Vector Graphics Research Articles

Tensor gradiometry with a diamond magnetometer

Vector magnetometry provides more information than scalar measurements for magnetic surveys utilized in space, defense, medical, geological, and industrial applications. These areas would benefit from a mobile vector magnetometer. Here we present a scanning fiber-coupled nitrogen-vacancy (N-V) center vector magnetometer. Feedback control of the microwave excitation frequency is used to improve dynamic range and maintain sensitivity during movement of the sensor head. Tracking of the excitation frequency shifts for all four orientations of the N-V center allows us to image the vector magnetic field of a damaged steel plate. We calculate the magnetic tensor gradiometry images in real time, and they allow us to detect smaller damage than is possible with vector or scalar imaging. Published by the American Physical Society 2024

Automatic extraction of flat sketch design element from clothing images using artificial intelligence

This study aims to develop a process that automatically extracts various flat sketch elements in vector format from clothing images. The approach of this study is to combine state-of-the-art image processing algorithms with the new algorithm devised in this study. First, a convolutional neural network based edge detection model trained with specially prepared fashion image data set was used to convert clothing images into edge maps. Then, a recurrent neural network based vectorization model and a rule-based image correction algorithm were used to convert the edge map into a vector image. Finally, a graph search algorithm was used to extract closed shapes from the vector image. As a result, the accuracy of edge extraction model has been improved by training with the special fashion image data set. The image correction rule was able to refine the vector images generated by the vectorization model. The graph algorithm was able to extract closed shapes from the vector image. This study is the first study to extract vector style flat sketch elements from clothing images using artificial intelligence and conventional computational geometry.

ТАКСОНОМІЧНИЙ МЕТОД ВИБОРУ СПОСОБУ ВЕКТОРИЗАЦІЇ ПОТОЧНОГО ЗОБРАЖЕННЯ ДЛЯ ОПТИКО-ЕЛЕКТРОННОГО МОДУЛЯ БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТА

In the article, a taxonomic method for selecting the optimal vectorization approach for processing current images in the optical-electronic module of unmanned aerial vehicles (UAVs) was developed. The primary goal of this method is to optimize UAV navigation systems, which is particularly relevant due to the need to improve data processing efficiency in conditions of limited access to satellite navigation systems. This is critically important for ensuring high autonomy and accuracy of UAVs during the performance of various tasks. The need for such a method arose from the specific challenges faced by UAVs when operating in complex and unpredictable conditions. Analysis has shown that the use of vector images can significantly reduce the time required for information processing. This is possible due to the smaller volume of data that needs to be processed compared to traditional raster images. Reducing information processing time is critically important for the operational capability of UAVs to respond to dynamic changes in the operational environment, especially in critical situations. The taxonomic approach applied in the study allows for systematic classification and evaluation of different vectorization methods in terms of their processing speed, accuracy of results, and data retention efficiency. This enables the identification of the most suitable method for the specific operational conditions in which the UAV is used, thereby optimizing the overall system performance. Furthermore, the results of this research are significant not only in the scientific field but also in practical applications. By improving autonomous navigation systems for UAVs, the study directly contributes to enhancing their effectiveness in complex and unpredictable conditions. This is particularly relevant for missions that require a high level of precision and reliability, such as those performed by military UAVs and emergency response. Enhanced navigation capabilities allow these UAVs to perform complex maneuvers and make real-time decisions based on processed vectorized images, thereby reducing the risk of errors and increasing mission success rates. An improved vectorization method could lead to significant enhancements in UAV navigation systems, making them more resistant to failures and capable of operating in less structured environments without heavy reliance on satellite signals. This enhancement is vitally important for both military and civilian UAV applications, where operational flexibility and reliability are extremely important.

Tracking plume-regolith interactions in near-vacuum conditions

An experiment to track and measure the transient phenomenon of plume-liberated regolith in near-vacuum conditions was performed in a dedicated plume-regolith facility housed at the University of Glasgow. This facility with a total volume of around 82 m3 can simulate a soft or hard landing event on “extraterrestrial” sub-atmospheric pressures. Particle image velocimetry method was used to estimate the ejection velocity and ejected angle of regolith particles, and its limitations are discussed. Glass microspheres that are matched with the size of the Lunar and Martian moon “Phobos” surface regoliths are used as simulants. With an exit Mach number of 6.6, a heated convergent–divergent nozzle represents the lander nozzle. Preliminary results capture ejecta development up to 30 ms from plume impingement. Flow visualization reveals the initial moments of plume boundary growth and regolith ejection. The vector images indicate a triangular-shaped sheet of particles sweeping from the regolith bed at a positive inclination with a local maximum velocity close to 100 m/s. The low-density “Phobos” simulant advances at a higher speed, reaches higher elevations, and covers a larger spatial area compared to a higher-density “Lunar” simulant. Observation of the crater formation reveals the difference in cohesive forces between the selected simulants. A higher inclination of particle ejection of more than 50° adjacent to the jet indicates particle entrainment originating from the interior of the crater. Stream traces reveal the deflection of ejected particles upon impingement on the lander surface at close proximity.

