Viewing Angle Research Articles

CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants.

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper proposes a novel visual marker called CylinderTag, which is designed for developable curved surfaces such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly attached to objects with a cylindrical shape. Leveraging the manifold assumption, the cross-ratio in projective invariance is utilized for encoding in the direction of zero curvature on the surface. Additionally, to facilitate the usage of CylinderTag, we propose a heuristic search-based marker generator and a high-performance recognizer as well. Moreover, an all-encompassing evaluation of CylinderTag properties is conducted by means of extensive experimentation, covering detection rate, detection speed, dictionary size, localization jitter, and pose estimation accuracy. CylinderTag showcases superior detection performance from varying view angles in comparison to traditional visual markers, accompanied by higher localization accuracy. Furthermore, CylinderTag boasts real-time detection capability and an extensive marker dictionary, offering enhanced versatility and practicality in a wide range of applications. Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects. The code is available at: https://github.com/wsakobe/CylinderTag.

HDDet: A More Common Heading Direction Detector for Remote Sensing and Arbitrary Viewing Angle Images

HDDet: A More Common Heading Direction Detector for Remote Sensing and Arbitrary Viewing Angle Images

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization.

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored. A copy of this paper, all code, all models used in our experiments, and all supplemental materials and videos are available at https://github.com/skywolf829/APMGSRN.

ANALISIS CFD PADA SAYAP MINI GLIDER DENGAN PENAMPANG LOW REYNOLD NUMBER AIRFOIL DAN FOLDED FLAT PLATE AIRFOIL

The purpose of this research is to make a very small unmanned aircraft as a teaching aid in aerospace engineering workshops, because of its very small size, this type of unmanned aircraft is abbreviated as Micro Air Vehicle, this MAV is only operated at low Reynolds numbers. MAV development research for smaller sizes continues to be carried out such as the Micro Glider. In this study, the author uses the Computational Fluid Dynamic method and the software used ANSYS Fluent and compares the existing DATCOM data, the author simulates the 4 types of airfoils into a wing object. The results of this study can be seen that based on the average deviation diff value that has been obtained in a population to determine the error from the comparison of the reference airfoil lift coefficient and its FFP by comparing the two methods, namely the IISHI airfoil reference geometry is 6.22% while the FFP is is 11.25% and the SD 7037 airfoil reference geometry is 3.71% while the FFP is 2.11%. Then for the comparison of the maximum lift to drag ratio values, it is obtained that the airfoil reference geometry (SD 7037) has a value of = 9.96 while the airfoil reference geometry (IISHI) has a value of = 8.81. In the Folded Flat Plate airfoil geometry (SD 7037) it has a value of = 7.61 while the Folded Flat Plate airfoil (IISHI) has a value of = 7.37. Then for the visualization of fluid flow can be obtained, namely the results of visualization of fluid flow in several parts of the angle of view / plane of the wing geometry when the AoA is high, namely the root plane to the half wing plane. early, while the geometry of SD 7037 (airfoil reference) is not easy to occur early turbulence flow. Second, that in general the fluid flow pattern seen in the FFP airfoil geometry (FFP IISHI) has the most dominant turbulence flow pattern (very much/large) while the FFP airfoil geometry (FFP SD 7037) has very little/small turbulence flow pattern..

ON THE SEARCH OF NEW AESTHETICS IN CONTEMPORARY UKRAINIAN LITERATURE (BASED ON THE NOVELS OF MAKSYM DUPESHKO)

