Volatile organic compound plume detection using wavelet analysis of video

A video based method to detect volatile organic compounds (VOC) leaking out of process equipments used in petrochemical refineries is developed. Leaking VOC plume from a damaged component causes edges present in image frames loose their sharpness. This leads to a decrease in the high frequency content of the image. The background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Plume regions in image frames are analyzed in low-band sub-images, as well. Image frames are compared with their corresponding low-band images. A maximum likelihood estimator (MLE) for adaptive threshold estimation is also developed in this paper. © 2008 IEEE

Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 74-82.Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.Günay, OsmanM.S

Preliminary development and testing of an open-path hydrocarbon sensor for oil and gas facility monitoring

2019 Summer.Includes bibliographical references.We developed an open-path laser absorption sensor for detection of unspeciated hydrocarbons for oil and gas production facility fence line monitoring. Such sensors can aid in maintaining air quality standards by quantifying greenhouse gas emissions and detecting emissions that cause adverse health effects. Our initial design employs a single-path detection system, though future implementations may use multiple paths for large-scale facility monitoring. The sensor uses a compact mid-infrared laser source in the spectral region of ~3.3 µm to measure absorption of several hydrocarbon species and is intended for open-paths of ~100 m to 1 km. Spectral simulations show that for typical conditions the hydrocarbons cause a transmission reduction of ~10% allowing for a robust measurement. The initial prototype system uses a helium-neon (He:Ne) laser at 3.391 µm for which signal contributions from methane and non-methane hydrocarbons are comparable. Closed-cell tests were performed with diluted methane (~150-250 ppm) to validate the transmission signals and showed good agreement with expected (calculated) values to within ~10%. The system employs a reference leg, with a 2nd detector (near the source), to normalize for laser power fluctuations. For improved signal-to-noise, particularly for detection of small concentrations and transmission changes, we employ phase-sensitive detection with a mechanical chopper and software based lock-in amplifier. This detection scheme, when employed in the field, allows measurement of transmission signals with stability <0.5% (based on coefficient of variation over 60 s). The portable field sensor system uses two refractive telescopes (2" diameter optics), a transmitter and receiver co-located on a mobile optical breadboard, and a reflector dictating the pathlength. We performed initial tests with pathlengths up to ~25 m (one way), though the design should allow paths in excess of 100 m. Methane was released for initial field tests at known flow rates near the center of the beam path. Transmission signals in agreement with expectations (given uncertainties in the wind and plume dispersion) were observed. The system should allow detection of leaks (emissions) for mass flows as low as ~0.1 g/s of methane (or equivalent optical signal from other species resulting in a 1% change in signal) for the case where the source is ~150 m from the beam path and under typical atmospheric conditions. Recommendations for future modifications are provided based on potential shortcomings identified by initial field testing. Initial field testing also proved that this technology could be a viable low-cost solution for hydrocarbon detection

Getting the most from medical VOC data using Bayesian feature learning

The metabolic processes in the body naturally produce a diverse set of Volatile Organic Compounds (VOCs), which are excreted in breath, urine, stool and other biological samples. The VOCs produced are odorous and influenced by disease, meaning olfaction can provide information on a person’s disease state. A variety of instruments exist for performing “artificial olfaction”: measuring a sample, such as patient breath, and producing a high dimensional output representing the odour. Such instruments may be paired with machine learning techniques to identify properties of interest, such as the presence of a given disease. Research shows good disease-predictive ability of artificial olfaction instrumentation. However, the statistical methods employed are typically off-the-shelf, and do not take advantage of prior knowledge of the structure of the high dimensional data. Since sample sizes are also typically small, this can lead to suboptimal results due to a poorly-learned model. In this thesis we explore ways to get more out of artificial olfaction data. We perform statistical analyses in a medical setting, investigating disease diagnosis from breath, urine and vaginal swab measurements, and illustrating both successful identification and failure cases. We then introduce two new latent variable models constructed for dimension reduction of artificial olfaction data, but which are widely applicable. These models place a Gaussian Process (GP) prior on the mapping from latent variables to observations. Specifying a covariance function for the GP prior is an intuitive way for a user to describe their prior knowledge of the data covariance structure. We also enable an approximate posterior and marginal likelihood to be computed, and introduce a sparse variant. Both models have been made available in the R package stpca hosted at https://github.com/JimSkinner/stpca. In experiments with artificial olfaction data, these models outperform standard feature learning methods in a predictive pipeline

