RMPC-Based Visual Servoing for Trajectory Tracking of Quadrotor UAVs With Visibility Constraints

Dear Editor,This letter deals with the tracking problem of quadrotors subject to external disturbances and visibility constraints by designing a robust model predictive control(RMPC) scheme. According to the imagebased visual servoing(IBVS) method, a virtual camera is constructed to express image moments of the tracking target.

High-visibility ghost imaging with phase-controlled discrete classical light sources

We take phase modulation to create discrete phase-controlled sources and realize the super-bunching effect by a phasecorrelated method. From theoretical and numerical simulations, we find the space translation invariance of the bunching effect is a key point for the ghost imaging realization. Experimentally, we create the orderly phase-correlated discrete sources which can realize high-visibility second-order ghost imaging than the result with chaotic sources. Moreover, some factors affecting the visibility of ghost image are discussed in detail.

Detection of EEG signals in normal and epileptic seizures with multiscale multifractal analysis approach via weighted horizontal visibility graph

Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.

Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding

A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations.

CAEFusion: A New Convolutional Autoencoder-Based Infrared and Visible Light Image Fusion Algorithm

To address the issues of incomplete information,blurred details,loss of details,and insufficient contrast in infrared and visible image fusion,an image fusion algorithm based on a convolutional autoencoder is proposed.The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map.A multi-scale convolution attention module is suggested to enhance the communication of feature information.At the same time,the feature transformation module is introduced to learn more robust feature representations,aiming to preserve the integrity of image information.This study uses three available datasets from TNO,FLIR,and NIR to perform thorough quantitative and qualitative trials with five additional algorithms.The methods are assessed based on four indicators:information entropy(EN),standard deviation(SD),spatial frequency(SF),and average gradient(AG).Object detection experiments were done on the M3FD dataset to further verify the algorithm’s performance in comparison with five other algorithms.The algorithm’s accuracy was evaluated using the mean average precision at a threshold of 0.5(mAP@0.5)index.Comprehensive experimental findings show that CAEFusion performs well in subjective visual and objective evaluation criteria and has promising potential in downstream object detection tasks.

Visibility graph approach to extreme event series

An extreme event may lead to serious disaster to a complex system.In an extreme event series there exist generally non-trivial patterns covering different time scales.Investigations on extreme events are currently based upon statistics,where the patterns are merged into averages.In this paper from extreme event series we constructed extreme value series and extreme interval series.And the visibility graph is then adopted to display the patterns formed by the increases/decreases of extreme value or interval faster/slower than the linear ones.For the fractional Brownian motions,the properties for the constructed networks are the persistence,threshold,and event-type-independent,e.g.,the degree distributions decay exponentially with almost identical speeds,the nodes cluster into modular structures with large and similar modularity degrees,and each specific network has a perfect hierarchical structure.For the volatilities of four stock markets(NSDQ,SZI,FTSE100,and HSI),the properties for the former three's networks are threshold-and market-independent.Comparing with the factional Brownian motions,their degree distributions decay exponentially but with slower speeds,their modularity behaviors are significant but with smaller modularity degrees.The fourth market behaves similar qualitatively but different quantitatively with the three markets.Interestingly,all the transition frequency networks share an identical backbone composed of nine edges and the linked graphlets.The universal behaviors give us a framework to describe extreme events from the viewpoint of network.

Two-dimensional horizontal visibility graph analysis of human brain aging on gray matter

Characterizing the trajectory of the healthy aging brain and exploring age-related structural changes in the brain can help deepen our understanding of the mechanism of brain aging.Currently,most structural magnetic resonance imaging literature explores brain aging merely from the perspective of morphological features,which cannot fully utilize the grayscale values containing important intrinsic information about brain structure.In this study,we propose the construction of two-dimensional horizontal visibility graphs based on the pixel intensity values of the gray matter slices directly.Normalized network structure entropy(NNSE)is then introduced to quantify the overall heterogeneities of these graphs.The results demonstrate a decrease in the NNSEs of gray matter with age.Compared with the middle-aged and the elderly,the larger values of the NNSE in the younger group may indicate more homogeneous network structures,smaller differences in importance between nodes and thus a more powerful ability to tolerate intrusion.In addition,the hub nodes of different adult age groups are primarily located in the precuneus,cingulate gyrus,superior temporal gyrus,inferior temporal gyrus,parahippocampal gyrus,insula,precentral gyrus and postcentral gyrus.Our study can provide a new perspective for understanding and exploring the structural mechanism of brain aging.

