Segmentation of a Speech Signal with Application of Fast Wavelet Transformation

The article describes the method of preliminary segmentation of a speech signal with wavelet transformation use, consisting of two stages. At the first stage there is an allocation of sibilants and pauses, at the second – the further segmentation of the rest signal parts.

Benefit segmentation of international travelers to Macau, China

The purpose of this study is threefold: (1) to identify the underlying benefits sought by international visitors to Macau, China, which has emerged as a popular gambling destination in Asia; (2) to segment tourists visiting Macau by employing a cluster analysis based on the benefits sought; and (3) to examine any salient differences between the segment groups with regard to their behavioral characteristics, socio-economic characteristics, and demographic profiles. A convenience sample was used to collect data in the Macau International Airport, in the Macau Ferry Terminal, and at the border gate with Mainland China. A total 1,513 useful surveys were retained for data analysis. Cluster analysis discloses four distinct clusters: "convention and business seekers," "family and vacation seekers," "gambling and shopping seekers," and "multi-purpose seekers." Based on the results of our findings, several managerial implications are discussed. © Taylor & Francis Group, LLC.

Application of Wavelet Decomposition to Document Line Segmentation

ACM Computing Classification System (1998): I.7, I.7.5.

The unequal segmentation of public preparedness for mass evacuation:a cross-national perspective

This thesis is concerned with understanding how Emergency Management Agencies (EMAs) influence public preparedness for mass evacuation across seven countries. Due to the lack of cross-national research (Tierney et al., 2001), there is a lack of knowledge on EMAs perspectives and approaches to the governance of public preparedness. This thesis seeks to address this gap through cross-national research that explores and contributes towards understanding the governance of public preparedness. The research draws upon the risk communication (Wood et al., 2011; Tierney et al., 2001) social marketing (Marshall et al., 2007; Kotler and Lee, 2008; Ramaprasad, 2005), risk governance (Walker et al., 2010, 2013; Kuhlicke et al., 2011; IRGC, 2005, 2007; Renn et al., 2011; Klinke and Renn, 2012), risk society (Beck, 1992, 1999, 2002) and governmentality (Foucault, 1978, 2003, 2009) literature to explain this governance and how EMAs responsibilize the public for their preparedness. EMAs from seven countries (Belgium, Denmark, Germany, Iceland, Japan, Sweden, the United Kingdom) explain how they prepare their public for mass evacuation in response to different types of risk. A cross-national (Hantrais, 1999) interpretive research approach, using qualitative methods including semi-structured interviews, documents and observation, was used to collect data. The data analysis process (Miles and Huberman, 1999) identified how the concepts of risk, knowledge and responsibility are critical for theorising how EMAs influence public preparedness for mass evacuation. The key findings grounded in these concepts include: - Theoretically, risk is multi-functional in the governance of public preparedness. It regulates behaviour, enables surveillance and acts as a technique of exclusion. - EMAs knowledge and how this influenced their assessment of risk, together with how they share the responsibility for public preparedness across institutions and the public, are key to the governance of public preparedness for mass evacuation. This resulted in a form of public segmentation common to all countries, whereby the public were prepared unequally.  - EMAs use their prior knowledge and assessments of risk to target public preparedness in response to particular known hazards. However, this strategy places the non-targeted public at greater risk in relation to unknown hazards, such as a man-made disaster. - A cross-national conceptual framework of four distinctive governance practices (exclusionary, informing, involving and influencing) are utilised to influence public preparedness. - The uncertainty associated with particular types of risk limits the application of social marketing as a strategy for influencing the public to take responsibility and can potentially increase the risk to the public.

Topic Segmentation: How Much Can We Do by Counting Words and Sequences of Words

In this paper, we present an innovative topic segmentation system based on a new informative similarity measure that takes into account word co-occurrence in order to avoid the accessibility to existing linguistic resources such as electronic dictionaries or lexico-semantic databases such as thesauri or ontology. Topic segmentation is the task of breaking documents into topically coherent multi-paragraph subparts. Topic segmentation has extensively been used in information retrieval and text summarization. In particular, our architecture proposes a language-independent topic segmentation system that solves three main problems evidenced by previous research: systems based uniquely on lexical repetition that show reliability problems, systems based on lexical cohesion using existing linguistic resources that are usually available only for dominating languages and as a consequence do not apply to less favored languages and finally systems that need previously existing harvesting training data. For that purpose, we only use statistics on words and sequences of words based on a set of texts. This solution provides a flexible solution that may narrow the gap between dominating languages and less favored languages thus allowing equivalent access to information.

