Evaluation of Augmented Reality for Rapid Assessment of Earthquake-Induced Building Damage

This paper discusses the feasibility of using augmented reality (AR) to evaluate earthquake-induced building damage. In the proposed approach, previously stored building information is superimposed onto a real structure in AR. Structural damage can then be quantified by measuring and interpreting key differences between the real and augmented views of the facility. Proof-of-concept experiments were performed in conjunction with large-scale cyclic shear wall tests. In these, CAD images of the walls were superimposed onto the wall specimens. Then, as the wall specimens were deformed under applied loading, the horizontal drifts between the walls and the augmented images were computed using two different techniques and compared with actual wall drifts. The obtained results highlight the potential of using AR for rapid damage detection and indicate that the accuracy of structural displacements measured using AR is a direct function of the accuracy with which augmented images can be registered with the real world. The limitations of the technology, considerations for field implementation, and the potential for other related applications of AR are also discussed.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

GRAIL: a scalable index for reachability queries in very large graphs

Given a large directed graph, rapidly answering reachability queries between source and target nodes is an important problem. Existing methods for reachability tradeoff indexing time and space versus query time performance. However, the biggest limitation of existing methods is that they do not scale to very large real-world graphs. We present a simple yet scalable reachability index, called GRAIL, that is based on the idea of randomized interval labeling and that can effectively handle very large graphs. Based on an extensive set of experiments, we show that while more sophisticated methods work better on small graphs, GRAIL is the only index that can scale to millions of nodes and edges. GRAIL has linear indexing time and space, and the query time ranges from constant time to being linear in the graph order and size. Our reference C++ implementations are open source and available for download at http://www.code.google.com/p/grail/.     

Identification Scheme for Fractional Hammerstein Models With the Delayed Haar Wavelet

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix(HWOM). To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.     

High-precision identification of contextual information in location-aware engineering applications

This paper presents research that investigated algorithms for high-precision identification of contextual information in location-aware engineering applications. The primary contribution of the presented work is the design and implementation of a dynamic user-viewpoint tracking scheme in which mobile users’ spatial context is defined not only by their position (i.e., location), but also by their three-dimensional head orientation (i.e., line of sight). This allows the identification of objects and artifacts visible in a mobile user’s field of view with much higher accuracy than was possible by tracking position alone. For outdoor applications, a georeferencing based algorithm has been developed using the Global Positioning System (GPS) and magnetic orientation tracking devices [5] to track a user’s dynamic viewpoint. For indoor applications, this study explored the applicability of wireless technologies, in particular Indoor GPS, for dynamic user position tracking in situations where GPS is unavailable. The objectives of this paper are to describe the details of the three-stage-algorithm that has been designed and implemented, and to demonstrate the extent to which positioning technologies such as GPS and Indoor GPS can be used together with high-precision orientation trackers to accurately interpret the fully-qualified spatial context of a mobile user in challenging environments such as those found on construction sites. The obtained results highlight the potential of using location-aware technologies for rapidly identifying and retrieving contextual information in engineering applications.     

Neural Network Method for Solving Partial Differential Equations

A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.     

Watch to Edit: Video Retargeting using Gaze

We present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re‐edit a wide‐angle video recording or a close‐up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re‐edit an entire movie in one shot. Our algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information, and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state‐of‐the‐art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to gaze driven re‐editing [ JSSH15 ] and letterboxing methods, especially for wide‐angle static camera recordings.     

Ferromagnetic resonance characterization of magnetostrictive metallic glass coatings

Amorphous ferromagnetic alloys are a promising class of materials that have been successfully used as magnetostrictive elements in fiber optic magnetic sensors. We have used ferromagnetic resonance (FMR) at about 9.5 GHz to characterize highly magnetostrictive film coatings of the amorphous ferromagnetic alloy Fe₈₁B_13.5Si_3.5C_2.R.f. sputtering was used to prepare films 0.1–0.6 μm thick on glass substrates and cladded single-mode optical fibers of diameter approximately 80 μm. Because of its inherent sensitivity, the FMR technique is shown to be an excellent non-destructive probe for investigating microscopic as well as macroscopic structural inhomogeneities that may arise from the fabrication process itself, subsequent handling or thermal aging of the coatings. An added advantage of the FMR technique is that it also allows a simultaneous measurement of such material parameters as the saturation magnetostriction coefficient, the saturation magnetization, the g factor and the uniaxial magnetic anisotropy energy. The effect of thermal annealing on some of these properties is also reported.     

A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis

An Artificial Neural Network (ANN) classifier trained by a hybrid GA-BP method for diagnosis of gear faults is presented here that can be incorporated in an online fault diagnostic system of vital gearboxes. The distinctive features obtained from vibration signals of a running gearbox; that was operated in normal and with faults induced conditions were used to feed the GA-BP hybrid classifier. Time domain vibration signals were divided in 40segments. From each segment features such as magnitude of peaks in time domain and spectrum along with statistical features such as central moments and standard deviations were extracted to feed the classifier. Based on the experimental results it was shown that the GA-BP hybrid classifier can successfully identify gear condition. It was also shown that the network trained by GA-BP hybrid method performs much better than ANN that is trained by standard BP or GA individually. Further, it was also shown that if prior to extraction of features; the vibration signals are pre-processed by Discrete Wavelet Transform (DWT) then efficacy of the GA-BP hybrid is significantly enhanced.     

Swift heavy ion induced structural modifications in zircon and scheelite phases of ThGeO4

Structural modifications in the zircon and scheelite phases of ThGeO₄ induced by swift heavy ions (93 MeV Ni⁷⁺) at different fluences as well as pressure quenching effects are reported. X-ray diffraction and Raman measurements at room temperature on the irradiated zircon phase of ThGeO₄ indicate the occurrence of stresses that lead to a reduction of the cell volume up to 2% followed by its transformation to a mixture of nano-crystalline and amorphous scheelite phases. Irradiation of the zircon phase at liquid nitrogen temperature induces amorphization at a lower fluence (7.5 × 10¹⁶ ions/m²), as compared to that at room temperature (6 × 10¹⁷ ions/m²). Scheelite type ThGeO₄ irradiated at room temperature undergoes complete amorphization at a lower fluence of 7.5 × 10¹⁶ ions/m² without any volume reduction. The track radii deduced from X-ray diffraction measurements on room temperature irradiated zircon, scheelite and low temperature irradiated zircon phases of ThGeO₄ are, 3.9, 3.5 and 4.5 nm, respectively. X-ray structural investigations on the zircon phase of ThGeO₄ recovered after pressurization to about 3.5 and 9 GPa at ambient temperature show the coexistence of zircon and disordered scheelite phases with a larger fraction of scheelite phase occurring at 9 GPa. On the other hand, the scheelite phase quenched from 9 GPa shows crystalline scheelite phase pattern.