An integrated approach for precise road reconstruction from aerial imagery and LiDAR data

Accurate and detailed road models play an important role in a number of geospatial applications, such as infrastructure planning, traffic monitoring, and driver assistance systems. In this thesis, an integrated approach for the automatic extraction of precise road features from high resolution aerial images and LiDAR point clouds is presented. A framework of road information modeling has been proposed, for rural and urban scenarios respectively, and an integrated system has been developed to deal with road feature extraction using image and LiDAR analysis. For road extraction in rural regions, a hierarchical image analysis is first performed to maximize the exploitation of road characteristics in different resolutions. The rough locations and directions of roads are provided by the road centerlines detected in low resolution images, both of which can be further employed to facilitate the road information generation in high resolution images. The histogram thresholding method is then chosen to classify road details in high resolution images, where color space transformation is used for data preparation. After the road surface detection, anisotropic Gaussian and Gabor filters are employed to enhance road pavement markings while constraining other ground objects, such as vegetation and houses. Afterwards, pavement markings are obtained from the filtered image using the Otsu's clustering method. The final road model is generated by superimposing the lane markings on the road surfaces, where the digital terrain model (DTM) produced by LiDAR data can also be combined to obtain the 3D road model. As the extraction of roads in urban areas is greatly affected by buildings, shadows, vehicles, and parking lots, we combine high resolution aerial images and dense LiDAR data to fully exploit the precise spectral and horizontal spatial resolution of aerial images and the accurate vertical information provided by airborne LiDAR. Objectoriented image analysis methods are employed to process the feature classiffcation and road detection in aerial images. In this process, we first utilize an adaptive mean shift (MS) segmentation algorithm to segment the original images into meaningful object-oriented clusters. Then the support vector machine (SVM) algorithm is further applied on the MS segmented image to extract road objects. Road surface detected in LiDAR intensity images is taken as a mask to remove the effects of shadows and trees. In addition, normalized DSM (nDSM) obtained from LiDAR is employed to filter out other above-ground objects, such as buildings and vehicles. The proposed road extraction approaches are tested using rural and urban datasets respectively. The rural road extraction method is performed using pan-sharpened aerial images of the Bruce Highway, Gympie, Queensland. The road extraction algorithm for urban regions is tested using the datasets of Bundaberg, which combine aerial imagery and LiDAR data. Quantitative evaluation of the extracted road information for both datasets has been carried out. The experiments and the evaluation results using Gympie datasets show that more than 96% of the road surfaces and over 90% of the lane markings are accurately reconstructed, and the false alarm rates for road surfaces and lane markings are below 3% and 2% respectively. For the urban test sites of Bundaberg, more than 93% of the road surface is correctly reconstructed, and the mis-detection rate is below 10%.

Utilization of bio-oil for cooking and lighting

Among the available alternative sources of energy in Bangladesh bio-oil is recognized to be a promising alternative energy source. Bio-oil can be extracted by pyrolysis as well as expelling or solvent extractionmethod. In these days bio-oil is merely used in vehicles and power plants after some up gradation .However, it is not used for domestic purposes like cooking and lighting due to its high density and viscosity. This paper outlines the design of a gravity stove to use high dense and viscous bio-oil for cooking purpose. For this, Pongamia pinnata (karanj) oil extracted by solvent extraction method is used as fuel fed under gravity force. Efficiency of gravity stove with high dense and viscous bio-oil (karanj) is 11.81% which of kerosene stove is 17.80% also the discharge of karanj oil through gravity stove is sufficient for continuous burning. Thus, bio-oil can be effective replacement of kerosene for domestic purposes.

Discovering relevant features for effective query formulation

The quality of discovered features in relevance feedback (RF) is the key issue for effective search query. Most existing feedback methods do not carefully address the issue of selecting features for noise reduction. As a result, extracted noisy features can easily contribute to undesirable effectiveness. In this paper, we propose a novel feature extraction method for query formulation. This method first extract term association patterns in RF as knowledge for feature extraction. Negative RF is then used to improve the quality of the discovered knowledge. A novel information filtering (IF) model is developed to evaluate the proposed method. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics confirm that the proposed model achieved encouraging performance compared to state-of-the-art IF models.

A novel representation of bioacoustic events for content-based search in field audio data

Bioacoustic data can provide an important base for environmental monitoring. To explore a large amount of field recordings collected, an automated similarity search algorithm is presented in this paper. A region of an audio defined by frequency and time bounds is provided by a user; the content of the region is used to construct a query. In the retrieving process, our algorithm will automatically scan through recordings to search for similar regions. In detail, we present a feature extraction approach based on the visual content of vocalisations – in this case ridges, and develop a generic regional representation of vocalisations for indexing. Our feature extraction method works best for bird vocalisations showing ridge characteristics. The regional representation method allows the content of an arbitrary region of a continuous recording to be described in a compressed format.

Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Methods for extracting genomic DNA from whole blood samples : current perspectives

Deoxyribonucleic acid (DNA) extraction has considerably evolved since it was initially performed back in 1869. It is the first step required for many of the available downstream applications used in the field of molecular biology. Whole blood samples are one of the main sources used to obtain DNA, and there are many different protocols available to perform nucleic acid extraction on such samples. These methods vary from very basic manual protocols to more sophisticated methods included in automated DNA extraction protocols. Based on the wide range of available options, it would be ideal to determine the ones that perform best in terms of cost-effectiveness and time efficiency. We have reviewed DNA extraction history and the most commonly used methods for DNA extraction from whole blood samples, highlighting their individual advantages and disadvantages. We also searched current scientific literature to find studies comparing different nucleic acid extraction methods, to determine the best available choice. Based on our research, we have determined that there is not enough scientific evidence to support one particular DNA extraction method from whole blood samples. Choosing a suitable method is still a process that requires consideration of many different factors, and more research is needed to validate choices made at facilities around the world.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

Determination of imidacloprid in paddy water and soil by liquid chromatography electrospray ionization-tandem mass spectrometry

A method for the determination of imidacloprid in paddy water and soil was developed using liquid chromatography electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS). Separation of imidacloprid was carried out on a Shimadzu C18 column (150 mm × 4.6 mm, 4.6 μm) with an acetonitrile?water (50 : 50, v/v) mobile phase containing 0.1% of acetic acid. The flow rate was 0.3 mL/min in isocratic mode. The product ion at 209 m/z was selected for quantification in multiple-reaction monitoring scan mode. Imidacloprid residues in soil were extracted by a solid-liquid extraction method with acetonitrile. Water samples were filtered and directly injected for analysis without extraction. Detection limits of 0.5 μg/kg and 0.3 μg/L were achieved for soil and water samples, respectively. The method had recoveries of 90 ± 2% (n = 4) for soil samples and 100 ± 2% (n = 4) for water samples. A linear relationship was observed throughout the investigated range of concentrations (1-200 μg/L), with the correlation coefficients ranging from 0.999 to 1.000. © Pleiades Publishing, Ltd., 2010.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Real-time PCR as a surveillance tool for the detection of Trichinella infection in muscle samples from wildlife

Trichinella nematodes are the causative agent of trichinellosis, a meat-borne zoonosis acquired by consuming undercooked, infected meat. Although most human infections are sourced from the domestic environment, the majority of Trichinella parasites circulate in the natural environment in carnivorous and scavenging wildlife. Surveillance using reliable and accurate diagnostic tools to detect Trichinella parasites in wildlife hosts is necessary to evaluate the prevalence and risk of transmission from wildlife to humans. Real-time PCR assays have previously been developed for the detection of European Trichinella species in commercial pork and wild fox muscle samples. We have expanded on the use of real-time PCR in Trichinella detection by developing an improved extraction method and SYBR green assay that detects all known Trichinella species in muscle samples from a greater variety of wildlife. We simulated low-level Trichinella infections in wild pig, fox, saltwater crocodile, wild cat and a native Australian marsupial using Trichinella pseudospiralis or Trichinella papuae ethanol-fixed larvae. Trichinella-specific primers targeted a conserved region of the small subunit of the ribosomal RNA and were tested for specificity against host and other parasite genomic DNAs. The analytical sensitivity of the assay was at least 100 fg using pure genomic T. pseudospiralis DNA serially diluted in water. The diagnostic sensitivity of the assay was evaluated by spiking log of each host muscle with T. pseudospiralis or T. papuae larvae at representative infections of 1.0, 0.5 and 0.1 larvae per gram, and shown to detect larvae at the lowest infection rate. A field sample evaluation on naturally infected muscle samples of wild pigs and Tasmanian devils showed complete agreement with the EU reference artificial digestion method (k-value = 1.00). Positive amplification of mouse tissue experimentally infected with T. spiralis indicated the assay could also be used on encapsulated species in situ. This real-time PCR assay offers an alternative highly specific and sensitive diagnostic method for use in Trichinella wildlife surveillance and could be adapted to wildlife hosts of any region. (C) 2012 Elsevier B.V. All rights reserved.