Сопоставительный анализ геоконцептов «Пекин» и «Шанхай» в «наивной» географии Китая

This article focuses on the reconstruction and comparative analysis of the geocognitive concepts ‘Beijing’ and ‘Shanghai’ in the ‘naïve’ geography of China. The study is conducted within the framework of geocognitive studies developed by the Perm School of Computer Linguistics. Geocognitive studies, as a science of conceptualizing mental representations of space, enable a better understanding of people's mental maps, their perceptions of the world and adaptation in the surrounding space, which can be valuable in the development of urban projects, tourist routes, marketing strategies, and many other areas where geographic aspects are important. The method of material collection employed in the study is geocognitive mapping – an experiment aimed at creating ‘naïve’ maps conducted using the vector graphics web editor Creative Maps Studio. The research material consists of 96 ‘naïve’ maps of China created by student informants from universities in China representing 45 regions. The informants were asked to draw a map of China using the web editor and supplement the drawn objects with associations or representations related to them. The processing of the textual layer of all collected maps, necessary for subsequent modeling of geocognitive concepts, was carried out in the information system Semograph, and graphical explication of graphosemantic models of the geocognitive concepts was performed using the SciVi program. In the informants’ perceptions, the geocognitive concept ‘Beijing’ is a more complex semantic formation, based on the political role of the capital, linking the country in social, cultural, and historical aspects. The geocognitive concept ‘Shanghai’ encapsulates perceptions of an economic and commercial metropolis – the ‘Magic City’ of the modern global world. During the analysis of the cities, it was found that historical, sociocultural components, and personal experience significantly influence the associations and/or representations of informants.

Comparative Study of Power Quality Event Characterization using Scalable Vector Graphics & Matlab

The research delves into the topic of power quality event extraction and compares two parameters—voltage sag/swell and harmonics—using data supplied by SVG and waveforms created by Matlab. When it comes to power quality analysis, the SVG method shines through in the qualitative comparison of findings.

A Novel Method for Spatial Structural Kinematic Analysis

Kinematic analysis is the first task that needs to be solved for further real-life problems such as machine dynamics and durability, machine balancing, and optimization, chemistry, etc. Currently, there are many methods to analyze the kinematic problem, for instance: vector graphics method, vector-analytic method, or Dennavit-Hartenberg-Craig matrix method. Although each method has its advantages, they have mostly encountered difficulties in establishing equations associated with complicated spatial structures. This research proposed a novel method, entitled “'perpendicular projection method” to solve the problem of spatial structural kinematic analysis. This is an approximate solution method that combines the use of generalized coordinates and direction cosine matrices. With this method, the problem of kinematic analysis of spatial structures will be solved simply and easily by establishing connection equations as well as simulating the motion of spatial structures. This method was then applied in conducting the kinematic analysis of a spatial mechanism with 1 rotating joint (R), 1 ball joint (S), 1 translational joint (P), and one cylindrical joint (C). The calculated results, including the velocity, acceleration of generalized coordinates, angular velocity, angular acceleration of the links, velocity, and acceleration of the center of mass of the links, showed the efficiency of the proposed method.

Correlation between melphalan chemotherapy with longitudinal global strain indices of the left ventricle in multiple myeloma patients: A velocity vector imaging (VVI) echocardiography study.

Correlation between melphalan chemotherapy with longitudinal global strain indices of the left ventricle in multiple myeloma patients: A velocity vector imaging (VVI) echocardiography study.

