Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.

Realizing precision pulse TIG welding with arc length control and visual image sensing based weld detection

Methods of arc length control and visual image based weld detection for precision pulse TIG welding were investigated. With a particular all hardware circuit, arc voltage during peak current stage is sampled and integrated to indicate arc length, deviation of arc length and adjusting parameters are calculated and output to drive a step motor directly. According to the features of welding image grabbed with CCD camera, a special algorithm was developed to detect the central line of weld fast and accurately. Then an application system were established, whose static arc length error is ±0.1 mm with 20 A average current and 1 mm given arc length, static detection precision of weld is 0.01 mm , processing time of each image is less than 120 ms . Precision pulse TIG welding of some given thin stainless steel components with complicated curved surface was successfully realized.

Visual sensing and morphological image processing of weld pool in laser spot welding

A visual sensing system was established to monitor the weld pool in laser spot welding. The top-hat and bottom-hat transformation algorithms based on mathematical morphology were used to compensate for non-uniform contrast of weld pool edge. Moreover, the canny edge detector was applied to extract the weld paol profile. The edge detected results show that the morphological operation is obviously superior to the traditional contrast enhancement method. In addition, the combination of dilation and erosion was applied to eliminate the irrelevant edge details, and the smooth weld pool edge was acquired. Based on the image processing technology described above, the dynamic process of weld pool diameter during laser spot welding was obtained.

Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010–2018

In order to apply satellite data to guiding navigation in the Arctic more effectively,the sea ice concentrations(SIC)derived from passive microwave(PM)products were compared with ship-based visual observations(OBS)collected during the Chinese National Arctic Research Expeditions(CHINARE).A total of 3667 observations were collected in the Arctic summers of 2010,2012,2014,2016,and 2018.PM SIC were derived from the NASA-Team(NT),Bootstrap(BT)and Climate Data Record(CDR)algorithms based on the SSMIS sensor,as well as the BT,enhanced NASA-Team(NT2)and ARTIST Sea Ice(ASI)algorithms based on AMSR-E/AMSR-2 sensors.The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons.The correlation coefficients(CC),biases and root mean square deviations(RMSD)between PM SIC and OBS SIC were compared in terms of the overall trend,and under mild/normal/severe ice conditions.Using the OBS data,the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness.Our results show that CC values range from 0.89(AMSR-E/AMSR-2 NT2)to 0.95(SSMIS NT),biases range from−3.96%(SSMIS NT)to 12.05%(AMSR-E/AMSR-2 NT2),and RMSD values range from 10.81%(SSMIS NT)to 20.15%(AMSR-E/AMSR-2 NT2).Floe size has a significant influence on the SIC retrievals of the PM products,and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions.Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products.Overall,the best(worst)agreement occurs between OBS SIC and SSMIS NT(AMSR-E/AMSR-2 NT2)SIC in the Arctic summer.

Detection and processing of molten-pool image of electron-beam deep-penetration welding based on visual sensing

A visual sensing system was developed. The system is suitable for titanium-alloy electron-beam welding, and senses and detects molten-pool dynamic processes. A suite of processing programs for colored molten-pool images in titanium-alloy electron-beam welding was developed using Matlab software; molten-pool edge images are completely obtained using the program. The Matlab software was used to write a program which could extract the molten-pool width. The functional relationship between the molten-pool width and penetration under the experimental conditions was obtained by a curve-fitting method, and provided the theoretical basis for further penetration control.

Computer-based sensing and visualizing of metal transfer mode in gas metal arc welding

Using Xenon lamp lights to overcome the strong interference from the welding arc, a computer-based system is developed to sense and visualize the metal transfer in GMAW. This system combines through-the-arc sensing of the welding current and arc voltage with high speed imaging of the metal transfer. It can simultaneously display the metal transfer processes and waveforms of electrical welding parameters in real-time The metal transfer videos and waveforms of electrical welding parameters can be recorded. Metal transfers under various welding conditions have been investigated with the system developed.

