced in this paper. By comparing the flow of Speed2000 and PowerSI/Hspice, PowerSI is chosen for the printed circuit board （PCB） board-level S-parameter extraction, while Tektronix oscilloscope （TDS7404） is used for the DDR3 waveform measurement. The lab measurement shows good agreement between simulation and measurement. The study shows that the combination of PowerSI and Hspice is recommended for quick system-level DDR3 SI simulation.展开更多
基金supported by National Natural Science Foundation of China(No.62171158)the project“The Major Key Project of PCL(PCL2021A03-1)”from Peng Cheng Laboratorysupported by the Science and the Research Fund Program of Guangdong Key Laboratory of Aerospace Communication and Networking Technology(2018B030322004).
文摘As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.
基金Supported by Science and Technology Open Cooperation Project of Henan Province(162106000017)Science and Technology People-benefiting Plan Project of Henan Province(152207110004)Puyang Science and Technology Plan Project(150109)~~
文摘The dairy herd improvement data from Henan Province were analyzed statistically to establish screening criteria for relevant data, thereby laying a foundation for genetic evaluation of dairy cows. With the 2 152 451 test-day records about 155 893 Chinese Holstein dairy cows collected by the Henan Dairy Herd Improvement Center from January 2008 to April 2016, the dynamics of test times during a complete lactation, test interval during a complete lactation, days in milk （DIM） of first test-day record, daughter descendant number and herd number of bull, age at first calving and pedigree integrity rate among different years and different herd sizes were analyzed by MEANS order of SAS 9.4. In addition, the data that were applicable to genetic evaluation were screened by SQL program. The results showed that during 2008-2015, the number of cow individuals participating in DHI in Henan Province increased from 7 379 to 93 706; the test-day milk yield increased from 19.91 to 24.05 kg; the somatic cell count reduced from 411.09×10^3 to 277.08×10^3 cells/ml; the percentage of cows with DIM ranging from 5-305 d reached 70.92%; the average test times increased from 3.20 to 6.31 times; the test interval decreased from 70.22 to 33.83 d; the dairy cows with age at first calving of 25 months were dominant, accounting for 12.57%; the bulls whose daughter descendant number was 20 or more and the daughters were distributed in 10 or more farms accounted for 6.05%; the one-generation pedigree integrity rate was 82.54%; the percentage of data that could be used for genetic evaluation was screened as 20.67%, which was lower than the results of other similar studies.
文摘Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights.While wearable device data help to monitor,detect,and predict diseases and health conditions,some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns.Moreover,wearable devices have been recently available as commercial products;thus large,diverse,and representative datasets are not available to most researchers.In this article,the authors propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers(e.g.,researchers)to make wearable device data more available to healthcare researchers.To secure the data transactions in a privacy-preserving manner,the authors use a decentralized approach using Blockchain and Non-Fungible Tokens(NFTs).To ensure data originality and integrity with secure validation,the marketplace uses Trusted Execution Environments(TEE)in wearable devices to verify the correctness of health data.The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share.To ensure user participation,we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs.The authors also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits.If widely adopted,it’s believed that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.
基金supported by the National Natural Science Foundation of China(62072475)the Fundamental Research Funds for the Central Universities of Central South University(CX20230356)。
文摘Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.
基金Supported by the Plan of Research on Science andTechnology and Development in Hebei Province (04213534)
文摘The aim is to solve the problem that how to share dispersive and heterogeneous data inside business information system or some other information source. On the basis of Web service, this paper adopts the notion of Data As ,Service to build service-oriented data integration architecture. According to this architecture, we develop data collection system which effectively integrates data from heterogeneous informa tion source and present a uniform data view to end users by implementing sharing data from heterogeneous systems and information source . At last, this paper gives an example of a compositive information collection platform system.
基金supported by the National Natural Science Foundation of China under Grant No.61161001
文摘---Double data rate synchronous dynamic random access memory （DDR3） has become one of the most mainstream applications in current server and computer systems. In order to quickly set up a system-level signal integrity （SI） simulation flow for the DDR3 interface, two system-level SI simulation methodologies, which are board-level S-parameter extraction in the frequency-domain and system-level simulation assumptions in the time domain, are introduced in this paper. By comparing the flow of Speed2000 and PowerSI/Hspice, PowerSI is chosen for the printed circuit board （PCB） board-level S-parameter extraction, while Tektronix oscilloscope （TDS7404） is used for the DDR3 waveform measurement. The lab measurement shows good agreement between simulation and measurement. The study shows that the combination of PowerSI and Hspice is recommended for quick system-level DDR3 SI simulation.
