Viewing Angle Research Articles

Военный опыт государственных лидеров и конфликтный потенциал авторитарных режимов (На примере Африки)

In the last decade, the International Political Science has witnessed an emergent trend to put an individual back to the focus of research. A growing number of researchers acknowledge that in order to understand the behavior of the state, it is necessary to take into account attitudes and behavior of state leaders. They assume that such a view angle will help to defy a cliché perception, according to which conflict situations arise solely due to the influence of exogenous factors, and if a certain event occurred, it only happened because it was determined by the external environment. The article attempts to analyze the influence of the military experience of state leaders on their countries’ participation in armed conflicts using the case of the authoritarian regimes in Africa. Employing the method of regression analysis, the author traces how the past military experience is reflected in the proneness of a state leader to conflict actions in the foreign policy sphere. He focuses on three types of such experience: (1) military service without participation in military actions (mainly staff work); (2) personal participation in military actions; (3) participation in rebel formations. The conducted research proves that military experience has a significant and robust influence on the subsequent behavior of leaders. If a state leader used to serve in the military as a staff group member, the probability that he will initiate an armed conflict is 2.7 times higher than in the absence of such experience. Participation in rebel groups increases the likelihood of initiating military actions as well. In contrast, combat experience in the military has the opposite effect: leaders with combat experience are only half as likely to initiate military conflicts as leaders who have not been to the battlefield.

La position médiale-latérale de la rotule est-elle un point de repère permettant une correction rotatoire lors de la planification d’une arthroplastie totale du genou varus ?

BackgroundLower limb malrotation can be observed in long leg radiographs, affecting the measurement of the angle between the mechanical and anatomical axes. The purposes were to analyze the effect of limb rotation and to evaluate the accuracy of the corrected angle between the mechanical and anatomical axes based on the patellar ML position. HypothesisThe hypothesis was that the correction of the angle between the mechanical and anatomical axes according to the patellar ML position can reduce the error from the angle in the true AP view in most of the knees. Patients and methodsA total of 100 consecutive knees with varus deformity undergoing primary total knee arthroplasty were included. Computed tomography images were digitally reconstructed in the neutral position and internally and externally rotated at 10° and 20°, respectively. The patellar ML position relative to the medial (0%) and lateral (100%) epicondyles and the angle between the mechanical and anatomical axes of the femur were measured. The corrected angle between the mechanical and anatomical axes was calculated using the averaged translational ratio. ResultsIn the neutral position, the patellar center position was 56.1% (standard deviation [SD]=4.7%), which was 31.4% (SD=7.2%) and 80.2% (SD=5.4%) in the 20° internal and external rotation, respectively. The angle between the mechanical and anatomical axes was 2.6° (SD=2.0°) and 8.1° (SD=2.1°) in the 20° internal and external rotation, respectively. On average, if the patellar center shifted 10%, the change of the angle between the mechanical and anatomical axes of the femur was 1.13°. Applying the corrected angle, a discrepancy from the neutral position decreased. ConclusionThe method to correct the angle between the mechanical and anatomical axes according to the patellar ML position can be used to reduce the measurement error for preoperative planning using a long leg radiograph.Level of evidence III.

Design Of Multi Sensor Quadcopter Navigation Data Acquisition For Early Fire Detection and Treatment

Recently, forest and land fires have become an event that has become a concern for the national and international community. This research creates a quadcopter as a tool for detecting forest and land fires. This system is an alternative tool for detecting forest and land fires that already exist. The advantage of this quadcopter is that it can collect data before the fire, during the fire, and after the fire. This quadcopter is designed and realized the developm­­­­­ent of an automatic navigation system for unmanned aircraft. This system is controlled by a microcontroller that processes IMU (Inertial Measurement Unit) sensor data in which there is a gyroscope and accelerometer, GPS, and magnetometer so that it can fly automatically. The purpose of this study is to acquire data on the navigation sensors used in the unmanned aerial vehicle (UAV) design, then the data from the navigation sensors will be combined using complement and median filters as position control input for the quadcopter. Based on this research, processing from sensor readings to sensor integration is able to produce better data, where the quadcopter's angle of view has an error percentage of 1.10° and 0.68°. This value is smaller than the percentage error of the magnetometer sensor alone or the IMU sensor alone which results in an error percentage of 1.18° and 0.76°.

