Burst event detection in wall turbulence by WVITA method

Wavelet Variable Interval Time Average (WVITA) is introduced as a method incorporating burst event detection in wall turbulence. Wavelet transform is performed to unfold the longitudinal fluctuating velocity time series measured in the near wall region of a turbulent boundary layer using hot-film anemometer. This unfolding is both in time and in space simultaneously. The splitted kinetic of the longitudinal fluctuating velocity time series among different scales is obtained by integrating the square of wavelet coefficient modulus over temporal space. The time scale that related to burst events in wall turbulence passing through the fixed probe is ascertained by maximum criterion of the kinetic energy evolution across scales. Wavelet transformed localized variance of the fluctuating velocity time series at the maximum kinetic scale is put forward instead of localized short time average variance in Variable Interval Time Average (VITA) scheme. The burst event detection result shows that WVITA scheme can avoid erroneous judgement and solve the grouping problem more effectively which is caused by VITA scheme itself and can not be avoided by adjusting the threshold level or changing the short time average interval.

Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

Improvements to Concrete Column Detection in Live Video

The automated detection of structural elements (e.g. concrete columns) in visual data is useful in many construction and maintenance applications. The research in this area is under initial investigation. The authors previously presented a concrete column detection method that utilized boundary and color information as detection cues. However, the method is sensitive to parameter selection, which reduces its ability to robustly detect concrete columns in live videos. Compared against the previous method, the new method presented in this paper reduces the reliance of parameter settings mainly in three aspects. First, edges are located using color information. Secondly, the orientation information of edge points is considered in constructing column boundaries. Thirdly, an artificial neural network for concrete material classification is developed to replace concrete sample matching. The method is tested using live videos, and results are compared with the results obtained with the previous method to demonstrate the new method improvements.

Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

An anomaly detection approach is considered for the mine hunting in sonar imagery problem. The authors exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of 'normal' natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier. © 2012 The Institution of Engineering and Technology.

Tropospheric aerosol detection over Southern Ireland using a backscatter Raman lidar

Lidar is an optical remote sensing instrument that can measure atmospheric parameters. A Raman lidar instrument (UCLID) was established at University College Cork to contribute to the European lidar network, EARLINET. System performance tests were carried out to ensure strict data quality assurance for submission to the EARLINET database. Procedures include: overlap correction, telecover test, Rayleigh test and zero bin test. Raman backscatter coefficients, extinction coefficients and lidar ratio were measured from April 2010 to May 2011 and February 2012 to June 2012. Statistical analysis of the profiles over these periods provided new information about the typical atmospheric scenarios over Southern Ireland in terms of aerosol load in the lower troposphere, the planetary boundary layer (PBL) height, aerosol optical density (AOD) at 532 nm and lidar ratio values. The arithmetic average of the PBL height was found to be 608 ± 138 m with a median of 615 m, while average AOD at 532 nm for clean marine air masses was 0.119 ± 0.023 and for polluted air masses was 0.170 ± 0.036. The lidar ratio showed a seasonal dependence with lower values found in winter and autumn (20 ± 5 sr) and higher during spring and winter (30 ± 12 sr). Detection of volcanic particles from the eruption of the volcano Eyjafjallajökull in Iceland was measured between 21 April and 7 May 2010. The backscatter coefficient of the ash layer varied between 2.5 Mm-1sr-1 and 3.5 Mm-1sr-1, and estimation of the AOD at 532 nm was found to be between 0.090 and 0.215. Several aerosol loads due to Saharan dust particles were detected in Spring 2011 and 2012. Lidar ratio of the dust layers were determine to be between 45 and 77 sr and AOD at 532 nm during the dust events range between 0.84 to 0.494.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Chemical composition observed over the mid-Atlantic and the detection of pollution signatures far from source regions

The atmospheric composition of the central North Atlantic region has been sampled using the FAAM BAe146 instrumented aircraft during the Intercontinental Transport of Ozone and Precursors (ITOP) campaign, part of the wider International Consortium for Atmospheric Research on Transport and Transformation (ICARTT). This paper presents an overview of the ITOP campaign. Between late July and early August 2004, twelve flights comprising 72 hours of measurement were made in a region from approximately 20 to 40°W and 33 to 47°N centered on Faial Island, Azores, ranging in altitude from 50 to 9000 m. The vertical profiles of O3 and CO are consistent with previous observations made in this region during 1997 and our knowledge of the seasonal cycles within the region. A cluster analysis technique is used to partition the data set into air mass types with distinct chemical signatures. Six clusters provide a suitable balance between cluster generality and specificity. The clusters are labeled as biomass burning, low level outflow, upper level outflow, moist lower troposphere, marine and upper troposphere. During this summer, boreal forest fire emissions from Alaska and northern Canada were found to provide a major perturbation of tropospheric composition in CO, PAN, organic compounds and aerosol. Anthropogenic influenced air from the continental boundary layer of the USA was clearly observed running above the marine boundary layer right across the mid-Atlantic, retaining high pollution levels in VOCs and sulfate aerosol. Upper level outflow events were found to have far lower sulfate aerosol, resulting from washout on ascent, but much higher PAN associated with the colder temperatures. Lagrangian links with flights of other aircraft over the USA and Europe show that such signatures are maintained many days downwind of emission regions. Some other features of the data set are highlighted, including the strong perturbations to many VOCs and OVOCs in this remote region.

