The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the posi...The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.展开更多
针对现有语音关键词检测方法定位精度低的问题,提出了一种基于多尺度距离矩阵的语音关键词检测与细粒度定位方法(spoken term detection and fine-grained localization method based on multi-scale distance matrices,MF-STD)。该方...针对现有语音关键词检测方法定位精度低的问题,提出了一种基于多尺度距离矩阵的语音关键词检测与细粒度定位方法(spoken term detection and fine-grained localization method based on multi-scale distance matrices,MF-STD)。该方法首先利用残差卷积网络提取特征并构建距离矩阵以建模输入之间的相关性;其次通过多尺度分割和解耦头学习不同尺度下的定位信息;最后根据多尺度加权定位损失、置信度损失和分类损失优化模型,实现对关键词存在性和时域边界的细粒度预测。在LibriSpeech数据集上的实验结果表明,MF-STD在集内词的检测中,精准率和交并比分别达到97.1%和88.6%;在集外词的检测中,精准率和交并比分别达到96.7%和88.2%。与现有的语音关键词检测与定位方法相比,MF-STD的检测准确率和定位精度显著提升,充分证明该方法的先进性,也证明了多尺度特征建模与细粒度定位约束在语音关键词检测任务中的有效性。展开更多
基金the Open Project of Sichuan Provincial Key Laboratory of Philosophy and Social Science for Language Intelligence in Special Education under Grant No.YYZN-2023-4the Ph.D.Fund of Chengdu Technological University under Grant No.2020RC002.
文摘The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor localization.However,the construction of the fingerprint database is quite time-consuming.Especially when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is difficult.An appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)applications.In this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization accuracy.Reference points(RPs)are generated in the grid by the FGGC model.Then,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex environments.As a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous methods.The proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy simultaneously.The mean error of the proposed model is 0.36 m,far lower than that of previous approaches.Thus,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor localization.It also shows high-accuracy performance with a fast running speed even under a large-size grid.The results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.
文摘针对现有语音关键词检测方法定位精度低的问题,提出了一种基于多尺度距离矩阵的语音关键词检测与细粒度定位方法(spoken term detection and fine-grained localization method based on multi-scale distance matrices,MF-STD)。该方法首先利用残差卷积网络提取特征并构建距离矩阵以建模输入之间的相关性;其次通过多尺度分割和解耦头学习不同尺度下的定位信息;最后根据多尺度加权定位损失、置信度损失和分类损失优化模型,实现对关键词存在性和时域边界的细粒度预测。在LibriSpeech数据集上的实验结果表明,MF-STD在集内词的检测中,精准率和交并比分别达到97.1%和88.6%;在集外词的检测中,精准率和交并比分别达到96.7%和88.2%。与现有的语音关键词检测与定位方法相比,MF-STD的检测准确率和定位精度显著提升,充分证明该方法的先进性,也证明了多尺度特征建模与细粒度定位约束在语音关键词检测任务中的有效性。
