Wavelet based spectral finite element modelling and detection of de-lamination in composite beams

In this paper, a model for composite beam with embedded de-lamination is developed using the wavelet based spectral finite element (WSFE) method particularly for damage detection using wave propagation analysis. The simulated responses are used as surrogate experimental results for the inverse problem of detection of damage using wavelet filtering. The WSFE technique is very similar to the fast fourier transform (FFT) based spectral finite element (FSFE) except that it uses compactly supported Daubechies scaling function approximation in time. Unlike FSFE formulation with periodicity assumption, the wavelet-based method allows imposition of initial values and thus is free from wrap around problems. This helps in analysis of finite length undamped structures, where the FSFE method fails to simulate accurate response. First, numerical experiments are performed to study the effect of de-lamination on the wave propagation characteristics. The responses are simulated for different de-lamination configurations for both broad-band and narrow-band excitations. Next, simulated responses are used for damage detection using wavelet analysis.

Spectral Finite Element Modeling of Complex Structures for Applications in Real-time Structural Health Monitoring

In this paper we discuss the recent progresses in spectral finite element modeling of complex structures and its application in real-time structural health monitoring system based on sensor-actuator network and near real-time computation of Damage Force Indicator (DFI) vector. A waveguide network formalism is developed by mapping the original variational problem into the variational problem involving product spaces of 1D waveguides. Numerical convergence is studied using a h()-refinement scheme, where is the wavelength of interest. Computational issues towards successful implementation of this method with SHM system are discussed.

On Convergence of Differential Evolution Over a Class of Continuous Functions With Unique Global Optimum

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

Multi-spectral satellite image classification using Glowworm Swarm Optimization

This paper investigates a new Glowworm Swarm Optimization (GSO) clustering algorithm for hierarchical splitting and merging of automatic multi-spectral satellite image classification (land cover mapping problem). Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to classify all the basic land cover classes of an urban region in a satisfactory manner. In unsupervised classification methods, the automatic generation of clusters to classify a huge database is not exploited to their full potential. The proposed methodology searches for the best possible number of clusters and its center using Glowworm Swarm Optimization (GSO). Using these clusters, we classify by merging based on parametric method (k-means technique). The performance of the proposed unsupervised classification technique is evaluated for Landsat 7 thematic mapper image. Results are evaluated in terms of the classification efficiency - individual, average and overall.

Evolution of 1/f α noise during electromigration stressing of metal film: Spectral signature of electromigration process

In this paper we report a systematic study of low-frequency 1/fα resistance fluctuation in a metal film at different stages of electromigration. The resistance fluctuation (noise) measurement was carried out in presence of a dc electromigration stressing current. We observe that in addition to the increase in the spectral power SV(f), the frequency dependence of the spectral power changes as the electromigration process progresses and the exponent α starts to change from 1 to higher value closer to 1.5. We interpret this change in α as arising due to an additional contribution to the spectral power with a 1/f3/2 component, which starts to contribute as the electromigration process progresses. This additional component SV(f) ∼ 1/f3/2 has been suggested to originate from long range diffusion that would accompany any electromigration process. The experimental observation finds support in a model simulation, where we also find that the enhancement of noise during electromigration stressing is accompanied by a change in spectral power frequency dependence.

Unified Spectral Efficiency Analysis of Cellular Systems with Channel-Aware Schedulers

Spectral efficiency is a key characteristic of cellular communications systems, as it quantifies how well the scarce spectrum resource is utilized. It is influenced by the scheduling algorithm as well as the signal and interference statistics, which, in turn, depend on the propagation characteristics. In this paper we derive analytical expressions for the short-term and long-term channel-averaged spectral efficiencies of the round robin, greedy Max-SINR, and proportional fair schedulers, which are popular and cover a wide range of system performance and fairness trade-offs. A unified spectral efficiency analysis is developed to highlight the differences among these schedulers. The analysis is different from previous work in the literature in the following aspects: (i) it does not assume the co-channel interferers to be identically distributed, as is typical in realistic cellular layouts, (ii) it avoids the loose spectral efficiency bounds used in the literature, which only considered the worst case and best case locations of identical co-channel interferers, (iii) it explicitly includes the effect of multi-tier interferers in the cellular layout and uses a more accurate model for handling the total co-channel interference, and (iv) it captures the impact of using small modulation constellation sizes, which are typical of cellular standards. The analytical results are verified using extensive Monte Carlo simulations.

