In the last two decades, light-weight magnesium matrix composites have been the hot issue of material field due to their excellent mechanical and physical properties, e.g., high-specific strength and modulus, good wea...In the last two decades, light-weight magnesium matrix composites have been the hot issue of material field due to their excellent mechanical and physical properties, e.g., high-specific strength and modulus, good wear resistance, and damping capacity. As compared with aluminum matrix composites, magnesium matrix composites have merit in their specific weight and have wide applications in aerospace and aeronautical fields. Generally, the processing techniques for magnesium matrix composites can be categorized as conventional and special processing routes. In recent years, as a special processing route, metal melt infiltration into porous ceramic preform featured by its low cost and availability of high-volume fraction of reinforced ceramics have been receiving much attention. Thus, in this review, one emphasis was put on the description of this processing technique in association with the means to obtain good wettability, the prerequisite for this kind of processing method. Based on the recognized fact that there exist clean interface and bonding ability between ceramics and matrix metal, in-situ reaction synthesis is usually utilized to fabricate magnesium matrix composites. Therefore, the interfacial feature was also reviewed for the in-situ reaction synthesis. Characterizations of microstructures and various mechanical-physical properties were finally summarized for magnesium matrix composites including tensile response, wear resistance, creep behavior, and damping capacity.展开更多
The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific f...The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.展开更多
With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and repr...With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.展开更多
基金financially supported by the National Natural Science Foundation of China (Grant No.51271051)
文摘In the last two decades, light-weight magnesium matrix composites have been the hot issue of material field due to their excellent mechanical and physical properties, e.g., high-specific strength and modulus, good wear resistance, and damping capacity. As compared with aluminum matrix composites, magnesium matrix composites have merit in their specific weight and have wide applications in aerospace and aeronautical fields. Generally, the processing techniques for magnesium matrix composites can be categorized as conventional and special processing routes. In recent years, as a special processing route, metal melt infiltration into porous ceramic preform featured by its low cost and availability of high-volume fraction of reinforced ceramics have been receiving much attention. Thus, in this review, one emphasis was put on the description of this processing technique in association with the means to obtain good wettability, the prerequisite for this kind of processing method. Based on the recognized fact that there exist clean interface and bonding ability between ceramics and matrix metal, in-situ reaction synthesis is usually utilized to fabricate magnesium matrix composites. Therefore, the interfacial feature was also reviewed for the in-situ reaction synthesis. Characterizations of microstructures and various mechanical-physical properties were finally summarized for magnesium matrix composites including tensile response, wear resistance, creep behavior, and damping capacity.
文摘The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
基金supported in part by the Fundamental Research Funds for the Central Universities(xcxjh20210104).
文摘With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.
