Detection and Evaluation of Impact damage in CFRP Laminates Using Ultrasound C-Scan and IR Thermography

Advanced composite structural components made up of Carbon Fibre Reinforced Polymers (CFRP) used in aerospace structures such as in Fuselage, Leading & Trailing edges of wing and tail, Flaps, Elevator, Rudder and entire wing structures encounter most critical type of damage induced by low velocity impact (<10 m/s) loads. Tool dropped during maintenance & service,and hailstone impacts on runways are common and unavoidable low-velocity impacts. These lowvelocity impacts induce defects such as delaminations, matrix cracking and debonding in the layered material, which are sub-surface in nature and are barely visible on the surface known as Barely Visible Impact Damage (BVID). These damages may grow under service load, leading to catastrophic failure of the structure. Hence detection, evaluation and characterization of these types of damage is of major concern in aerospace industries as the life of the component depends on the size and shape of the damage.In this paper, details of experimental investigations carried out and results obtained from a low-velocity impact of 30 Joules corresponding to the hailstone impact on the wing surface,simulated on the 6 mm CFRP laminates using instrumented drop-weight impact testing machine are presented. The Ultrasound C-scan and Infrared thermography imaging techniques were utilized extensively to detect, evaluate and characterize impact damage across the thickness of the laminates.

Detection and determination of manganese concentration in water using a fiber Bragg grating coupled with nanotechnology

Through this paper we experimentally demonstrate the fabrication of a fiber Bragg grating (FBG) chemical sensor to detect and determine the manganese concentration in water and compare our results with sophisticated spectroscopic methods, such as atomic absorption spectrometry and the inductively coupled plasma method. Here we propose a simple method to develop a thin layer of gold nanoparticles above the etched grating region to enhance the sensitivity of the reflected spectrum of the FBG. By doing so, we achieve a sensitivity of 1.26 nm/parts per million in determining the trace level of Mn in water. Proper reagents are used to detect manganese in water. (C) 2011 Optical Society of America

Electrochemical detection and removal of lead in water using poly(propylene imine) modified re-compressed exfoliated graphite electrodes

Modification of exfoliated graphite (EG) electrode with generation 2 poly(propylene imine) dendrimer by electrodeposition resulted in an electrochemical sensor which was used to detect lead ions in water to a limit of 1 ppb and a linear response between 2.5 and 40 ppb using square wave anodic stripping voltammetry (SW-ASV). Pb(II) was also removed from spiked water sample using a 40-mm diameter unmodified EG electrode with an applied potential of -1,000 mV for 180 min. A removal efficiency of 99% was calculated from a 150 mL sample. The results obtained in both cases using SW-ASV, correlated with atomic absorption spectroscopy.

Efficient Runtime Detection and Toleration of Asymmetric Races

We introduce ToleRace, a runtime system that allows programs to detect and even tolerate asymmetric data races. Asymmetric races are race conditions where one thread correctly acquires and releases a lock for a shared variable while another thread improperly accesses the same variable. ToleRace provides approximate isolation in the critical sections of lock-based parallel programs by creating a local copy of each shared variable when entering a critical section, operating on the local copies, and propagating the appropriate copies upon leaving the critical section. We start by characterizing all possible interleavings that can cause races and precisely describe the effect of ToleRace in each case. Then, we study the theoretical aspects of an oracle that knows exactly what type of interleaving has occurred. Finally, we present software implementations of ToleRace and evaluate them on multithreaded applications from the SPLASH2 and PARSEC suites.

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.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Joint data detection and dominant singular mode estimation in time varying reciprocal MIMO systems

This paper proposes an algorithm for joint data detection and tracking of the dominant singular mode of a time varying channel at the transmitter and receiver of a time division duplex multiple input multiple output beamforming system. The method proposed is a modified expectation maximization algorithm which utilizes an initial estimate to track the dominant modes of the channel at the transmitter and the receiver blindly; and simultaneously detects the un known data. Furthermore, the estimates are constrained to be within a confidence interval of the previous estimate in order to improve the tracking performance and mitigate the effect of error propagation. Monte-Carlo simulation results of the symbol error rate and the mean square inner product between the estimated and the true singular vector are plotted to show the performance benefits offered by the proposed method compared to existing techniques.

Resonance raman detection and estimation in the aqueous phase using water dispersible cyclodextrin: reduced-graphene oxide sheets

Resonance Raman spectroscopy is a powerful analytical tool for detecting and identifying analytes, but the associated strong fluorescence background severely limits the use of the technique. Here, we show that by attaching beta-cyclodextrin (beta-CD) cavities to reduced graphene-oxide (rGO) sheets we obtain a water dispersible material (beta-CD: rGO) that combines the hydrophobicity associated with rGO with that of the cyclodextrin cavities and provides a versatile platform for resonance Raman detection. Planar aromatic and dye molecules that adsorb on the rGO domains and nonplanar molecules included within the tethered beta-CD cavities have their fluorescence effectively quenched. We show that it is possible using the water dispersible beta-CD: rGO sheets to record the resonance Raman spectra of adsorbed and included organic chromophores directly in aqueous media without having to extract or deposit on a substrate. This is significant, as it allows us to identify and estimate organic analytes present in water by resonance Raman spectroscopy.

