Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,th...Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the city.The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations.But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms.In light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter degradation.The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion.Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting.The model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.展开更多
Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature ...Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.展开更多
Skin melanoma is one of the most common malignant tumorsoriginating from melanocytes, and the incidence of the Chinese populationis showing a continuous increasing trend. Early and accurate diagnosisof melanoma has gr...Skin melanoma is one of the most common malignant tumorsoriginating from melanocytes, and the incidence of the Chinese populationis showing a continuous increasing trend. Early and accurate diagnosisof melanoma has great significance for guiding clinical treatment.However, the symptoms of malignant melanoma are not obvious in theearly stage. It is difficult to be diagnosed with human observation. Meanwhile,it is easy to spread due to missed diagnosis. In order to accuratelydiagnose melanoma, end-to-end skin lesion attribute segmentation frameworkis presented in this paper. It is applied to facilitate the digitalizationprocess of attributes segmentation. The framework was improved on theU-Net construction that use the channel context feature fusion modulebetween the encoder and decoder to further merge context information. Adual-domain attention module is proposed to get more effective informationfrom the feature map. It shows that the proposed method effectivelysegments the lesion attributes and achieves good result in the ISIC2018task2 dataset.展开更多
【目的】由于天然孔隙介质中存在物理化学非均质性,在这种复杂的非均质性含水层中,以往的现场试验数据显示溶质在非均质介质运移过程中无法用菲克扩散定律对流弥散方程(Advection-Dispersion Equation,ADE)来描述。本研究采用高密度电...【目的】由于天然孔隙介质中存在物理化学非均质性,在这种复杂的非均质性含水层中,以往的现场试验数据显示溶质在非均质介质运移过程中无法用菲克扩散定律对流弥散方程(Advection-Dispersion Equation,ADE)来描述。本研究采用高密度电法证实溶质在非均质介质中非菲克运移。【方法】本研究采用石英砂、沸石两种不同基质构建双重介质物理模型(Models of Dual-Domain Mass Transfer,DDMT),采用高密度电法测定系统ERT21实时检测和采集数据,在实验室利用Nacl溶液开展示踪试验,利用阿尔奇定律分析溶质运移试验研究。【结果】试验结果浓度穿透曲线在后期发生“拖尾”现象;在沸石柱实验中,观察到流体电导率(σ_(f))和体积电导率(σ_(b))之间的滞后现象,这表明流体在不可动领域和可动领域之间的交换。而在沙子柱试验中,未观察到σ_(f)和σ_(b)之间的滞后现象,可以忽略质量传递行为;滞后现象的形状与大小由水动力学特征和基质属性控制,水动力学是影响拖尾时长的因素之一,渗透系数会影响溶质运移的过程。【结论】通过试验观察和地球物理数据分析,直接量化了实验室尺度下的异常质量传递行为,通过地球物理方法测量的导电率(σ_(b))对于移动和不动领域都具有敏感性,从而提供了与标准采样方法相比的独特优势。展开更多
基金supported by the National Natural Science Foundation of China under Grant 62072256Natural Science Foundation of Nanjing University of Posts and Telecommunications(Grant Nos.NY221057,NY220003).
文摘Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the city.The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations.But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms.In light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter degradation.The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion.Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting.The model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.
基金supported by National Nature Science Foundation of China(No.62276093)in part by Natural Science Foundation of Shandong Province,China(No.2022MF86).
文摘Photoplethysmography(PPG)biometrics have received considerable attention.Although deep learning has achieved good performance for PPG biometrics,several challenges remain open:1)How to effectively extract the feature fusion representation from time and frequency PPG signals.2)How to effectively capture a series of PPG signal transition information.3)How to extract timevarying information from one-dimensional time-frequency sequential data.To address these challenges,we propose a dual-domain and multiscale fusion deep neural network(DMFDNN)for PPG biometric recognition.The DMFDNN is mainly composed of a two-branch deep learning framework for PPG biometrics,which can learn the time-varying and multiscale discriminative features from the time and frequency domains.Meanwhile,we design a multiscale extraction module to capture transition information,which consists of multiple convolution layers with different receptive fields for capturing multiscale transition information.In addition,the dual-domain attention module is proposed to strengthen the domain of greater contributions from time-domain and frequency-domain data for PPG biometrics.Experiments on the four datasets demonstrate that DMFDNN outperforms the state-of-the-art methods for PPG biometrics.
基金The paper is supported by the National Natural Science Foundation of China under Grant No.62072135 and No.61672181.
文摘Skin melanoma is one of the most common malignant tumorsoriginating from melanocytes, and the incidence of the Chinese populationis showing a continuous increasing trend. Early and accurate diagnosisof melanoma has great significance for guiding clinical treatment.However, the symptoms of malignant melanoma are not obvious in theearly stage. It is difficult to be diagnosed with human observation. Meanwhile,it is easy to spread due to missed diagnosis. In order to accuratelydiagnose melanoma, end-to-end skin lesion attribute segmentation frameworkis presented in this paper. It is applied to facilitate the digitalizationprocess of attributes segmentation. The framework was improved on theU-Net construction that use the channel context feature fusion modulebetween the encoder and decoder to further merge context information. Adual-domain attention module is proposed to get more effective informationfrom the feature map. It shows that the proposed method effectivelysegments the lesion attributes and achieves good result in the ISIC2018task2 dataset.
文摘【目的】由于天然孔隙介质中存在物理化学非均质性,在这种复杂的非均质性含水层中,以往的现场试验数据显示溶质在非均质介质运移过程中无法用菲克扩散定律对流弥散方程(Advection-Dispersion Equation,ADE)来描述。本研究采用高密度电法证实溶质在非均质介质中非菲克运移。【方法】本研究采用石英砂、沸石两种不同基质构建双重介质物理模型(Models of Dual-Domain Mass Transfer,DDMT),采用高密度电法测定系统ERT21实时检测和采集数据,在实验室利用Nacl溶液开展示踪试验,利用阿尔奇定律分析溶质运移试验研究。【结果】试验结果浓度穿透曲线在后期发生“拖尾”现象;在沸石柱实验中,观察到流体电导率(σ_(f))和体积电导率(σ_(b))之间的滞后现象,这表明流体在不可动领域和可动领域之间的交换。而在沙子柱试验中,未观察到σ_(f)和σ_(b)之间的滞后现象,可以忽略质量传递行为;滞后现象的形状与大小由水动力学特征和基质属性控制,水动力学是影响拖尾时长的因素之一,渗透系数会影响溶质运移的过程。【结论】通过试验观察和地球物理数据分析,直接量化了实验室尺度下的异常质量传递行为,通过地球物理方法测量的导电率(σ_(b))对于移动和不动领域都具有敏感性,从而提供了与标准采样方法相比的独特优势。