Damage Detection in Flexible Plate Using Lamb Waves

This paper presents an experimental technique for structural health monitoring (SHM) based on Lamb waves approach in an aluminum plate using piezoelectric material as actuators and sensors. Lamb waves are a form of elastic perturbation that remains guided between two parallel free surfaces, such as the upper and lower surfaces of a plate, beam or shelf. Lamb waves are formed when the actuator excites the surface of the structure with a pulse after receiving a signal. Two PZTs were placed in the plate surface and one of them was used to send a predefined wave through the structure. Thus, the other PZT (adjacent) becomes the sensor. Using this methodology, this paper presents one case of damage detection considering the aluminum plate in the free-free-free-free boundary condition. The damage was simulated by adding additional mass on the plate. It is proposed two damage detection indexes obtained from the experimental signal, involving the Fast Fourier Transform (FFT) and the power spectral density (PSD) that were computed using the output signal. The results show the viability of the presented methodology to damage detection in smart structures

A well-balanced flow equation for noise removal and edge detection

In this paper, an anisotropic nonlinear diffusion equation for image restoration is presented. The model has two terms: the diffusion and the forcing term. The balance between these terms is made in a selective way, in which boundary points and interior points of the objects that make up the image are treated differently. The optimal smoothing time concept, which allows for finding the ideal stop time for the evolution of the partial differential equation is also proposed. Numerical results show the proposed model's high performance.

Iodine speciation in atmospheric aerosols in the marine boundary layer

Iodine chemistry plays an important role in the tropospheric ozone depletion and the new particle formation in the Marine Boundary Layer (MBL). The sources, reaction pathways, and the sinks of iodine are investigated using lab experiments and field observations. The aims of this work are, firstly, to develop analytical methods for iodine measurements of marine aerosol samples especially for iodine speciation in the soluble iodine; secondly, to apply the analytical methods in field collected aerosol samples, and to estimate the characteristics of aerosol iodine in the MBL. Inductively Coupled Plasma – Mass Spectrometry (ICP-MS) was the technique used for iodine measurements. Offline methods using water extraction and Tetra-methyl-ammonium-hydroxide (TMAH) extraction were applied to measure total soluble iodine (TSI) and total insoluble iodine (TII) in the marine aerosol samples. External standard calibration and isotope dilution analysis (IDA) were both conducted for iodine quantification and the limits of detection (LODs) were both 0.1 μg L-1 for TSI and TII measurements. Online couplings of Ion Chromatography (IC)-ICP-MS and Gel electrophoresis (GE)-ICP-MS were both developed for soluble iodine speciation. Anion exchange columns were adopted for IC-ICP-MS systems. Iodide, iodate, and unknown signal(s) were observed in these methods. Iodide and iodate were separated successfully and the LODs were 0.1 and 0.5 μg L-1, respectively. Unknown signals were soluble organic iodine species (SOI) and quantified by the calibration curve of iodide, but not clearly identified and quantified yet. These analytical methods were all applied to the iodine measurements of marine aerosol samples from the worldwide filed campaigns. The TSI and TII concentrations (medians) in PM2.5 were found to be 240.87 pmol m-3 and 105.37 pmol m-3 at Mace Head, west coast of Ireland, as well as 119.10 pmol m-3 and 97.88 pmol m-3 in the cruise campaign over the North Atlantic Ocean, during June – July 2006. Inorganic iodine, namely iodide and iodate, was the minor iodine fraction in both campaigns, accounting for 7.3% (median) and 5.8% (median) in PM2.5 iodine at Mace Head and over the North Atlantic Ocean, respectively. Iodide concentrations were higher than iodate in most of the samples. In the contrast, more than 90% of TSI was SOI and the SOI concentration was correlated significantly with the iodide concentration. The correlation coefficients (R2) were both higher than 0.5 at Mace Head and in the first leg of the cruise. Size fractionated aerosol samples collected by 5 stage Berner impactor cascade sampler showed similar proportions of inorganic and organic iodine. Significant correlations were obtained in the particle size ranges of 0.25 – 0.71 μm and 0.71 – 2.0 μm between SOI and iodide, and better correlations were found in sunny days. TSI and iodide existed mainly in fine particle size range (< 2.0 μm) and iodate resided in coarse range (2.0 – 10 μm). Aerosol iodine was suggested to be related to the primary iodine release in the tidal zone. Natural meteorological conditions such as solar radiation, raining etc were observed to have influence on the aerosol iodine. During the ship campaign over the North Atlantic Ocean (January – February 2007), the TSI concentrations (medians) ranged 35.14 – 60.63 pmol m-3 among the 5 stages. Likewise, SOI was found to be the most abundant iodine fraction in TSI with a median of 98.6%. Significant correlation also presented between SOI and iodide in the size range of 2.0 – 5.9 μm. Higher iodate concentration was again found in the higher particle size range, similar to that at Mace Head. Airmass transport from the biogenic bloom region and the Antarctic ice front sector was observed to play an important role in aerosol iodine enhancement. The TSI concentrations observed along the 30,000 km long cruise round trip from East Asia to Antarctica during November 2005 – March 2006 were much lower than in the other campaigns, with a median of 6.51 pmol m-3. Approximately 70% of the TSI was SOI on average. The abundances of inorganic iodine including iodine and iodide were less than 30% of TSI. The median value of iodide was 1.49 pmol m-3, which was more than four fold higher than that of iodate (median, 0.28 pmol m-3). Spatial variation indicated highest aerosol iodine appearing in the tropical area. Iodine level was considerably lower in coastal Antarctica with the TSI median of 3.22 pmol m-3. However, airmass transport from the ice front sector was correlated with the enhance TSI level, suggesting the unrevealed source of iodine in the polar region. In addition, significant correlation between SOI and iodide was also shown in this campaign. A global distribution in aerosol was shown in the field campaigns in this work. SOI was verified globally ubiquitous due to the presence in the different sampling locations and its high proportion in TSI in the marine aerosols. The correlations between SOI and iodide were obtained not only in different locations but also in different seasons, implying the possible mechanism of iodide production through SOI decomposition. Nevertheless, future studies are needed for improving the current understanding of iodine chemistry in the MBL (e.g. SOI identification and quantification as well as the update modeling involving organic matters).