Visual sensing of weld pool in variable polarity TIG welding of aluminium alloy

The passive visual sensing method was successfully applied to monitor the weld pool in TIG welding of aluminium alloy. In order to reduce the disturbance from the arc and acquire clear image, two techniques were used: one was the own-developed computer-controlled variable polarity power(VPP), the other was the composite filter technology. The VPP source did not have high-frequency electromagnetic interference from arc stabilizer in traditional AC welding power, and its output current wave was controlled by computer, so the imaging current and the imaging time could be adjusted easily. Filter spectral window was identified at 650 nm by analyzing the measured arc light spectral distribution. At the end, the high quality and clear images of welding pool of aluminium alloy were successfully acquired at low imaging current.

Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing

Compressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we show the usage in groundwater mapping. Due to scarcity of water in many regions of the world, including southwestern United States, monitoring and management of groundwater is of utmost importance. A complete mapping of groundwater is difficult since the monitored sites are far from one another, and thus the data sets are considered extremely “sparse”. To overcome this difficulty in complete mapping of groundwater, compressive sensing is an ideal tool, as it bypasses the classical Nyquist criterion. We show that compressive sensing can effectively be used for reconstructions of groundwater level maps, by validating against data. This approach can have an impact on geographical sensing and information, as effective monitoring and management are enabled without constructing numerous or expensive measurement sites for groundwater.

Remote Sensing and GIS Application to Superlarge Mineral Deposits Prediction in Western Slope of Great Xing'an Mountains, China

Supported by Geographic Information Systems (GIS), the multi-source geoscience information in the Western Slope of Great Xing’an Mountains are visual analyzed, including remote sensing data, magnetic data, gravity data and gamma-ray spectrometry data. Thus the structural framework is built up. And the remote sensing image pattern of mineral deposits is established by comparing the remote sensing geological features of five large or superlarge mineral deposits located in the same metallogenic belt in China, Russia and Mongolia. Guided by the image pattern, seven prospective locations of large or superlarge mineral resource are delineated.

基于金纳米便携功能水凝胶的酪胺可视化监测

为建立生物胺(biogenic amines,BAs)实时监测的比色传感方法,以功能性水凝胶为基底,将β-D-半乳糖五乙酸酯(β-D-galactose pentaacetate,β-D-GP)作为还原剂前体和氯金酸一起负载于功能性水凝胶上,BAs能将β-D-GP水解为还原性β-D-半乳糖,β-D-半乳糖进一步将氯金酸中的金离子(Au^(3+))还原为金纳米粒子(Au nanoparticles,AuNPs),导致颜色从无色变为紫红色,使用智能手机的图像处理软件获得试验体系红/绿/蓝(red/green/blue,RGB)数据,水凝胶的G值用于量化BAs的测定。进一步用紫外分光光度法验证该方法的可行性,并建立可视化探针。该文以酪胺(tyramine,Tyr)为模型,RGB法和紫外分光光度法分别在酪胺浓度为2~200μg/mL和0.25~160μg/mL范围内呈现良好的线性相关性,相关系数R^(2)大于0.980,且显色体系稳定,应用于发酵食品中生物胺的测定,其加标回收率在96.0%~124.0%。

论兰波诗歌的视觉语言

兰波诗歌的视觉特征是显而易见的。他诗画通融意识的产生,受浪漫主义、巴那斯派、印象主义等艺术流派的启发,但他坚守自己独特的审美情趣,不追求情景交融,而将视觉呈现作为“发明”诗歌新语言的手段。他有意将视觉错轨,以便改变“看”的角度,摆脱“看”的旧习惯和旧观念。他的视觉呈现是空间化文本构图和时间化文本叙述的完美结合,时空的交叉和置换实现了诗人表达的自由,扩大了语言的能指,促进了诗意的延伸。空间艺术的开发和拓展是兰波实践其诗学主张的重要途径,但当诗人发现视觉语言的极限时,便主动放弃诗歌,而放弃亦是空间艺术的一种表现形式。

