Design and Realization of Block Level Augmented Reality Three-Dimensional Map

In response to the construction needs of “Real 3D China”, the system structure, functional framework, application direction and product form of block level augmented reality three-dimensional map is designed. Those provide references and ideas for the later large-scale production of augmented reality three-dimensional map. The augmented reality three-dimensional map is produced based on skyline software. Including the map browsing, measurement and analysis and so on, the basic function of three-dimensional map is realized. The special functional module including housing management, pipeline management and so on is developed combining the need of residential quarters development, that expands the application fields of augmented reality three-dimensional map. Those lay the groundwork for the application of augmented reality three-dimensional map. .

Experimental Study on the Wavelengths of Two-Dimensional and Three-Dimensional Freak Waves

Freak waves are commonly characterized by strong-nonlinearity, and the wave steepness, which is calculated from the wavelength, is a measure of the degree of the wave nonlinearity. Moreover, the wavelength can describe the locally spatial characteristics of freak waves. Generally, the wavelengths of freak waves are estimated from the dispersion relations of Stokes waves. This paper concerns whether this approach enables a consistent estimate of the wavelength of freak waves. The two-(unidirectional, long-crested) and three-dimensional(multidirectional, shortcrested) freak waves are simulated experimentally through the dispersive and directional focusing of component waves, and the wavelengths obtained from the surface elevations measured by the wave gauge array are compared with the results from the linear, 3rd-order and 5th-order Stokes wave theories. The comparison results suggest that the 3rd-order theory estimates the wavelengths of freak waves with higher accuracy than the linear and 5th-order theories. Furthermore, the results allow insights into the dominant factors. It is particularly noteworthy that the accuracy is likely to depend on the wave period, and that the wavelengths of longer period freak waves are overestimated but the wavelengths are underestimated for shorter period ones. In order to decrease the deviation, a modified formulation is presented to predict the wavelengths of two-and three-dimensional freak waves more accurately than the 3rd-order dispersion relation, by regression analysis. The normalized differences between the predicted and experimental results are over 50% smaller for the modified model suggested in this study compared with the 3rd-order dispersion relation.

Three-Dimensional Scene Encryption Algorithm Based on Phase Iteration Algorithm of the Angular-Spectral Domain

In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional(3 D) scene encryption algorithm based on the phase iteration of the angular spectrum domain is proposed in this paper. The algorithm, which adopts the layer-oriented method, generates the computer generated hologram by encoding the three-dimensional scene. Then the computer generated hologram is encoded into three pure phase functions by adopting the phase iterative algorithm based on angular spectrum domain,and the encryption process is completed. The three-dimensional scene encryption can improve the capacity of the information,and the three-phase iterative algorithm can guarantee the security of the encryption information. The numerical simulation results show that the algorithm proposed in this paper realized the encryption and decryption of three-dimensional scenes. At the same time, it can ensure the safety of the encrypted information and increase the capacity of the encrypted information.

Three-Dimensional Scenes Restore Using Digital Image

Encryption and decryption method of three-dimensional objects uses holograms computer-generated and suggests encoding stage. Information obtained amplitude and phase of a three-dimensional object using mathematically stage transforms overlap stored on a digital computer. Different three-dimensional images restore and develop the system for the expansion of the three-dimensional scenes and camera movement parameters. This article talks about these kinds of digital image processing algorithms as the reconstruction of three-dimensional model of the scene. In the present state, many such algorithms need to be improved in this paper proposing one of the options to improve the accuracy of such reconstruction.

Scene 3-D Reconstruction System in Scattering Medium

Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and extensions.The NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also evolving.Existing underwater 3D reconstruction systems still face challenges such as long training times and low rendering efficiency.This paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D reconstruction.First,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across frames.Then,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction results.After pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and rendering.In terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same data.In terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 18.40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.

YOLOv5ST:A Lightweight and Fast Scene Text Detector

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness

The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos.Grounded in the H.264 video coding standard,the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel.Subsequently,it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information,enabling successful watermark extraction and lossless recovery of the original video content.Experimental results demonstrate the algorithm’s strong imperceptibility,with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 7.59%and 0.4%on average.At the same time,the proposed algorithm has strong robustness to both offline and online attacks:In the face of offline attacks,the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989,and the average bit error rate is 0.0089.In the face of online attacks,the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840,and the mean bit error rate is 0.2269.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 1.27%and 18.16%on average,highlighting the algorithm’s robustness.Furthermore,the algorithm exhibits low computational complexity,with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s,respectively,underscoring its practical utility.

Intelligent Sensing and Control of Road Construction Robot Scenes Based on Road Construction

Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.

Ground target localization of unmanned aerial vehicle based on scene matching

In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.

