The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
The titled test equipment is designed based on the microcontrollor MCS 51.It can be use for three models of pulse test, pause discharge test and continuous discharge test. It works on many forms with one model by ente...The titled test equipment is designed based on the microcontrollor MCS 51.It can be use for three models of pulse test, pause discharge test and continuous discharge test. It works on many forms with one model by entering different parameters. It has not only printing function, but also ICL232 interface, and can be controlled at a long distance.展开更多
This paper investigates the untraditional approach of contention resolution in Wavelength Division Multiplexing (WDM) Optical Packet Switching (OPS). The most striking characteristics of the developed switch architect...This paper investigates the untraditional approach of contention resolution in Wavelength Division Multiplexing (WDM) Optical Packet Switching (OPS). The most striking characteristics of the developed switch architecture are: (1) Contention resolution is achieved by a combined sharing of Fiber Delay-Lines (FDLs) and Tunable Optical Wavelength Converters (TOWCs); (2) FDLs are arranged in non-degenerate form, i.e., non-uniform distribution of the delay lines; (3) TOWCs just can perform wavelength conversion in partial continuous wavelength channels, i.e., sparse wavelength conversion. The concrete configurations of FDLs and TOWCs are described and analyzed under non-bursty and bursty traffic scenarios. Simulation results demonstrate that for a prefixed packet loss probability constraint, e.g., 10-6, the developed architecture provides a different point of view in OPS design. That is, combined sharing of FDLs and TOWCs can, effectively, obtain a good tradeoff between the switch size and the cost, and TOWCs which are achieved in sparse form can also decrease the implementing complexity.展开更多
Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering th...Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering that when faced with large-scale sentiment analysis tasks,the high accuracy rate often requires long experimental time.In view of the weakness,a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed,and singular value decomposition(SVD)is used to find the sparse feature of the image,which are sparse vectors with strong discriminativeness and effectively reduce redundant information;The authors propose the Fast Dictionary Learning algorithm(FDL),which can combine neural network with sparse representation.This method is based on K-Singular Value Decomposition,and through iteration,it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy.Moreover,the effectiveness of the proposed method is evaluated on the FER2013 dataset.By adding singular value decomposition,the accuracy of the test suite increased by 0.53%,and the total experiment time was shortened by 8.2%;Fast Dictionary Learning shortened the total experiment time by 36.3%.展开更多
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
文摘The titled test equipment is designed based on the microcontrollor MCS 51.It can be use for three models of pulse test, pause discharge test and continuous discharge test. It works on many forms with one model by entering different parameters. It has not only printing function, but also ICL232 interface, and can be controlled at a long distance.
基金Supported by the National Natural Science Foundation of China (No.69990540).
文摘This paper investigates the untraditional approach of contention resolution in Wavelength Division Multiplexing (WDM) Optical Packet Switching (OPS). The most striking characteristics of the developed switch architecture are: (1) Contention resolution is achieved by a combined sharing of Fiber Delay-Lines (FDLs) and Tunable Optical Wavelength Converters (TOWCs); (2) FDLs are arranged in non-degenerate form, i.e., non-uniform distribution of the delay lines; (3) TOWCs just can perform wavelength conversion in partial continuous wavelength channels, i.e., sparse wavelength conversion. The concrete configurations of FDLs and TOWCs are described and analyzed under non-bursty and bursty traffic scenarios. Simulation results demonstrate that for a prefixed packet loss probability constraint, e.g., 10-6, the developed architecture provides a different point of view in OPS design. That is, combined sharing of FDLs and TOWCs can, effectively, obtain a good tradeoff between the switch size and the cost, and TOWCs which are achieved in sparse form can also decrease the implementing complexity.
基金supported by the National Natural Science Foundation of China(No.61801440)the High‐quality and Cutting‐edge Disciplines Construction Project for Universities in Beijing(Internet Information,Communication University of China),State Key Laboratory of Media Convergence and Communication(Communication University of China)the Fundamental Research Funds for the Central Universities(CUC2019B069).
文摘Many methods based on deep learning have achieved amazing results in image sentiment analysis.However,these existing methods usually pursue high accuracy,ignoring the effect on model training efficiency.Considering that when faced with large-scale sentiment analysis tasks,the high accuracy rate often requires long experimental time.In view of the weakness,a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed,and singular value decomposition(SVD)is used to find the sparse feature of the image,which are sparse vectors with strong discriminativeness and effectively reduce redundant information;The authors propose the Fast Dictionary Learning algorithm(FDL),which can combine neural network with sparse representation.This method is based on K-Singular Value Decomposition,and through iteration,it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy.Moreover,the effectiveness of the proposed method is evaluated on the FER2013 dataset.By adding singular value decomposition,the accuracy of the test suite increased by 0.53%,and the total experiment time was shortened by 8.2%;Fast Dictionary Learning shortened the total experiment time by 36.3%.
