The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
The objective of this study was to carry out taste masking of ofloxacin(Ofl) by ion exchange resins(IERs)followed by sustained release of Ofl by forming interpenetrating polymer network(IPN) beads. Drug-resin complexe...The objective of this study was to carry out taste masking of ofloxacin(Ofl) by ion exchange resins(IERs)followed by sustained release of Ofl by forming interpenetrating polymer network(IPN) beads. Drug-resin complexes(DRCs) with three different ratios of Ofl to IERs(1:1, 1:2, 1:4) were prepared by batch method and investigated for in vivo and in vitro taste masking. DRC of methacrylic acid-divinyl benzene(MD) resin and Ofl prepared at a ratio of 1:4 was used to form IPN beads. IPN beads of MD 1:4 were prepared by following the ionic cross-linking method using sodium carboxymethyl xanthan gum(SCMXG) and SCMXG-sodium carboxymethyl cellulose(SCMXG-SCMC). IPN beads were characterized with FT-IR and further studied on sustained release of Ofl at different pH. In vivo taste masking carried out by human volunteers showed that MD 1:4 significantly reduced the bitterness of Ofl. Characterization studies such as FT-IR, DSC, P-XRD and taste masking showed that complex formation took place between drug and resin. In vitro study at gastric pH showed complete release of drug from MD 1:4 within 30 min whereas IPN beads took 5 h at gastric pH and 10 h at salivary pH for the complete release of drug. As the crosslinking increased the release kinetics changed into non-Fickian diffusion to zero-order release mechanism. MD 1:4 showed better performance for the taste masking of Ofl and IPNs beads prepared from it were found useful for the sustained release of Ofl at both the pH, indicating a versatile drug delivery system.展开更多
Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it...Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.展开更多
An otoscope is traditionally used to examine the eardrum and ear canal.A diagnosis of otitis media(OM)relies on the experience of clinicians.If an examiner lacks experience,the examination may be difficult and time-co...An otoscope is traditionally used to examine the eardrum and ear canal.A diagnosis of otitis media(OM)relies on the experience of clinicians.If an examiner lacks experience,the examination may be difficult and time-consuming.This paper presents an ear disease classification method using middle ear images based on a convolutional neural network(CNN).Especially the segmentation and classification networks are used to classify an otoscopic image into six classes:normal,acute otitis media(AOM),otitis media with effusion(OME),chronic otitis media(COM),congenital cholesteatoma(CC)and traumatic perforations(TMPs).The Mask R-CNN is utilized for the segmentation network to extract the region of interest(ROI)from otoscopic images.The extracted ROIs are used as guiding features for the classification.The classification is based on transfer learning with an ensemble of two CNN classifiers:EfficientNetB0 and Inception-V3.The proposed model was trained with a 5-fold cross-validation technique.The proposed method was evaluated and achieved a classification accuracy of 97.29%.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
基金Council of Scientific and Industrial Research (CSIR), New Delhi, India, for providing Senior Research FellowshipCentralized Analytical Facility of CSIRCSMCRI for analytical support
文摘The objective of this study was to carry out taste masking of ofloxacin(Ofl) by ion exchange resins(IERs)followed by sustained release of Ofl by forming interpenetrating polymer network(IPN) beads. Drug-resin complexes(DRCs) with three different ratios of Ofl to IERs(1:1, 1:2, 1:4) were prepared by batch method and investigated for in vivo and in vitro taste masking. DRC of methacrylic acid-divinyl benzene(MD) resin and Ofl prepared at a ratio of 1:4 was used to form IPN beads. IPN beads of MD 1:4 were prepared by following the ionic cross-linking method using sodium carboxymethyl xanthan gum(SCMXG) and SCMXG-sodium carboxymethyl cellulose(SCMXG-SCMC). IPN beads were characterized with FT-IR and further studied on sustained release of Ofl at different pH. In vivo taste masking carried out by human volunteers showed that MD 1:4 significantly reduced the bitterness of Ofl. Characterization studies such as FT-IR, DSC, P-XRD and taste masking showed that complex formation took place between drug and resin. In vitro study at gastric pH showed complete release of drug from MD 1:4 within 30 min whereas IPN beads took 5 h at gastric pH and 10 h at salivary pH for the complete release of drug. As the crosslinking increased the release kinetics changed into non-Fickian diffusion to zero-order release mechanism. MD 1:4 showed better performance for the taste masking of Ofl and IPNs beads prepared from it were found useful for the sustained release of Ofl at both the pH, indicating a versatile drug delivery system.