Modern Ukrainian literature reacts to the collapse of the postmodern project and the emergence of a new cultural logic - metamodernism. This article is an attempt to prove it on the example of an analysis of the work of a modern Ukrainian writer. Maksym Dupeshko’s novels «The Story Worth an Entire Apple Orchard» [Istoriia varta tsiloho yablunevoho sadu] (2017) and «Near the Carriage under a Dead Fox» [Bilia karety pid mertvym lysom] (2021) served as the artistic material for the study. A scrupulous analysis of the novels testifies to the writer’s sensitivity to tectonic shifts in modern aesthetics. Both novels work with the phenomenon of the influence of historical memory on the present. And the strategies used by the author fully illustrate the theses of metamodernism theorists: oscillation between pre-modern, modern, and post-modern practices; fragmented «I» and attempts to integrate it into newmeaningful configurations; finding the multiplicity of human subjectivity; transition from parataxis to metaxy; a movement from melancholy to hope. Maksym Dupeshko’s texts testify listening to a new sensuality. The writer is not afraid to search for the truth, to be simple (both at the level of language and at the level of transmitted ideas), he consciously uses banality, he is deliberately pathetic, oscillates from irony to seriousness, from naivety to sincerity, always, however, without approaching cynicism. «The Story Worth an Entire Apple Orchard» is a novel-reflex on the modern Russian-Ukrainian war. The credit of the novel is that in its area there is a very important for our literature a change of modes in the artistic interpretation of reality — a transition from the obvious to the significant, after that we will no longer be ashamed to be heroes, to be from the provinces, to seek God and places of strength, we willstop confusing naivety and sincerity. «Near the Carriage under the Dead Fox» is a novel-anamorphosis - a construction that seems absurd to us if we look at it directly, but when we change the angle of view, it acquires clear and recognizable outlines. This novel engages the reader in the quest, it demonstrates a way to look into our modern existence - an endless chaos of things, events, ideas, aesthetics, fragments of sleeps, fragments of destinies, parts of dreams, hallucinations and fantasies.

Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations

We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coefficient discovery in order to overcome deficiency of the discrete characterization of the PDE loss in neural networks and improve accuracy of function feature description, which offers a new angle of view for gPINNs. The TL-gPINN algorithm is applied to infer the unknown variable coefficients of various forms (the polynomial, trigonometric function, hyperbolic function and fractional polynomial) and multiple variable coefficients simultaneously with abundant soliton solutions for the well-known variable coefficient nonlinear Schrödinger equation. Compared with the PINN and gPINN, TL-gPINN yields considerable improvement in accuracy. Moreover, our method leverages the advantage of the transfer learning technique, which can help to mitigate the problem of inefficiency caused by extra loss terms of the gradient. Numerical results fully demonstrate the effectiveness of the TL-gPINN method in significant accuracy enhancement, and it also outperforms gPINN in efficiency even when the training data was corrupted with different levels of noise or hyper-parameters of neural networks are arbitrarily changed.

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework.

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor's width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot's velocities, the robot's orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model's good performance and applicability in the real world.

Optical Phased Array-Based Laser Beam Array Subdivide Pixel Method for Improving Three-Dimensional Imaging Resolution

The small number of pixels in the current linear mode avalanche photodiode (LM-APD) array limits its three-dimensional (3D) imaging resolution. We use an optical phased array-based beam array subdivided pixel method to improve the 3D imaging resolution, using an optical phased array to generate a beam array with the same number of pixels as the LM-APD array and matching positions and controlling each sub-beam in the beam array to scan in the field of view of the corresponding pixel. The sub-beam divergence angle in the beam array is smaller than the instantaneous field of view angle of a single pixel in the LM-APD array. The sub-beam scanning in a single pixel’s field of view realizes the multiple acquisition of the target 3D information by the LM-APD array, thus improving the resolution of the LM-APD array. The distribution of the beam array in the far field is simulated, and the main performance parameters of 3D imaging are analyzed. Finally, a liquid crystal phase modulator is used as an optical phased array device to conduct experiments on a target 20 m away, and the results prove that our method can improve the resolution from 4 × 4 to 8 × 8, which can be improved at least four times.

CFD and radiation field modeling of the NETmix milli-photocatalytic reactor: Evaluation of illumination systems composed of LEDs with distinct view angles

The coupling of ray tracing and computational fluid dynamics simulations was explored for the NETmix milli-photoreactor. Three LEDs plates composed of 15 inline LEDs were simulated and the LEDs-to-reactor distance varied. The radiation field obtained through ray tracing was incorporated into CFD simulations to elucidate the impact of LEDs light distribution patterns on the reactor performance. The employed LEDs emitted the same amount of power with the same light spectrum in all cases. The evaluated LEDs present a 48∘, 63∘ and 120∘ view angle. The impact of the distinct radiation fields on the photocatalytic oxidation of the n-decane over a thin TiO2 catalyst film was elucidated. Power absorption efficiency, homogeneity index, global illumination efficiency and the pollutant conversion were evaluated for each of the proposed illumination systems. The registered irradiance values varied from 154 W m−2 to 331 W m−2 with homogeneity indexes between 28.4% and 73.1%. Simulations have shown the dependency of the photocatalytic process on the homogeneity of the radiation field and how LED source selection is an important step in the modeling of photoreactors. The characteristics of the LEDs lamps had a significant impact on the pollutant degradation results.

