Unified Dispersion Characteristics of Structural Acoustic Waveguides

In this article, we show with some formalism that infinite flexible structural acoustic waveguides have a general form for the dispersion equation. The dispersion equation of all such waveguides should conform to a generic form. This allows us to bring out the common features of structural acoustic waveguides. We take three examples to demonstrate this fact, namely, the rectangular, the circular cylindrical and the elliptical geometries. Where necessary, the equations are simplified for applicability to a particular frequency-regime before demonstrating the conformance to the generic form of the dispersion relation. It is then shown that the coupled wavenumber solutions of all these systems can be represented on a single schematic.

3-D acoustic analysis of elliptical chamber mufflers having an end-inlet and a side-outlet: An impedance matrix approach

The acoustical behavior of an elliptical chamber muffler having an end-inlet and side-outlet port is analyzed semi-analytically. A uniform piston source is assumed to model the 3-D acoustic field in the elliptical chamber cavity. Towards this end, we consider the modal expansion of acoustic pressure field in the elliptical cavity in terms of angular and radial Mathieu functions, subjected to rigid wall condition, whereupon under the assumption of a point source, Green's function is obtained. On integrating this function over piston area of the side or end port and dividing it by piston area, one obtains the acoustic field, whence one can find the impedance matrix parameters characterizing the 2-port system. The acoustic performance of these configurations is evaluated in terms of transmission loss (TL). The analytical results thus obtained are compared with 3-D HA carried on a commercial software for certain muffler configurations. These show excellent agreement, thereby validating the 3-D semi-analytical piston driven model. The influence of the chamber length as well as the angular and axial location of the end and side ports on TL performance is also discussed, thus providing useful guidelines to the muffler designer. (c) 2011 Elsevier B.V. All rights reserved.

Does acoustic adaptation drive vertical stratification? A test in a tropical cricket assemblage

In species-rich assemblages, differential utilization of vertical space can be driven by resource availability. For animals that communicate acoustically over long distances under habitat-induced constraints, access to an effective transmission channel is a valuable resource. The acoustic adaptation hypothesis suggests that habitat acoustics imposes a selective pressure that drives the evolution of both signal structure and choice of calling sites by signalers. This predicts that species-specific signals transmit best in native habitats. In this study, we have tested the hypothesis that vertical stratification of calling heights of acoustically communicating species is driven by acoustic adaptation. This was tested in an assemblage of 12 coexisting species of crickets and katydids in a tropical wet evergreen forest. We carried out transmission experiments using natural calls at different heights from the forest floor to the canopy. We measured signal degradation using 3 different measures: total attenuation, signal-to-noise ratio (SNR), and envelope distortion. Different sets of species supported the hypothesis depending on which attribute of signal degradation was examined. The hypothesis was upheld by 5 species for attenuation and by 3 species each for SNR and envelope distortion. Only 1 species of 12 provided support for the hypothesis by all 3 measures of signal degradation. The results thus provided no overall support for acoustic adaptation as a driver of vertical stratification of coexisting cricket and katydid species.

A review of recent developments in parametric based acoustic emission techniques applied to concrete structures

This article presents a review of recent developments in parametric based acoustic emission (AE) techniques applied to concrete structures. It recapitulates the significant milestones achieved by previous researchers including various methods and models developed in AE testing of concrete structures. The aim is to provide an overview of the specific features of parametric based AE techniques of concrete structures carried out over the years. Emphasis is given to traditional parameter-based AE techniques applied to concrete structures. A significant amount of research on AE techniques applied to concrete structures has already been published and considerable attention has been given to those publications. Some recent studies such as AE energy analysis and b-value analysis used to assess damage of concrete bridge beams have also been discussed. The formation of fracture process zone and the AE energy released during the fracture process in concrete beam specimens have been summarised. A large body of experimental data on AE characteristics of concrete has accumulated over the last three decades. This review of parametric based AE techniques applied to concrete structures may be helpful to the concerned researchers and engineers to better understand the failure mechanism of concrete and evolve more useful methods and approaches for diagnostic inspection of structural elements and failure prediction/prevention of concrete structures.

Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network-oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network-aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network-oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network-oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network-aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation.