文摘传统的夜间车辆检测基于车灯特征的提取和识别,这类方法容易发生误判、检测精度和检测实时性不高。针对上述问题,本文研究了基于改进Mask RCNN(mask RCNN-night vehicle detection,Mask RCNN-NVD)的夜间车辆检测算法。将残差网络(residual network,ResNet)结构中的普通卷积修改为数量为16组的分组卷积,通过16组1×1卷积实现通道数叠加,将网络参数降至普通卷积的1/16,提升检测速度,并实现与普通卷积相同的效果;将通道注意力机制模块(squeeze-and-excitation,SE)嵌入ResNet结构中,通过2个全连接层构建瓶颈结构,将归一化权重加权到各通道特征,增强网络表征能力;在特征金字塔网络(feature pyramid networks,FPN)后加入自底向上结构,将底层特征强定位信息传递到高层语义特征中;加入自适应池化层,根据区域候选网络(region proposal network,RPN)产生的候选区域分配至不同尺度特征图中,并在底层特征与各阶段最高层特征之间加入跳跃连接结构,实现缩减模型参数的同时保留模型的全局表征能力。通过对开源数据集Microsoft common objects in context(MS COCO)、Berkeley deep drive 100K(BDD100K)的夜间行车图像进行数据增强,构建用于评估检测性能的测试集2000张。实验结果表明:算法在测试集上的平均精度(mean Average Precsion,mAP)值高达92.62,每秒图像处理帧数(Frames Per Second,FPS)值高达30帧。相比于原始Mask RCNN算法分别在mAP值上提高1.68,FPS值提高4帧,验证提出的方法可以有效提升夜间车辆检测的准确性和实时性。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R442),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Face mask detection has several applications,including real-time surveillance,biometrics,etc.Identifying face masks is also helpful for crowd control and ensuring people wear them publicly.With monitoring personnel,it is impossible to ensure that people wear face masks;automated systems are a much superior option for face mask detection and monitoring.This paper introduces a simple and efficient approach for masked face detection.The architecture of the proposed approach is very straightforward;it combines deep learning and local binary patterns to extract features and classify themasmasked or unmasked.The proposed systemrequires hardware withminimal power consumption compared to state-of-the-art deep learning algorithms.Our proposed system maintains two steps.At first,this work extracted the local features of an image by using a local binary pattern descriptor,and then we used deep learning to extract global features.The proposed approach has achieved excellent accuracy and high performance.The performance of the proposed method was tested on three benchmark datasets:the realworld masked faces dataset(RMFD),the simulated masked faces dataset(SMFD),and labeled faces in the wild(LFW).Performancemetrics for the proposed technique weremeasured in terms of accuracy,precision,recall,and F1-score.Results indicated the efficiency of the proposed technique,providing accuracies of 99.86%,99.98%,and 100%for RMFD,SMFD,and LFW,respectively.Moreover,the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.
基金This study was supported by a Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT&Future Planning NRF-2020R1A2C1014829the Soonchunhyang University Research Fund.
文摘An otoscope is traditionally used to examine the eardrum and ear canal.A diagnosis of otitis media(OM)relies on the experience of clinicians.If an examiner lacks experience,the examination may be difficult and time-consuming.This paper presents an ear disease classification method using middle ear images based on a convolutional neural network(CNN).Especially the segmentation and classification networks are used to classify an otoscopic image into six classes:normal,acute otitis media(AOM),otitis media with effusion(OME),chronic otitis media(COM),congenital cholesteatoma(CC)and traumatic perforations(TMPs).The Mask R-CNN is utilized for the segmentation network to extract the region of interest(ROI)from otoscopic images.The extracted ROIs are used as guiding features for the classification.The classification is based on transfer learning with an ensemble of two CNN classifiers:EfficientNetB0 and Inception-V3.The proposed model was trained with a 5-fold cross-validation technique.The proposed method was evaluated and achieved a classification accuracy of 97.29%.