The use of serface fuintionalised silica nano-particlate powders for the identification of gunshot residues from fingerprints

Gunshot residue (GSR) mixture consists of partially burned particles of propellant and characteristic particles of elements originating from the primer, bullet, propellant and some additives in the propellant. Since Harrison and Gillory [1] drew forensic scientists’ attention to the fact that GSR contained trace amounts of inorganic compounds such as lead, barium and antimony, a number of analytical techniques have been tested trying to find and establish sensitive, selective and reliable methods to identify and analyse gunshot residues. The standard procedure for the analysis of gunshot residues involves imaging these small metallic particles using scanning electron microscopy (SEM) and subsequent compositional analysis using Energy Dispersive X-ray Analysis (EDX). This study focuses on the analysis organic compounds in GSR. It is motivated by the increasing need to overcome the problems with the analysis of lead-free ammunitions. A comprehensive literature review was performed in order to determine the most commonly encountered organic compounds in GSR. These compounds include diphenylamine, methylcentralite, ethylcentralite, nitroglycerine, 2-nitrodiphenylamine and 4-nitrodiphenylamine. It has been clearly demonstrated using standard materials and appropriate calibration curves that gas chromatograph and mass spectrometry (GC/MS) is capable of providing limits of detection that are consistent with the concentrations of the key organic constituents found in gunshot residues. Furthermore, we have demonstrated that the relative concentrations of seven key components can be used to provide branding information on the shotgun cartridges. A strong relationship was found between the chemical composition of fired and unfired powder. Therefore, it is possible to differentiate between two ammunition brands through the analysis of the organic constituents. Traditional fingerprint powders such as titanium dioxide, aluminium, carbon black, iron oxide, lycopodium spores and rosin are used to enhance fingerprint left at the scene of crime. More recently nanoparticles have been demonstrated to be highly effective for the enhancement of the fingerprints [2]. Silica nano-particulates of defined size and shape were synthesised and functionalised with two different functional groups (phenyl and long chain hydrocarbon) using a Tri- phasic Reverse Emulsion (TPRE) method. These nano-particulates were characterised using scan electron microscope (SEM), transmission electron microscopy (TEM), elemental analysis, particles size analyser, BET surface area and solid-state nuclear magnetic resonance (NMR) spectroscopy. These powders were used as an effective agent to visualise latent fingerprints on different surfaces. Furthermore, they have been utilised to absorb any organic materials within the fingerprint from the discharged of weapon. Analyses of the adsorbed organic residues were performed using GC/MS and Raman spectroscopy. The results showed that the synthesised silica nano-particulate fingerprint powder gave better result in term of their ability to absorb organic materials in GSR and enhance the visualisation of the latent fingerprint compared to a single commercial powder

