Simulations using meshfree methods

In this paper, attempt is made to solve a few problems using the Polynomial Point Collocation Method (PPCM), the Radial Point Collocation Method (RPCM), Smoothed Particle Hydrodynamics (SPH), and the Finite Point Method (FPM). A few observations on the accuracy of these methods are recorded. All the simulations in this paper are three dimensional linear elastostatic simulations, without accounting for body forces.

Maximum group velocity in a one-dimensional model with a sinusoidally varying staggered potential

We use Floquet theory to study the maximum value of the stroboscopic group velocity in a one-dimensional tight-binding model subjected to an on-site staggered potential varying sinusoidally in time. The results obtained by numerically diagonalizing the Floquet operator are analyzed using a variety of analytical schemes. In the low-frequency limit we use adiabatic theory, while in the high-frequency limit the Magnus expansion of the Floquet Hamiltonian turns out to be appropriate. When the magnitude of the staggered potential is much greater or much less than the hopping, we use degenerate Floquet perturbation theory; we find that dynamical localization occurs in the former case when the maximum group velocity vanishes. Finally, starting from an ``engineered'' initial state where the particles (taken to be hard-core bosons) are localized in one part of the chain, we demonstrate that the existence of a maximum stroboscopic group velocity manifests in a light-cone-like spreading of the particles in real space.

VAM Applied to Dimensional Reduction of Nonlinear Multifunctional Film-fabric Laminates

This work aims at asymptotically accurate dimensional reduction of non-linear multi-functional film-fabric laminates having specific application in design of envelopes for High Altitude Airships (HAA). The film-fabric laminate for airship envelope consists of a woven fabric core coated with thin films on each face. These films provide UV protection and Helium leakage prevention, while the core provides required structural strength. This problem is both geometrically and materially non-linear. To incorporate the geometric non-linearity, generalized warping functions are used and finite deformations are allowed. The material non-linearity is handled by using hyper-elastic material models for each layer. The development begins with three-dimensional (3-D) nonlinear elasticity and mathematically splits the analysis into a one-dimensional through-the-thickness analysis and a two-dimensional (2-D) plate analysis. The through-the-thickness analysis provides the 2-D constitutive law which is then given as an input to the 2-D reference surface analysis. The dimensional reduction is carried out using Variational Asymptotic Method (VAM) for moderate strains and very small thickness-to-wavelength ratio. It features the identification and utilization of additional small parameters such as ratio of thicknesses and stiffness coefficients of core and films. Closed form analytical expressions for warping functions and 2-D constitutive law of the film-fabric laminate are obtained.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Three-dimensional numerical analysis of geocell-reinforced soft clay beds by considering the actual geometry of geocell pockets

Due to its complex honeycomb structure, the numerical modeling of the geocell has always been a big challenge. Generally, the equivalent composite approach is used to model the geocells. In the equivalent composite approach, the geocellsoil composite is treated as the soil layer with improved strength and stiffness values. Though this approach is very simple, it is unrealistic to model the geocells as the soil layer. This paper presents a more realistic approach of modeling the geocells in three-dimensional (3D) framework by considering the actual curvature of the geocell pocket. A square footing resting on geocell reinforced soft clay bed was modeled using the ``fast Lagrangian analysis of continua in 3D'' (FLAC(3D)) finite difference package. Three different material models, namely modified Cam-clay, Mohr-Coulomb, and linear elastic were used to simulate the behaviour of foundation soil, infill soil and the geocell, respectively. It was found that the geocells distribute the load laterally to the wider area below the footing as compared to the unreinforced case. More than 50% reduction in the stress was observed in the clay bed in the presence of geocells. In addition to geocells, two other cases, namely, only geogrid and geocell with additional basal geogrid cases were also simulated. The numerical model was systematically validated with the results of the physical model tests. Using the validated numerical model, parametric studies were conducted to evaluate the influence of various geocell properties on the performance of reinforced clay beds.

