Dimensional Scene Research Articles

An energy consumption optimization strategy for Wireless sensor networks via multi-objective algorithm

Deploying relay nodes is a significant mechanism to prolong the network lifetime of wireless sensor networks (WSNs). However, most existing studies overlook the energy consumption of relay nodes, leading to imperfections in the optimization process. Additionally, there is also a lack of analysis of conflicts between different optimization objectives. In this regard, a multi-objective antlion with fuzzy clustering algorithm (MOALO-FCM) is designed to obtain a better trade-off between different optimization objectives. And an adaptive membership function revision strategy is introduced to improve the energy balance of relay nodes. To verify the performance of the proposed algorithm, simulation experiments are set in 2-dimensional and 3-dimensional scenes with correlative algorithms, respectively. The main evaluation indexes include performance of Pareto optimal solution sets, the life cycle of network, the energy consumption of sensor nodes, the energy consumption of relay nodes, the number of living nodes, and the running time of algorithms. The results indicate that the proposed algorithm has better performance in various indexes.

Effects and safety of virtual reality-based mindfulness in patients with psychosis: a randomized controlled pilot study

Virtual reality (VR) technology can be a supporting tool to enhance mindfulness. Recently, many research using VR-based mindfulness (VBM) has been carried out in various psychiatric disorders but not in psychosis. We investigated safety and effects of virtual reality-based mindfulness (VBM) in patients with psychosis as a pilot study. Sixty-four patients were randomly assigned to VBM or to VR control. For VBM, education and meditation videos were provided. For VR control, 3-dimensional natural scenes were shown. Both programs consisted of 8 weekly sessions, each lasting about 30 min. Pre- and post-assessments were performed using the experiences questionnaire (EQ), psychotic symptom rating scales-delusion (PSYRATS-D), PSYRATS-auditory hallucinations (AH), motivation and pleasure scale-self rating (MAP-SR) and etc. The safety questionnaire was also surveyed after 1st and 8th session. Physiological measures such as skin conductance level (SCL), heart rate (HR) and RR interval, were collected during the VR interventions. Limited individuals participated in the safety questionnaire and physiological measures. All the results were presented in mean and standard deviation. We did not observe significant results in group x time interaction and main effects of group and time in the decentering and clinical scales. However, within group comparison showed that patients randomized to VBM showed increased decentering (p = 0.029) and decreased amount (p = 0.032) and duration of preoccupation (p = 0.016) in the PSYRATS-D. For the feelings and motivations about close caring relationships of the MAP-SR, we observed a significant group x time interaction (p = 0.027). The frequency of VR sickness was high but its severity was mild. There were significant differences only in HR over time in the VBM group (p = 0.01). These results suggest that VBM was not more effective in reducing decentering and psychiatric symptoms than VR control but its adversity was modest.

Recurrent Neural Networks for Snapshot Compressive Imaging.

Conventional high-speed and spectral imaging systems are expensive and they usually consume a significant amount of memory and bandwidth to save and transmit the high-dimensional data. By contrast, snapshot compressive imaging (SCI), where multiple sequential frames are coded by different masks and then summed to a single measurement, is a promising idea to use a 2-dimensional camera to capture 3-dimensional scenes. In this paper, we consider the reconstruction problem in SCI, i.e., recovering a series of scenes from a compressed measurement. Specifically, the measurement and modulation masks are fed into our proposed network, dubbed BIdirectional Recurrent Neural networks with Adversarial Training (BIRNAT) to reconstruct the desired frames. BIRNAT employs a deep convolutional neural network with residual blocks and self-attention to reconstruct the first frame, based on which a bidirectional recurrent neural network is utilized to sequentially reconstruct the following frames. Moreover, we build an extended BIRNAT-color algorithm for color videos aiming at joint reconstruction and demosaicing. Extensive results on both video and spectral, simulation and real data from three SCI cameras demonstrate the superior performance of BIRNAT.

