Dimensional Depth Research Articles

Fire Suppression Simulation Application Development Stereo Graphics Based (Virtual Reality)

Modern man today is very dependent on his life with computers. Because with the help of computer technology can ease their task. A computer can create a situation similar to the original. This technology is commonly called simulation. Serious simulations depict a system that is difficult to perform in the real world and is at high risk. These serious simulations are usually much more developed in the making. This development technology is called Virtual Reality (VR). Virtual Reality (VR) is a development of stereo display techniques. Where users can see an object with dimensional depth that will give a 3D effect on the human brain. Based on the background above, the author tries to implement stereo displays in firefighting simulation (FFS) applications. The simulation engine used is Open Scene Graph with Microsoft Visual Studio 2008 compiler, which uses the C++ programming language. The GUI display design uses Qt 4.7.3. Users will try the stereo display effect in the FFS application and interact between the fire particle system and the water particle system to compare factors that cause fire particle system outages. Of all the implementations and trials carried out, the author succeeded in implementing stereoscopic fire suppression simulation applications. Because all objects in this application already have a red shadow for the left side of the thing and a blue shadow for the right side. The interaction between the user and the fire suppression simulation application was also successfully displayed in this Fire Fighting Simulation (FFS) application. The fire particle system will be extinguished if the user performs fire suppression interactions.

Using Noninvasive Depth–Sensors to Quantify Human Productivity Levels in Desk–Related Workspaces

Numerous neuroscientific studies demonstrate the interaction between movement, memory, and environmental conditions. We present a new in–the–field tool that can be used by interior designers, environmental psychologists, and architects to understand and quantify human productivity in desk–related work and study spaces. In this investigation, an interior space, equipped with a new generation of nonintrusive sensors and analytical algorithms synchronously measured multiple individuals and working groups. We tested the hypothesis that a positive correlation exists between stillness and cognitive function associated with self–reported productivity. We explored, which design attributes, Seat Orientation, Zones, Table Crowdedness, and Table Types were associated with statistically significant changes in users’ self–reported productivity and micro–movements. A mathematical model was developed to map a “stillness value” for each person across time, deriving a measurable productivity factor (PF) that could be used to synchronously assess the impact of design features in multiple users. Experiments were conducted over 12 days involving 640 academic students in a semi–enclosed reading/study room within a recently built city public library with extensive views of the waterfront, harbor, and bay. Three–dimensional depth–cameras anonymously measured head and body movement while subjects worked and completed productivity surveys. Methods were validated using video recordings, sensor data, questionnaires, and observations. The results showed the PF was significantly correlated to the time spent in different Zones and was greatest at Seat Orientations furthest away from walk–through traffic and in Zones with the highest exposure to the window wall and views. Users in these settings experienced a higher and longer–lasting PF during the high–performance phase of their stay.

P14‐5: A method of analysis of pulmonary function by movement of the left and right lungs using a three dimensional depth sensor

P14‐5: A method of analysis of pulmonary function by movement of the left and right lungs using a three dimensional depth sensor

An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

BACKGROUND CONTEXTTimely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. PURPOSEThis study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm, in predicting the Cobb angle in AIS. STUDY DESIGNRetrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. PATIENT SAMPLEOne hundred and-sixty human subjects suspected to have AIS were included. OUTCOME MEASURESPatient demographics, radiographic measurements, and predicted Cobb angle derived from the deep learning algorithm were the outcome measures for this study. METHODSOne hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error and root mean square error between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest. RESULTSThe correlation between the actual and the mean predicted Cobb angles was 0.91. The mean absolute error and root mean square error were 4.0° and 5.4°, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10° and 89% for that of ≥20°. CONCLUSIONSThe three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools.

