Visual Scheme Research Articles

A novel global multi-level terrain data tile production method

Abstract. Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services.

Advancements and Applications of Anti-Fraud Graphical Passwords

Visual cryptography represents a sophisticated and innovative approach to secure information sharing, gaining traction within the field of information security. This technology adeptly transforms sensitive data into visually interpretable graphics or images. These images are then segmented into multiple independent parts, each distributed among different stakeholders. This strategy ensures that no single party can access the complete data without collaboration, thereby enhancing security. This dissertation delves into several anti-fraud visual password schemes designed to mitigate risks associated with deceptive behaviors by participants and distributors alike. By addressing these vulnerabilities, the proposed schemes enhance the integrity and reliability of the data sharing process. The application of visual passwords in real-life scenarios not only bolsters data security but also shields private information from potential leaks. By reducing the complexity and cost associated with traditional decryption methods, visual cryptography offers substantial support for a wide array of application scenarios. This dissertation presents a comprehensive exploration of visual cryptography's potential to revolutionize data protection strategies, illustrating its importance and utility in contemporary digital environments.

A Data-Driven Image-Based Visual Servoing Scheme for Redundant Manipulators With Unknown Structure and Singularity Solution

A Data-Driven Image-Based Visual Servoing Scheme for Redundant Manipulators With Unknown Structure and Singularity Solution

Profiling Dynamic Patterns of Single-Cell Motility.

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n = 74253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.

Research on Visual Recognition of Service Robots Based on YOLO Algorithm

In modern society, based on the aging of population and the rapid development of intelligent technology, the demand for intelligent service robots is increasing, and visual recognition is one of the key technologies of service robots. The rapid development of deep learning technology provides new solutions for visual recognition. Existing target recognition algorithms have the problem of complex model and slow inference speed. According to the above problems, this paper proposes a visual recognition algorithm based on YOLO series, which is used for the visual recognition design scheme of service robot. It can ensure the detection accuracy, reduce the speed of the model complexity, speed up the reasoning, and determine whether to meet the actual needs. Through randomly selected pictures, the results show that the design scheme can effectively improve the detection accuracy and real-time of the service robot on targets, and enhance its autonomous service ability in various complex environments.

Surface Coloring and Plasmonic Information Encryption at 50000 dpi Enabled by Direct Femtosecond Laser Printing.

Femtosecond (fs) laser pulses drive matter into a highly nonequilibrium state, allowing precise sculpturing of irradiated surface sites with sophisticated nanomorphologies. Here, we used fs-laser patterning to create diverse plasmonic morphologies on the top Au layer of the metal-insulator-metal sandwich. Mutual action of laser-driven thermomechanical effects and ultrafast solid-to-liquid transition allows control of the morphology resulting in pronounced surface reflectivity modulation, i.e., in a structural color effect. This enables template-free high-resolution color printing at a superior lateral resolution up to 50000 dots per inch and facile tunability of the color tone and saturation. Moreover, precise control over the orientation of the printed nanostructures within subwavelength lattices allows modulation of their local plasmonic response encrypting the optical information within the colorful images. The hidden information can be unveiled using a facile cross-polarized optical visualization scheme, rendering the proposed method with extra modalities combining high resolution information encryption, coloring, and security labeling.

Toward Understanding the Impact of Visualized Focus Levels in Virtual Reality on User Presence and Experience

Neurofeedback refers to the process of feeding a sensory representation of brain activity back to users in real time to improve a particular brain function, e.g., their focus and/or attention on a particular task. This study addressed the notable lack of research on methods used to visualize EEG data and their effects on the immersive quality of VR. We developed an algorithm to quantify focus, yielding a focus score. A pre-study with twenty participants confirmed its effectiveness in distinguishing between focused and relaxed mental states. Subsequently, we used this focus score to prototype a VR experience system visualizing the focus score in preconfigured manners, which was utilized in an exploratory study to assess the impact of different neurofeedback visualization methods on user engagement and focus in VR. Among all the visualization methods evaluated, the environmental scheme stood out due to its superior usability during task execution, its ability to evoke positive emotions through the visualization of objects or scenes, and its minimal deviation from user expectations. Additionally, we explored design guidelines based on collected results for future research to further refine the visualization scheme, ensuring effective integration of the focus score within the VR environment. These enhancements are crucial for designing neurofeedback visualization schemes that aim to boost participant focus in VR settings, offering significant insights into the optimization of such technologies.

