Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

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.

Effect of Viral Antigen Levels on the Serological Response and Efficiency of the Binary Ethylenimine-Inactivated Bluetongue Virus Serotype-16 Vaccine

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.

Bio-Inspired Binary Bees Algorithm for a Two-Level Distribution Optimisation Problem

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.

健康赋权模式联合二元应对干预对老年脑卒中患者神经功能、应对方式及希望水平的影响

目的 探讨健康赋权模式联合二元应对干预对老年脑卒中患者神经功能、应对方式及希望水平的影响。方法 选取2020年10月-2022年10月邯郸市中心医院收治的144例老年脑卒中患者作为研究对象,按照随机数字表法分为观察组和对照组,各72例。对照组给予常规护理模式干预,观察组给予健康赋权模式联合二元应对干预,评估干预前后两组的神经功能[美国国立卫生研究院脑卒中量表(NIHSS)]、应对方式[简易应对方式问卷(SCSQ)]及希望水平[Herth希望量表(HHI)]。结果 干预前两组NIHSS、SCSQ及HHI评分比较,差异均无统计学意义(P>0.05);干预后,观察组NIHSS评分低于对照组(P<0.05),SCSQ的积极应对方式评分高于对照组(P<0.05),SCSQ的消极应对方式评分低于对照组(P<0.05),HHI中各维度评分均高于对照组(P<0.05)。结论 健康赋权模式联合二元应对干预能够显著改善老年脑卒中患者的神经功能,提高积极应对方式,有效增强患者的希望水平。

Novel non-coherent integration method using binary phase-coded radar signal

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.

Evaluation of regional water resources carrying capacity based on binary index method and reduction index method

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.

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs

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(R) 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.

标签到标签通信系统中相位对消问题研究

在反向散射标签到标签(BBTT)通信系统中,标签与标签之间通过反向散射和接收环境信号进行通信。来自信号源的环境信号和来自发射标签的反向散射信号在接收标签处产生叠加信号,由于相位的不确定性,在接收标签处可能造成相位对消问题,导致解调错误。首先,本文分析了相位对消问题存在的原因和造成的影响。其次,针对低信噪比(SNR)、接收标签对误符号率(SER)要求高的场景提出基于三电平二进制的二阶调制方案。针对高阶调制下的相位对消问题,本文采用非对称星座图,并在发射标签端采用最小二分法确定延迟并主动增加延迟来降低相位对消问题对解调的影响。最后,设计了动态调制方法以适应不同信道环境下的反向散射通信。仿真实验表明,本文设计的调制方案能有效降低标签到标签反向散射通信的误符号率。

掩码生成动态调控弱监督视频实例分割

针对全监督视频实例分割网络训练数据高度依赖精细掩码标注,时间和人工成本过高,导致智能机器无法快速适应新场景的问题,提出一种端到端的掩码生成动态调控弱监督视频实例分割(Weakly Supervised Video Instance Segmentation,WSVIS)网络。为克服初始掩码预测层通道维度突降导致的实例激活特征丢失问题,构建多级特征融合模块,利用特征复用策略预测初始实例特征并融合相对位置信息生成初始预测掩码。然后,提出动态调控机制在通道和空间维度上建立掩码特征依赖关系,强化初始预测掩码与实例感知信息之间的动态交互。最后,网络设计二元颜色相似性生成伪亲和标签取代精细掩码标注,联合边界框与掩码一致性损失实现仅边界框标注的弱监督视频实例分割。实验结果表明,在BoxSet和YT-VIS数据集上,WSVIS网络能达到与全监督网络相近的分割精度和分割效果,同时能够满足实时推理要求,为智能机器快速适应新场景实现实时环境感知和理解提供了理论支撑和算法依据。

基于深度学习特征融合的遥感图像场景分类应用

针对传统手工特征方法无法有效提取整体图像深层信息的问题,本文提出一种基于深度学习特征融合的场景分类新方法.利用灰度共生矩阵(GLCM)和局部二值模式(LBP)提取具有相关空间特性的纹理特征和局部纹理特征的浅层信息;通过基于AlexNet迁移学习网络提取图像的深层信息,在去除最后一层全连接层的同时加入一层256维的全连接层作为特征输出;将两种特征进行自适应融合,最终输入到网格搜索算法优化的支持向量机(GS-SVM)中对遥感图像进行场景分类识别.在公开数据集UC Merced的21类目标数据和RSSCN7的7类目标数据的实验结果表明,5次实验的平均准确率分别达94.77%和93.79%.该方法可有效提升遥感图像场景的分类精度.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

Hybrid Color Texture Features Classification Through ANN for Melanoma

Melanoma is of the lethal and rare types of skin cancer.It is curable at an initial stage and the patient can survive easily.It is very difficult to screen all skin lesion patients due to costly treatment.Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of melanoma.These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease.The trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reflections,air bubbles,and oils on almost all images.Active contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular shapes.An entropy and morphology-based automated mask selection is pro-posed for the active contour method.The proposed method can improve the overall segmentation along with the boundary of melanoma images.In this study,features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been combined.Therefore,hybrid fused texture features;such as local,color feature as global,shape features,and Artificial neural network(ANN)as classifiers have been proposed for the categorization of the malignant and non-malignant.Experimentations had been carried out on datasets Dermis,DermQuest,and PH2.The results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