The software for improvement of image quality after enlargement

In the paper digital images of various formats were investigated. The different vector image formats have different color rendering capabilities. The main task was to achieve a result of refinement of the random low resolution color raster image without quality and resolution loss. The biggest advantage of using specific vector or compressed raster formats is the ability of scaling without quality loss and comparatively small file size. This eases vector images transfer through networks. In the article a specific algorithm of raster images refinement was investigated, particularly the method of raster images refinement based on combination of interpolation algorithms with and without square root of the color values. The key point of the method is comparison and combination of vertical, horizontal and diagonal interpolation that allows to achieve better precision on color depth calculation. This exact method was never used in commercial of scientific software though there are different variation of combined interpolation methods similar to current one. In this paper two different approaches to image matrix re-calculation during image refinement were tested, in order to research how root squaring the value of color depth would affect the target color value. The result shows that this approach allows to keep more details in shadows and save contours during interpolation though the images lose somewhat of color depth. The experiment shows that this interpolation method with square rooting color values allows to enlarge and refine color images with complex tone curve structure and keep details of the objects in place, though color depth is worsened especially in deepest shades and blacks. On the opposite the method of combined interpolation without root squaring gives significantly better result with color interpolation but loses details in the dark areas of the initial image. The suggested method can be used in a number of different areas

Study of the blood flow structure in personalized models of graft branch from the femoral artery

The paper presents the methodology and results of calculating the pulsating flow of blood-mimicking fluid in three personalized models of proximal anastomosis of the femo-ral artery after femoral popliteal bypass surgery performed using synthetic grafts. Per-sonalized models were built based on computed tomography data and ultrasound meas-urements of blood flow velocity in the common femoral artery and in the graft. The char-acteristics of the pulsating flow of blood-mimicking fluid were calculated by numerically solving the three-dimensional unsteady Navier-Stokes equations. Comparison of the calculated velocity field obtained for one of the personalized models with clinical meas-urement data using ultrasonic high-speed vector imaging (applying the V Flow technol-ogy implemented in the Mindray scanner) showed satisfactory agreement of the results. A comparative analysis of the flow structure in two personalized models built for the sec-ond patient one and seven months after surgery is given. It was established that the nar-rowing of the flow sections of the vascular bed, detected after seven months, due to the growth of neointima in the common femoral artery (immediately before the anastomosis) and in the initial section of the graft, led to a significant increase in gradients of the ve-locity field and the time-averaged wall shear stress (TAWSS) in the area of the anastomo-sis; at the same time, the value of the oscillatory shear index (OSI) on the wall throughout the entire anastomosis area decreased. It was also revealed that at a distance of several calibers from the anastomosis, a single-vortex, weakly swirling flow is formed in the graft, the direction of swirl in which is determined by the individual geometry of the anastomo-sis area.

IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers

Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text → raster image → vector graphics) combining text-to-image generation models with image vectorization, or language-based methods (i.e., text → vector graphics script) through pretrained large language models. Nevertheless, these methods suffer from limitations in terms of generation quality, diversity, and flexibility. In this paper, we introduce IconShop, a text-guided vector icon synthesis method using autoregressive transformers. The key to success of our approach is to sequentialize and tokenize SVG paths (and textual descriptions as guidance) into a uniquely decodable token sequence. With that, we are able to exploit the sequence learning power of autoregressive transformers, while enabling both unconditional and text-conditioned icon synthesis. Through standard training to predict the next token on a large-scale vector icon dataset accompanied by textural descriptions, the proposed IconShop consistently exhibits better icon synthesis capability than existing image-based and language-based methods both quantitatively (using the FID and CLIP scores) and qualitatively (through formal subjective user studies). Meanwhile, we observe a dramatic improvement in generation diversity, which is validated by the objective Uniqueness and Novelty measures. More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.

Discontinuity-Aware 2D Neural Fields

Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadratic, or cubic Bézier curves, and construct a feature field that is discontinuous across these locations and smooth everywhere else. Finally, we use a shallow multi-layer perceptron to decode the features into the signal value. To construct the feature field, we develop a new data structure based on a curved triangular mesh, with features stored on the vertices and on a subset of the edges that are marked as discontinuous. We show that our method can be used to compress a 100, 000 2 -pixel rendered image into a 25MB file; can be used as a new diffusion-curve solver by combining with Monte-Carlo-based methods or directly supervised by the diffusion-curve energy; or can be used for compressing 2D physics simulation data.