U-Net for temperature estimation from simulated infrared images in tokamaks

Surface temperature measurement is critical for the safety and proper operation of nuclear fusion reactors such as tokamaks, which operate at high temperature [100–3600 °C] in the presence of complex and unknown surface reflectance properties, causing a reflected flux that disturbs measurement interpretations. This paper describes a numerical approach based on machine learning techniques to estimate the surface temperature of in-vessel components from infrared images, despite the presence of reflections and the unknown emissivity of materials. Our contributions are two-fold: for the first time in the infrared domain, we generate a huge dataset of simulated images thanks to a novel GPU implementation of a Monte Carlo infrared ray tracer. This implementation offers the great advantage of enabling the generation of a very large amount of synthetic images for different plasma scenarios. Hence the second contribution of this paper: we propose an infrared inverse lighting model, based on a Convolutional Neural Network (CNN) trained with the images generated by our ray tracer. The proposed approach is applied on a numerical prototype of W Environment Steady state Tokamak (WEST), including complex simulated thermal scenes. Using synthetic test data, and without providing the surface optical properties, the mean temperature error predicted by the CNN on ITER-like wide angle view of WEST is evaluated to 9% with standard plasma scenarios and 22% in case of exotic thermal scenes.

Comparative analysis of two parameter-dependent split window algorithms for the land surface temperature retrieval using MODIS TIR observations

MODIS Land Surface Temperature (LST) product is extensively used in agricultural studies like crop health assessment, soil moisture estimation, irrigation management, land use land cover change, air-temperature retrieval and crop water stress detection. Numerous studies have used Split Window (SW) algorithms to retrieve LST from MODIS TIR bands. Among them, some utilize Sensor View Angle Dependent (SVAD) or Columnar Water Vapor Dependent (CWVD) SW algorithms. Present study aims to make use of SVAD and CWVD SW algorithms and compare them to evaluate the LST retrieval accuracy over various land surface type. Theoretical accuracy assessment of the CWVD and SVAD algorithms demonstrates a good accuracy with the RMSE of 1.09K and 1.42K, respectively. The experimental retrieval of LST achieves exceptionally good accuracy, with a RMSE of 1.45K in the CWVD algorithm and 1.80K in the SVAD algorithm, particularly in heterogeneous regions. In homogeneous regions, the RMSE values are 1.14K in CWVD and 1.10K in SVAD. Both algorithms exhibit satisfactory accuracy; nevertheless, the application of these algorithms may vary in agricultural contexts. Based on the obtained results and the inclusion of required parameters, we have arrived at a conclusion regarding the superior performance of the SVAD compared to the CWVD for LST retrieval.

Machine learning framework for precise localization of bleached corals using bag-of-hybrid visual feature classification

Corals are sessile invertebrates living underwater in colorful structures known as reefs. Unfortunately, coral’s temperature sensitivity is causing color bleaching, which hosts organisms that are crucial and consequently affect marine pharmacognosy. To address this problem, many researchers are developing cures and treatment procedures to restore bleached corals. However, before the cure, the researchers need to precisely localize the bleached corals in the Great Barrier Reef. The researchers have developed various visual classification frameworks to localize bleached corals. However, the performance of those techniques degrades with variations in illumination, orientation, scale, and view angle. In this paper, we develop highly noise-robust and invariant robust localization using bag-of-hybrid visual features (RL-BoHVF) for bleached corals by employing the AlexNet DNN and ColorTexture handcrafted by raw features. It is observed that the overall dimension is reduced by using the bag-of-feature method while achieving a classification accuracy of 96.20% on the balanced dataset collected from the Great Barrier Reef of Australia. Furthermore, the localization performance of the proposed model was evaluated on 342 images, which include both train and test segments. The model achieved superior performance compared to other standalone and hybrid DNN and handcrafted models reported in the literature.