褐土全氮含量Vis/NIRS组合预测模型的构建

准确掌握农田土壤全氮含量对于评估土壤肥力,合理施用氮肥具有重要意义。为综合利用各个单预测模型的优势,提升整体预测性能、降低模型方差,提高鲁棒性,以农田褐土土壤为研究对象,基于近红外-可见光高光谱数据,提出了一种基于标准差的预测有效度组合预测模型(CPM),用于预测土壤全氮含量。对原高光谱数据采用Savitzky-Golay平滑和一阶微分变换,采用树模型进行特征波段提取,利用决策树回归(DTR)(模型1)、高斯核回归(GKR)(模型2)、随机森林回归(RF)(模型3)、LASSO回归(模型4)、多层感知器回归(MLP)(模型5)5个单预测模型,通过单预测模型的线性组合建立组合预测模型。结果表明:(1)通过广义简约梯度优化算法求得组合预测模型中5个单预测模型的权重分别为ω_(1)*=0.407,ω_(2)*=0.378,ω_(30*=0.215,ω_(4)*=0,ω_(5)*=0;(2)对于所有数据,基于5种单预测模型和组合预测模型对土壤全氮含量预测的预测有效度分别为M_(1)=0.855,M_(2)=0.856,M_(3)=0.847,M_(4)=0.785,M_(5)=0.796,M_(CPM)=0.880;与单模型预测有效度最大值相比,组合预测模型预测有效度提高了2.924%;(3)对于所有数据,基于5种单预测模型和组合预测模型对土壤全氮含量预测的预测精度和标准差分别为E(A_(1))=0.924,σ(A_(1))=0.075,E(A_(2))=0.928,σ(A_(2))=0.077,E(A_(3))=0.923,σ(A_(3))=0.082,E(A_(4))=0.882,σ(A_(4))=0.109,E(A_(5))=0.889,σ(A_(5))=0.104,E(A_(CPM))=0.937,σ(A_(CPM))=0.066,与单模型预测精度最大值相比,组合预测模型预测精度提高了0.970%,模型稳定性提高了12.000%,且为优性组合预测。组合预测模型可用于可见光-近红外光谱数据的农田褐土土壤全氮含量的有效估测,可为农田土壤全氮含量的快速监测提供依据和参考。

Analysis of the Causes of a Low Cloud and Low Visibility Weather at Cangyuan Washan Airport

At 11:00 am on August 5, 2017, Cangyuan Washan Airport experienced low cloud and low visibility weather, accompanied by aircraft turbulence, which affected the normal operation of flights, which was closely related to the meteorological conditions at that time. Using the hourly reanalysis data of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5), including Geopotential height, temperature, precipitation, wind field, specific humidity, vorticity and other elements, with a spatial resolution of 0.25° × 0.25°, this paper focuses on the horizontal distribution and vertical configuration of various physical quantities before and after the occurrence of low cloud and low visibility weather at the airport. The results indicate that the main influencing system of this low cloud and low visibility weather is the westward tropical depression. Before the occurrence of low cloud and low visibility weather, low-level water vapor converges and is accompanied by precipitation. The temperature decreases with precipitation, the near-surface wind direction changes, and the wind speed decreases.

Seeing invisible objects with intelligent optics IsaacNape&AndrewForbes

Transparent objects are invisible to traditional cameras because they can only detect intensity fluctuations,necessitating the need for interferometry followed by computationally intensive digital image processing.Now it is shown that the necessary transformations can be performed optically by combining machine learning and diffractive optics,for a direct in-situ measurement of transparent objects with conventional cameras.

Bio-inspired micropatterned thermochromic hydrogel for concurrent smart solar transmission and rapid visible-light stealth at all-working temperatures

Thermochromic hydrogels exhibit a smart capacity for regulating solar spectrum transmission,enabling automatically change their transmissivity in response to the ambient temperature change.This has great importance for energy conservation purposes.Military and civilian emergency thermochromic applications require rapid visible-light stealth(VLS);however,concurrent smart solar transmission and rapid VLS is yet to be realized.Inspired by squid-skin,we propose a micropatterned thermochromic hydrogel(MTH)to realize the concurrent control of smart solar transmittance and rapid VLS at all-working temperatures.The MTH possesses two optical regulation mechanisms:optical property regulation and optical scattering,controlled by temperature and pressure,respectively.The introduced surface micropattern strategy can arbitrarily switch between normal and diffuse transmission,and the VLS response time is within 1 s compared with previous~180 s.The MTH also has a high solar-transmission regulation range of 61%.Further,the MTH preparation method is scalable and cost-effective.This novel regulation mechanism opens a new pathway towards applications with multifunctional optical requirements.