Efficient method of moving shadow detection and vehicle classification

Moving shadow detection and removal from the extracted foreground regions of video frames, aim to limit the risk of misconsideration of moving shadows as a part of moving objects. This operation thus enhances the rate of accuracy in detection and classification of moving objects. With a similar reasoning, the present paper proposes an efficient method for the discrimination of moving object and moving shadow regions in a video sequence, with no human intervention. Also, it requires less computational burden and works effectively under dynamic traffic road conditions on highways (with and without marking lines), street ways (with and without marking lines). Further, we have used scale-invariant feature transform-based features for the classification of moving vehicles (with and without shadow regions), which enhances the effectiveness of the proposed method. The potentiality of the method is tested with various data sets collected from different road traffic scenarios, and its superiority is compared with the existing methods. (C) 2013 Elsevier GmbH. All rights reserved.

Molecular flexibility tuned emission in ``V'' shaped naphthalimides: Hg(II) detection and aggregation-induced emission enhancement (AIEE)

Four ``V'' shaped 1,8-naphthalimides (1-4) have been synthesized and their fluorescence quantum-yields correlated to their molecular flexibility. The correlation was used for detection of Hg(II) via a chemodosimetric approach. 4 was found to be an AIE active molecule with the formation of fluorescent nanoaggregates.

Trace Driven Dynamic Deadlock Detection and Reproduction

Dynamic analysis techniques have been proposed to detect potential deadlocks. Analyzing and comprehending each potential deadlock to determine whether the deadlock is feasible in a real execution requires significant programmer effort. Moreover, empirical evidence shows that existing analyses are quite imprecise. This imprecision of the analyses further void the manual effort invested in reasoning about non-existent defects. In this paper, we address the problems of imprecision of existing analyses and the subsequent manual effort necessary to reason about deadlocks. We propose a novel approach for deadlock detection by designing a dynamic analysis that intelligently leverages execution traces. To reduce the manual effort, we replay the program by making the execution follow a schedule derived based on the observed trace. For a real deadlock, its feasibility is automatically verified if the replay causes the execution to deadlock. We have implemented our approach as part of WOLF and have analyzed many large (upto 160KLoC) Java programs. Our experimental results show that we are able to identify 74% of the reported defects as true (or false) positives automatically leaving very few defects for manual analysis. The overhead of our approach is negligible making it a compelling tool for practical adoption.

Polyfunctional Lewis Acids: Intriguing Solid-State Structure and Selective Detection and Discrimination of Nitroaromatic Explosives

Synthesis and crystal structures of three porphyrin-based polyfunctional Lewis acids 1-3 are reported. Intermolecular HgClHgCl (linear and -type) interactions in the solid state of the peripherally ArHgCl-decorated compound 3 lead to a fascinating 3D supramolecular architecture. Compound3 shows a selective fluorescence quenching response to picric acid and discriminates other nitroaromatic-based explosives. For the first time, an electron-deficient polyfunctional Lewis acid is shown to be useful for the selective detection and discrimination of nitroaromatic explosives. The Stern-Volmer quenching constant and detection limits of compound3 for picric acid are the best among the reported small-molecular receptors for nitroaromatic explosives. The electronic structure, Lewis acidity, and selective sensing characteristics of 3 are well corroborated by DFT calculations.

An enzyme-linked immuno focus assay for rapid detection and enumeration, and a newborn mouse model for human non-polio enteroviruses associated with acute diarrhea

We have recently reported significant association of non-polio enteroviruses (NPEVs) with acute and persistent diarrhea (18-21% of total diarrheal cases), and non-diarrheal Increased Frequency of Bowel Movements (IFoBM-ND) (about 29% of the NPEV infections) in children and that the NPEV-associated diarrhea was as significant as rotavirus diarrhea. However, their diarrhea-causing potential is yet to be demonstrated in an animal model system. Since the determination of virus titers by the traditional plaque assay takes 4-7 days, there is a need for development of a rapid method for virus titer determination to facilitate active clinical research on enterovirus-associated diarrhea. The goal of this study is to develop a cell-based rapid detection and enumeration method and to demonstrate the diarrhea-inducing potential of purified and characterized non-polio enteroviruses, which were isolated from diarrheic children. Here we describe generation of monoclonal and polyclonal antibodies against purified strains belonging to different serotypes, and development of an enzyme-linked immuno focus assay (ELIFA) for detection and enumeration of live NPEV particles in clinical and purified virus samples, and a newborn mouse model for NPEV diarrhea. Plaque-purified NPVEs, belonging to different serotypes, isolated from children with diarrhea, were grown in cell culture and purified by isopycnic CsCl density gradient centrifugation. By ELIFA, NPEVs could be detected and enumerated within 12 h post-infection. Our results demonstrated that Coxsackievirus B1 (CVB1) and CVB5 strains, isolated from diarrheic children, induced severe diarrhea in orally-inoculated 9-12 day-old mouse pups, fulfilling Koch's postulates. The methods described here would facilitate studies on NPEV-associated gastrointestinal disease. (C) 2015 Elsevier B.V. All rights reserved.