Analysis and Evaluation Methods of Seismic Subsidence Characteristics of Loess and Field Seismic Subsidence

The objective of this research is to analyze the dynamic degeneration of loess and the evaluation method of field seismic subsidence. In this study, Q3 loess is taken as the research object, and the dynamic properties of loess with 10%, 20%, 30% and 35% moisture content are tested by triaxial experiment. In addition, seismic subsidence characteristics of loess with dry densities of 1.4g/cm3, 1.6g/cm3, and 1.8g/cm3 and consolidation stress ratios of 1.0, 1.2, 1.4, and 1.6 are analyzed. Then the simplified seismic subsidence estimation method is used to calculate the relationship between seismic subsidence coefficients at different soil depth in one dimensional field, cycle times, and subsidence depth. The results show that the higher the water content of loess is, the greater the change of seismic subsidence appears. The larger the dry density of loess is, the smaller the change degree of seismic subsidence appears. The larger the consolidation stress ratio is, the greater the change of seismic subsidence occurs in loess. When the depth of soil reaches 9.5m, the maximum seismic subsidence coefficient can reach 0.8%. When the depth of soil layer is 10m, the degree of seismic subsidence is the largest. When the depth of soil layer is 12~16m, the settlement depth caused by earthquake subsidence is small. While the depth of soil layer is 8~12m, the settlement degree is large.

基于二维几何特征与深度特征的人脸识别技术研究

基于二维几何特征与深度特征的人脸识别技术研究

High-accurate and robust fingerprint anti-spoofing system using Optical Coherence Tomography

Traditional commercial automated fingerprint recognition systems (AFRSs) are vulnerable to fake attacks by the use of artificial fingerprints, due to its limitation on resolution and the difficulty of obtaining depth information. Developing new systems with strong anti-spoofing ability are of increasing concern in the current digital age. Inspired by the observation and comparison of fingertip skin structure and the one dimensional (1D) depth signals derived from OCT acquisition images between real finger and artificial fingerprints, two novel anti-spoofing features, namely depth-double-peak feature and sub-single-peak feature, were specifically defined. Depth-double-peak feature refers that there must be two and only two peaks in the 1D depth signal which reflects the double-peak structure of real fingertip skin. While sub-single-peak feature means that there must be a peak in the 1D depth signal intercepted before the maximum peak which distinguishes the extra layer covered on a real finger. Real and fake images could be presented by these two features and classified by a pre-learned threshold. Four types of artificial fingerprint, i.e., Normal Thickness Fingerprint Layer (NTFL), thin Fingerprint Layer (TFL), Ultra-Thin Fingerprint Layer (UTFL and Artificial Fingers Model (AFM) were collected to verify system robustness. 30 sets of real finger data (each set includes 400 images) and 60 sets of artificial fingerprint data (15 sets of each type) were collected by our designed OCT device. Experimental results show that our proposed anti-spoofing system could achieve 100% accuracy over all four types of artificial fingerprints and outperform the other automated anti-spoofing method in comparison.

Multimodal Sensor System for Pressure Ulcer Wound Assessment and Care

We present a multimodal sensor system for wound assessment and pressure ulcer care. Multiple imaging modalities including RGB, three- dimensional (3-D) depth, thermal, multispectral, and chemical sensing are integrated into a portable hand-held probe for real-time wound assessment. Analytic and quantitative algorithms for various assessments including tissue composition, wound measurement in 3-D, temperature profiling, spectral, and chemical vapor analysis are developed. After each assessment scan, 3-D models of the wound are generated on the fly for geometric measurement, while multimodal observations are analyzed to estimate healing progress. Collaboration between developers and clinical practitioners was conducted at the Charlie Norwood VA Medical Center for in-field data collection and experimental evaluation. A total of 133 assessment sessions from 23 enrolled subjects were collected, on which the multimodal data were analyzed and validated with respect to clinical notes associated with each subject. The system can be operated by nontechnical caregivers on a regular basis to aid wound assessment and care. A web portal front-end was developed for clinical decision and telehealth support, where all historical patient data including wound measurements and analysis can be organized online.

Dynamic mesh grids for laser head tomography

In this work, new image reconstruction schema has been proposed for back-reflected diffuse optical tomography geometry. 100 source and 100 detector points have been selected as bifurcated probe positions in xy plane. An x, y, and z cubic coordinate grid system has 10 × 10 × 30 mesh grids. In this work, a dynamic mesh grid concept has been introduced first. Each time source and detector positions are changed, x and y coordinate positions have been reassigned automatically. Centers between source and detector positions have been recalculated in xy plane. x and y grid positions have been reassigned around this center points. When using predefined static mesh grid photon fluencies which are coming from Monte Carlo (MC) simulation output or diffusion equation are transferred into static voxels which is only partially correct. Hence there is no symmetry around source–detector matchup center in xy plane and so is not totally correct. In this work, dynamic mesh grids have been created and photon fluencies have been assigned into automatically redefined dynamic mesh grid voxels. One dimensional depth profile has been used for simplicity. Median values of transport functions for each z depth grids have been used. Transport functions of photon fluencies from each source to detector positions have been calculated and each voxel value has been assigned in three dimensional dynamic mesh grid array. We innovated the use of dynamic mesh grid array instead of using static mesh grids which were being used previously, and now have assigned the photon fluencies into the dynamic mesh grid voxels. This also gave us an opportunity to use different transport functions for one dimensional image reconstruction schema. Before, we were using the sum of transport weight functions which are derived from each of the z depth layers. Now we have opportunity to use mean or median values also. Thus we are centering the dynamic mesh grid array by finding the center of each source and detector position successfully. This work gave us encouragement to build tomography devices.