The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition

For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Three-dimensional Computational Fluid Dynamics Modeling of Two-phase Flow in a Structured Packing Column

Characterizing the complex two-phase hydrodynamics in structured packed columns requires a power- ful modeling tool. The traditional two-dimensional model exhibits limitations when one attempts to model the de- tailed two-phase flow inside the columns. The present paper presents a three-dimensional computational fluid dy- namics （CFD） model to simulate the two-phase flow in a representative unit of the column. The unit consists of an CFD calculations on column packed with Flexipak 1Y were implemented within the volume of fluid （VOF） mathe- matical framework. The CFD model was validated by comparing the calculated thickness of liquid film with the available experimental data. Special attention was given to quantitative analysis of the effects of gravity on the hy- drodynamics. Fluctuations in the liquid mass flow rate and the calculated pressure drop loss were found to be quali- tatively in agreement with the experimental observations.

Three-Dimensional Tidal Model and Its Application to Numerical Simulation of Water Quality in Coastal Waters

The turbulence mechanism plays an important part in the mixing process and momentum transfer of turbulence. A three-dimensional Prandtl mixing length tidal model has been developed to simulate tidal flows and water quality. The eddy viscosities and diffusivities are computed from the Prandtl mixing length model. In order to model the water quality of an estuary or coastal area many interdependent processes need to be simulated. These may be conveniently separated into three main groups: transport and mixing processes, biochemical interaction of water quality variables and the utilization and re-cycling of nutrients by living matter. The model simulates full oxygen and nutrient balance, primary productivity and the transport, reaction mechanism and fate of pollutants over tidal time-scales. The model is applied to numerical simulation of tidal flows and water quality in Dalian Bay. The model has been calibrated against a limited data set of historical water quality observations and in general demonstrates excellent agreement with all available data.

Three-Dimensional and Cross-sectional Characteristics of Normal Grain Growth Based on Monte Carlo Simulation

An appropriate Monte Carlo method was developed to simulate the three-dimensional normal grain growth more completely. Comparative investigation on the three-dimensional and the cross-sectional characteristics of normal grain growth was done. It was found that the time exponent of grain growth determined from cross-section exhibits the same rule of increasing slowly with time and approaching the theoretical value n = 0.5 of steadygrain growth as the three-dimensional (3-D) system. From change of the number of grains per unit area with timemeasured in cross-section, the state of 3-D normal grain growth may be predicted. The gtain size distribution incross-section is different from that in 3-D system and can not express the evolution characteristic of the 3-D distribution. Furthermore, there exists statistical connection between the topological parameters in cross-section and thosein three-dimensions.

Three-dimensional frequency-domain full waveform inversion based on the nearly-analytic discrete method

The nearly analytic discrete(NAD)method is a kind of finite difference method with advantages of high accuracy and stability.Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.This study applies the NAD method to solving three-dimensional(3D)acoustic wave equations in the frequency-domain.This forward modeling approach is then used as the“engine”for implementing 3D frequency-domain full waveform inversion(FWI).In the numerical modeling experiments,synthetic examples are first given to show the superiority of the NAD method in forward modeling compared with traditional finite difference methods.Synthetic 3D frequency-domain FWI experiments are then carried out to examine the effectiveness of the proposed methods.The inversion results show that the NAD method is more suitable than traditional methods,in terms of computational cost and stability,for 3D frequency-domain FWI,and represents an effective approach for inversion of subsurface model structures.

The three-dimension model for the rock-breaking mechanism of disc cutter andanalysis of rock-breaking forces

To study the rock deformation with three- dimensional model under rolling forces of disc cutter, by car- rying out the circular-grooving test with disc cutter rolling around on the rock, the rock mechanical behavior under rolling disc cutter is studied, the mechanical model of disc cutter rolling around the groove is established, and the the- ory of single-point and double-angle variables is proposed. Based on this theory, the physics equations and geometric equations of rock mechanical behavior under disc cutters of tunnel boring machine （TBM） are studied, and then the bal- ance equations of interactive forces between disc cutter and rock are established. Accordingly, formulas about normal force, rolling force and side force of a disc cutter are de- rived, and their validity is studied by tests. Therefore, a new method and theory is proposed to study rock- breaking mech- anism of disc cutters.