Campus Network Traffic Prediction and Anomaly Detection Based on Deep Learning

This paper proposes an intelligent solution for network traffic prediction and anomaly detection in campus networks, addressing the increasingly severe network security challenges. The proposed approach innovatively integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) to simultaneously extract local features and capture dynamic temporal dependencies of network traffic, significantly improving prediction accuracy. Based on this, an adaptive threshold anomaly detection algorithm is designed to automatically adjust detection sensitivity according to traffic variations, achieving a better balance between accuracy and recall rates. Additionally, an anomaly visualization scheme is presented, intuitively displaying the spatiotemporal distribution of network anomalies through heatmaps, assisting administrators in decision-making. Large-scale experiments demonstrate that this approach can effectively identify various security threats such as DDoS attacks, scanning probes, and botnets, with an overall detection rate exceeding 90% while maintaining a low false positive rate. Compared to traditional statistical and machine learning methods, the proposed approach exhibits stronger adaptability and generalization capabilities, providing crucial support for building an intelligent, precise, and reliable campus network security protection system. Future work will focus on further improving the real-time performance and robustness of the solution, expanding its application in new network scenarios such as IoT and edge computing.

Visually security privacy medical data protection scheme using compressive sensing and improved duffing chaotic system

Health and medical data frequently contain sensitive patient information that must be protected. Existing visual security schemes for medical images exhibit limitations in the imperceptibility of cipher images and the performance of image reconstruction. This paper introduces and evaluates a novel approach called Visual Meaningful Image Encryption (VMIE) for securing medical images. The proposed VMIE scheme employs a chaotic system based on the Duffing equation for initial encryption. Medical images are processed and encrypted in a sparse domain. A Bidirectional Chaotic Magic Transformation (BCMT) algorithm is then applied to scramble the sparse medical images. The scrambled data undergoes compression and diffusion. An adaptive embedding strategy employing the Discrete Cosine Stockwell Transform (DCST) integrates confidential data into the host image. The performance of the proposed chaotic system is validated through theoretical analysis and numerical simulation. Simulation results, along with comparisons to existing schemes, demonstrate the efficacy of the VMIE method in enhancing visual security and its suitability for natural images. The VMIE approach presented in this paper offers a promising solution for securing medical images, effectively addressing the limitations of current visual security schemes.

Adaptive Visual Control for Robotic Manipulators with Consideration of Rigid-Body Dynamics and Joint-Motor Dynamics

A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

Planning space exploration: Appraising and appreciating a curated collection of NASA's roadmap visualizations

For supporting strategic planning and communication amongst stakeholders, roadmapping is a widespread practice across many industries. However, there is a notable gap in acknowledging and appreciating the pioneering roadmap visualizations published by NASA. This paper will address this gap by presenting and critiquing a curated collection of 10 exemplar visualizations, each representing a distinctive design form. Despite potential aesthetic limitations, these examples offer themselves as convenient and useful templates for other organizations seeking to develop their own roadmaps. By deconstructing these formats and emphasizing their unique features, this paper aims to broaden the adoption and adaptation of these foundational visual schemes, while recognizing NASA's contributions to the practice of roadmapping.

Dual-band complex-amplitude metasurface empowered high security cryptography with ultra-massive encodable patterns

Abstract The significance of a cryptograph method lies in its ability to provide high fidelity, high security, and large capacity. The emergence of metasurface-empowered cryptography offers a promising alternative due to its unparalleled wavefront modulation capabilities and easy integration with traditional schemes. However, the majority of reported strategies suffer from limited capacity as a result of restricted independent information channels. In this study, we present a novel method of cryptography that utilizes a dual-band complex-amplitude meta-hologram. The method allows for the encoding of 225 different patterns by combining a modified visual secret-sharing scheme (VSS) and a one-time-pad private key. The use of complex-amplitude modulation and the modified VSS enhances the quality and fidelity of the decrypted results. Moreover, the transmission of the private key through a separate mechanism can greatly heighten the security, and different patterns can be generated simply by altering the private key. To demonstrate the feasibility of our approach, we design, fabricate, and characterize a meta-hologram prototype. The measured results are in good agreement with the numerical ones and the design objectives. Our proposed strategy offers high security, ultra-capacity, and high fidelity, making it highly promising for applications in information encryption and anti-counterfeiting.