An Adaptive Fast-Multipole-Accelerated Hybrid Boundary Integral Equation Method for Accurate Diffusion Curves

In theory, diffusion curves promise complex color gradations for infinite-resolution vector graphics. In practice, existing realizations suffer from poor scaling, discretization artifacts, or insufficient support for rich boundary conditions. Previous applications of the boundary element method to diffusion curves have relied on polygonal approximations, which either forfeit the high-order smoothness of Bézier curves, or, when the polygonal approximation is extremely detailed, result in large and costly systems of equations that must be solved. In this paper, we utilize the boundary integral equation method to accurately and efficiently solve the underlying partial differential equation. Given a desired resolution and viewport, we then interpolate this solution and use the boundary element method to render it. We couple this hybrid approach with the fast multipole method on a non-uniform quadtree for efficient computation. Furthermore, we introduce an adaptive strategy to enable truly scalable infinite-resolution diffusion curves.

Biomechanical properties of the ascending aorta in patients with arterial hypertension by velocity vector imaging.

Aortic stiffness is an important risk factor for cardiovascular events and morbidity. Increased aortic stiffness is associated with an increase in cardiac and vascular hypertension-related organ damage.To evaluate the biomechanical properties of the ascending aorta (AA) in patients with arterial hypertension (AH) by velocity vector imaging (VVI).Ninety-five patients with AH and 53 normal healthy control participants were prospectively enrolled. AA biomechanical properties, i.e., ascending aortic global longitudinal strain (ALS), ascending aortic global circumferential strain (ACS), and fractional area change (FAC), were evaluated by VVI. Relative wall thickness (RWT) and left ventricular mass (LVM) were calculated. Pulsed Doppler early transmitral peak flow velocity (E), early diastolic mitral annular velocity (e'), left ventricular global longitudinal strain (GLS), distensibility (D) and stiffness index (SI) of AA were also obtained.The ALS, ACS and FAC were significantly lower in the AH patients, especially in those with ascending aorta dilatation (AAD), than in the normal healthy control subjects. The patients with AAD had a higher E/e' ratio, RWT, LVM and SI and a lower GLS and D than patients without AAD and normal healthy volunteers (p < 0.05). There were significant associations between biomechanical properties and D, SI, E/e' and GLS (ALS and D: r = 0.606, ALS and SI: r = - 0.645, ALS and E/e': r = - 0.489, ALS and GLS: r = 0.466, ACS and D: r = 0.564, ACS and SI: r = - 0.567, ACS and E/e': r = - 0.313, ACS and GLS: r = 0.320, FAC and D: r = 0.649, FAC and SI: r = - 0.601, FAC and E/e': r = - 0.504, FAC and GLS: r = 0.524, respectively, p < 0.05).The biomechanical properties of AA were impaired in patients with AH, especially patients with ascending aorta dilatation. Hypertension is associated with a high prevalence of diastolic and systolic dysfunction and increased arterial stiffness. Further study is needed to evaluate the clinical application of AA biomechanical properties by VVI.

BIGNet: A Deep Learning Architecture for Brand Recognition with Geometry-Based Explainability

Abstract Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics’ curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens’ location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet’s advantage of being a more explainable style-capturing agent.

SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. We first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation, which also enables efficient editing of segmentation results. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. We efficiently accomplish accurate segmentation outcomes by utilizing minimal clicks and straightforward strokes, eliminating the necessity for editing polygon vertices. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results. The code for training the superpixel and graph networks will be made publicly available upon publication.11https://vrlab.org.cn/~hanhu/projects/spgraph/.

Salad Fingers: Pre-YouTube digital uncanny and the ‘weird’ future of animation

In 2004, artist David Firth launched the lo-fi animation Salad Fingers in the user-generated content sharing platform Newgrounds. The series, focused on a strange, unsettling narrative about a character that acts as a child but commits unhinged violent acts, went on to become viral in the earliest days of YouTube. Created with the software Flash (launched by Macromedia and then acquired by Adobe), which generates vector images, Salad Fingers is a significant stylistic and generic contribution to the early period of participatory digital cultures. The series operates as a bridge between analogue and digital artforms that privilege the sensorial over narrative cohesion, while also cultivating a distinctive ‘uncanny-weird’ mode tied to early participatory digital cultures, trends that perdure in contemporary animation. We articulate how Salad Fingers operates in the distinctive ‘digital uncanny’, an aesthetic and cultural mode that would become pervasive in visual media cultures on YouTube and beyond.