Assessment and control of Bacillus cereus emetic toxin in food

Despite of improving levels of hygiene, the incidence of registered food borne disease has been at the same level for many years: there were 40 to 90 epidemics in which 1000-9000 persons contracted food poisoning through food or drinking water in Finland. Until the year 2004 salmonella and campylobacter were the most common bacterial causes of food borne diseases, but in years 2005-2006 Bacillus cereus was the most common. Similar developement has been published i.e. in Germany already in the 1990´s. One reason for this can be Bacillus cereus and its emetic toxin, cereulide. Bacillus cereus is a common environmental bacterium that contaminates raw materials of food. Otherwise than salmonella and campylobacter, Bacillus cereus is a heat resistant bacterium, capable of surviving most cooking procedures due to the production of highly thermo resistant spores. The food involved has usually been heat treated and surviving spores are the source of the food poisoning. The heat treatment induces germination of the spore and the vegetative cells then produce toxins. This doctoral thesis research focuses on developing methods for assessing and eliminating risks to food safety by cereulide producing Bacillus cereus. The biochemistry and physiology of cereulide production was investigated and the results were targeted to offer tools for minimizing toxin risk in food during the production. I developed methods for the extraction and quantitative analysis of cereulide directly from food. A prerequisite for that is knowledge of the chemical and physical properties of the toxin. Because cereulide is practically insoluble in water, I used organic solvents; methanol, ethanol and pentane for the extraction. For extraction of bakery products I used high temperature (100C) and pressure (103.4 bars). Alternaties for effective extraction is to flood the plain food with ethanol, followed by stationary equilibration at room temperature. I used this protocol for extracting cereulide from potato puree and penne. Using this extraction method it is also possible also extract cereulide from liquid food, like milk. These extraction methods are important improvement steps for studying of Bacillus cereus emetic food poisonings. Prior my work, cereulide extraction was done using water. As the result, the yield was poor and variable. To investigate suspected food poisonings, it is important to show actual toxicity of the incriminated food. Many toxins, but not cereulide, inactivate during food processing like heating. The next step is to identify toxin by chemical methods. I developed with my colleague Maria Andesson a rapid assay for the detection of cereulide toxicity, within 5 to 15 minutes. By applying this test it is possible to rapidly detect which food was causing the food poisoning. The chemical identification of cereulide was achieved using mass spectrometry. I used cereulide specific molecular ions, m/z (+/-0.3) 1153.8 (M+H+), 1171.0 (M+NH4+), 1176.0 (M+Na+) and 1191.7 (M+K+) for reliable identification. I investigated foods to find out their amenability to accumulate cereulide. Cereulide was formed high amounts (0.3 to 5.5 microg/g wet wt) when of cereulide producing B. cereus strains were present in beans, rice, rice-pastry and meat-pastry, if stored at non refrigerated temperatures (21-23C). Rice and meat pastries are frequently consumed under conditions where no cooled storage is available e.g. picnics and outdoor events. Bacillus cereus is a ubiquitous spore former and is therefore difficult to eliminate from foods. It is therefore important to know which conditions will affect the formation of cereulide in foods. My research showed that the cereulide content was strongly (10 to 1000 fold differences in toxin content) affected by the growth environment of the bacterium. Storage of foods under nitrogen atmosphere (> 99.5 %) prevented the production of cereulide. But when also carbon dioxide was present, minimizing the oxygen contant (< 1%) did not protect the food from formation of cereulide in preliminary experiments. Also food supplements affected cereulide production at least in the laboratory. Adding free amino acids, leucine and valine, stimulated cereulide production 10 to 20 fold. In peptide bonded form these amino acids are natural constituents in all proteins. Interestingly, adding peptide bonded leucine and valine had no significant effect on cereulide production. Free amino acids leucine and valine are approved food supplements and widely used as flawour modifiers in food technology. My research showed that these food supplements may increase food poisoning risk even though they are not toxic themselves.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Estudo em Escala Piloto de Parâmetros de Monitoramento e Operação de Biopilhas no Tratamento de Solos Contaminados por Hidrocarbonetos de Petróleo