Modeling of Residential Environment Artistic Design Based on Multisensor Data Fusion

The development of a 3D city model is accompanied by the continuous progress of technical knowledge, the continuous increase of public interest in it, and the rapid progress of national knowledge. The use of a 3D city model has become the common concern of academia, enterprises, and most users. The article takes the community as an example to summarize the research overview and basic knowledge of the 3D model. From the aspects of 3D city location data collection technology, 3D city model technology, 3D exhibition, etc., the advanced technology in 3D city model, 3DGIS software system, and other issues are discussed and studied, and the current state-of-the-art technology development level is summarized and analyzed. The article first introduces the purpose and importance of the 3D town model and further analyzes the development context, research background, and research content at home and abroad. Secondly, the theoretical basis of the 3D data model, study location data characteristics, location data model configuration, location data survey structure, and location data model construction requirements were put forward. Then, 3DMax software was used to build a house model and finally find it in OpenGL software. The effective distance of the detector is 0.4~3.5 m, and the maximum resolution is 640 × 480 . It also introduces the realization of 3D architectural model storage and the visual expression of 3D city modeling. The article uses the Kinect depth sensor to recreate a simple static dimensional scene.

Unveiling functions of the visual cortex using task-specific deep neural networks.

The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.

Çocuk Cerrahisi için Ayna Dünya Simülasyonu

Objective: To create contents that allow students to be experienced in 3D with 360-degree video and 3-dimensional scenes in a real environment for student/assistant training and distance online medicine education. Method: The 360 degree video recordings were made with Samsung Gear 360 in three different areas: Classroom, Operating Room and Case Meeting in the medical school. A 3D scene was designed with Autodesk 3dsMax. 360-degree videos and 3D scene were combined in Unity program. The Augmented Reality (AR) application was developed within the Unity and the mobile application was tested by different users. AR experience is now available with the mobile application. Results: As part of this study, three 360-degree videos were recorded and the augmented reality mobile application was developed. Conclusion: In this study, the pilot study was completed by producing content with 360-degree videos and augmented reality technology for use in medical education. We plan to investigate the eficacy of these mirror world contents to medical education with further studies.

Deep Learning for Camera Autofocus

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or ultrasound, to directly measure focus state. However, such sensors increase cost and complexity without directly optimizing final image quality. This paper proposes a new pipeline for image-based autofocus and shows that neural image analysis finds focus 5-10x faster than traditional contrast enhancement. We achieve this by learning the direct mapping between an image and its focus position. In further contrast with conventional methods, AI methods can generate scene-based focus trajectories that optimize synthesized image quality for dynamic and three dimensional scenes. We propose a focus control strategy that varies focal position dynamically to maximize image quality as estimated from the focal stack. We propose a rule-based agent and a learned agent for different scenarios and show their advantages over other focus stacking methods.

Large depth effects on lightness in the absence of a large luminance range.

To investigate the role of luminance range for lightness computations in complex 3-dimensional scenes we measured the change in lightness of a surface embedded within a relatively low-luminance-range context, as its perceived spatial position shifted from one plane to another. Our experiment tested conflicting claims between the coplanar ratio principle, according to which large depth effects require a high overall luminance range, and the anchoring theory, which predicts that depth effects can occur with a low overall range, given a sufficiently large difference between the highest luminance values in the 2 planes. Our results show decisive support for the anchoring theory but also hint at a large expansion of the perceived range of reflectances (gray shades) relative to the actual range within each plane. This expansion is qualitatively consistent with anchoring theory's scale normalization principle, but it is surprising in magnitude. Together with our earlier findings showing a massive compression of the perceived reflectance range in unsegmented high-dynamic-range Mondrians, our results underline the urgency of the scaling problem in lightness theory (how luminance range is mapped onto lightness range), a companion of the anchoring problem (which point on the lightness scale is anchored to which point on the luminance scale). (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Noise-tolerant single photon sensitive three-dimensional imager

Active imagers capable of reconstructing 3-dimensional (3D) scenes in the presence of strong background noise are highly desirable for many sensing and imaging applications. A key to this capability is the time-resolving photon detection that distinguishes true signal photons from the noise. To this end, quantum parametric mode sorting (QPMS) can achieve signal to noise exceeding by far what is possible with typical linear optics filters, with outstanding performance in isolating temporally and spectrally overlapping noise. Here, we report a QPMS-based 3D imager with exceptional detection sensitivity and noise tolerance. With only 0.0006 detected signal photons per pulse, we reliably reconstruct the 3D profile of an obscured scene, despite 34-fold spectral-temporally overlapping noise photons, within the 6 ps detection window (amounting to 113,000 times noise per 20 ns detection period). Our results highlight a viable approach to suppress background noise and measurement errors of single photon imager operation in high-noise environments.