Station-viewpoint joint coverage path planning towards mobile visual inspection

Coverage path planning (CPP) has been widely studied due to its significant impact on the efficiency of automated surface quality inspection. However, these researches mostly concentrate on fixed-base visual robotic schemes, with limited focus on the widely utilized mobile-base schemes which require considerations of inherent constraints between stations (base positions) and viewpoints. Therefore, this article models a station-viewpoint joint coverage path planning problem and proposes a workflow to solve it. Within this workflow, firstly, a viewpoint selection genetic algorithm based on alternating evolution strategy is presented to optimize both the viewpoint quantity and view quality; secondly, a novel genetic algorithm is devised to accomplish joint assignment and sequence planning for stations and viewpoints. Several experimental studies are conducted to validate the effectiveness and efficiency of the proposed methods, and the proposed genetic algorithms exhibit notable superiorities compared to the benchmark methods in terms of viewpoint quantity, mean view quality, motion cost, and computational efficiency.

The strategy of traditional Chinese settlement digitization: a landscape gene information chain theory-based perspective

Traditional Chinese settlements (TCSs) embody millennia of human and cultural history, with the core culture serving as their essence. However, any damage incurred by these TCSs can result in the loss of core culture. In the era of China’s digital transformation, there has recently been a significant push toward digital reforms within TCSs. Thus, this paper introduces a traditional settlement digitalization (TSD) framework with a specific focus on enhancing the preservation of TCS landscape gene through the perspective of landscape gene information chain theory. This research uses the Nanxun traditional settlement (TS), Huzhou, Zhejiang, China, the first TS listed as a World Cultural Heritage Site in China, as a case study, extracting and identifying the area landscape gene and digital visualization status. This article identifies the landscape gene information chain of the Nanxun TS and redesigns its digital visualization and application scheme under the TSD framework. This study reveals the notion of a digital landscape gene characterized by four key attributes: identical, interactive, immersive, and intelligent. The results of this study will help guide the sustainable protection, application and planning of the Nanxun TS and promote the study of other sections of landscape gene theory. This research not only contributes to the development of landscape gene information chain theory but also provides guidance for the digital transformation of other TCSs and cultural heritage sites.

Automated Piezo-Assisted Sperm Immobilization

Sperm immobilization is a crucial procedure in clinical cell surgery for infertility treatment. Current immobilization is implemented by tapping the sperm tail with a glass micropipette, but its effectiveness is restricted by sperm orientation and ineffective membrane ablation. Ineffective ablation leads to limited release of oocyte activating factors and lowers fertilization rate; and sperm swim in small angles relative to the micropipette tip cannot be tapped due to the risk of damaging the sperm’s genetic materials contained in the sperm head. This paper reports automated piezo-assisted sperm immobilization with enhanced efficacy of cell membrane ablation and sperm orientation control. The designed piezo drill consists of two orthogonal vibration modules to generate controlled micropipette vibration along axial and lateral axes. Through stiffness modeling, the flexure joints guide the motion of the central beam of each vibration module. To achieve sperm orientation control, whirl flow is induced by both axial and lateral vibration of the micropipette tip. To immobilize sperm, only micropipette’s axial vibration is generated to prevent lateral vibration from damaging sperm head. A visual servoing scheme is developed by decoupling sperm wiggling from positioning error for immobilization. Experimental results showed that sperm orientation control by the piezo drill achieved an error of 1.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> and a time cost of 2.5 s. Visual servoing with sperm wiggling decoupling achieved a positioning error of 1.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$</tex-math> </inline-formula> m. Furthermore, the piezo-assisted sperm immobilization technique led to effective membrane ablation. With membrane-impermeable stains, it took 5.6 s for the immobilized sperm to be stained after piezo-assisted immobilization, significantly less than 49.2 s by conventional micropipette tapping. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work tackled the challenge of ineffective membrane ablation and orientation limit in clinical cell surgeries. Conventional manual immobilization suffers from low membrane ablation efficacy, which leads to limited release of oocyte activating factors and lowers fertilization rate. Moreover, sperm swim in small angles relative to the micropipette tip cannot be tapped due to the risk of damaging the sperm’s genetic materials contained in the sperm head. In this paper, we propose automation techniques for effective membrane ablation and orientation control of sperm. A clinically compatible piezo drill is developed to generate controllable micropipette motion along both axial and lateral directions. The whirl flow generated by micropipette vibration is employed to rotate sperm, which greatly increased the number of available sperm for immobilization. A visual servoing controller is developed to keep the sperm at the center of field of view for immobilization by decoupling sperm wiggling from positioning error. The developed methods can be generalized to the manipulation of other types of cells. The piezo drill can be used for effective membrane ablation of oocyte, embryo, yeast cell and so on. The orientation control strategy leveraging piezo-induced whirl flow is applicable to non-contact rotation of a variety of microorganism.