O objetivo do trabalho foi identificar ferramentas e indicadores adequados ao monitoramento e à otimização de processos de biorremediação, incluindo parâmetros físicos, químicos e microbiológicos definidos em estudos de tratabilidade de solos contaminados por óleo cru em escala de laboratório e comparar estratégias de biorremediação, tais como bioestímulo e bioaumento conduzidas em simulações de biopilhas dinâmicas ou estáticas. Quando três métodos de extração de hidrocarbonetos de petróleo de solo arenoso e franco-argiloso para análise cromatográfica (Soxhlet-SOX, microondas-MARS e extração acelerada por solvente-ASE) foram comparados entre si, concluiu-se que a técnica que promove a melhor recuperação depende da fração de interesse (n-alcanos, HRP, MCNR, HPA), das características texturais do solo (teores de areia, silte e argila) e da idade da contaminação. Dentre os indicadores de densidade populacional microbiana (microrganismos heterotróficos totais-PHT, população de fungos-PF e população microbiana degradadora de óleo (PDO) passíveis de utilização para indicar a taxa de degradação de compostos orgânicos presentes no solo tais como os hidrocarbonetos de petróleo, o PDO mostrou-se o mais adequado em conjunto com a produção de CO2 aferida pelo método respirométrico. Quando a estratégia de biorremediação de solo franco-argiloso contaminado com óleo cru a 3% (m m-1) utilizando bioestímulo (ajuste de pH, umidade e taxa C:N:P) foi comparada ao bioaumento (bioestímulo e adição de inóculo de microrganismos extraídos, enriquecidos e aclimatizados ao óleo cru como fonte de carbono), em sistemas de bancada simulando biopilha dinâmica (microcosmo M) e biopilha estática com aeração forçada (reator B), o tratamento que apresentou melhor remoção (32%) de HTP após 121 dias foi o bioaumento em biopilha estática. Para HPA, o tratamento que alcançou a melhor remoção (33%) foi com bioestímulo também em biopilha estática. A avaliação da taxa de mortalidade (%) de Eisenia andrei exposta tanto a solos recém-contaminados por óleo cru e preparados para bioestímulo (BIOS) e bioaumento (BIOA) a serem tratados em biopilhas dinâmicas e estáticas em escala de laboratório mostrou que após 56 dias de exposição da E. andrei, todos os solos produziram letalidade de 100%, quer fossem os solos recém-contaminados e preparados para os diferentes tratamentos (BIOS M, BIOS B, BIOA M, BIOA B) ou após 121 dias de tratamento. Tal resultado confirma que a biorremediação foi incipiente também do ponto de vista de remoção da ecotoxicidade. Em linhas gerais, a biorremediação de solo franco-argiloso contaminado por óleo cru, contendo tanto contaminação antiga quanto recente, reúne os maiores desafios à biorremediação, tanto do ponto de vista da composição textural do solo quanto da natureza do contaminante. Os processos são aparentemente lentos e requerem ferramentas auxiliares para aceleração dos mesmos. Recomenda-se no futuro, condução de experimentos com o uso de diferentes surfactantes, com ênfase em biosurfactantes

Aplicação da metodologia de dissociação em alta resolução (HRM) para determinação de perfis genéticos com interesse forense

A genética forense tem grande importância na geração de provas em casos de violência sexual, paternidade criminal, identificação de cadáveres e investigação de evidências de locais de crime. A análise de STRs apresenta grande poder de discriminação, mas é uma metodologia multi-etapas, trabalhosa, cara e em muitos casos a análise genética é prejudicada pela baixa quantidade e qualidade das evidências coletadas. Este estudo teve como objetivo desenvolver e caracterizar uma metodologia de triagem de amostras forenses através da análise de perfis de dissociação em alta resolução (HRM) de regiões do DNA mitocondrial, o qual está presente em maior número de cópias e mais resistente a degradação. Para tanto, foram extraídos DNAs de 68 doadores. Estas amostras foram sequenciadas e analisadas por HRM para sete alvos no DNA mitocondrial. Também foram realizados ensaios para determinar a influência do método de extração, da concentração e nível de degradação do DNA no perfil de HRM obtido para uma amostra. Os resultados demonstraram a capacidade da técnica de excluir indivíduos com sequências diferentes da referência comparativa em cinco regiões amplificadas. Podem ser analisadas em conjunto, amostras de DNA com variação de concentração de até a ordem de 100 vezes e extraídas por diferentes metodologias. Condições de degradação de material genético não prejudicaram a obtenção de perfis de dissociação em alta resolução. A sensibilidade da técnica foi aprimorada com a análise de produtos de amplificação de tamanho reduzido. A fim de otimizar o ensaio foi testada a análise de HRM em reações de PCR duplex. Um dos pares de amplificação forneceu perfis de HRM compatíveis com resultados obtidos de reações com amplificação de apenas um dos alvos. Através da análise conjunta das cinco regiões, esta metodologia visa a identificação de indivíduos não relacionados com as referências comparativas, diminuindo o número de amostras a serem analisadas por STRs, reduzindo gastos e aumentando a eficiência da rotina de laboratórios de genética forense.

浅论区域水土流失模型设计及参数采集方法

在已有区域试验研究和观测数据、水土流失因子研究成果基础上,基于坡面土壤侵蚀机理对区域水土流失过程进行分析,基于结合GIS空间分析功能,进行了区域尺度上水土流失模型设计的初步尝试,并提出了模型所需参数的提取方法及精度要求,本模型在延河流域试运算结果反映了接近现实的土壤侵蚀时空分布趋势和分异。