Preprocedural simulation of a mitral transcatheter edge-to-edge repair using computed tomography imaging

Abstract Mitral transcatheter edge-to-edge repairs (TEERs) are a widespread treatment option for heart failure complicated with mitral regurgitation. Although we generally use trans-oesophageal echocardiography (TOE) to understand structural heart disease conditions, TOE does not provide a complete view of the heart and surrounding structures. As the TEER device moves in complex ways during the procedure, it is difficult to imagine the actual behavior of the device only from the images obtained from TOE because of its restricted angle of view. Contrast-enhanced CT, on the other hand, is a modality that can capture the image of the heart as a whole, as if looking at a bird’s eye view. The purpose of our study was to create a TEER device simulator using ECG-gated computed tomography imaging. As the first step, we created a virtual inferior vena cava and fossa ovalis model. We obtained 192 possible CT image patterns of the TEER device with this model, and they all were extracted into an STL file format. As the second step, the ECG-gated cardiac CT images of the patients were segmented into each cardiac structure and also extracted into STL files. Finally, both STL file images were fused by some 3D software, and we made a simulation of the TEER procedure preoperatively. By piling up each knob operation and rotation, we could reproduce the serial movement of the TEER device. We could successfully reproduce the TEER device with each knob operation and rotation on a patient-specific cardiac CT image. The positional relationship between the heart and device were visualized, showing the entire trajectory of the device. It could also allow preoperative simulations to be tailored to each patient. Compared to some previous reports about TEER simulations using 3D printing models, our method could save time and cost 1)2). Another report demonstrated a simulator using a cardiac CT image, but that report did not mention how to obtain the device image 3). Our method would be more precise because our simulator was based on real TEER device images. We will apply this method for upcoming real cases and will then confirm whether a preoperative simulation of the ideal manipulation would enable optimal clipping. We successfully created a prototype of a TEER simulator using ECG-gated computed tomography imaging.en face view of the mitral valve (TOE)en face view (simulator)

Representations of Reification in Pre-Islamic Poetry: Selected Samples

This study deals with the concept of reification, its motifs, aspects and the mechanisms for resisting it, to reveal the pre-Islamic poetry in confrontation with the state of reification that was imposed on these poets, and how they expressed it artistically and aesthetically, taking into account that psychological, social or economic perspective, as well as the angle of view that surrounded their poetic expression.

Hierarchical Assembly of Patternable Chiroptical Biotextiles with Extreme Environment Stability.

Flexible photonic textiles constructed by sustainable cholesteric organization are very promising to achieve a combination of chiroptical structural colors, mechanical robustness, sustainability, and environment stability. However, the efficient assembly of well-ordered cholesteric nanoarchitectures on flexible textiles in a scalable and patternable manner remains a grand challenge. In this study, we develop an efficient and scalable approach to construct large area chiroptical biotextiles using renewable and bioenabled cellulose nanocrystals (CNCs) as building blocks. This hierarchical assembly enables cholesteric photonic CNCs "cast" in situ, in a seamlessly tessellated design, onto topography-tailored textiles to form a strong interlocked multilayered structure. The resulting hierarchical architecture not only comprises strong photonic-photonic coupling to synergistically enhance the chiroptical properties with tunable wavelengths but also leads to impressive mechanical and optical stability against external mechanical forces and extreme environments. More importantly, through regulating the localized photonic band of the preformed chiroptical textiles by small molecules (e.g., water and glucose), customized colored patterns can be easily generated in large scale that are highly responsive to multistimuli, including chiral polarized light, view angle, and solvent. This chiroptical biotextile is a promising next-generation biomimetic photonic material for defense, aviation, and marine and aerospace special applications.

Dense-and-Similar Object detection in aerial images

The general object detection performance has been improving significantly due to the prosperity of deep learning. When applied to aerial images, these algorithms perform poorly. There are, as we summarized, two practical reasons: (1) photographed from far above, objects are commonly small in size, dense in clusters, but sparsely scattered in the whole image; (2) some classes are naturally similar in appearance, not to mention the insufficiency of distinguishable visual features captured by cameras due to factors such as bad weather, various angles of view and long distances, etc. To address the two issues, we propose a dense-and-similar object detector with four key components: a coarse detector, an adaptive clustering procedure, a similar-class classifier, and a fine detector. At first, we input the original image into both the coarse and fine detectors. Then we cluster and patch the coarsely detected results adaptively to form a foreground region image. We feed the patched image into the coarse detector again and use the similar-class classifier to re-identify the labels of detected bounding boxes. At last, we put re-identified detections and outputs of the fine detector together, and obtain the final detections after non-maximal suppression. The approach proposed in this study achieved excellent results on both the VisDrone 2019 and DIOR datasets through experimental validation.