Three dimensional measurement with an electrically tunable focused plenoptic camera.

A liquid crystal microlens array (LCMLA) with an arrayed microhole pattern electrode based on nematic liquid crystal materials using a fabrication method including traditional UV-photolithography and wet etching is presented. Its focusing performance is measured under different voltage signals applied between the electrodes of the LCMLA. The experimental outcome shows that the focal length of the LCMLA can be tuned easily by only changing the root mean square value of the voltage signal applied. The developed LCMLA is further integrated with a main lens and an imaging sensor to construct a LCMLA-based focused plenoptic camera (LCFPC) prototype. The focused range of the LCFPC can be shifted electrically along the optical axis of the imaging system. The principles and methods for acquiring several key parameters such as three dimensional (3D) depth, positioning, and motion expression are given. The depth resolution is discussed in detail. Experiments are carried out to obtain the static and dynamic 3D information of objects chosen.

Muscovite single layer resolution: Secondary ion mass spectrometry depth profile

Three dimensional depth profile of chemical composition by secondary ion mass spectrometry is still of high interest. Spatial resolution is evaluated on muscovite K0.70Na0.11Ca0.01{[Si3.07Al0.93][Al1.88Fe0.12Mg0.05]O10(OH)2}, forming periodically repeating layers with a single layer of 0.98nm. The depth profile determines the periodic structure as the oscillating ion mass intensities of all constituents which are well represented by Na, O, Si, SiO, and Fe cations, with assigned the absolute (depth profile/correlated) positions from the surface 0.00 (1.60/0.00) nm, 0.16 (1.76/0.20) nm, 0.19 (1.79/0.04) nm, 0.24 (1.84/0.06) nm, and 0.39 (1.99/0.19) nm, respectively. The sequence correlates well with the structure of the single muscovite layer with estimated spatial resolution of 0.07nm. Moreover a novel approach by Fourier transform provides frequency pattern with time and chirp parameters which might be a potential tool to evaluate an atomic mixing process in determination of periodic structures of clay minerals.

生命化建築のための深度データを用いた居住者の行動の分類と行動認識に関する研究

The number of single households consisting of elderly people is increasing in Japan. We are conducting series of research on the “Biofied Building” that can control the living spaces suitable for each resident's needs. This system utilizes the algorithm learnt from living organisms with the help of small sensor agent robots. In this system, every information in living space is collected by the robots for the space control. The information about the resident's activity is one of the most important data for the “Biofied Building”. When we acquire the information on activities, we have to add tags on the data such as meaning of these activities. For this purpose, we proposed an activity recognition method for residents in living spaces. An activity is defined as an action done aimed at some target, and consists of several movements. A movement is a motion defined by the unit body form. In this study, we defined two types of activities, activities consisting of single movement and activities consisting of multiple movements. For example, the activity of “eating” consisting of multiple movements, such as; “holding chopsticks” and “catching food” etc., is classified into “Multiple type”. On the other hand, the activity of “standing up” consisting of a single movement of “standing up”, is classified into “Single type”. The activities in living space are thus categorized into two. In this study, we used Kinect for Windows by Microsoft to acquire the depth data. After the noise rejection, R transformation or variance, PCA, LDA, and k-means classification were applied to recognize the activities. Our proposed method has two phases, the activity categorization phase, and the activity recognition phase. The ten activities including the six “Single type” activities (“standing up”, “sitting down”, “crunching”, “standing up from crunching”, “picking up something” and “standing up from picking up something”), and the four “Multiple type” activities (“handling a smart phone”, “reading a book”, ”writing” and “eating”) were performed by eleven subjects. We proved that it was effective to categorize the activities into each activity type for activity recognition, because each activity type has own features. We had 100% of mean recognition rate of activity categorization. And we had better mean recognition rates of activity classification with the activity categorization than without it. After the activity categorization phase, the activity classification using R transformation and variance was applied. These two methods were used for transforming two dimensional depth data into one dimensional parameters. R transformation is often used for image analysis in several conventional methods. However, the R transformation for “Multiple type” activities was rarely referred. We applied both methods to “Multiple type” activities and found that they worked well. In addition, we examined the effect of variance for the transformation of two dimensional matrix. As “Multiple type” activities consist of continuous movements in limited space, we hypothesized that variance of several consecutive depth data may be able to detect the features of “Multiple type” activities. As the results, we got 91.4% of mean recognition rate of “Single type” activities in case of using R transformation. And we also got 77.3% of mean recognition rate of “Multiple type” activities in case of using variance. Although some improvement is needed, the proposed method showed a certain level of feasibility for practical applications.