Evaluation of damage in reinforced concrete bridge beams using acoustic emission technique

Acoustic emission (AE) testing is a well-known method for damage identification of various concrete structures including bridges. This article presents a method to assess damage in reinforced concrete (RC) bridge beams subjected to incremental cyclic loading. The specifications in the standard NDIS-2421 were used to classify the damage in RC bridge beams. Earlier researchers classified the damage occurring in bridge beams by using crack mouth opening displacement (CMOD) and AE released and proposed a standard (NDIS-2421: the Japanese Society for NonDestructive Inspection). In general, multiple cracks take place in RC beams under bending; therefore, utilisation of CMOD for crack detection may not be appropriate. In the present study, the damage in RC beams is classified by using the AE released, deflection, strains in steel and concrete, because the measurement of the strains in steel and concrete is easy and the codes of practice are specified for different limit states (IS-456:2000). The observations made in the present experimental study have some important practical applications in assessing the state of damage of concrete structural members.

Use of acoustic emissions in flexural fatigue crack growth studies on concrete

The acoustic emission technique is used for monitoring the fatigue crack growth in plain concrete beams under three-point loading. Variable amplitude loading with step-wise increase in the maximum load is applied. The fatigue crack growth is continuously monitored using six acoustic sensors. The results of load, displacement, crack mouth opening displacement, acoustic events, and acoustic energy are simultaneously acquired during the test. It is seen that a Paris law type of relationship exists between the rate of increase of acoustic emission count per cycle and the stress intensity factor range. Using b-value analysis, different stages of fatigue fracture is explained. (C) 2012 Elsevier Ltd. All rights reserved.

An experimental study on cracking evolution in concrete and cement mortar by the b-value analysis of acoustic emission technique

Notched three point bend (TPB) specimens made with plain concrete and cement mortar were tested under crack mouth opening displacement (CMOD) control at a rate of 0.0004 mm/s and simultaneously acoustic emissions (AE) released were recorded during the experiments. Amplitude distribution analysis of AE released during concrete was carried out to study the development of fracture process in concrete and mortar specimens. The slope of the log-linear frequency-amplitude distribution of AE is known as the AE based b-value. The AE based b-value was computed in terms of physical process of time varying applied load using cumulative frequency distribution (Gutenberg-Richter relationship) and discrete frequency distribution (Aki's method) of AE released during concrete fracture. AE characteristics of plain concrete and cement mortar were studied and discussed and it was observed that the AE based b-value analysis serves as a tool to identify the damage in concrete structural members. (C) 2012 Elsevier Ltd. All rights reserved.

Experimental Investigation on the Effect of Advanced Bus-Clamping Pulsewidth Modulation on Motor Acoustic Noise

Voltage source inverters (VSIs) supply nonsinusoidal voltages to induction motor drives, leading to line current distortion and torque pulsation. Conventional space vector pulsewidth modulation (PWM) techniques are widely used in VSIs on the account of good waveform quality and high dc bus utilization. In a conventional space vector PWM technique, the switching sequence begins with one zero state and ends with the other zero state in a subcycle. Some novel switching sequences have been proposed, which employ only one zero state but apply one of the two active states twice in a subcycle. One pair of such special switching sequences has recently been shown to reduce the pulsating torque considerably. In this paper, the conventional and special switching sequences are compared experimentally in terms of acoustic noise. In the low-and medium-speed ranges, the special switching sequence is seen to reduce the amplitude of the tonal component of noise at the switching frequency considerably and is also found to result in spread spectrum.

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Analysis of large-amplitude stratospheric mountain wave event observed from the AIRS and MLS sounders over the western Himalayan region

Mountain waves in the stratosphere have been observed over elevated topographies using both nadir-looking and limb-viewing satellites. However, the characteristics of mountain waves generated over the Himalayan Mountain range and the adjacent Tibetan Plateau are relatively less explored. The present study reports on three-dimensional (3-D) properties of a mountain wave event that occurred over the western Himalayan region on 9 December 2008. Observations made by the Atmospheric Infrared Sounder on board the Aqua and Microwave Limb Sounder on board the Aura satellites are used to delineate the wave properties. The observed wave properties such as horizontal (lambda(x), lambda(y)) and vertical (lambda(z)) wavelengths are 276 km (zonal), 289 km (meridional), and 25 km, respectively. A good agreement is found between the observed and modeled/analyzed vertical wavelength for a stationary gravity wave determined using the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis winds. The analysis of both the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis and MERRA winds shows that the waves are primarily forced by strong flow across the topography. Using the 3-D properties of waves and the corrected temperature amplitudes, we estimated wave momentum fluxes of the order of similar to 0.05 Pa, which is in agreement with large-amplitude mountain wave events reported elsewhere. In this regard, the present study is considered to be very much informative to the gravity wave drag schemes employed in current general circulation models for this region.