A multi-view human gait recognition using hybrid whale and gray wolf optimization algorithm with a random forest classifier

Gait recognition has become one of the furthest promising behavioral biometric techniques for identifying individuals. Gait recognition models are capable of identifying humans at a distance based on their walking manner without their permission or interference. However, it is usually noted that the performance of a gait recognition approach will drop drastically in the presence of covariates such as carrying conditions, clothing conditions, and variations in the view angle. Therefore, it is necessary to develop a robust gait recognition system in order to identify the most significant gait features. In this paper, we introduced a recently developed hybrid whale and gray wolf optimization algorithm (WGWOA) for determining the optimal subset of gait features by combining the advantages of whale optimization and gray wolf optimization techniques. Moreover, we employed principal component analysis (PCA) to extract the essential gait features from the gradient gait energy image (GGEI) and random forest (RF) approach to classify the optimal gait features. The proposed method has been assessed on the publicly available largest multi-view CASIA-B and OU-MVLP benchmark datasets. Experimental results indicate that the proposed model achieved an accuracy of 99.25%, 98.39%, and 97.97% under normal, carrying a bag and wearing coat walking conditions, respectively on the CASIA-B dataset. Furthermore, the proposed model achieved an accuracy of 97.63% under normal conditions on the OU-MVLP dataset. The comparative results also confirm that the proposed algorithm is superior to the contemporary approaches.

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

AbstractFor autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial‐temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real‐time and outperforms state‐of‐the‐art approaches.

DeepRetention: A Deep Learning Approach for Intron Retention Detection

As the least understood mode of alternative splicing, Intron Retention (IR) is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease studies. Existing methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained introns. These metrics are not able to describe the pattern of sequencing depth of intronic reads, which is an intuitive and informative characteristic of retained introns. We hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR detection. Here we present DeepRetention, a novel approach for IR detection by modeling the pattern of sequencing depth of introns. Due to the lack of a gold standard dataset of IR, we first compare DeepRetention with two state-of-the-art methods, i.e. iREAD and IRFinder, on simulated RNA-seq datasets with retained introns. The results show that DeepRetention outperforms these two methods. Next, DeepRetention performs well when it is applied to third-generation long-read RNA-seq data, while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing data. Further, we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer's Disease (AD) samples. The differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene network. The parent genes of differential IRs are enriched in AD-related functions. In summary, DeepRetention detects IR from a new angle of view, providing a valuable tool for IR analysis.

P‐90: Evaluating Maxwellian view design with a non‐equal weighting matrix

In this work, we proposed an algorithm to evaluate any reflective polarization volume hologram (R‐PVH) Maxwellian view design based on a non‐equal weighting matrix of the field of view and gaze angle. A continuous eye movement from 0 to 17 degrees towards the ear is considered. The comparison of the eyebox of the example designs is presented after using the weighting matrix of the field of view and gaze angle. This algorithm gives us a method to evaluate any design based on the weighted distance to the center of the pupil.

11‐3: A Novel e‐Privacy LCD Based on the Smart Viewing Angle Controllable Cell with IPS Electrode Structure

In this paper, we proposed a novel e‐Privacy LCD, based on the smart viewing angle controllable (SVC) cell with IPS electrode structure, which can be electrically switched between the wide viewing angle (share mode) and the narrow viewing angle (privacy mode). The path of the collimated light emitted from collimated microstructural LGP BLU is not be changed when the SVC cell is off state, it is privacy mode; the light will be scattered when it pass through the SVC cell that be applied voltage, it is share mode. This phenomenon is very consistent with the simulated results. We also study the influence of the operating voltage or the cell gap on share performance. With the increase of the operating voltage or the cell gap of the SVC cell, the share performance is obviously improved.

A new hybrid algorithm integrating genetic algorithm with Tabu search to solve imbalanced k‐coverage problem in directional sensor networks

AbstractThe target coverage problem is considered as one of the major issues in directional sensor networks (DSNs), which is caused by the nature of these networks, including their limited angle of view. Due to the fault tolerance characteristic of some coverage applications, the target coverage is required to be performed using multiple sensors. This challenge is discussed in the literature under the title of k‐coverage problem. Under certain conditions, the number of sensors may suffer some changes due to various factors such as power depletion of the sensors, sensors' malfunctioning, and harshness of the environment. This can result in unavailability of adequate sensors for providing k‐coverage for all targets. The network suffering from such problem is referred to as under‐provisioned network. This paper was aimed at studying such networks by adopting the network conditions to the real environments. To solve this problem, the present paper proposes a hybrid model integrating the genetic algorithm (GA) and Tabu search (TS). The proposed algorithm generally aimed to identify a subset of sensors with appropriate working directions in order to provide a balanced coverage for all the targets available in the network. In order to evaluate the performance of the algorithm several experiments were conducted and the results have been compared with greedy and learning automat‐abased algorithms. . The results of the experiments show the superiority of the algorithm.

