In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic s...In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.展开更多
Bluetongue (BT) is a serious hemorrhagic disease of ruminants caused by bluetongue virus (BTV). Inactive BTV vaccines have been successful in field trials in some areas, and inactivated vaccines are considered safer. ...Bluetongue (BT) is a serious hemorrhagic disease of ruminants caused by bluetongue virus (BTV). Inactive BTV vaccines have been successful in field trials in some areas, and inactivated vaccines are considered safer. However, information about the effect of the viral antigen level on the serological response and efficiency of the inactive BTV-16 vaccine is lacking. In the present study, the serological response and efficiency of the viral antigen concentration in the binary ethylenimine-inactivated Chinese BTV serotype-16 vaccine were investigated. The viral antigens in the viral suspension (VS) were quantified using a modified BTV AC-ELISA method. Four batches of vaccine containing 1, 5, 10, and 50 μg/ml of viral antigen were generated from the VS. Four groups of naive Chinese sheep were vaccinated with the different vaccine batches, and the serological response and vaccine efficiency were investigated before and after challenge infection. The vaccines containing 10 and 50 μg/ml of viral antigen induced significant ELISA and neutralizing antibody titers 14 days after vaccination, whereas the vaccines containing 1 and 5 μg/ml of viral antigen did not have these effects. A booster immunization at 21 days enhanced all groups’ antibody titers;however, the increased titer was related to the viral antigen level. In contrast to the serological response, the viral antigen level of the vaccines did not have a significant effect on the vaccine efficiency. With the exception of one sheep from the 5 μg/ml viral antigen group, all vaccinated sheep from the four antigen level groups showed strong resistance to infection based on their clinical symptoms, rectal temperatures and viremia. Collectively, these data suggested that viral antigen levels from 1 to 50 μg/ml had a significant effect on the serological response of the animals but a limited effect on the vaccine efficiency. The BTV-16 vaccine containing 1 μg/ml of viral antigen was sufficient to achieve high efficiency, but only the vaccines with more than 10 μg/ml of antigen induced a significant antibody response. To obtain a better serological response, we suggest the use of vaccines with more than 10 μg/ml of viral antigen. The findings in the study will be useful for BTV vaccine production.展开更多
Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and fiv...Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.展开更多
The m series with 511 bits is taken as an example being applied in non-coherent integra- tion algorithm. A method to choose the bi-phase code is presented, which is 15 kinds of codes are picked out of 511 kinds of m s...The m series with 511 bits is taken as an example being applied in non-coherent integra- tion algorithm. A method to choose the bi-phase code is presented, which is 15 kinds of codes are picked out of 511 kinds of m series to do non-coherent integration. It is indicated that the power in- creasing times of larger target sidelobe is less than the power increasing times of smaller target main- lobe because of the larger target＇ s pseudo-randomness. Smaller target is integrated from larger tar- get sidelobe, which strengthens the detection capability of radar for smaller targets. According to the sidelobes distributing characteristic, a method is presented in this paper to remove the estimated sidelobes mean value for signal detection after non-coherent integration. Simulation results present that the SNR of small target can be improved approximately 6. 5 dB by the proposed method.展开更多
Based on the regional water resources carrying capacity(WRCC)evaluation principles and evaluation index system in the National Technical Outline of Water Resources Carrying Capacity Monitoring and Early Warning(hereaf...Based on the regional water resources carrying capacity(WRCC)evaluation principles and evaluation index system in the National Technical Outline of Water Resources Carrying Capacity Monitoring and Early Warning(hereafter referred to as the Technical Outline),this paper elaborates on the collection and sorting of the basic data of water resources conditions,water resources development and utilization status,social and economic development in basins,analysis and examination of integrity,consistency,normativeness,and rationality of the basic data,and the necessity of WRCC evaluation.This paper also describes the technique of evaluating the WRCC in prefecture-level cities and city-level administrative divisions in the District of the Taihu Lake Basin,which is composed of the Taihu Lake Basin and the Southeastern River Basin.The evaluation process combines the binary index evaluation method and reduction index evaluation method.The former,recommended by the Technical Outline,uses the total water use and the amount of exploited groundwater as evaluation indices,showing stronger operability,while the latter is developed by simplifying and optimizing the comprehensive index system with greater systematicness and completeness.The mutual validation and adjustment of the results of the above-mentioned two evaluation methods indicate that the WRCC of the District of the Taihu Lake Basin is overloaded in general because some prefecture-level cities and city-level administrative divisions in the Taihu Lake Basin and the Southeastern River Basin are in a severely overloaded state.In order to explain this conclusion,this paper analyzes the causes of WRCC overloading from the aspects of basin water environment,water resources development and utilization,water resources regulation and control ability,water resources utilization efficiency,and water resources management.展开更多
The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landm...The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.展开更多
现有脉内复合调制波形存在调制方式简单、自相关旁瓣电平高的不足,致使发射波形在应用中易被截获且对弱目标检测性能较差。针对上述问题,在线性调频(linear frequency modulation,LFM)-Costas雷达波形中引入二相编码(binary phase shift...现有脉内复合调制波形存在调制方式简单、自相关旁瓣电平高的不足,致使发射波形在应用中易被截获且对弱目标检测性能较差。针对上述问题,在线性调频(linear frequency modulation,LFM)-Costas雷达波形中引入二相编码(binary phase shift keying,BPSK),设计了LFM-Costas-BPSK波形。一方面,降低了子脉冲间的相关性从而有效抑制了自相关旁瓣电平;另一方面,提高了波形复杂度,因而降低了被截获概率。仿真实验分析了关键参数对波形性能的影响,验证了所设计的LFM-Costas-BPSK波形的有效性。展开更多
基金This work is supported by the National Natural Science Foundation of China(No.U1736118)the Natural Science Foundation of Guangdong(No.2016A030313350)+3 种基金the Special Funds for Science and Technology Development of Guangdong(No.2016KZ010103)the Key Project of Scientific Research Plan of Guangzhou(No.201804020068)the Fundamental Research Funds for the Central Universities(No.16lgjc83 and No.17lgjc45)the Science and Technology Planning Project of Guangdong Province(Grant No.2017A040405051).