2 3D VIEW: Designing of a Deception from Distorted View-dependent Images and Explaining interaction with virtual World

This paper presents an intuitive and interactive computer simulated augmented reality interface that gives the illusion of a 3D immersive environment. The projector displays a rendered virtual scene on a flat 2D surface (floor or table) based on the user’s viewpoint to create a head coupled perspective. The projected image is view-dependent which changes and deforms relative to user’s position in space. The nature of perspective projection is distorted and anamorphic such that the deformations in the image give an illusion of a virtual three dimensional holographic scene in which the objects are popping out or floating above the projection plane like real 3D objects. Also, the user can manipulate and interact with 3D objects in a virtual environment by controlling the position and orientation of 3D models, interacting with GUI incorporated in virtual scene and can view, move, manipulate and observe the details of objects from any angle naturally by using his hands. The head and hand tracking is achieved by a low cost 3D depth sensor ‘Kinect’. We describe the implementation of the system in OpenGL and Unity3D game engine. Stereoscopic 3D along with other enhancements are also introduced which further improves the 3D perception. The approach does not require head mounted displays or expensive 3D hologram projectors as it is based on perspective projection technique. Our experiments show the potential of the system providing users a powerful, realistic illusion of 3D.

To compute lightness, illumination is not estimated, it is held constant.

The light reaching the eye from a surface does not indicate the black-gray-white shade of a surface (called lightness) because the effects of illumination level are confounded with the reflectance of the surface. Rotating a gray paper relative to a light source alters its luminance (intensity of light reaching the eye) but the lightness of the paper remains relatively constant. Recent publications have argued, as had Helmholtz (1866/1924), that the visual system unconsciously estimates the direction and intensity of the light source. We report experiments in which this theory was pitted against an alternative theory according to which illumination level and surface reflectance are disentangled by comparing only those surfaces that are equally illuminated, in other words, by holding illumination level constant. A 3-dimensional scene was created within which the rotation of a target surface would be expected to become darker gray according to the lighting estimation theory, but lighter gray according to the equi-illumination comparison theory, with results clearly favoring the latter. In a further experiment cues held to indicate light source direction (cast shadows, attached shadows, and glossy highlights) were completely eliminated and yet this had no effect on the results. (PsycINFO Database Record

Ecological Virtual Reality Evaluation of Neglect Symptoms (EVENS): Effects of Virtual Scene Complexity in the Assessment of Poststroke Unilateral Spatial Neglect

Background. Unilateral spatial neglect (USN) is a highly prevalent and disabling poststroke impairment. USN is traditionally assessed with paper-and-pencil tests that lack ecological validity, generalization to real-life situations and are easily compensated for in chronic stages. Virtual reality (VR) can, however, counteract these limitations. Objective. We aimed to examine the feasibility of a novel assessment of USN symptoms in a functional shopping activity, the Ecological VR-based Evaluation of Neglect Symptoms (EVENS). Methods. EVENS is immersive and consists of simple and complex 3-dimensional scenes depicting grocery shopping shelves, where joystick-based object detection and navigation tasks are performed while seated. Effects of virtual scene complexity on navigational and detection abilities in patients with (USN+, n = 12) and without (USN−, n = 15) USN following a right hemisphere stroke and in age-matched healthy controls (HC, n = 9) were determined. Results. Longer detection times, larger mediolateral deviations from ideal paths and longer navigation times were found in USN+ versus USN− and HC groups, particularly in the complex scene. EVENS detected lateralized and nonlateralized USN-related deficits, performance alterations that were dependent or independent of USN severity, and performance alterations in 3 USN− subjects versus HC. Conclusion. EVENS’ environmental changing complexity, along with the functional tasks of far space detection and navigation can potentially be clinically relevant and warrant further empirical investigation. Findings are discussed in terms of attentional models, lateralized versus nonlateralized deficits in USN, and tasks-specific mechanisms.