多传感器融合技术在智能交通系统中的应用

阐述多传感器融合技术在高速公路智能交通中的应用,包括对路面传感器数据、视觉传感器数据、环境传感器数据的融合和分析。探讨多传感器融合技术在高速公路智能交通中的优化方法。

基于PEGPEA-高灵敏仿生光子晶体薄膜制备及应用

采用乳液聚合结合改良的Stӧber方法制备了单分散聚苯乙烯(PS)@SiO2核壳纳米微球,然后将其分散在聚(乙二醇)苯基醚丙烯酸酯(PEGPEA)中,形成非密集颗粒阵列后快速光聚合捕获规则阵列,制备了一种机械变色的光子晶体薄膜。采用SEM、光纤耦合光谱仪对光子晶体薄膜进行了表征,考察了PS@SiO2核壳纳米微球体积分数和粒径对光子晶体薄膜结构色的影响,并对光子晶体薄膜的力致变色机理进行了推测。结果表明,热处理改善了PS@SiO2核壳纳米微球的微观秩序,加快形成蛋白石光子晶体结构;光子晶体薄膜结构色随着PS@SiO2核壳纳米微球体积分数的增加或粒径的减小由红色向蓝色渐变;光子晶体薄膜具有的结构色反射率>60%、拉伸应变至少可达70%、灵敏度2.16 nm/%、响应速度1.9 nm/ms,反射波长调谐范围为151 nm;在100次拉伸/释放循环实验和25℃下维持拉伸状态3 d,恢复后光子晶体薄膜结构色保持不变。

基于混合神经网络的多维视觉传感信号模式分类

传感器采集的数字信号分类精度差,导致关键信息的丢失。为了提高传感数据的可靠性和有效性,提出基于混合神经网络的多维视觉传感信号模式分类方法。结合卷积神经网络(CNN)、循环神经网络(RNN)构建混合神经网络,以更有效地表示多维视觉数据中的特征;其中,卷积神经网络负责对多维的空间信号进行去噪处理并提取特征;循环神经网络负责对时域和频域信号进行特征提取;混合神经网络通过联合训练CNN和RNN各自的参数,以调整其权重,并且结合两者从不同层级提取的特征来实现多维视觉传感信号模式的分类。仿真结果表明,使用所提方法进行分类时,信号光滑度保持在0.9以上,传感信号分类结果与实际结果拟合度较高,有效实现多维视觉传感信号模式分类。

基于轻量化NDFEDet-SOLOv2的遥感图像建筑物提取方法

目的在地籍测绘和灾害管理等领域中,建筑物轮廓和位置的自动提取是至关重要的一环。为了解决高分辨率遥感图像建筑物因环境因素导致分割精度不准确等问题,提出了一种改进的轻量化SOLOv2实例分割模型——NDFEDet-SOLOv2。方法该模型选用双向特征金字塔网络(BiFPN)特征融合方式的轻量级EfficientDet网络,其中将骨干网络部分的EfficientNet升级为EfficientNetv2,EfficientNetv2中的三层MBConv模块SE注意力更换为含有DropBlock正则化的轻量级标准化注意力机制(NAM),构成NAD-MBConv模块。BiFPN特征融合部分,向其尾端各特征层并入双水平路由注意视觉变压器(BiFormer),形成双向水平路由注意特征金字塔网络结构(Bi-FPN-Former),从而聚焦微小建筑物轮廓信息,以实现更高层次的特征融合。结果NDFEDet-SOLOv2模型相较于传统轻量级SOLOv2实例分割算法,平均精度mAP、mAP 50和mAP 75分别提高了3.9%、3.7%和2.5%,检测帧率(FPS)提高了2.7帧/s。结论轻量化NDFEDet-SOLOv2实例分割算模型消除了建筑物边角的图像畸变,在地理环境空间不均等复杂情况下也能准确提取出遥感图像建筑物的基本轮廓,从而为城市布局更新和建筑变化检测提供理论参考。

