Visual Scheme Research Articles

Building Prestige Values for Eatsambel Products through Instagram Advertising: A Critical Discourse Analysis

This study aims to analyze how prestige value is built through Eatsambel product advertisements on Instagram, focusing on thematic, schematic, and semantic aspects. A qualitative approach is used, where Teun A. van Dijk's critical discourse analysis is applied to explore the key elements used in the advertisement. The data was taken from Eatsambel's Instagram posts in the period from November 2023 to January 2024, covering various advertising campaigns. The results show that themes such as quality of ingredients, authenticity of the manufacturing process, and rich culinary culture are used to build a prestigious image. Elegant visual schemes and narratives relevant to everyday life were also found to be strategies to reinforce the image of exclusivity. Semantic aspects, such as word choice that emphasizes the superiority of taste, also play a role in shaping the perception of the product's prestige in the eyes of the audience. The combination of these elements creates a comprehensive marketing approach, where prestige value is not only built through exclusivity, but also through proximity to the audience. The findings have important implications for marketing strategies in the culinary industry, demonstrating that the integration between digital elements and prestige imagery can increase product appeal and strengthen emotional connections with consumers. The research encourages the development of dynamic and relevant advertising narratives, combining traditional and innovative elements to reach a wider market segment.

SVB: Self-Supervised Real CT Denoising via Similarity-Based Visual Blind-Spot Scheme

SVB: Self-Supervised Real CT Denoising via Similarity-Based Visual Blind-Spot Scheme

Camera-based control system of a planar cable-driven parallel robot intended for functional rehabilitation

Abstract This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.

Design of Traditional Inkblot Pattern Plane System Based on Graphics and Visual Perception

In order to improve the graphic design effect of traditional pattern with ink and wash elements, this paper puts forward a graphic and visual perception-based graphic system design scheme of traditional pattern with ink and wash elements. Taking plane space media parameters, creative display patterns, characters and other elements as constraint parameters, this paper constructs a plane design model under traditional pattern fusion ink elements, extracts the graphic and visual perception element feature space structure of plane design graphics under traditional pattern fusion ink elements, and combines the three-dimensional internal structure characteristics of plane design graphics under traditional pattern fusion ink elements to carry out information reorganization. The method of color space color feature decomposition is used to decompose the visual elements of graphic design under traditional pattern fusion ink elements, and the virtual reconstruction model of graphic design under traditional pattern fusion ink elements and RGB-D image of visual perception element is established by the method of image aesthetic and emotional factors fusion. The feature recombination method of converting graphic design into materialized image is used to design the three-dimensional reconstruction model of graphic design under traditional pattern fusion ink elements. The 3D multi-viewpoint tracking and recognition method is adopted to reconstruct the graphic design and visual perception elements under the traditional pattern fusion ink elements, so as to improve the graphic design ability under the traditional pattern fusion ink elements. The simulation results show that this method has good visual expression ability, good graphic design effect, and good visual communication ability of graphic design with traditional pattern and ink elements.

Whole-brain responses to visual and auditory stimuli in anesthetized and minimally restrained awake mice using quiet zero echo time fMRI

Abstract Functional MRI (fMRI) is a flexible tool for sensory perception studies in animal models. However, animal fMRI studies are generally performed under anesthesia. Unfortunately, anesthesia affects brain function and sensory processing, complicating the interpretation of the findings. Since there is a growing need for fMRI protocols applicable for awake animals, we optimized a zero echo time Multi-Band Sweep Imaging with a Fourier Transformation (MB-SWIFT) fMRI approach for imaging awake mice. We implemented a 14-day habituation protocol that resulted in merely moderate motion of the mice while being head-fixed with the animals’ body and limbs being free to move. The sensory responsiveness between different states of consciousness was compared by imaging mice with visual and auditory stimulation schemes in the awake state and under ketamine–xylazine anesthesia. In awake mice, we observed a robust whole-brain activation of the ascending auditory and visual pathways, as well as higher sensory processing areas. Under ketamine–xylazine anesthesia, auditory responses were suppressed, and the temporal shapes of fMRI responses were different from those obtained in awake mice. Our results suggest that the quiet and motion-tolerant zero echo time MB-SWIFT approach allows complex behavioral fMRI designs in the awake state that promise to improve our understanding of the underlying mechanisms of perception.

Modeling and Visual Simulation of Bifurcation Aneurysms Using Smoothed Particle Hydrodynamics and Murray's Law.

Aneurysm modeling and simulation play an important role in many specialist areas in the field of medicine such as surgical education and training, clinical diagnosis and prediction, and treatment planning. Despite the considerable effort invested in developing computational fluid dynamics so far, visual simulation of blood flow dynamics in aneurysms, especially the under-explored aspect of bifurcation aneurysms, remains a challenging issue. To alleviate the situation, this study introduces a novel Smoothed Particle Hydrodynamics (SPH)-based method to model and visually simulate blood flow, bifurcation progression, and fluid-structure interaction. Firstly, this research consider blood in a vessel as a kind of incompressible fluid and model its flow dynamics using SPH; and secondly, to simulate bifurcation aneurysms at different progression stages including formation, growth, and rupture, this research models fluid particles by using aneurysm growth mechanism simulation in combination with vascular geometry simulation. The geometry incorporates an adjustable bifurcation structure based on Murray's Law, and considers the interaction between blood flow, tissue fluid, and arterial wall resistance. Finally, this research discretizes the computation of wall shear stress using SPH and visualizes it in a novel particle-based representation. To examine the feasibility and validity of the proposed method, this research designed a series of numerical experiments and validation scenarios under varying test conditions and parameters. The experimental results based on numerical simulations demonstrate the effectiveness and efficiency of proposed method in modeling and simulating bifurcation aneurysm formation and growth. In addition, the results also indicate the feasibility of the proposed wall shear stress simulation and visualization scheme, which enriches the means of blood analysis.

Application of Different Sensors in Obstacle Avoidance of Delivery Robots

People's living conditions are constantly rising as the global economy and society evolve at a rapid pace, and improving production efficiency has become a crucial concern. This paper proposes a combination of multiple obstacle avoidance schemes and algorithms to achieve dynamic obstacle avoidance for delivery robots. By analysing three different obstacle avoidance schemes including visual obstacle avoidance scheme, ultrasonic obstacle avoidance scheme and laser obstacle avoidance scheme, three robot usage scenarios including hotels, restaurants and hospitals, and three vital algorithms including Dijkstras algorithm, ORCA algorithm and PID algorithm. Then by analyzing the advantages and disadvantages of each scheme and algorithm and the characteristics of different usage scenarios, this paper obtains a robot obstacle avoidance and algorithm usage plan in a specific scenario.

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

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.

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.

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.

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.