Exploring Multi-Dimensional Remote Sensing Data with a Virtual Reality System

In modern database applications the similarity or dissimilarity of complex objects is examined by performing distance-based queries (DBQs)on data of high dimensionality. The R-tree and its variations are commonly cited multidimensional access methods that can be used for answering such queries. Although, the related algorithms work well for low-dimensional data spaces, their performance degrades as the number of dimensions increases (dimensionality curse). In order to obtain accept- able response time in high-dimensional data spaces, algorithms that ob- tain approximate solutions can be used. Three approximation techniques (α-allowance, N-consider and M-consider) and the respective recursive branch-and-bound algorithms for DBQs are presented and studied in this paper. We investigate the performance of these algorithms for the most representative DBQs (the K-nearest neighbors query and the K-closest pairs query) in high-dimensional data spaces, where the point data sets are indexed by tree-like structures belonging to the R-tree family:R*- trees and X-trees. The searching strategy is tuned according to several parameters, in order to examine the trade-off between cost (I/O activ- ity and response time) and accuracy of the result. The outcome of the experimental evaluation is the derivation of the outperforming DBQ ap- proximate algorithm for large high-dimensional point data sets.

This paper presents a new approach to multiple objects tracking that combines particle filters and k-means. The ap-proach has been tested under an important real world sit-uation, related to pharmacological development, which has also proved to serve as an interesting ground-truth dataset provider for the evaluation of tracking algorithms. The obtained results are then compared to other models. The promising results of these experiments are presented. 1

Massive amount of Earth science data open an unprecedented opportunity to discover potentially valuable information. Earth science data are complex, nonlinear, high-dimensional data, and the sparsity of data in high-dimensional space poses major challenge in clustering of the data. Shared nearest neighbor clustering (SNN) algorithm is one of the well-known and efficient methods to handle high-dimensional spatiotemporal data. The SNN clustering method does not cluster all the data forming rigid boundary selection. This paper reports fuzzy shared nearest neighbor (FSNN) algorithm which is an enhancement of the SNN clustering method that has the capability of handling the data lying in the boundary regions by means of a fuzzy concept. The clusters obtained can be characterized by the cluster centroid, which summarizes the behavior of the ocean points in the cluster. The statistical measure is used to find the significant relation between the cluster centroids and the existing climate indices. In this study, correlation measure is used to find the significant pattern, such as teleconnection or dipole. The experimentation is performed on Indian continent latitude range $$7.5^{\circ }{-}37.5^{\circ }\hbox {N}$$ and longitude range $$67.5^{\circ }{-}97.5^{\circ }\hbox {E}$$ . Extensive experiments are carried out to compare the proposed approach with existing clustering methods such as K-means, fuzzy C-means and SNN. The proposed method, FSNN algorithm, not only handles the data lying in the overlapping region, but it also finds more compact and well-separated clusters. FSNN shows better results in terms of finding a significant correlation between cluster centroids and existing climate indices and validated by ground truth dataset.

It is estimated that more than 20,000 natural lakes are found across China. Most of these lakes are undergoing eutrophication or other severe environmental nuisances owing to natural and/or anthropogenic processes. In order to prevent or to minimize such damaging impacts, and to ascertain a proper quality management of the lake water and the associated fish resources, it is required to have access to up-to-date, accurate, and relevant data and information on the aquatic ecosystem in a timely manner. The “Dragon 3” project, supported by the European Space Agency, is focusing on <i>Xin’an</i> river watershed and investigates the impact of water quality and land cover/use change on the spatio-temporal distribution of the fishing grounds in <i>Qiandao Lake</i>. In this paper, the land use changes derived from satellite images is presented. Initially, Landsat 5 TM and Landsat 8 LDCM have been analyzed for the last 20 years in the vicinity of the <i>Xin’an</i> river watershed. Following the radiometric calibration of the images, several pixel-base classification algorithms have been evaluated including Spectral Angle Mapper (SAM), Support Vector Machine (SVM) as well Neural Network (NN). As it was found using the multi-temporal satellite imagery, the SVM algorithm was able to give high kappa accuracy estimated at around 0.90. In addition EO-Hyperion images over the western part of the <i>Xin’an</i> River were evaluated using hyperspectral vegetation indices as well using linear spectral un-mixing techniques. In addition, ENVISAT radar images have been evaluated in terms of land use change. The final outcomes indicate a significant urban expansion in the surrounding area of the Xin’an River which impacts the water quality investigation. Finally, a Landsat image was processed in order to estimate the Trophic State Index (TSI) values over the water bodies and the highest values were observed over the <i>Xin’an</i> river watershed and more specifically for the urban sites.

The sparsity and the problem of the curse of dimensionality of high-dimensional data, which make the most of the traditional clustering algorithm, lose action in high-dimensional space. Therefore, clustering of data in high-dimensional space is becoming the hot research areas. By utilizing the subtractive clustering as initialized method, and combine with the revised clustering validation indices, this paper offers a subspace clustering algorithm for automatically determining the optimal number of clusters on high dimensional data. The experiment results show that the proposed clustering algorithm can get better cluster validation performance than that of conventional indices.