Development of D_α band symmetrical visible optical diagnostic for boundary reconstruction on EAST tokamak

To investigate the potential of utilizing visible spectral imaging for controlling the plasma boundary shape during stable operation of plasma in future tokamak, a D_α band symmetric visible light diagnostic system was designed and implemented on the Experimental Advanced Superconducting Tokamak(EAST). This system leverages two symmetric optics for joint plasma imaging. The optical system exhibits a spatial resolution less than 2 mm at the poloidal cross-section, distortion within the field of view below 10%, and relative illumination of 91%.The high-quality images obtained enable clear observation of both the plasma boundary position and the characteristics of components within the vacuum vessel. Following system calibration and coordinate transformation, the image coordinate boundary features are mapped to the tokamak coordinate system. Utilizing this system, the plasma boundary was reconstructed, and the resulting representation showed alignment with the EFIT(Equilibrium Fitting) results. This underscores the system's superior performance in boundary reconstruction applications and provides a diagnostic foundation for boundary shape control based on visible spectral imaging.

Deep learning-assisted common temperature measurement based on visible light imaging

Real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection methods.This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light images.Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN.Moreover,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training.Compared to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

An organic visible-photocatalytic-adsorbence mechanism to high-efficient removal of heavy metal antimony ions

Purification of emerging heavy metal antimony contaminated water based on advanced ingenious strategies.An activated modified coconut shell charcoal(CSC)was synthesized and evaluated as a substrate-supported loaded organic photovoltaic material,PM6:PYIT:PM6-b-PYIT,to prepare a surprisingly highly efficient,stable,environmentally friendly,and recyclable organic photocatalyst(CSC–N–P.P.P),which showed excellent effects on the simultaneous removal of Sb(Ⅲ)and Sb(Ⅴ).The removal efficiency of CSC-N-P.P.P on Sb(Ⅲ)and Sb(Ⅴ)reached an amazing 99.9%in quite a short duration of 15 min.At the same time,under ppb level and indoor visible light(~1 W m^(2)),it can be treated to meet the drinking water standards set by the European Union and the U.S.National Environmental Protection Agency in 5 min,and even after 25 cycles of recycling,the efficiency is still maintained at about 80%,in addition to the removal of As(Ⅲ),Cd(Ⅱ),Cr(Ⅵ),and Pb(Ⅱ)can also be realized.The catalyst not only solves the problems of low reuse rate,difficult structure adjustment and high energy consumption of traditional photocatalysts but also has strong applicability and practical significance.The pioneering approach provides a much-needed solution strategy for removing highly toxic heavy metal antimony pollution from the environment.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks:Design and analysis

Hybrid Power-line/Visible-light Communication(HPVC)network has been one of the most promising Cooperative Communication(CC)technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency.Current research on HPVC networks focuses on the performance analysis and optimization of the Physical(PHY)layer,where the Power Line Communication(PLC)component only serves as the backbone to provide power to light Emitting Diode(LED)devices.So designing a Media Access Control(MAC)protocol remains a great challenge because it allows both PLC and Visible Light Communication(VLC)components to operate data transmission,i.e.,to achieve a true HPVC network CC.To solve this problem,we propose a new HPC network MAC protocol(HPVC MAC)based on Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA)by combining IEEE 802.15.7 and IEEE 1901 standards.Firstly,we add an Additional Assistance(AA)layer to provide the channel selection strategies for sensor stations,so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism,respectively.Based on this,we give a detailed working principle of the HPVC MAC,followed by the construction of a joint analytical model for mathematicalmathematical validation of the HPVC MAC.In the modeling process,the impacts of PHY layer settings(including channel fading types and additive noise feature),CSMA/CA mechanisms of 802.15.7 and 1901,and practical configurations(such as traffic rate,transit buffer size)are comprehensively taken into consideration.Moreover,we prove the proposed analytical model has the solvability.Finally,through extensive simulations,we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62%between the simulation and analytical results.