Outdoor sound propagation with analytic ray curve tracer and Gaussian beam.

Outdoor sound propagation benefits from algorithms that can handle, in a computationally efficient manner, inhomogeneous media, complex boundary surfaces, and large spatial expanse. One recent work by Mo, Yeh, Lin, and Manocha [Appl. Acoust. 104, 142-151 (2016)] proposed a ray tracing method using analytic ray curves as tracing primitives, which improved the performance of propagation paths computation over rectilinear ray tracers. In this paper, an algorithm is developed that extends the performance improvement to field computation; it combines the analytic ray curve tracer with fast pressure computation based on the Gaussian beam model. The algorithm is validated against published results on benchmarks in atmospheric and ocean acoustics, and its application is demonstrated on a scene with terrains and buildings of realistic complexity and under a variety of atmospheric conditions. This algorithm is able to compute characteristic sound fields for fully general media profiles and complex three dimensional scenes at close-to-interactive speed.

How plant structure impacts the biochemical leaf traits assessment from in-field hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes

Light propagation modeling in 3-dimensional virtual scenes has been successfully applied to many fields, including plant canopies. However, its application to detailed analyses on how multiple scattering affects spectral-based biochemistry assessments has never been proposed.In this article, a wheat canopy model has been built using simulation models included in the open source software platform Open-Alea. Adel-Wheat, a 3D dynamic model of the aerial growth of winter wheat, has been associated with spectra collected on wheat leaves with an ASD spectrometer, and then used as input of the Caribu light propagation model. Caribu calculates the proportion of direct and scattered light for all polygons of the 3D scene. Principal component analysis was first applied to analyze the distribution of resulting spectra in the spectral feature space. Then the influence of canopy structure on quantitative regression models has been considered. For this purpose, a typical agronomical problem, i.e. nitrogen content retrieval, was addressed, using a Partial Least Square regression model.This study exhibits some important results concerning the distribution of collected spectra in the spectral feature space due to multiple scattering, and underlines the physical interpretation of these results. In the short term, it shows that satisfactory nitrogen content prediction (error about 0.5% of dry matter) can be obtained at the plant level, when considering only the plant top leaves. Moreover, its paves the way for future researches to develop spectral analysis tools able to overcome such multiple scattering phenomena.

An Improved Method of Vehicle Ego-motion Estimation Based on Stereo Vision

Intelligent vehicle is a senior development form in intelligent transportation system. Acquisition of accurate positioning information with cameras is a core technology in intelligent vehicles. In this paper, we propose a novel method to recover the vehicle trajectory using stereo vision technology. Our method takes advantage of structure and motion-based approaches, so the pose sequence can be computed without prior information on the structure of the relevant scene. In order to improve the efficiency and robustness, a robust feature detection procedure is conducted between image triples. Estimating vehicle ego-motion based on its epipolar geometric constraint makes the presented method not require the time-consuming reconstruction of 3-dimensional scene structure. An improved random sample consensus (RANSAC) algorithm based on geometrical constraints is also employed, which can effectively remove those outliers that are mismatched features or belong to moving objects. For nonlinear problems, an innovative Extended Kalman Filter (EKF) is then adopted to refine the estimated position. Overall, the improvements enable the algorithm to robustly estimate motion in a dynamic environment. Our experiments show that the improved visual odometry (VO) approach performs better than other state-of-the-art positioning methods in terms of computational complexity and accuracy.

Methods of spatial indexing of dynamic scenes based on regular octrees

The paper is devoted to study and development of spatial indexing methods as applied to three dimensional scenes arising in computer graphics, CAD/CAM systems, robotics, virtual and augmented reality applications, nD-modeling systems, and in project planning. Such scenes are compositions of a great number of extended geometrical objects exhibiting individual dynamic behaviors. The main focus is placed on algorithms for executing typical spatial queries with the use of regular dynamic octrees. In particular, algorithms for determining collisions, region search and nearest neighbor search are studied. For the model datasets introduced, average complexity estimates of index construction and execution of typical queries are derived based on probabilistic analysis. The estimates obtained significantly improve known pessimistic results and justify the suitability of regular octrees to spatial indexing of large-scale dynamic scenes. Results of computational experiments substantiate theoretical results and demonstrate possibilities of creating efficient computer graphics applications under the condition of permanently growing complexity of visual models.

