Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
地外文明搜寻(Search for Extra Terrestrial Intelligence,SETI)是射电天文的重要子领域。为了获得尽可能多的观测时间,SETI采用共时观测(Commensal Survey),即不单独占用望远镜时间,在望远镜进行其他观测任务的同时进行SETI信号的搜...地外文明搜寻(Search for Extra Terrestrial Intelligence,SETI)是射电天文的重要子领域。为了获得尽可能多的观测时间,SETI采用共时观测(Commensal Survey),即不单独占用望远镜时间,在望远镜进行其他观测任务的同时进行SETI信号的搜寻。介绍了SETI共时观测的概念以及SETI后端的整体框架,分析了SETI共时观测的主要策略;对实时数据接收系统SERENDIP进行了分析说明;同时分析了数据去射电干扰和候选目标提取方法;通过对500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)的5 h漂移扫描数据处理,验证了SETI后端的有效性。最后对SETI的未来发展趋势进行了展望:FAST的高灵敏度不仅是对其它望远镜针对该项目观测数据的有效验证,更增加了探测到微弱地外文明信号的可能性。展开更多
针对目前智能脱扣器中采集数据需要高效、高保真、低时延压缩传输的现状,采用FAST(FIX Adapted for Streaming)协议处理数据,该协议利用数据内容与结构描述分离的方法,用发送和接受双方都理解的模板描述数据结构,对数据内容进行字节编...针对目前智能脱扣器中采集数据需要高效、高保真、低时延压缩传输的现状,采用FAST(FIX Adapted for Streaming)协议处理数据,该协议利用数据内容与结构描述分离的方法,用发送和接受双方都理解的模板描述数据结构,对数据内容进行字节编码以及二进制序列化压缩。描述了用C++语言实现FAST算法的方法,试验结果表明FAST协议能够有效解决传输数据的大量重复,在工业高速海量实时数据的应用中具有巨大的优越性。展开更多
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘地外文明搜寻(Search for Extra Terrestrial Intelligence,SETI)是射电天文的重要子领域。为了获得尽可能多的观测时间,SETI采用共时观测(Commensal Survey),即不单独占用望远镜时间,在望远镜进行其他观测任务的同时进行SETI信号的搜寻。介绍了SETI共时观测的概念以及SETI后端的整体框架,分析了SETI共时观测的主要策略;对实时数据接收系统SERENDIP进行了分析说明;同时分析了数据去射电干扰和候选目标提取方法;通过对500 m口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,FAST)的5 h漂移扫描数据处理,验证了SETI后端的有效性。最后对SETI的未来发展趋势进行了展望:FAST的高灵敏度不仅是对其它望远镜针对该项目观测数据的有效验证,更增加了探测到微弱地外文明信号的可能性。
文摘针对目前智能脱扣器中采集数据需要高效、高保真、低时延压缩传输的现状,采用FAST(FIX Adapted for Streaming)协议处理数据,该协议利用数据内容与结构描述分离的方法,用发送和接受双方都理解的模板描述数据结构,对数据内容进行字节编码以及二进制序列化压缩。描述了用C++语言实现FAST算法的方法,试验结果表明FAST协议能够有效解决传输数据的大量重复,在工业高速海量实时数据的应用中具有巨大的优越性。