Optimization of Visual Communication Design Scheme by Combining Computer-Aided Design and Deep Learning

Optimization of Visual Communication Design Scheme by Combining Computer-Aided Design and Deep Learning

Visual double image encryption scheme based on multilayer complex networks and wavelet transform

Visual double image encryption scheme based on multilayer complex networks and wavelet transform

UAV vision-based crack quantification and visualization of bridges: system design and engineering application

Accurately measuring visible cracks in bridges is crucial for their structural health diagnosis, damage detection, performance evaluation, and maintenance planning. The primary means of visual crack detection still relies heavily on manual visual inspection, an inefficient process that can pose significant safety risks. This article develops a unmanned aerial vehicle (UAV) vision-based surface crack measurement methodology and visualization scheme for the bridges that can detect and measure cracks automatically with improved efficiency. The surface crack measurement methodology is achieved by designing a three-stage crack sensing system including the You Only Look Once-based crack recognition, U-shaped network-based crack segmentation, and deep-vision-based crack width calculation. This workflow is integrated into a comprehensive UAV inspection system, which is intended for operation at the field. The surface crack visualization scheme is accomplished by taking advantage of time-series image fusion, GPS information migration, and three-dimensional (3D) point cloud technique to reconstruct the 3D geometrical model of the tested bridge, which is convenient for unveiling the crack information in the bridge. The proposed methodology was successfully validated by a case study on an arch bridge. The achievement of this article promotes the UAV vision-based bridge’s surface crack inspection technology to a new status that no preparation for pasting calibration marker is needed, and crack identification, segmentation, and width calculation are realized promptly during the UAV flying on-site, as well as damage evaluation for bridges is visually fulfilled based on the reconstructed digital-graphical 3D model. The working environments and influencing factors to the developed system are sufficiently discussed. Certain limitations in the current application are pointed out for future improvements.

LLM enabled generative collaborative design in a mixed reality environment

In the collaborative design process, diverse stakeholder backgrounds often introduce inefficiencies in collaboration, such as delays in design delivery and decreased creativity, primarily due to misunderstandings and communication barriers caused by this diversity. To respond, this study proposes an AI-augmented Multimodal Collaborative Design (AI-MCD) framework. This framework utilizes Large Language Models (LLM) to establish an iterative prompting mechanism that provides professional design prompts for Generative AI (GAI) to generate precise visual schemes. On this basis, the GAI cooperates with Mixed Reality (MR) technology to form an interactive and immersive environment for enabling full participation in the design process. By integrating these technologies, the study aims to help stakeholders form a unified cognition and optimize the traditional collaborative design process. Through a case study involving the development of heart education products for children, the effectiveness of the framework is emphasized, and the practical application and effectiveness of the proposed method innovation are demonstrated.

Two-phase visual servoing for capturing tumbling non-cooperative satellites with a space manipulator

In this paper, a visual servoing approach is developed to capture the docking rings of tumbling non-cooperative satellites with a space manipulator. The primary challenge addressed is the potential for the docking ring to leave the monocular camera’s field-of-view as the manipulator approaches the target, due to the ring’s large size. To solve this issue, a two-phase visual servoing scheme combining a monocular camera and a three-line structured light vision system is proposed. In an effort to augment the success rate and safety of capture operations, several constraints are formulated, encompassing manipulator’s kinematics, monocular camera’s field-of-view, obstacle avoidance, structured light’s breakpoints and smooth capture. Subsequently, a nonlinear model predictive controller is proposed to manage these constraints in real-time and regulate the system. System models are established based on image moments and pose for each phase, selecting these features as visual feedback to simplify the formulation of servo constraints and avoid the complex circle-based pose measurement. Furthermore, to ensure unbiased predictions, the model disturbances arising from the imprecise estimation of target motion parameter are observed using an extended Kalman filter, which are then incorporated into the predictive control framework. The simulation results demonstrate the effectiveness of this scheme.