Direct Measurement of Three-Dimensional Forces in Atomic Force Microscopy

Direct measurement of three-dimensional (3-D) forces between an atomic force microscope (AFM) probe and the sample benefits diverse applications of AFM, including force spectroscopy, nanometrology, and manipulation. This paper presents the design and evaluation of a measurement system, wherein the deflection of the AFM probe is obtained at two points to enable direct measurement of all the three components of 3-D tip-sample forces in real time. The optimal locations for measurement of deflection on the probe are derived for a conventional AFM probe. Further, a new optimal geometry is proposed for the probe that enables measurement of 3-D forces with identical sensitivity and nearly identical resolution along all three axes. Subsequently, the designed measurement system and the optimized AFM probe are both fabricated and evaluated. The evaluation demonstrates accurate measurement of tip-sample forces with minimal cross-sensitivities. Finally, the real-time measurement system is employed as part of a feedback control system to regulate the normal component of the interaction force, and to perform force-controlled scribing of a groove on the surface of polymethyl methacrylate.

Three-Dimensional Nanoarchitecture of BiFeO3 Anchored TiO2 Nanotube Arrays for Electrochemical Energy Storage and Solar Energy Conversion

Here, we report the synthesis of TiO2/BiFeO3 nano-heterostnicture (NH) arrays by anchoring BiFeO3 (BFO) particles on on TiO2 nanotube surface and investigate their pseudocapacitive and photoelectrochemical properties considering their applications in green energy fields. The unique TiO2/BFO NHs have been demonstrated both as energy conversion and storage materials. The capacitive behavior of the NHs has been found to be significantly higher than that of the pristine TiO2 NTs, which is mainly due to the anchoring of redox active BFO nanoparticles. A specific capacitance of about 440 F g(-1) has been achieved for this NHs at a current density of 1.1 A g(-1) with similar to 80% capacity retention at a current density of 2.5 A g(-1). The NHs also exhibit high energy and power performance (energy density of 46.5 Wh kg(-1) and power density of 1.2 kW kg(-1) at a current density of 2.5 A g(-1)) with moderate cycling stability (92% capacity retention after 1200 cycles). Photoelectrochemical investigation reveals that the photocurrent density of the NHs is almost 480% higher than the corresponding dark current and it shows significantly improved photoswitching performance as compared to pure TiO2 nanotubes, which has been demonstrated based the interfacial type-II band alignment between TiO2 and BFO.

NON-DIMENSIONAL KINETOELASTOSTATIC MAPS FOR COMPLIANT MECHANISMS

How do we assess the capability of a compliant mechanism of given topology and shape? The kinetoelastostatic maps proposed in this paper help answer this question. These maps are drawn in 2D using two non-dimensional quantities, one capturing the nonlinear static response and the other the geometry, material, and applied forces. Geometrically nonlinear finite element analysis is used to create the maps for compliant mechanisms consisting of slender beams. In addition to the topology and shape, the overall proportions and the proportions of the cross-sections of the beam segments are kept fixed for a map. The finite region of the map is parameterized using a non-dimensional quantity defined as the slenderness ratio. The shape and size of the map and the parameterized curves inside it indicate the complete kinetoelastostatic capability of the corresponding compliant mechanism of given topology, shape, and fixed proportions. Static responses considered in this paper include input/output displacement, geometric amplification, mechanical advantage, maximum stress, etc. The maps can be used to compare mechanisms, to choose a suitable mechanism for an application, or re-design as may be needed. The usefulness of the non-dimensional maps is presented with multiple applications of different variety. Non-dimensional portrayal of snap-through mechanisms is one such example. The effect of the shape of the cross-section of the beam segments and the role of different segments in the mechanism as well as extension to 3D compliant mechanisms, the cases of multiple inputs and outputs, and moment loads are also explained. The effects of disproportionate changes on the maps are also analyzed.