基于高分六号数据的东海县植被覆盖度分析

为了对中国连云港市东海县地区植被进行植被覆盖度的研究及分析,以高分六号(GF-6)卫星数据为原始数据源,在归一化植被指数(normalized vegetation index, NDVI)和像元二分模型分析方法的传统研究基础上,进一步通过不同置信度法来获取像元二分模型数据中所对应的纯土壤像元(S_(soil))值和纯植被像元(S_(veg))值,从而对植被覆盖度进行遥感估测分析。结果表明:植被覆盖度的估测结果对置信度的取值非常敏感,在选取置信度时,应结合数据源的卫星特征、影像特征、地域特征等合理选择,置信度应控制在2%~10%;高分六号卫星影像能较好的估测出植被覆盖度,东海县植被覆盖等级主要呈西高东低的空间状态,这也为后续高分六号卫星在林业应用方面提供价值参考。

顺序存储二叉树的遍历及其应用研究

顺序存储二叉树非常适用于二叉树的树形接近于满二叉树时的处理.本文介绍了二叉树的顺序存储结构及其优点、二叉树的遍历方法、顺序存储二叉树的层次遍历和递归遍历算法,以及层次遍历算法的一些简单应用.

基于二元线性回归的谐波发射水平估计方法

区分电力系统中系统侧与用户侧谐波发射水平十分必要。该文提出了基于二元线性回归的系统谐波阻抗及用户谐波发射水平的估算方法。根据此方法,利用在公共联接点测量的各次谐波的电压和电流信号,运用二元线性回归统计可得到系统侧的谐波阻抗,进而计算出用户侧谐波发射水平。相对于“双线性回归法”忽略电阻分量,该文提出的方法能够估计出谐波复阻抗中实部与虚部,更为合理。通过对实验电路仿真分析以及实测数据的分析结果,并对比“波动法”的计算结果,验证了其有效性。

基于二元应对模式的食管癌患者及伴侣术前希望水平与应对方式影响因素研究

目的基于二元应对模式调查食管癌术前患者及其伴侣的希望水平应对现状,为护理人员提供参考。方法采用便利抽样法,选取2018年1月至6月入住成都市某三级甲等综合医院胸外科被确诊为食管癌的患者及伴侣作为研究对象,采用一般资料调查表,简易应对方式量表,Herth希望量表进行问卷调查。结果食管癌患者术前希望水平得分为(31.70±3.758)分,应对方式得分为(47.49±5.440)分,食管癌患者伴侣希望水平得分(30.97±4.471),食管癌患者伴侣应对方式得分(48.07±7.214)。多元线性逐步回归结果显示,肿瘤分期及N分期2个变量进入到希望水平回归方程,可解释希望水平总分的61.9%的变异,肿瘤分期及文化程度2个变量进入到应对方式回归方程,可解释应对方式总分的41.3%的变异,患者希望水平与应对方式得分与患者伴侣得分呈正相关。结论食管癌患者及家属在术前面对癌症这一压力事件的希望水平及应对方式处于中等水平,医护人员应当基于二元应对模式关注患者及家属的希望水平,提高其积极应对方式。

基于GLCM和LBP的局部放电灰度图像特征提取

针对变压器局部放电模式识别中传统统计谱图特征提取维数高、识别率差等问题,提出基于灰度共生矩阵和局部二值模式的局部放电灰度图像纹理特征提取方法。该方法从宏观角度将灰度图像转化为灰度共生矩阵并获取其8维特征,从微观角度计算邻域像素相对灰度响应并获取其10维特征量。搭建四种局部放电实验模型,通过脉冲电流法采集局部放电信号;结合两类特征,以支持向量机作为分类器来识别放电类型并用传统特征提取方法作为对比。结果表明利用该方法提取灰度图像特征在避免特征灾难的同时仍有较高识别率,能有效识别四种放电模型,验证了该方法的有效性。

基于形状约束和局部演化的二值水平集运动目标分割

针对水平集分割模型运算效率较低且易出现过分割的现象,结合视频中运动目标分割的应用背景,该文提出一种将运动目标检测作为先验形状约束和曲线局部演化方法相结合的二值水平集分割模型。该模型提出将运动目标检测的区域作为先验形状信息对水平集分割进行约束,并使用二值函数替换传统水平集函数提高运算效率,同时融入曲线的局部演化方法解决二值水平集模型缺乏曲线演化渐进性的问题。实验结果表明,该文方法在分割准确性、鲁棒性和运算效率等方面与相关模型相比均有不同程度的提高。

融合LBP和GLCM的纹理特征提取方法

为提取有效的特征用于纹理描述和分类,提出一种融合局部二进制模式(LBP)和灰度共生矩阵(GLCM)的纹理特征提取方法。利用旋转不变的LBP算子处理纹理图像,得到LBP图像及其GLCM,采用对比度、相关性、能量和逆差矩描述图像的纹理特征。实验结果表明,与其他方法相比,该方法提取的纹理特征具有更强的纹理鉴别能力,平均分类正确率达到93%。

局部熵驱动的GAC模型在生物医学图像分割中的应用

针对测地线活动轮廓(GAC)模型不能对包含噪声和灰度不均匀现象这类复杂背景图像成功提取目标的问题,本文提出局部熵驱动的GAC模型.首先提取图像的局部信息熵来刻画图像的灰度变化,再利用局部熵构造符号压力函数来指导轮廓曲线向目标边界靠近,实现目标的分割.为降低计算复杂度并提高模型对水平集变化的鲁棒性,采用二值水平集方法进行求解.实验结果表明,本文方法可以克服噪声和灰度不均匀对图像分割的影响,实现快速准确的分割.