IMPACT CHARACTERISTICS OF FREE OVER-FALL IN POOL ZONE WITH UPSTREAM BED SLOPE

This paper presents the theoretical equations for the impact characteristics of free over-fall (ICFOF) in the pool zone with sloping upstream bed. The various impact characteristics considered are the brink depth, the impact position and impact angle, the tail water depth and the depth of water pool. In order to overcome the complexity in determining the ICFOF, they were expressed in dimensionless forms as functions of upstream bed slope (UBS), upstream Froude number and Drop number. The multiple regression analysis was used to obtain the empirical expressions for the ICFOF using the laboratory experimental results. It was found that the dimensionless brink depth decreases with an increase in upstream Froude number and UBS. With the increase in both the Drop number and UBS, the dimensionless impact position increases, while the impact angle decreases. On the other hand, the dimensional tail water depth and the dimensional depth of water pool vary directly with the Drop number and inversely with the UBS. In addition, the empirical equations proposed for horizontal upstream slope were compared with those of previous investigators. Further, a parameter called slope effect ratio is defined to state the effect of UBS on the ICFOF. Moreover, the empirical equation derived in this study can be used to design the structure of downstream apron practically. An example illustrates the calculation of the impact position/tail water depth for downstream apron.

Three Dimensional Numerical Modeling of Oil Spill Behavior in Marine Environment

In this research a three dimensional model is explained which simulate the oil spill behavior inseas. This model is the developed version of previous two-dimensional study of Aghajanloo et al., 2013.Because of three-dimensional nature of the oil dispersion/movement in water, this is a more realistic model of oil spill transport and fate in marine environment. The model is based on Eulerian approach and the governing equations have been discretized by finite volume method. The main model is a combination of several major sub-models that predict the oil slick transport on the sea surface, oil losses due to natural weathering processes and oil dispersion in water column. The oil pollution distribution in water column have been modeled in a sigma-coordinate system and the effects of current, wave and wind forces and turbulent diffusion have been included. Also a hydrodynamic model is provided to simulate the tidal current which is based on two dimensional depth averaged equations of shallow water. A test case including analytical solution is chosen to ensure the model capability in oil spill simulation. The oil spill accident in the Persian Gulf has been simulated by the model.

Supercritical flow in circular pipe bends

In this paper a complex flow pattern occurring in a closed conduit bend with supercritical flow is analyzed. The research was done by a scale (physical hydraulic) model, and a numerical model developed in Ansys (Fluent) program surrounding. This article compares the results obtained by numerical and physical (scale) models, providing comments and recommendations for their application. Based on these results, simple empirical relationships were developed, describing the effects of the bend deflection angle and approach flow conditions (non dimensional depth and the Froude number upstream from the bend) on the considered flow.

Recognition of Human Body Movements Trajectory Based on the Three-dimensional Depth Data

Abstract based on the traditional machine vision recognition technology about human body movement trajectory, the paper finds out the shortcomings of the traditional recognition technology. By combining the three - dimensional depth data, the three-dimensional motion history image and the invariant moments of the three - dimensional motion history image computed as the eigenvector of body movements, the paper applies the method to the machine vision of the human body movements trajectory. In detail, the paper describes the algorithm and realization scheme of the human body movements trajectory recognition based on the three-dimensional depth data. Finally, comparing with the results of the recognition experiment, we verify that the method of human body movement trajectory recognition technology based on the three-dimensional data has a more accurate recognition rate and a better robustness.