A parallelized screen-based method for rendering polylines and polygons on terrain surfaces

Two dimensional (2D) vector features, when rendered on terrain as reference or highlight features, can improve the intelligibility of three dimensional (3D) scenes. This research proposes a new screen-space based method to render solid color 2D polylines and polygons on terrain. Using the z-buffer value and window coordinates in screen space, a pixel's spatial extent in real world coordinates can be calculated. The pixel's color is changed, if its spatial extent intersects with a polyline or polygon feature, to reflect the fact that there is a polyline going through or a polygon covering the pixel. Compared to other existing methods, our method eliminates undesirable rendering artifacts. A parallel computing schema was proposed and implemented as well to improve rendering performance.

Westminster Serious Games Platform (wmin-SGP) a tool for real-time authoring of roleplay simulations for learning

The use of serious games in education and their pedagogical benefit is being widely recognized. However, effective integration of serious games in education depends on addressing two big challenges: the successful incorporation of motivation and engagement that can lead to learning; and the highly specialised skills associated with customised development to meet the required pedagogical objectives. This paper presents the Westminster Serious Games Platform (wmin-SGP) an authoring tool that allows educators/domain experts without games design and development technical skills to create bespoke roleplay simulations in three dimensional scenes featuring fully embodied virtual humans capable of verbal and non-verbal interaction with users fit for specific educational objectives. The paper presents the wmin-SGP system architecture and it evaluates its effectiveness in fulfilling its purpose via the implementation of two roleplay simulations, one for Politics and one for Law. In addition, it presents the results of two types of evaluation that address how successfully the wmin-SGP combines usability principles and game core drives based on the Octalysis gamification framework that lead to motivating games experiences. The evaluation results shows that the wmin-SGP: provides an intuitive environment and tools that support users without advanced technical skills to create in real-time bespoke roleplay simulations in advanced graphical interfaces; satisfies most of the usability principles; and provides balanced simulations based on the Octalysis framework core drives. The paper concludes with a discussion of future extension of this real time authoring tool and directions for further development of the Octalysis framework to address learning.

Shell: A Spatial Decomposition Data Structure for Ray Traversal on GPU

Shared memory many-core processors such as GPUs have been extensively used in accelerating computation-intensive algorithms and applications. When porting existing algorithms from sequential or other parallel architecture models to shared memory many-core architectures, non-trivial modifications are often needed to match the execution patterns of the target algorithms with the characteristics of many-core architectures. Ray traversal is a fundamental process in many applications, and is commonly accelerated by spatial decomposition schemes captured in hierarchical data structures (e.g., kd-trees). However, ray traversal using hierarchical data structures needs to conduct repeated hierarchical searches. Such search process is time-consuming on shared memory many-core architectures since it incurs considerable amounts of expensive memory accesses and execution divergence. In this paper, we propose a novel spatial decomposition based data structure, called Shell, which completely avoids hierarchical search for ray traversal. In Shell, a structure is built on the boundary of each region in the decomposed space, which allows any ray traversing in a region to find the next neighboring region to traverse using table lookup schemes, without any hierarchical search. While our ray traversal approach works for other spatial decomposition paradigms and many-core processors in higher dimensional scenes, we illustrate it using kd-tree on GPU for 3D scenario and compare with the fastest known kd-tree searching algorithms for ray traversal. Experimental results in graphics ray tracing and radiation dose calculation show that our approach improves the performance by 3.5-5.5 $\times$ over the fastest known kd-tree based approaches.

Realization of wire selection platform based on three dimensional visualization technology

The three dimensional wire selection platform of the power transmission line is constructed by modelling technology and three dimensional scene render technology in combination with characteristics of the digital elevation, the digital ortho image and the earth geography information system. Through application of design and wire selection in some 330kV I and II circuit line engineering, it shows the design technical person can see scene of the three dimensional power transmission line clearly and livingly when the three dimensional visualization technology is applied in the optimized wire selection practice of the power transmission line, optimizes route of the line, saves investment of the line, reduces labour strength of person and finally improves design quality of the line.