Acoustic and optical phonon dynamics from femtosecond time-resolved optical spectroscopy of the superconducting iron pnictide Ca(Fe0.944Co0.056)(2)As-2

We report the temperature evolution of coherently excited acoustic and optical phonon dynamics in the superconducting iron pnictide single crystal Ca(Fe0.944Co0.056)(2)As-2 across the spin density wave transition at T-SDW similar to 85 K and the superconducting transition at T-SC similar to 20 K. The strain pulse propagation model applied to the generation of the acoustic phonons yields the temperature dependence of the optical constants, and longitudinal and transverse sound velocities in the temperature range from 3.1 K to 300 K. The frequency and dephasing times of the phonons show anomalous temperature dependence below T-SC indicating a coupling of these low-energy excitations with the Cooper-pair quasiparticles. A maximum in the amplitude of the acoustic modes at T similar to 170 is seen, attributed to spin fluctuations and strong spin-lattice coupling before T-SDW. Copyright (c) EPLA, 2012

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T-2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness, and the saddle point based algorithm outperforms the IP method significantly.

Nanoparticle agglomeration in an evaporating levitated droplet for different acoustic amplitudes

Radiatively heated levitated functional droplets with nanosilica suspensions exhibit three distinct stages namely pure evaporation, agglomeration, and finally structure formation. The temporal history of the droplet surface temperature shows two inflection points. One inflection point corresponds to a local maximum and demarcates the end of transient heating of the droplet and domination of vaporization. The second inflection point is a local minimum and indicates slowing down of the evaporation rate due to surface accumulation of nanoparticles. Morphology and final precipitation structures of levitated droplets are due to competing mechanisms of particle agglomeration, evaporation, and shape deformation. In this work, we provide a detailed analysis for each process and propose two important timescales for evaporation and agglomeration that determine the final diameter of the structure formed. It is seen that both agglomeration and evaporation timescales are similar functions of acoustic amplitude (sound pressure level), droplet size, viscosity, and density. However, we show that while the agglomeration timescale decreases with initial particle concentration, the evaporation timescale shows the opposite trend. The final normalized diameter can be shown to be dependent solely on the ratio of agglomeration to evaporation timescales for all concentrations and acoustic amplitudes. The structures also exhibit various aspect ratios (bowls, rings, spheroids) which depend on the ratio of the deformation timescale (t(def)) and the agglomeration timescale (t(g)). For t(def) < t(g), a sharp peak in aspect ratio is seen at low concentrations of nanosilica which separates high aspect ratio structures like rings from the low aspect ratio structures like bowls and spheroids. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4775791]

Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset

In the design of practical web page classification systems one often encounters a situation in which the labeled training set is created by choosing some examples from each class; but, the class proportions in this set are not the same as those in the test distribution to which the classifier will be actually applied. The problem is made worse when the amount of training data is also small. In this paper we explore and adapt binary SVM methods that make use of unlabeled data from the test distribution, viz., Transductive SVMs (TSVMs) and expectation regularization/constraint (ER/EC) methods to deal with this situation. We empirically show that when the labeled training data is small, TSVM designed using the class ratio tuned by minimizing the loss on the labeled set yields the best performance; its performance is good even when the deviation between the class ratios of the labeled training set and the test set is quite large. When the labeled training data is sufficiently large, an unsupervised Gaussian mixture model can be used to get a very good estimate of the class ratio in the test set; also, when this estimate is used, both TSVM and EC/ER give their best possible performance, with TSVM coming out superior. The ideas in the paper can be easily extended to multi-class SVMs and MaxEnt models.