Synthesis of 3-dimensional porous graphene nanosheets using electron cyclotron resonance plasma enhanced chemical vapour deposition

Microwave plasma driven chemical vapour deposition was used to synthesize graphene nanosheets from a mixture of acetylene and hydrogen gas molecules. In this plasma, acetylene decomposes to carbon atoms that form nanostructures in the outlet plasma stream and get deposited on the substrate. The GNS consists of a few layers of graphene aligned vertically to the substrate. Graphene layers have been confirmed by high-resolution transmission electron microscopy, and Raman spectral studies were conducted to observe the defective nature of the sample. The growth of nanosheets in a vertical direction is assumed to be due to the effect of electric field and from the difference in the deposition rate in the axial and parallel directions. These vertical graphene sheets are attractive for various applications in energy storage and sensors.

Tailored nitrogen dioxide sensing response of three-dimensional graphene foam

Ultralight and macroporous three-dimensional reduced graphene oxide (rGO) foams are prepared by lyophilization (freeze-drying) technique to avoid a conventional template method. This method allows tailoring the porosity of the foams by varying the weight percentages of graphene oxide dispersions in water. Three different rGO foams of 0.2, 0.5 and 1.0 wt% are used for NO2 sensing. Sensing response from the tailored structure of rGO is found to be directly related to the density. A maximum of 20% sensing response is observed for a higher porosity of the structure, better than the known results so far on graphene foams in the literature. (C) 2015 Elsevier B.V. All rights reserved.

Multi-dimensional Goodness-of-Fit Tests for Spectrum Sensing Based on Stochastic Distances

In this paper, we study two multi-dimensional Goodness-of-Fit tests for spectrum sensing in cognitive radios. The multi-dimensional scenario refers to multiple CR nodes, each with multiple antennas, that record multiple observations from multiple primary users for spectrum sensing. These tests, viz., the Interpoint Distance (ID) based test and the h, f distance based tests are constructed based on the properties of stochastic distances. The ID test is studied in detail for a single CR node case, and a possible extension to handle multiple nodes is discussed. On the other hand, the h, f test is applicable in a multi-node setup. A robustness feature of the KL distance based test is discussed, which has connections with Middleton's class A model. Through Monte-Carlo simulations, the proposed tests are shown to outperform the existing techniques such as the eigenvalue ratio based test, John's test, and the sphericity test, in several scenarios.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.

Temperature dependent compressive behavior of graphene mediated three-dimensional cellular assembly

Mechanical behavior of three-dimensional cellular assembly of graphene foam (GF) presented temperature dependent characteristics evaluated at both low temperature and room temperature conditions. Cellular structure of GF comprised of polydimethyl siloxane polymer as a flexible supporting material demonstrated 94% enhancement in the storage modulus as compared to polymer foam alone. Evaluation of frequency dependence revealed an increase in both storage modulus and tan delta with the increase in frequency. Moreover, strain rate independent highly reversible behavior is measured up to several compression cycles at larger strains. It is elucidated that the interaction between graphene and polymer plays a crucial role in thermo-mechanical stability of the cellular structure. (C) 2015 Elsevier Ltd. All rights reserved.

Ultrasonic Guided Wave Based Damage Detection Using Low-dimensional Data from Laser Doppler Scan

In the current state of the art, it remains an open problem to detect damage with partial ultrasonic scan data and with measurements at coarser spatial scale when the location of damage is not known. In the present paper, a recent development of finite element based model reduction scheme in frequency domain that employs master degrees of freedom covering the surface scan region of interests is reported in context of non-contact ultrasonic guided wave based inspection. The surface scan region of interest is grouped into master and slave degrees of freedom. A finite element wise damage factor is derived which represents damage state over distributed areas or sharp condition of inter-element boundaries (for crack). Laser Doppler Vibrometer (LDV) scan data obtained from plate type structure with inaccessible surface line crack are considered along with the developed reduced order damage model to analyze the extent of scan data dimensional reduction. The proposed technique has useful application in problems where non-contact monitoring of complex structural parts are extremely important and at the same time LDV scan has to be done on accessible surfaces only.