目的探讨肺部GGO及GGN超高分辨率CT扫描技术,提高对其细节的显示。方法采用Philips i CT 256机型经低剂量肺部查体筛查检出的56例GGO及GGN患者(男23例,女33例,年龄30～75岁,平均62岁)进行超高分辨CT扫描研究,病灶最大径4～31mm,均结合...目的探讨肺部GGO及GGN超高分辨率CT扫描技术,提高对其细节的显示。方法采用Philips i CT 256机型经低剂量肺部查体筛查检出的56例GGO及GGN患者(男23例,女33例,年龄30～75岁,平均62岁)进行超高分辨CT扫描研究,病灶最大径4～31mm,均结合生理通气辅助,28例对病灶范围进行常规超高分辨率靶扫描,余28例对病灶范围进行优化超高分辨率靶扫描,再经2位高年资诊断医师共同评价所得图像的细节及清晰度显示,并进行统计学分析。结果常规超高分辨率靶扫描较常规扫描在空间分辨率方面稍有提高,细节显示较常规扫描无明显改善,部分甚至有负面影响,而经优化的超高分辨率扫描在肺局灶性GGO及GGN细节显示上均有较明显提高。结论经优化的超高分辨率CT扫描对局灶性GGO及GGN细节的显示较常规超高分辨率扫描明显提高,进而提高局灶性GGO及GGN的诊断准确率。展开更多
Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent years.One popular evasion method is to execute malicious code and perform malicious actions only in memor...Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent years.One popular evasion method is to execute malicious code and perform malicious actions only in memory.Mali-cious programs that use this attack method are called memory-resident malware,with excellent evasion capability,and have posed huge threats to cyber security.Traditional static and dynamic methods are not effective in detect-ing memory-resident malware.In addition,existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory analysis.This paper proposes MRm-DLDet,a state-of-the-art memory-resident malware detection framework,to overcome these drawbacks.MRm-DLDet first builds a virtual machine environment and captures memory dumps,then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping,followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect them.MRmNet receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18,which extracts the features of the sub-images.Then trains a network of gated recurrent units with an attention mechanism.Finally,it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting layer.We created a high-quality dataset consisting of 2,060 benign and memory-resident programs.In other words,the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB images.We implement MRm-DLDet for Windows 10,and it performs better than the latest methods,with a detection accuracy of up to 98.34%.Moreover,we measured the effects of mimicry and adversarial attacks on MRm-DLDet,and the experimental results demonstrated the robustness of MRm-DLDet.展开更多
We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser（AMLL）. The transmitted signal and sampling clock in the rada...We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser（AMLL）. The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression（MLS） algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.展开更多
Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting...Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods.This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye.Based on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage assessment.We optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster levels.The results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 ha.Severely damaged buildings accounted for 15.67%of the total building area in the affected areas.WorldPop population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,respectively.Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.展开更多
Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination ...Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography(UHR-OCT)imaging data.Methods:A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups:normal(50 eyes),with keratoconus(38 eyes)or with subclinical keratoconus(33 eyes).All eyes were imaged with a Scheimpflug camera and UHR-OCT.Corneal morphological features were extracted from the imaging data.A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes.Fisher’s score was used to rank the differentiable power of each feature.The receiver operating characteristic(ROC)curves were calculated to obtain the area under the ROC curves(AUCs).Results:The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus(AUC=0.93).The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.Conclusion:The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes.The epithelial features extracted from the OCT images were the most valuable in the discrimination process.This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.展开更多
文摘目的探讨肺部GGO及GGN超高分辨率CT扫描技术,提高对其细节的显示。方法采用Philips i CT 256机型经低剂量肺部查体筛查检出的56例GGO及GGN患者(男23例,女33例,年龄30～75岁,平均62岁)进行超高分辨CT扫描研究,病灶最大径4～31mm,均结合生理通气辅助,28例对病灶范围进行常规超高分辨率靶扫描,余28例对病灶范围进行优化超高分辨率靶扫描,再经2位高年资诊断医师共同评价所得图像的细节及清晰度显示,并进行统计学分析。结果常规超高分辨率靶扫描较常规扫描在空间分辨率方面稍有提高,细节显示较常规扫描无明显改善,部分甚至有负面影响,而经优化的超高分辨率扫描在肺局灶性GGO及GGN细节显示上均有较明显提高。结论经优化的超高分辨率CT扫描对局灶性GGO及GGN细节的显示较常规超高分辨率扫描明显提高,进而提高局灶性GGO及GGN的诊断准确率。
基金supported by the Youth Innovation Promotion Association CAS(No.2019163)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDC02040100)the Key Laboratory of Network Assessment Technology at Chinese Academy of Sciences and Beijing Key Laboratory of Network security and Protection Technology.