Analysing far-red SIF directional anisotropy of three structurally contrasting forest canopies towards improved GPP estimation

Solar-induced chlorophyll fluorescence (SIF) is used to estimate terrestrial gross primary production (GPP) due to its physiological link to photosynthesis. However, strong angular variations in satellite-observed SIF (SIFobs), especially for forest canopies with a high structural heterogeneity, hampers robust estimation of GPP. Here, we use directional SIFobs datasets from OCO-2 and TROPOMI satellite sensors and the 3D discrete anisotropic radiative transfer model to investigate the directional properties of far-red SIFobs and their relationships with two GPP products obtained from MODIS and FluxCom datasets for three European forest types with contrasting canopy structures, i.e., boreal, temperate and Mediterranean forests. We found bowl-like angular distribution patterns of SIFobs in both observed and simulated boreal and temperate forests, but no generic distribution pattern of SIFobs was found for the Mediterranean forest. In our GPP estimation of boreal forest, oblique SIFobs performed better than the widely used nadir SIFobs. However, we did not find an optimal view angle suitable for all three forest types. Further, the angular dependencies and forest structure impacts present in SIFobs were found to still propagate into the estimation of total emitted SIF (SIFtotal), which generally regarded as free of these confounding effects. Finally, our results demonstrate that two commonly used variables, i.e., SIFobs with a constant viewing angle and the approximated SIFtotal, both fail to produce a robust relationship with GPP for the Mediterranean forest. These findings highlight the importance of forest canopy structural heterogeneity, including forest floor, on interpretation of directional satellite SIF observations, even when corrected and expressed as a total canopy SIF emission.

A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors

Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants’ attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head).

Limited‐angle tomography reconstruction with aU‐Conv‐Swin‐Net

AbstractX‐ray computed tomography (CT) is widely used as a non‐destructive inspection technology. However, due to various limitations such as sample dimensions and blocking of in‐situ instruments, projections can be acquired only in a limited‐angle range during CT acquisition. When such projections are reconstructed with conventional algorithms, the details and contours are deteriorated by artefacts due to lack of information at certain view angles. To address this problem, we propose a reconstruction method based on U‐Conv‐Swin‐Net (UCSN) in this paper. Validated through synthetic and experimental data, the proposed method can effectively remove the degrading artefacts and fully restore the sample details and edges. In addition, the UCSN method exhibits superior reconstruction effect for different scanning ranges compared with conventional reconstruction methods.

TS[formula omitted]HGRNet: A paradigm of two stream best deep learning feature fusion assisted framework for human gait analysis using controlled environment in smart cities

Industrial advancements and large financial stakes are rousing to grasp the aims of smart cities. Their key aim is to increase efficiency and citizens’ quality of life. Human Gait Recognition (HGR) is an important application of smart cities in which a person is recognized with the help of walking styles. The main advantage of HGR is an individual’s inconspicuous distant gait data acquisition. It is being utilized to mitigate security threats at airports, banks, embassies & various corporate vicinities. In this paper, a deep learning method is proposed for HGR. The proposed methodology utilizes a large Multiview gait database CASIA-B comprising video sequences. Initial video processing in frames precedes the implementation of the pre-trained deep model DenseNet-201. Reutilizing the same for gait recognition using Transfer Learning (TL) yields the target model, which further acts as the source for feature extraction from the global average pool layer. Feature extraction as a dominant step is responsible here for accurate gait recognition. Poor lighting, varying view angles, diverse clothing conditions, carrying luggage, etc., are some constraints that adversely affect systems’ efficiency. To tackle this situation, we use two parallel techniques in the process as- improved BAT algorithm (IBAT) and Entropy selection. We use a Canonical Correlation Analysis (CAA) to fuse these carefully selected features into a matrix finally. Final gait recognition is achieved by passing these fused features from the Softmax classifier. The proposed technique reaches 96.13% and 95.2% accuracy by utilizing all angles of the CASIA-B and CASIA-C datasets. The authenticity of this work is cross-examined with the existing methods.