Shallow Seismic Refraction, Two-Dimensional Electrical Resistivity Imaging, and Ground Penetrating Radar for Imaging the Ancient Monuments at the Western Shore of Old Luxor City, Egypt

A combined near surface geophysical survey conducted in archaeological site at the western bank of Luxor area shows that, the geophysical methods offer the possibility to characterize and reconstruct the geometry of subsurface structures without destroying the deposits, providing a way to find solutions to the questions of archaeological or engineering significance. The two dimensional electrical imaging (R2D) and the shallow seismic refraction (SSR) techniques were carried out through twelve profiles measured across the study area in NW-SE directions with 5 m profile spacing. The interpretation of the 2-D resistivity imaging shows that there are two layers which are arranged from the top to the bottom as soil layer consists of weathered clay and its thickness varies between 3.82 m and 4 m; wetted clay and mud with depth varies from 25.3 m to 26.23 m and a massive body may composed of Alabaster, limestone or granite is present within the second layer. Following, the inverted 2-D models were merged to form a quasi-3D resistivity model which was visualized as depth slices. A high resistive anomalous feature was detected in successive depth slices. Moreover, analysis of shallow seismic data shows that, P-velocity ranges from 400 - 1350 m/sec whereas S-wave ranges from 280 - 460 m/sec. The massive body with high resistivity is detected and shows also a high velocity. Following a detailed Ground Penetrating Radar (GPR) a survey was carried out to image the subsurface. 200 MHz antenna frequency was used on the survey. Inspection of the 2-D GPR profile shows the presence of hyperbolic anomalous features is present at a depth nearly 4 m. Moreover, three dimensional depth slices shows also the presence of an anomalous feature which appears at depth slices ranging from 3.5 m to 5 m. Finally, the interpreted geophysical results have been verified through archaeological direct excavation started by the Egyptian supreme council of Antiquities in cooperation with the European Commission working in the restoration project of Memnon’s and the Temple of Amenhotep III. They have found a body of Pharaonic statue, the statue has been known by the archaeologists as a statue of Queen Tie, wife of Amenhotep III and mother of Pharaoh Akhenaton unification, carved in Alabaster stone, and this result confirmed that the integrated geophysical method can be effectively contributed in the archaeological prospection in Egypt.

Finite Element Analysis of Tidal Currents over the Red Sea

Hydrodynamic models represent the core of any simulation for water quality, siltation, and morphology studies. In this study a finite element model was setup for the Red Sea to predict the tidal currents and tidal water level variations. The boundary for the model is located at the Straits of Bab-al-Mandeb. The model was simulated using two dimensional depth averaged model with an element size varying from 15 km to less than 1 km. The model was shown to provide good results for the water level variations at many stations in the Red Sea.

Numerical Simulation of Oil Spill Behavior in the Persian Gulf

The aim of this research is to simulate oil spill behavior in the Persian Gulf due to the wind andtidal currents. This is achieved by coding an oil spill mathematical model consisting of several major submodels including hydrodynamic model, oil slick dynamic model and oil weathering model. The base of all of them is Eulerian approach and they are coupled together to simulate spill incident in the Persian Gulf. Thehydrodynamic model provided to simulate the tidal current is based on two dimensional depth averagedequations of shallow water discretized by finite volume method that is applied on rectangular structured mesh. The tidal current in the Gulf is made by imposing tidal fluctuation to the main open boundary at the Hormuz Strait. This model verification is carried out by comparison the resultant predictions of model with the available measured data and it shows a good agreement. Oil slick transport on the sea surface is simulated by an advection-diffusion model. Also, the most effective natural processes on oil fate and their effect on oil properties are estimated by an oil weathering model. Then, a test case including analytical solution is chosen to ensure the model capability on oil spill simulation. Finally, the model is implemented for a spillage event in the Persian Gulf. By explanation of the presented results, oil fate, the slick transport and other predicted data are demonstrated. The results show the model capability on modelling of an oil spill incident in marine environment.

Computing the Three Dimensional Depth Measurement by the Multi Stereo Images

Computing the Three Dimensional Depth Measurement by the Multi Stereo Images