Visibility的航空遥感相机自动焦面检测方法

为了解决航空遥感相机自动焦面检测的问题,提出了一种利用空间滤波测速(SFV)原理进行航空遥感相机自动焦面检测的方法。首先,在焦面检测过程中,采集线阵CCD输出图像的Visibility值,利用SFV信号的Visibility值与离焦量之间的关系,通过搜寻SFV信号Visibility最大值找出最佳的成像焦面位置;其次,对空间滤波测速原理及Visibility与离焦量的关系进行了介绍;最后,对设计的实验装置在5～53.2 mm/s的典型像移速度下进行了20次焦面检测,结果表明最大测量误差均方值为46.25μm,小于航空遥感相机光学系统的检焦误差宽容度(76.8μm),能够满足航空遥感相机的自动焦面检测精度要求。

Analysis of the Low Visibility and Air Pollution Process in Shanghai during December 14-15,2006

A process of continuous heavy fog and air pollution occurred in the eastern China including Shanghai,Nanjing,Hefei,etc.during December 14-15,2006.Based on the GTS synoptic data,sounding data and NCEP/NCAR reanalyzed dataset,from the aspects of the weather situation,vapor condition,dynamic factor,temperature stratification,and air quality the contribution of foggy conditions and air pollution in the fog process to continuous heavy fog were analyzed.The results showed that 1 000 hPa fluid flux divergence (FD),vertical velocity (ω) and divergence difference(△DIV) between 1 000 hPa and 500 hPa had not significantly correlative with visibility,while relative humidity (RH) near ground had significant negative correlative,temperature lapse rate (γ) near ground had significant positive correlation,therefore,RH≥85%,γ<0.2 ℃/100m could be regarded as the necessary conditions of fog formation.In addition,the lowest air visibility had intense negative correlation with daily averaged API in the meantime,'API rising up to 150' could be an important criterion of fog formation in Shanghai Hongqiao international airport.

Characteristics and Application of Self-Developed Fast-Response Visibility Meter

The measuring principle and development process of self-developed fast-response visibility meter was introduced,and the comparative test with FD12 visibility meter was carried out.Meanwhile,by using the observational data from automatic weather station from October 2004 to March 2005,the evolution characteristics of visibility and its relationship with relative humidity,wind speed and temperature in autumn and winter in northern Beijing were discussed.The results showed that self-developed visibility meter could reflect the variation trend of visibility,with good comparison results,and could be used to measure visibility,while its frequency response was over 1 Hz,meeting the fast-response requirement of atmospheric visibility measurement and relevant detection.In northern Beijing,atmospheric visibility was significantly negatively correlated with relative humidity but significantly positively correlated with wind speed,while temperature could affect visibility indirectly by changing relative humidity and atmospheric stability.Gale and heavy fog had important effects on visibility.

Quantitative relationship between visibility and mass concentration of PM2.5 in Beijing

The pollution of particulate matter less than 2.5μm （PM2.5） is a serious environmental problem in Beijing. The annual average concentration of PM2.5 in 2001 from seasonal monitor results was more than 6 times that of the U,S, national ambient air quality standards proposed by U.S. EPA. The major contributors to mass of PM2.5 were organics, crustal elements and sulfate. The chemical composition of PM2.5 varied largely with season, but was similar at different monitor stations in the same season. The fine particles （PM2.5） cause atmospheric visibility deterioration through light extinction, The mass concentrations of PM2.5 were anti-correlated to the visibility, the best fits between atmospheric visibility and the mass concentrations of PM2.5 were somehow different： power in spring, exponential in summer, logarithmic in autumn, power or exponential in winter. As in each season the meteorological parameters such as air temperature and relative humidity change from day to day, probably the reason of above correlations between PM2.5 and visibility obtained at different seasons come from the differences in chemical compositions of PM2.5.

基于障碍物边界采样改进的Visibility-PRM算法

首先分析解决机器人运动规划问题的Visibility-PRM算法。然后针对其在复杂工作空间运动规划中表现出的roadmap连通子图数多、可视性判断效率低等局限性,提出基于障碍物边界采样改进的Visibility-PRM算法(Obs-Visi-PRM)。通过改进采样和连通的策略,减少了roadmap的连通子图数,降低了碰撞检测次数。最后通过实例分析,证明Obs-Visi-PRM算法的有效性。