Dynamic Characteristic Mechanism of Atrial Septal Defect Using Real-Time Three-Dimensional Echocardiography and Evaluation of Right Ventricular Functions

The dynamic characteristics of the area of the atrial septal defect（ASD） were evaluated using the technique of real-time three-dimensional echocardiography（RT 3DE）, the potential factors responsible for the dynamic characteristics of the area of ASD were observed, and the overall and local volume and functions of the patients with ASD were measured. RT 3DE was performed on the 27 normal controls and 28 patients with ASD. Based on the three-dimensional data workstations, the area of ASD was measured at P wave vertex, R wave vertex, T wave starting point, and T wave terminal point and in the T-P section. The right atrial volume in the same time phase of the cardiac cycle and the motion displacement distance of the tricuspid annulus in the corresponding period were measured. The measured value of the area of ASD was analyzed. The changes in the right atrial volume and the motion displacement distance of the tricuspid annulus in the normal control group and the ASD group were compared. The right ventricular ejection fractions in the normal control group and the ASD group were compared using the RT 3DE long-axis eight-plane（LA 8-plane） method. Real-time three-dimensional volume imaging was performed in the normal control group and ASD group（n=30）. The right ventricular inflow tract, outflow tract, cardiac apex muscular trabecula dilatation, end-systolic volume, overall dilatation, end-systolic volume, and appropriate local and overall ejection fractions in both two groups were measured with the four-dimensional right ventricular quantitative analysis method（4D RVQ） and compared. The overall right ventricular volume and the ejection fraction measured by the LA 8-plane method and 4D RVQ were subjected to a related analysis. Dynamic changes occurred to the area of ASD in the cardiac cycle. The rules for dynamic changes in the area of ASD and the rules for changes in the right atrial volume in the cardiac cycle were consistent. The maximum value of the changes in the right atrial volume occurred in the end-systolic period when the peak of the curve appeared. The minimum value of the changes occurred in the end-systolic period and was located at the lowest point of the volume variation curve. The area variation curve for ASD and the motion variation curve for the tricuspid annulus in the cardiac cycle were the same. The displacement of the tricuspid annulus exhibited directionality. The measured values of the area of ASD at P wave vertex, R wave vertex, T wave starting point, T wave terminal point and in the T-P section were properly correlated with the right atrial volume（P〈0.001）. The area of ASD and the motion displacement distance of the tricuspid annulus were negatively correlated（P〈0.05）. The right atrial volumes in the ASD group in the cardiac cycle in various time phases increased significantly as compared with those in the normal control group（P=0.0001）. The motion displacement distance of the tricuspid annulus decreased significantly in the ASD group as compared with that in the normal control group（P=0.043）. The right ventricular ejection fraction in the ASD group was lower than that in the normal control group（P=0.032）. The ejection fraction of the cardiac apex trabecula of the ASD patients was significantly lower than the ejection fractions of the right ventricular outflow tract and inflow tract and overall ejection fraction. The difference was statistically significant（P=0.005）. The right ventricular local and overall dilatation and end-systolic volumes in the ASD group increased significantly as compared with those in the normal control group（P=0.031）. The a RVEF and the overall ejection fraction decreased in the ASD group as compared with those in the normal control group（P=0.0005）. The dynamic changes in the area of ASD and the motion curves for the right atrial volume and tricuspid annulus have the same dynamic characteristics. RT 3DE can be used to accurately evaluate the local and overall volume and functions of the right ventricle. The local and overall volume loads of the right ventricle in the ASD patients increase significantly as compared with those of the normal people. The right ventricular cardiac apex and the overall systolic function decrease.

Clinical Value of Stereoscopic Three-dimensional Echocardiography in Assessment of Atrial Septal Defects: Feasibility and Efficiency

Stereoscopic three-dimensional echocardiography（S-3DE） is a novel displaying technol-ogy based on real-time 3-dimensional echocardiography （RT-3DE）. Our study was to evaluate the feasibility and efficiency of S-3DE in the diagnosis of atrial septal defect （ASD） and its use in the guidance for transcatheter ASD occlusion. Twelve patients with secundum ASD underwent RT-3DE examination and 9 of the 12 were subjected to transcatheter closure of ASD. Stereoscopic vision was generated with a high-performance volume renderer with red-green stereoscopic glasses. S-3DE was compared with standard RT-3D display for the assessment of the shape, size, and the surrounding tis-sues of ASD and for the guidance of ASD occlusion. The appearance rate of coronary sinus and the mean formation time of the IVC, SVC were compared. Our results showed that S-3DE could measure the diameter of ASD accurately and there was no significant difference in the measurements between S-3DE and standard 3D display （2.89±0.73 cm vs 2.85±0.72 cm, P〉0.05; r=0.96, P〈0.05）. The appearance of coronary sinus for S-3DE was higher as compared with the standard 3D display （93.3% vs 100%）. The mean time of the IVC, SVC for S-3DE monitor was slightly shorter than that of the standard 3D display （11.0±3.8 s vs 10.3±3.6 s, P〉0.05）. The mean completion time of interven-tional procedure was shortened with S-3DE display as compared with standard 3D display （17.3±3.1 min vs 23.0±3.9 min, P〈0.05）. Stereoscopic three-dimensional echocardiography could improve the visualization of three-dimensional echocardiography, facilitate the identification of the adjacent structures, decrease the time required for interventional manipulation. It may be a feasible, safe, and efficient tool for guiding transcatheter septal occlusion or the surgical interventions.