Evidence of direct detection of interstellar deuterium in the local interstellar medium by IBEX

We report the first in situ measurements of neutral deuterium originating in the local interstellar medium (LISM) in Earth’s orbit. These measurements were performed with the IBEX-Lo camera on NASA’s interstellar boundary explorer (IBEX) satellite. All data from the spring observation periods of 2009 through 2011 have been analysed. In the three years of the IBEX mission time, the observation geometry and orbit allowed for a total observation time of 115.3 days for the LISM. However, the effects of the spinning spacecraft and the stepping through 8 energy channels mean that we are only observing the interstellar wind for a total time of 1.44 days, in which 2 counts for interstellar deuterium were collected. We report here a conservative number, because a possibility of systematic error or additional noise, though eliminated in our analysis to the best of our knowledge, only supports detection at a 1-sigma level. From these observations, we derive a ratio D/H = (5.8 ± 4.4) × 10-4 at 1 AU. After modelling the transport and loss of D and H from the termination shock to Earth’s orbit, we find that our result of D/HLISM = (1.6 ± 1.2) × 10-5 agrees with D/HLIC = (1.6 ± 0.4) × 10-5 for the local interstellar cloud. This weak interstellar signal is extracted from a strong terrestrial background signal consisting of sputter products from the sensor’s conversion surface. As reference, we accurately measure the terrestrial D/H ratio in these sputtered products and then discriminate this terrestrial background source. Because of the diminishing D and H signal at Earth’s orbit during the rising solar activity due to photoionisation losses and increased photon pressure, our result demonstrates that in situ measurements of interstellar deuterium in the inner heliosphere are only possible during solar minimum conditions.

Test Facility to Study Surface-Interaction Processes for Particle Detection in Space

The Imager for Low Energetic Neutral Atoms test facility at the University of Bern was developed to investigate, characterize, and quantify physical processes on surfaces that are used to ionize neutral atoms before their analysis in neutral particle-sensing instruments designed for space research. The facility has contributed valuable knowledge of the interaction of ions with surfaces (e.g., fraction of ions scattered from surfaces and angular scattering distribution) and employs a novel measurement principle for the determination of secondary electron emission yields as a function of energy, angle of incidence, particle species, and sample surface for low particle energies. Only because of this test facility it was possible to successfully apply surface-science processes for the new detection technique for low-energetic neutral particles with energies below about 1 keV used in space applications. All successfully flown spectrometers for the detection of low-energetic neutrals based on the particle–surface interaction process use surfaces evaluated, tested, and calibrated in this facility. Many instruments placed on different spacecraft (e.g., Imager for Magnetopause-to-Aurora Global Exploration, Chandrayaan-1, Interstellar Boundary Explorer, etc.) have successfully used this technique.