文摘In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.
文摘Bluetongue (BT) is a serious hemorrhagic disease of ruminants caused by bluetongue virus (BTV). Inactive BTV vaccines have been successful in field trials in some areas, and inactivated vaccines are considered safer. However, information about the effect of the viral antigen level on the serological response and efficiency of the inactive BTV-16 vaccine is lacking. In the present study, the serological response and efficiency of the viral antigen concentration in the binary ethylenimine-inactivated Chinese BTV serotype-16 vaccine were investigated. The viral antigens in the viral suspension (VS) were quantified using a modified BTV AC-ELISA method. Four batches of vaccine containing 1, 5, 10, and 50 μg/ml of viral antigen were generated from the VS. Four groups of naive Chinese sheep were vaccinated with the different vaccine batches, and the serological response and vaccine efficiency were investigated before and after challenge infection. The vaccines containing 10 and 50 μg/ml of viral antigen induced significant ELISA and neutralizing antibody titers 14 days after vaccination, whereas the vaccines containing 1 and 5 μg/ml of viral antigen did not have these effects. A booster immunization at 21 days enhanced all groups’ antibody titers;however, the increased titer was related to the viral antigen level. In contrast to the serological response, the viral antigen level of the vaccines did not have a significant effect on the vaccine efficiency. With the exception of one sheep from the 5 μg/ml viral antigen group, all vaccinated sheep from the four antigen level groups showed strong resistance to infection based on their clinical symptoms, rectal temperatures and viremia. Collectively, these data suggested that viral antigen levels from 1 to 50 μg/ml had a significant effect on the serological response of the animals but a limited effect on the vaccine efficiency. The BTV-16 vaccine containing 1 μg/ml of viral antigen was sufficient to achieve high efficiency, but only the vaccines with more than 10 μg/ml of antigen induced a significant antibody response. To obtain a better serological response, we suggest the use of vaccines with more than 10 μg/ml of viral antigen. The findings in the study will be useful for BTV vaccine production.
文摘Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.
基金Supported by the National Natural Science Foundation of China(Youth Science Fund)(61001190)
文摘The m series with 511 bits is taken as an example being applied in non-coherent integra- tion algorithm. A method to choose the bi-phase code is presented, which is 15 kinds of codes are picked out of 511 kinds of m series to do non-coherent integration. It is indicated that the power in- creasing times of larger target sidelobe is less than the power increasing times of smaller target main- lobe because of the larger target＇ s pseudo-randomness. Smaller target is integrated from larger tar- get sidelobe, which strengthens the detection capability of radar for smaller targets. According to the sidelobes distributing characteristic, a method is presented in this paper to remove the estimated sidelobes mean value for signal detection after non-coherent integration. Simulation results present that the SNR of small target can be improved approximately 6. 5 dB by the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.51379181)Phase Ⅲ Project(2018-2021)of the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Based on the regional water resources carrying capacity(WRCC)evaluation principles and evaluation index system in the National Technical Outline of Water Resources Carrying Capacity Monitoring and Early Warning(hereafter referred to as the Technical Outline),this paper elaborates on the collection and sorting of the basic data of water resources conditions,water resources development and utilization status,social and economic development in basins,analysis and examination of integrity,consistency,normativeness,and rationality of the basic data,and the necessity of WRCC evaluation.This paper also describes the technique of evaluating the WRCC in prefecture-level cities and city-level administrative divisions in the District of the Taihu Lake Basin,which is composed of the Taihu Lake Basin and the Southeastern River Basin.The evaluation process combines the binary index evaluation method and reduction index evaluation method.The former,recommended by the Technical Outline,uses the total water use and the amount of exploited groundwater as evaluation indices,showing stronger operability,while the latter is developed by simplifying and optimizing the comprehensive index system with greater systematicness and completeness.The mutual validation and adjustment of the results of the above-mentioned two evaluation methods indicate that the WRCC of the District of the Taihu Lake Basin is overloaded in general because some prefecture-level cities and city-level administrative divisions in the Taihu Lake Basin and the Southeastern River Basin are in a severely overloaded state.In order to explain this conclusion,this paper analyzes the causes of WRCC overloading from the aspects of basin water environment,water resources development and utilization,water resources regulation and control ability,water resources utilization efficiency,and water resources management.
文摘The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam<sup>(R)</sup> Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we compared the performance of our best CNN algorithm versus our same goal algorithm based on HSV colorimetric lesions features extracted of pixel-level annotations, both algorithms trained and tested on the same data. It is evaluated that the CNN trained on image level annotated images, is 9% less sensitive, achieves 2.6% less precision, 1.2% less FPR, and 7% less FNR, than that based on HSV filters, extracted from on pixel-level annotated training data.
