From the last few years, RGB-D cameras are widely used by researchers in various fields. Their reasonable cost and the ability to estimate distances at a high frame rate have made these sensors recommendable for applications in gaming accessories, robotics, computer vision, etc. In addition to color, these sensors also provide depth information. Aspects like the stability, accuracy and reliability of depth-sensing cameras like Kinect v2 must also be considered before using the device for applications like that of 3D space modelling. In this paper, an analysis of the error in the depth measurement as well as calculation of Depth Entropy given by Kinect v2 sensor in different mediums viz. air, glass and water has been done. We have validated our findings using the theories of optics. The findings from error analysis are used to make an error compensation model which can correct depth at each pixel of the image. The error analysis and error compensation model proposed herewith will help in improving the accuracy of present and future depth sensing devices.
Tensegrity structures are lightweight structures composed of cables in tension and struts in compression. Since tensegrity
systems exhibit geometrically nonlinear behavior, finding optimal structural designs is difficult. This paper focuses on the
use of stochastic search for the design of tensegrity systems. A pedestrian bridge made of square hollow-rope tensegrity ring
modules is studied. Two design methods are compared in this paper. Both methods aim to find the minimal cost solution. The
first method approximates current practice in design offices. More specifically, parametric analysis that is similar to a
gradient-based optimization is used to identify good designs. Parametric studies are executed for each system parameter in
order to identify its influence on response. The second method uses a stochastic search strategy called probabilistic global
search Lausanne. Both methods provide feasible configurations that meet civil engineering criteria of safety and serviceability.
Parametric studies also help in defining search parameters such as appropriate penalty costs to enforce constraints while
optimizing using stochastic search. Traditional design methods are useful to gain an understanding of structural behavior.
However, due to the many local minima in the solution space, stochastic search strategies find better solutions than parametric
studies. 相似文献
The present study attempts to improve the ignition properties of diesel fuel by investigating the influence of adding aluminum and aluminum oxide nanoparticles to diesel. As part of this study, droplet ignition experiments were carried out atop a heated hot plate. Different types of fuel mixtures were used; both particle size (15 and 50 nm) as well as the volume fraction (0%, 0.1%, and 0.5%) of nanoparticles added to diesel were varied. For each type of fuel mixture, several droplets were dropped on the hot plate from a fixed height and under identical conditions, and the probability of ignition of that fuel was recorded based on the number of droplets that ignited. These experiments were repeated at several temperatures over the range of 688-768 degrees C. It was observed that the ignition probability for the fuel mixtures that contained nanoparticles was significantly higher than that of pure diesel. 相似文献
In this paper, small printed flower-shape triple notch ultra-wideband (UWB) monopole antenna with high band rejection is presented. Notch bands include WiMAX (IEEE802.16 3.30–3.80 GHz), WLAN IEEE802.11a/h/j/n (5.15–5.35, 5.25–5.35, 5.47–5.725, 5.725–5.825 GHz), and X-band downlink satellite system (7.1–7.9 GHz). By including inverted T-shape stub and etching two C-shaped slots on the radiating patch, triple band-notch function is obtained with measured high band rejection (VSWR = 14.52 at 3.58 GHz, VSWR = 15.88 at 5.69 GHz and VSWR = 6.95 at 7.61 GHz) and covers a UWB useable fractional bandwidth of 114.30% (2.74–10.57 GHz = 7.83 GHz). In short the antenna offers triple band-notch UWB systems as a compact multifunctional antenna to reduce the number of antennas installed in wireless devices for accessing multiple wireless networks with wide radiation pattern. The proposed antenna has a small size of about 0.25λ × 0.30λ at 4.2 GHz (first resonance frequency), which has a size reduction of 30% with respect to the earlier published antenna. Both the experimental and simulated results of the proposed antenna are presented, indicating that the antenna is a good candidate for various UWB applications.
Sparse representations have gained much interest due to the rapid growth of intelligent embedded systems, the need to reduce the time to mine large datasets, and for reducing the footprint of recognition based applications on portable devices. Computational learning theory tells us that the Vapnik–Chervonenkis (VC) dimension of a learning model directly impacts the structural risk and the generalization ability of a learning model. The minimal complexity machine (MCM) was recently proposed as a way to learn a hyperplane classifier by minimizing a tight bound on the VC dimension; results show that it learns very sparse representations that yield test set accuracies that are comparable to the state-of-the-art. The MCM formulation works in the primal itself, both when the classifier is learnt in the input space and when it is learnt implicitly in a higher dimensional feature space. In the latter case, the hyperplane is constructed in the empirical feature space (EFS). In this paper, we examine the hyperplane restricted to the EFS. The EFS is a finite dimensional vector space spanned by the image vectors in a higher dimensional feature space. Since the VC dimension of a linear hyperplane classifier is exactly the number of features used by the classifier, the dimension of the EFS is a direct measure of both the sparsity of the model and the VC dimension. This allows us to formulate optimization problems that focus on learning sparse representations, and yet generalize well. We derive an EFS version of the MCM, that allows us to minimize the model complexity and improve sparsity. We also propose a novel least squares version of the MCM in the EFS. Experimental results demonstrate that the EFS variants yield sparse models with generalization comparable to the state-of-the-art. 相似文献
Microsystem Technologies - The aim of this work is to design, simulate, and analyze a bi-axial piezoresistive MEMS (Micro-Electro-Mechanical System) force sensor, which has the capability of... 相似文献
Multimedia Tools and Applications - The text present in natural scenes contains semantic information about its surrounding environment. For example, the majority of questions asked by blind people... 相似文献
Mica sample containing 34% alumina and 9% potash has been investigated as a potential dual source of alumina and potash. It was found that planetary milling had a strong effect on structure breakdown as well as on the release of alumina and potash values. Both alumina and potash were extracted from mica using mechanical milling followed by hydrochloric acid leaching and precipitation process. The optimal conditions derived from statistical design yielded 72% alumina and 75% potash values in the form of γ-alumina and sylvinite. The analysis of the final precipitate was carried out by different characterization techniques such as XRD, SEM–EDS, XRF, BET surface area analysis.