Impact of a generalised SVG-based large-scale super-resolution algorithm on the design of light-weight medical image segmentation DNNs

ABSTRACT Setting up a complex CNN requires a powerful platform, several hours of run-time, and a lot of data for training. Here, we propose a generalised lightweight solution that exploits super-resolution and scalable vector graphics and uses a small-scale UNet as the baseline framework to segment different organs in MR and CT data. We selected the UNet since many researchers use it as the baseline, modify it in their proposal, and perform an ablation study to show the effectiveness of the proposed modification. First, we downsample the input 2D CT slices by bicubic interpolation. Using the architecture of the conventional UNet, we reduce the size of the network’s input, and the number of layers and filters to construct a lightweight UNet. The network segments the low-resolution images and prepares the mask of an organ. Then, we upscale the boundary of the output mask by the Scalable Vector Graphics technique to obtain the final border. This design reduces the number of parameters and the run-time by a factor of two. We segmented several tissues to prove the stability of our method to the type of organ. The experiments proved the feasibility of setting up complex deep neural networks with conventional platforms.

Reducing the burden of inconclusive smart-device single-lead ECG tracings via a novel atificial intelligence algorithm

Abstract Introduction Multiple smart-devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by the manufacturers’ algorithms is currently too high to be clinically useful. Reliable artificial intelligence (AI) algorithms may be valuable for managing the large amount of data created by smart-devices. Method This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart-device agnostic AI-based algorithm for detecting AF from four different commercially available smart-devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and four smart-device SL-ECGs. Single-lead ECGs were exported as PDF files and converted into Scalable Vector Graphics (SVG) images for signal extraction. The novel AI-algorithm was compared with each manufacturer’s algorithm. Results We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI-algorithm vs. manufacturer algorithm were: 88% vs. 81% (p=0.34) and 97% vs. 77% (p&amp;lt;0.001) for the AliveCor KardiaMobile, 86% vs. 81% (p=0.45) and 95% vs. 83% (p&amp;lt;0.001) for the Apple Watch 6, 91% vs. 67% (p&amp;lt;0.01) and 94% vs. 82% (p&amp;lt;0.001) for the Fitbit Sense, 86% vs. 82% (p=0.63), and 94% vs. 80% (p&amp;lt;0.001) for the Samsung Galaxy Watch3, respectively. (Figure 1) In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart-devices using the AI-based algorithm compared to manufacturer’s algorithms (14-17%) p&amp;lt;0.001. Conclusion A novel AI-algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers’ algorithms.Central Illustration

Additional effects of combination therapy of antihypertensive medication on aortic elastic properties beyond blood pressure lowering effect in spontaneously hypertensive rats

Abstract Background Combination therapy in the treatment of hypertension provides better therapeutic efficacy, improved tolerability by lowering the dose and reduced potential for side effects than monotherapy. Especially, combinations of calcium channel blocker (CCB), angiotensin receptor blocker (ARB) or diuretics (thiazide) are generally effective and well tolerated regimen in antihypertensive treatment. However, we have focused more on the antihypertensive power of the combination therapy rather than the effect on the vessel itself. Purpose The purpose of this study is to determine the efficacy of antihypertensive medications as ARB and CCB and its combination with or without thiazide on the aortic structural and functional properties beyond the effect of blood pressure (BP) lowering. Methods Fourteen-week-old male spontaneous hypertensive rats were randomly divided into 5 groups, and treated with 1) vehicle (V), 2) amlodipine besylate (A), 3) olmesartan (O), 4) combined amlodipine and olmesartan (AO) or 5) combined amlodipine, olmesartan and thiazide (AOD), respectively. After 8 weeks of treatment, hemodynamic parameters and structural and functional properties of aorta as intima-media thickness (IMT) and velocity vector imaging (VVI) parameters of aorta including fractional area change (FAC), circumferential strain (CS) and strain rate (CSR) were assessed in each group and collagen contents of aorta were quantified as well. Results During the study period, systolic and diastolic BP were serially decreased in all treatment groups. Especially, AOD group showed lowest systolic and diastolic BP levels and also showed smallest IMT and vessel area and highest elasticity (FAC and CS) among the other groups (p&amp;lt;0.01) at 8 weeks (Table 1). In histologic analysis, collagen contents of aorta in all treatment groups showed significant associations with BP, IMT and VVI parameters (FAC, CS, CSR) (p&amp;lt;0.05), respectively. Particularly, aorta of AO and AOD groups showed significantly lower levels of collagen contents than that of control group. (p&amp;lt; 0.05) (Figure 1) Conclusion(s) Beyond the BP lowering effect, combination of amlodipine and olmesartan with or without thiazide minimize structural changes and shows better protection for the progression of arteriosclerosis than individual monotherapy in treatment of hypertension.Table 1Figure 1