In-plane ESPI with unlimited angle of view for multi-object rotation angle determination

The in-plane electronic speckle pattern interferometry (ESPI), implemented in a Michelson stellar interferometer-like configuration, offers high sensitivity and dynamic measurement. However, its limited angle of view (AOV) remains a major challenge for the rotation angle determination of multiple objects. In this Letter, we analyze the main factors that influence the AOV of the in-plane ESPI and propose an “image transmitting” approach to enlarge the AOV. With the aid of a folded dual-telescope imaging system, we develop an AOV-unlimited interferometer that can determine multi-object rotation angles in real time. The practicability of the interferometer is demonstrated by the application in real-time measuring of the rotation angles of the disks within a 2D granular system.

Correlated random walk and noise suppression in detecting electric fields

Detecting a weak electric field in a strong noisy background is always an interesting but hard task. We investigate the collective effects of charged Brownian particles in the presence of a weak electric field together with a strong noisy background field by numerical simulation. We show that the pattern formed by snapshots touched by the Brownian particles on one boundary surface can manifest the direction of an applied weak electric field but will be spoiled if there exists a strong noisy background. Moreover, we find that the influence of noise can be suppressed effectively if the correlations caused by the inter-particle electric interaction are taken into account. This work is expected to provide a new angle of view: correlated random walk plays a constructive role in noise suppression.

Improved Surface Temperature Identification Method for Remote Observations of Sample Return Capsules

A new method for the identification of the peak temperature of the heat shield based on remote spectral irradiance measurements is proposed and tested through the analysis of results from the Hayabusa sample return capsule. To identify the peak temperature of the heat shield, a parameterized empirical model for the surface temperature distribution on the geometry is developed, and it is then used to simulate spectra for optimized fitting with a measured spectral radiance. This new peak temperature identification method is shown to be insensitive to the wavelengths resolved, the view of the geometry, and measurement noise. The peak temperature of the Hayabusa capsule geometry at the representative condition considered (3330 K) was resolved to within 20 K for the three instruments investigated, while the effective temperature varied by around 600 K depending on which instrument was used. The error in the peak temperature model is also shown to be an order of magnitude less sensitive to measurement noise than the effective temperature approach. The new peak temperature method facilitates the direct comparison of results from instruments with different bandwidths and/or different view angles of the capsule, which was previously not possible.

Increased ankle pain after total knee arthroplasty is associated with a preoperative lateralized gait and talar tilt, but not with ankle laxity or the range of motion of the subtalar joint.

Total knee arthroplasty (TKA) may provoke ankle symptoms. The aim of this study was to validate the impact of the preoperative mechanical tibiofemoral angle (mTFA), the talar tilt (TT) on ankle symptoms after TKA, and assess changes in the range of motion (ROM) of the subtalar joint, foot posture, and ankle laxity. Patients who underwent TKA from September 2020 to September 2021 were prospectively included. Inclusion criteria were primary end-stage osteoarthritis (Kellgren-Lawrence stage IV) of the knee. Exclusion criteria were missed follow-up visit, post-traumatic pathologies of the foot, and neurological disorders. Radiological angles measured included the mTFA, hindfoot alignment view angle, and TT. The Foot Function Index (FFI) score was assessed. Gait analyses were conducted to measure mediolateral changes of the gait line and ankle laxity was tested using an ankle arthrometer. All parameters were acquired one week pre- and three months postoperatively. A total of 69 patients (varus n = 45; valgus n = 24) underwent TKA and completed the postoperative follow-up visit. Of these, 16 patients (23.2%) reported the onset or progression of ankle symptoms. Varus patients with increased ankle symptoms after TKA had a significantly higher pre- and postoperative TT. Valgus patients with ankle symptoms after TKA showed a pathologically lateralized gait line which could not be corrected through TKA. Patients who reported increased ankle pain neither had a decreased ROM of the subtalar joint nor increased ankle laxity following TKA. The preoperative mTFA did not correlate with the postoperative FFI (r = 0.037; p = 0.759). Approximately one-quarter of the patients developed ankle pain after TKA. If patients complain about ankle symptoms after TKA, standing radiographs of the ankle and a gait analysis could help in detecting a malaligned TT or a pathological gait.