The Horror Writing of Strange Tales from Liaozhai

Strange Tales from Liaozhai is a representative work of the Zhiguai Chuanqi of Chinese classical literature, and the writing of horror stories is one of its most essential parts, which contains adequate meaning, showing its rich details and vivid writing characteristics, which also reflects a certain extent of the author, Pu Songling's unique aesthetic and value orientation. This paper analyzes the setting of the time and space environment of the original text, the use of the third-person limited angle of view in the narrative, and the selection of horror images, respectively, from the scene of the incident, the subject of experience and the object of experience in the horror story. This paper analyzes the original texts of Strange Tales from Liaozhai by summarizing writing techniques of horror stories in work to explore the process of creating the sense of horror in Strange Tales from Liaozhai and how the author uses the original source of human fear to create a horror story. It reveals the alienation and abnormality of time and space, the immersive sense of subjective, limited perspective, and the deadly threat behind horrific imagery. While appreciating the artistic beauty and realizing the diversity of stories types of this work, its implicit humanistic concern is not ignored.

Remote wide angle view broad wavelength viewing system compatible with D-T operations in JET

Imaging diagnostics in Joint European Torus have become essential in the study of plasma wall interactions and the protection of the plasma facing components. During deuterium operations, the location of these diagnostics was at close proximity to the vessel. However, for the 50–50 deuterium-tritium operation, the neutron yield impact onto the electronics of these imaging diagnostics would have caused them to fail completely at these locations. Shielding these systems from neutrons is achieved by relocating a selected number of these imaging systems to outside the Torus Hall (i.e. to the other side of the biological shield wall). The relocation encompasses the construction of two new lines of sight crossing through the biological shield wall and each of them using an optical relay which extends to separate ports in the machine. This article starts with a brief description of two new viewing systems. However, it focuses on the optical modelling and engineering design of one of them which is a multiple camera system producing a wide angle view (WAV) of the inside of the vessel. This WAV system has been used successfully during the recent D-T campaign. It operates in a broadband wavelength range extending from the visible to the middle infrared. It also incorporates a novel in-house horizontal kinematic mirror mount made from glass reinforced plastic as part of the optical relay. The operational stability of the WAV system has also been assessed and some post design improvements implemented.

Software-assisted preoperative planning of S1 Alar Iliac screws: a 3D morphometric and anatomical study.

S1 alar iliac (S1AI) trajectory has gained popularity as a salvage technique for revision surgeries and failed constructs in the lumbopelvic region. This study aims to investigate the morphometry of this new trajectory based on 3D models. The possible role of gender, ethnicity and view angle (surgeon's vs. radiologist's) was investigated. Computed tomography-driven virtual 3D models of spinopelvic region were created applying Materialize MIMICS software, and assessed for coronal and sagittal radiographic versus surgeon's view angles, and morphometry of the screw trajectory. Independent-samples t test was used to analyze the results. P value was set at < = 0.05. The Statistical Package for the Social Sciences Software (SPSS version 24.0) was used for the statistical analysis. A total of 164 3D models were simulated with a total 328 screws inserted satisfactorily within the S1AI trajectory. S1AI instrumentation was feasible in 96.48%. The mean radiological coronal angle was 50.619' ± 8.590' and the mean coronal angle for surgeons' perspective was 10.263' ± 5.860'. The mean radiological and surgeon's perspective sagittal angles were found to be 44.532' ± 6.424' and 31.164' ± 5.455', respectively. A statistically significant difference was found between anatomical and surgeon's perspective trajectories. Neither the pelvic laterality nor the gender influence the screw angles, length and diameter in radiological versus surgeon's view angles. Preoperative 3D modeling would be an invaluable adjunct to increase the accuracy of S1AI screw placement. Surgeon's perspective of the trajectory differs from standard CT sections and should be considered in preoperative planning.

Implementation of an Optical Measurement Method for Monitoring Mechanical Behaviour

There is a gap between mechanical measurement methods under laboratory conditions and those under real conditions of structural monitoring. This paper proposes a method that applies well-established computer vision developments to photomechanics in difficult conditions. It is therefore a technique that is flexible and versatile while maintaining satisfying accuracy. It consists in marker tracking using ArUco fiducial markers as measurement points. It allows locating markers in non-optimal conditions of camera orientation and position. A homography process was used to analyse pictures taken with a view angle. The accuracy of the method was estimated, especially in case of out-of-plane motions, and the impact of the view angle and of the distance between camera and markers on the location error was studied. The method was applied in creep tests to measure crack parameters as well as the transverse expansion of wooden beams. In the application example presented, it enabled to compute distances between markers with only 0.28% of relative error and hence to measure the crack parameters and the long-term shrinkage-swelling of the wooden beams. However, the impacts of brightness variations and camera parameters have not been estimated. This method is very promising when experimental conditions are variable and when multiple measurements are necessary.