This work explores the use of data-driven methods, including machine learning and sparse sampling, for systems in fluid dynamics. In particular, camera images of a transitional separation bubble are used with dimensionality reduction and supervised classification techniques to discriminate between an actuated and an unactuated flow. After classification is demonstrated on full-resolution image data, similar classification performance is obtained using heavily subsampled pixels from the images. Finally, a sparse sensor optimization based on compressed sensing is used to determine optimal pixel locations for accurate classification. With 5–10 specially selected sensors, the median cross-validated classification accuracy is ≥ 97 %, as opposed to a random set of 5–10 pixels, which results in classification accuracy of 70–80 %. The methods developed here apply broadly to high-dimensional data from fluid dynamics experiments. Relevant connections between sparse sampling and the representation of high-dimensional data in a low-rank feature space are discussed.

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.

In order to solve the problem that traditional grid-based clustering techniques lack of the capability of dealing with data of high dimensionality, we propose an intersecting grid partition method and a density estimation method. The partition method can greatly reduce the number of grid cells generated in high dimensional data space and make the neighbor-searching easily. On basis of the two methods, we propose grid-based clustering algorithm (GCOD), which merges two intersecting grids according to density estimation. The algorithm requires only one parameter and the time complexity is linear to the size of the input data set or data dimension. The experimental results show that GCOD can discover arbitrary shapes of clusters and scale well.

A wide variety of techniques for manipulating 3D objects in VR (Virtual Reality) systems have been implemented recently. But the shortcomings of the present interaction devices limit the types of manipulation that the user can perform in the virtual world. In this paper an interaction device that enables the user to interact with the virtual environment with different gestures is presented. By measuring the joint angles of the individual fingers with fiber optic sensors and tracking the relative position and orientation of the palm with AC (Alternating Current) pulsed magnetic tracker, the proposed interaction device can track an enormous variety of gestures and give the VR system remarkably rich expressive power. The relationship between the output of the fiber optic sensor and the actual flexure angle is studied. A digital phase sensitive detector is constructed and an iteration algorithm is used to extract the amplitudes of the received signals of the magnetoresistive sensors. The algorithm to calculate the spherical coordinates from the output of the accelerometers and magnetoresistive sensors is deducted. The design of the proposed interaction device and the experiment result are also presented.

The Reality of Virtual Reality in Echocardiography Education?

Although virtual reality (VR) programs to provide firefighting training continue to be developed and adopted, our investigation with 15 firefighters indicates that a current VR training system tends to convey behavioral tips and does not sufficiently reflect actual firefighters’ needs and realities in the field. It often provides somewhat simplified fire simulations and actually lowers the effectiveness of the training. In this paper, we employ Human-Computer Interaction (HCI) methods to examine and identify core elements in firefighting scenarios and develop a VR system that incorporates such elements. We evaluate our system with respect to presence and three design components of the VR simulation (i.e., reality, meaning, play) through a user study with 22 participants. Our study results demonstrate greater user experience and perception toward the four elements in firefighting training with our VR system compared to the existing one. We discuss design implications (e.g., move control, degree of freedom, sight hindrance by smoke, unexpected events) of our study that are expected to help implement and provide an effective VR training system for firefighters.

Various machine learning, cloud computing and big data models are most utilized control parameters in software applications. Therefore these systems are most demand at current trending decades. But these models are needs very low data access time, speed for process. Day to day life data storage servers and devices are costly and hardware complex, so dimensionality is increases with rapid manner. Any type of optimization techniques access takes more time consumption for high dimensional data. So, more applications related problems are occurs only at high dimensional data space does not at low space dimensional data. In this research proposes a dimensional reduction technique with Logistic regression (LR) model. This LR model is most helpful for dimensional reduction and clustering problems. LRML method has reduced the dimensional data size and achieved thee efficiency by 95.3% and ratio of reduction by 35.76%.

Multi-level adaptive neuro-fuzzy inference system-based reconstruction of 1D ISOMAP representations

&lt;p&gt;Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to addressing this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

This paper presents a fusion method generating unobservable sharpened hyperspectral remote sensing data with high spatial and spectral resolutions. This method, related to linear spectral unmixing (LSU) techniques, and based on nonnegative matrix factorization (NMF), introduces Joint-Variables NMF (JVNMF) for fusing observable low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes a joint-variables criterion that exploits spatial and spectral degradation models between the two considered images, and therefore considers a reduced number of unknown variables. This approach, called Grd-JVNMF, is a gradient-based method and uses iterative update rules. The proposed method is applied to realistic synthetic and semi-real data, and its effectiveness, in spatial and spectral domains, is evaluated with established performance criteria. Experimental results show that the proposed Grd-JVNMF method yields multi-sharpened hyperspectral data with good spectral and spatial fidelities. These tests also show that the proposed method outperforms tested literature ones.