SST: Integrated Fluorocarbon Microsensor System Using Catalytic Modification

Selective, sensitive, and reliable sensors are urgently needed to detect air-borne halogenated volatile organic compounds (VOCs). This broad class of compounds includes chlorine, fluorine, bromine, and iodine containing hydrocarbons used as solvents, refrigerants, herbicides, and more recently as chemical warfare agents (CWAs). It is important to be able to detect very low concentrations of halocarbon solvents and insecticides because of their acute health effects even in very low concentrations. For instance, the nerve agent sarin (isopropyl methylphosphonofluoridate), first developed as an insecticide by German chemists in 1938, is so toxic that a ten minute exposure at an airborne concentration of only 65 parts per billion (ppb) can be fatal. Sarin became a household term when religious cult members on Tokyo subway trains poisoned over 5,500 people, killing 12. Sarin and other CWAs remain a significant threat to the health and safety of the general public. The goal of this project is to design a sensor system to detect and identify the composition and concentration of fluorinated VOCs. The system should be small, robust, compatible with metal oxide semiconductor (MOS) technology, cheap, if produced in large scale, and has the potential to be versatile in terms of low power consumption, detection of other gases, and integration in a portable system. The proposed VOC sensor system has three major elements that will be integrated into a microreactor flow cell: a temperature-programmable microhotplate array/reactor system which serves as the basic sensor platform; an innovative acoustic wave sensor, which detects material removal (instead of deposition) to verify and quantify the presence of fluorine; and an intelligent method, support vector machines, that will analyze the complex and high dimensional data furnished by the sensor system. The superior and complementary aspects of the three elements will be carefully integrated to create a system which is more sensitive and selective than other CWA detection systems that are commercially available or described in the research literature. While our sensor system will be developed to detect fluorinated VOCs, it can be adapted for other applications in which a target analyte can be catalytically converted for selective detection. Therefore, this investigation will examine the relationships between individual sensor element performance and joint sensor platform performance, integrated with state-of-the-art data analysis techniques. During development of the sensor system, the investigators will consider traditional reactor design concepts such as mass transfer and residence time effects, and will apply them to the emerging field of microsystems. The proposed research will provide the fundamental basis and understanding for examining multifunctional sensor platforms designed to provide extreme selectivity to targeted molecules. The project will involve interdisciplinary researchers and students and will connect to K-12 and RET programs for underrepresented students from rural areas

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

RV SONNE 241 Cruise Report / Fahrtbericht, Manzanillo, 23.6.2015 – Guayaquil, 24.7.2015 : SO241 - MAKS: Magmatism induced carbon escape from marine sediments as a climate driver – Guaymas Basin, Gulf of California

SO241 set out to test the hypothesis that rift-related magmatism is able to increase carbon emissions from sedimentary basins to the extent that they can actively force climate. To this end we investigated a study area in the Guaymas Basin in the Gulf of California which is one of very few geological settings where rift-related magmatism presently leads to magmatic intrusions into a sediment basin. During the cruise we collected 1100 km of 2D seismic lines to image the extent and volume of magmatic intrusions as well as the extent of metamorphic overprinting of the surrounding sediments and associated subsurface sediment mobilization. We selected three typical seep sites above magmatic intrusions for detailed geochemical studies using gravity corers, multicorers and TV grab. With these samples we will be able to determine the pore water composition to assess the amount and composition of hydrocarbon compounds that are released from these systems. Detailed ocean bottom seismometer measurements at a seep site in the center of the Guaymas Basin will provide further insights into effects of magmatic intrusions on carbon release and diagenetic overprinting of the sediments. It will be possible to reconstruct its long-term seepage history from big carbonate blocks that we have collected with a TV-grab. The northeastern margin of the Guaymas Basin is known for the presence of gas hydrates. During the cruise we collected several seismic lines, which show a clear but unusually shallow BSR indicating high heat flow in the region. Using the seismic data we discovered a previously unknown geological structure on the flank of the northern rift segment: a large mound that seems to consist entirely of black smoker deposits. It seems to be the result of a recent intrusion into the underlying sediments and changes the view how such systems function. The structure was investigated with a comprehensive geochemical, geothermal, and video surveying program which revealed at least seven vents that are active simultaneously. These vents inject methane and helium-rich vent fluids several hundred meters up into the water column. These findings suggest that large-scale magmatism, for example during the opening of an ocean basin under the influence of a hot spot, can be an effective way of liberating large amounts of carbon high up into the water column. The data collected during SO241 will allow us to constrain the amount of carbon that can escape into the atmosphere during LIP emplacement and their relevance on a global scale can be assessed. In addition to reaching the main objectives of the project we discovered a large landslide complex that was probably associated with a tsunami