Simulating satellite urban land surface temperatures: sensitivity to sensor view angle and assumed landscape complexity

Urban land surface temperature (LST) from satellite earth observation (EO) varies with sensor view angle. Where these variations are not accounted for, urban LST products are inconsistent through time, limiting their use in urban weather and climate model evaluations and process studies (e.g. urban heat island, building energy balance, human thermal comfort). Obstacle-resolving numerical models (ORM) of urban form and radiation exchanges are being used to: (a) understand relations between EO view angle, the 3-dimensional urban surface, and the surface temperatures from urban land surface models, and (b) evaluate parameterisations (parametric models) that aim to account for LST angular effects for the next operational satellite products. Most ORM are limited to simplified buildings (e.g. cuboids) and surface temperatures by lack of datasets. Novelly, we use both a realistic urban form model and observed surface temperatures to assess the impact of simplifying the urban form and temperature on the modelled LST anisotropy. We test various sets of assumptions in central London by combining ground-based thermal camera observations and the state-of-the-art Discrete Anisotropic Radiative Transfer (DART) model. The high resolution realistic model (< 1 m) includes thermal (surface temperatures varying by sun-surface geometry, shadow history and materials every 30 min) and geometry (sloped roofs, chimneys and vegetation) data. These data are used to simulate brightness temperatures of EO pixels to quantify LST view angle variations.During daytime, a change of view angle of 47° off-nadir corresponds to a difference in LST of up to 5.1 K for the realistic building model. The intermediate-complexity landscape (easily obtainable building geometry/footprints and more idealised surface temperature distributions driven only by shadow patterns) gives the best agreement in simulated LST to the realistic landscape. The directional variations are still captured in total (daytime mean absolute error 0.44 K) when using an idealised ORM representation of the same landscape (cuboid buildings, simplified surface temperature) except for roofs which are near-isotropic. Results suggest that geometry assumptions used in current ORM are suited for evaluation of parametric models used to develop and verify operational LST sensor view angle corrections. Future work should consider more realistic materials and scattering processes including low emissivity glass and metals with challenging specular properties.

Generalization of the shortest path approach for superpixel segmentation of omnidirectional images

With the growing use of image capture devices using wide angles and the need for fast and accurate image analysis in computer vision, there is a demand for dedicated under-representation methods. Most decomposition methods segment an image into a small number of irregular homogeneous regions, called superpixels. Nevertheless, these approaches are generally designed to process natural 2D planar images, i.e., captured with a 90o angle view without distortion. In this work, we present SphSPS, a new general decomposition method (for Spherical Shortest Path-based Superpixels)11Available code at: https://github.com/rgiraud/sphsps, that is dedicated to wide 360o omnidirectional or spherical images. The produced superpixels respect both the geometry of the 3D spherical acquisition space, and the boundaries of image objects. To fastly extract relevant clustering features, we generalize the shortest path approach between a pixel and a superpixel center. We demonstrate that considering the geometry of the acquisition space to compute the shortest path enables to jointly improve the segmentation accuracy and the shape regularity of superpixels. To evaluate this regularity property, we propose a generalization of a standard 2D regularity metric to the spherical space, addressing the limitations of the only existing spherical compactness measure. Finally, SphSPS is validated on reference 360o images from the PSD (Panorama Segmentation Dataset) and also on synthetic road omnidirectional images. Our method significantly outperforms both planar and spherical state-of-the-art approaches in terms of segmentation accuracy, robustness to noise and regularity, providing a very interesting tool for superpixel-based applications on 360o images.

Multi‐Task and Multi‐Scale Intelligent Electromagnetic Sensing with Distributed Multi‐Frequency Reprogrammable Metasurfaces

AbstractReprogrammable metasurface (RM) has been demonstrated as a key enabling technology for resolving the difficulties arising in conventional electromagnetic (EM) sensing strategies, and the RM‐enabled intelligent EM sensing has emerged as a new paradigm with many advanced functionalities that are thought to be impossible. However, the RM‐enabled sensing strategies developed so far are usually limited to the single‐scale sensing task and the cooperative target, since they are designed to operate at a single frequency band and have the relatively small view angle. To address the formidable challenges, here the concept of multi‐task multi‐scale intelligent sensing is proposed with algorithm‐driven distributed multi‐frequency RMs. For principle illustration, a proof‐of‐principle system working at S‐, C‐, and X‐bands is designed, and it is experimentally demonstrated that the system is capable of monitoring continuously the action behavior and healthy status of the target acting freely in real‐world indoor settings. It is faithfully expected that the presented strategy could open a new avenue in the areas of internet‐of‐things (IoTs), digital twins, and so on.