单电极电致发光纤维的连续制备及其可视化液体传感应用

随着柔性电子产品、智能传感器、智能穿戴等领域的兴起,柔性电致发光器件得到了蓬勃的发展。柔性电致发光纤维以其便携性、柔软性及可编织性,为可视化传感领域带来了巨大的发展潜力。但受限于其大多为双电极结构,制备工艺复杂,使其在成本、器件均匀性、灵敏性及柔软性方面仍存在不足。本文开发了一种单电极电致发光纤维,通过简单、可扩展和低成本的制造工艺,实现了电致发光纤维的连续制备。且通过无线驱动的方式实现了电致发光,单电极纤维接受无线电场能量,通过接地线与大地、无线发射源形成闭合回路,从而消除了对外部电源的依赖。单电极电致发光纤维由导电层、介质层和发光层构成,发光波长为456 nm,具有良好的力学性能、发光性能及柔软性。单电极电致发光纤维可应用于溶液的可视化传感,其发光强度对于氯化钠浓度的识别度可达到0.001%(质量分数),具有出色的灵敏度,在汗液检测及生物等方面具有重要意义。发光纤维可进一步编织成发光织物,其透气性及力学方面均与普通的商用面料相媲美,并可实现不同浓度氯化钠的可视化传感。在可穿戴设备领域,无线驱动的单电极电致发光纤维具有实现视觉显示和通信功能的巨大潜力。

遥感影像水体识别研究进展及热点分析

洪涝灾害是严重危害人类社会发展的自然灾害之一,从遥感影像中准确识别水体信息对提升突发洪涝灾害事件的应急处理、灾害预警具有重要意义,一直受到专家学者的关注。针对国内外专家学者利用遥感影像进行水体识别的相关研究成果,文章从传统方法以及深度学习法两大方面,对前人提出的水体识别理论和方法进行分析研究;在此基础上,利用文献计量的方法对水体识别当前的研究热点进行梳理分析,针对现存问题和当前热点对基于遥感影像的水体识别未来发展趋势进行了展望。

基于三维点云的磁极焊缝识别及机器人轨迹生成技术

针对大型水电站发电机磁极变长度、变间隙的复杂焊缝存在的示教编程效率低、精度差的问题,开发了一种基于光栅视觉传感的焊缝识别及机器人轨迹免示教生成技术.采用安装于机器人末端的光栅传感器获取不同部位的磁极焊缝点云,提出了一种结合机器人工具位姿变换矩阵和迭代最近点算法(ICP)的点云配准算法,得到大尺寸磁极焊缝完整点云数据.基于随机采样一致性(RANSAC)开发了焊缝识别算法,实现了机器人焊接轨迹的自动生成.结果表明,该算法可识别出多种复杂工况的磁极焊缝,识别率高,抗干扰能力强,平均识别误差在±0.4 mm范围内,满足焊接要求.

温致颜色与润湿性可切换智能织物涂层的构制与应用

多丙烯酸酯基化合物和多氨基化合物在饱和电解质溶液与醇类液相组成的混合体系中发生室温凝胶反应,通过长链烷基胺修饰制得超支化聚合物(HBPs)。以荧烷染料ODB-1、双酚A、十四醇为原料,采用混合方式制备温致变色复合物(TCC)。将超支化聚合物与温致变色复合物进行物理共混,构筑智能响应性变色颗粒产物,基于热黏合法构制温致颜色与润湿性可切换智能织物涂层,并展开可视化传感应用。结果表明:HBPs基颗粒涂层具有抗污自清洁性能和化学稳定性。TCC最低相变温度为35.9℃、变色时间为82 s、复色时间为39 s、变色前后色差为9.73,达到较好的变色效果。HBPs-TCC基织物涂层温致变色和亲疏水转换特性显著,可在不同温度的金属、玻璃和皮肤表面实施可视化传感应用,且能够循环使用。