Evidence for a thermally reversing window in bulk Ge–Te–Si glasses revealed by alternating differential scanning calorimetry

Experiments on Ge15Te85− x Si x glasses (2 ≤ x ≤ 12) using alternating differential scanning calorimetry (ADSC) indicate that these glasses exhibit one glass transition and two crystallization reactions upon heating. The glass transition temperature has been found to increase almost linearly with silicon content, in the entire composition tie-line. The first crystallization temperature (T c1) exhibits an increase with silicon content for x < 5; T c1 remains almost a constant in the composition range 5 < x ≤ 10 and it increases comparatively more sharply with silicon content thereafter. The specific heat change (ΔC p) is found to decrease with an increase in silicon content, exhibiting a minimum at x = 5 (average coordination number, r = 2.4); a continuous increase is seen in ΔC p with silicon concentration above x = 5. The effects seen in the variation with composition of T c1 and ΔC p at x = 5, are the specific signatures of the mean-field stiffness threshold at r = 2.4. Furthermore, a broad trough is seen in the enthalpy change (ΔH NR), which is indicative of a thermally reversing window in Ge15Te85− x Si x glasses in the composition range 2 ≤ x ≤ 6 (2.34 ≤ r ≤ 2.42).

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).

The spectral index-luminosity relationship for steep-spectrum cores in extra-galactic radio-sources

The spectral index-luminosity relationship for steep-spectrum cores in galaxies and quasars has been investigated, and it is found that the sample of galaxies supports earlier suggestions of a strong correlation, while there is weak evidence for a similar relationship for the quasars. It is shown that a strong spectral index-luminosity correlation can be used to set an upper limit to the velocities of the radio-emitting material which is expelled from the nucleus in the form of collimated beams or jets having relativistic bulk velocities. The data on cores in galaxies indicate that the Lorentz factors of the radiating material are less than about 2.

Radio-Optical Imaging of ATLBS Survey

We present the radio-optical imaging of ATLBS, a sensitive radio survey (Subrahmanyan et al. 2010). The primary aim of the ATLBS survey is to image low-power radio sources which form the bulk of the radio source population to moderately high red-shifts (z similar to 1.0). The accompanying multiband optical and near infra-red observations provide information about the hosts and environments of the radio sources. We give here details of the imaging of the radio data and optical data for the ATLBS survey.

Differential Reaction Kinetics, Cleavage Complex Formation, and Nonamer Binding Domain Dependence Dictate the Structure-Specific and Sequence-Specific Nuclease Activity of RAGs

During V(D)J recombination, RAG (recombination-activating gene) complex cleaves DNA based on sequence specificity. Besides its physiological function, RAG has been shown to act as a structure-specific nuclease. Recently, we showed that the presence of cytosine within the single-stranded region of heteroduplex DNA is important when RAGs cleave on DNA structures. In the present study, we report that heteroduplex DNA containing a bubble region can be cleaved efficiently when present along with a recombination signal sequence (RSS) in cis or trans configuration. The sequence of the bubble region influences RAG cleavage at RSS when present in cis. We also find that the kinetics of RAG cleavage differs between RSS and bubble, wherein RSS cleavage reaches maximum efficiency faster than bubble cleavage. In addition, unlike RSS, RAG cleavage at bubbles does not lead to cleavage complex formation. Finally, we show that the ``nonamer binding region,'' which regulates RAG cleavage on RSS, is not important during RAG activity in non-B DNA structures. Therefore, in the current study, we identify the possible mechanism by which RAG cleavage is regulated when it acts as a structure-specific nuclease. (C) 2011 Elsevier Ltd. All rights reserved.