文摘Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent years.One popular evasion method is to execute malicious code and perform malicious actions only in memory.Mali-cious programs that use this attack method are called memory-resident malware,with excellent evasion capability,and have posed huge threats to cyber security.Traditional static and dynamic methods are not effective in detect-ing memory-resident malware.In addition,existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory analysis.This paper proposes MRm-DLDet,a state-of-the-art memory-resident malware detection framework,to overcome these drawbacks.MRm-DLDet first builds a virtual machine environment and captures memory dumps,then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping,followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect them.MRmNet receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18,which extracts the features of the sub-images.Then trains a network of gated recurrent units with an attention mechanism.Finally,it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting layer.We created a high-quality dataset consisting of 2,060 benign and memory-resident programs.In other words,the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB images.We implement MRm-DLDet for Windows 10,and it performs better than the latest methods,with a detection accuracy of up to 98.34%.Moreover,we measured the effects of mimicry and adversarial attacks on MRm-DLDet,and the experimental results demonstrated the robustness of MRm-DLDet.
基金partially supported by the National Natural Science Foundation of China(Nos.61571292and 61535006)by the State Key Lab Project of Shanghai Jiao Tong University(No.2014ZZ03016)by STCSM
文摘We experimentally demonstrate the ultra-high range resolution of a photonics-based microwave radar using a high repetition rate actively mode-locked laser（AMLL）. The transmitted signal and sampling clock in the radar originate from the same AMLL to achieve a large instantaneous bandwidth. A Ka band linearly frequency modulated signal with a bandwidth up to 8 GHz is successfully generated and processed with the electro-optical upconversion and direct photonic sampling. The minor lobe suppression（MLS） algorithm is adopted to enhance the dynamic range at a cost of the range resolution. Two-target discrimination with the MLS algorithm proves the range resolution reaches 2.8 cm. The AMLL-based microwave-photonics radar shows promising applications in high-resolution imaging radars having the features of high-frequency band and large bandwidth.
基金supported by the Third Xinjiang Scientific Expedition Program(Grant 2022xjkk0600)。
文摘Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses.In February 2023,two magnitude-7.8 earthquakes struck Turkey in quick succession,impacting over 30 major cities across nearly 300 km.A quick and comprehensive understanding of the distribution of building damage is essential for e fficiently deploying rescue forces during critical rescue periods.This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye.Based on high-resolution remote sensing data from WorldView2,BDANet used predisaster imagery to extract building outlines;the image features before and after the disaster were then combined to conduct building damage assessment.We optimized these results to improve the accuracy of building edges and analyzed the damage to each building,and used population distribution information to estimate the population count and urgency of rescue at different disaster levels.The results indicate that the building area in the Islahiye region was 156.92 ha,with an affected area of 26.60 ha.Severely damaged buildings accounted for 15.67%of the total building area in the affected areas.WorldPop population distribution data indicated approximately 253,297,and 1,246 people in the collapsed,severely damaged,and lightly damaged areas,respectively.Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.
基金This study was supported by research grants from Key R&D Program Projects in Zhejiang Province(2019C03045)the National Major Equipment Program of China(2012YQ12008004)+1 种基金the National Key Research and Development Program of China(2016YFE0107000)the National Nature Science Foundation of China(Grant No.81570880).
文摘Purpose:To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography(UHR-OCT)imaging data.Methods:A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups:normal(50 eyes),with keratoconus(38 eyes)or with subclinical keratoconus(33 eyes).All eyes were imaged with a Scheimpflug camera and UHR-OCT.Corneal morphological features were extracted from the imaging data.A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes.Fisher’s score was used to rank the differentiable power of each feature.The receiver operating characteristic(ROC)curves were calculated to obtain the area under the ROC curves(AUCs).Results:The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus(AUC=0.93).The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes.Conclusion:The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes.The epithelial features extracted from the OCT images were the most valuable in the discrimination process.This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.