Fractal Analysis for Cancer Research: Case Study and Simulation of Fractals

2010 Mathematics Subject Classification: 65D18.

Fuzzy c-means variants for medical image segmentation

Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques exist. One group of segmentation algorithms is based on clustering concepts. In this article we investigate several fuzzy c-means based clustering algorithms and their application to medical image segmentation. In particular we evaluate the conventional hard c-means (HCM) and fuzzy c-means (FCM) approaches as well as three computationally more efficient derivatives of fuzzy c-means: fast FCM with random sampling, fast generalised FCM, and a new anisotropic mean shift based FCM. © 2010 by IJTS, ISDER.

Strategic segmentation: when two monopolies are better than one

In this article we show that the price and the profit of an incumbent firm may increase after a new firm enters its market. Our analysis suggests that a well-established firm after competition emerges on its market might benefit from excluding some consumers from the low- end segment and concentrate only on its loyal consumers. We also find that strategic de-marketing can increase social welfare.

A high efficient biometrics approach for unconstrained iris segmentation and recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineates the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.

A novel 3-D segmentation algorithm for anatomic liver and tumor volume calculations for liver cancer treatment planning

Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. Present manual techniques for segmentation of the liver from Computed Tomography (CT) tend to be tedious and greatly dependent on the skill of the technician/doctor performing the task. ^ This dissertation presents the development and implementation of a fully integrated algorithm for 3-D liver and tumor segmentation from tri-phase CT that yield highly accurate estimations of the respective volumes of the liver and tumor(s). The algorithm as designed requires minimal human intervention without compromising the accuracy of the segmentation results. Embedded within this algorithm is an effective method for extracting blood vessels that feed the tumor(s) in order to plan effectively the appropriate treatment. ^ Segmentation of the liver led to an accuracy in excess of 95% in estimating liver volumes in 20 datasets in comparison to the manual gold standard volumes. In a similar comparison, tumor segmentation exhibited an accuracy of 86% in estimating tumor(s) volume(s). Qualitative results of the blood vessel segmentation algorithm demonstrated the effectiveness of the algorithm in extracting and rendering the vasculature structure of the liver. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out to determine if the manual initialization has any impact on the accuracy showed user initialization independence in the results. ^ The dissertation thus provides a complete 3-D solution towards liver cancer treatment planning with the opportunity to extract, visualize and quantify the needed statistics for liver cancer treatment. Since SIRT requires highly accurate calculation of the liver and tumor volumes, this new method provides an effective and computationally efficient process required of such challenging clinical requirements.^

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Fractal analysis of flocs formed in edible oil -water emulsions by image analysis techniques

Edible oil is an important contaminant in water and wastewater. Oil droplets smaller than 40 μm may remain in effluent as an emulsion and combine with other contaminants in water. Coagulation/flocculation processes are used to remove oil droplets from water and wastewater. By adding a polymer at proper dose, small oil droplets can be flocculated and separated from water. The purpose of this study was to characterize and analyze the morphology of flocs and floc formation in edible oil-water emulsions by using microscopic image analysis techniques. The fractal dimension, concentration of polymer, effect of pH and temperature are investigated and analyzed to develop a fractal model of the flocs. Three types of edible oil (corn, olive, and sunflower oil) at concentrations of 600 ppm (by volume) were used to determine the optimum polymer dosage and effect of pH and temperature. To find the optimum polymer dose, polymer was added to the oil-water emulsions at concentration of 0.5, 1.0, 1.5, 2.0, 3.0 and 3.5 ppm (by volume). The clearest supernatants obtained from flocculation of corn, olive, and sunflower oil were achieved at polymer dosage of 3.0 ppm producing turbidities of 4.52, 12.90, and 13.10 NTU, respectively. This concentration of polymer was subsequently used to study the effect of pH and temperature on flocculation. The effect of pH was studied at pH 5, 7, 9, and 11 at 30°C. Microscopic image analysis was used to investigate the morphology of flocs in terms of fractal dimension, radius of oil droplets trapped in floc, floc size, and histograms of oil droplet distribution. Fractal dimension indicates the density of oil droplets captured in flocs. By comparison of fractal dimensions, pH was found to be one of the most important factors controlling droplet flocculation. Neutral pH or pH 7 showed the highest degree of flocculation, while acidic (pH 5) and basic pH (pH 9 and pH 11) showed low efficiency of flocculation. The fractal dimensions achieved from flocculation of corn, olive, and sunflower oil at pH 7 and temperature 30°C were 1.2763, 1.3592, and 1.4413, respectively. The effect of temperature was explored at temperatures 20°, 30°, and 40°C and pH 7. The results of flocculation of oil at pH 7 and different temperatures revealed that temperature significantly affected flocculation. The fractal dimension of flocs formed in corn, olive and sunflower oil emulsion at pH 7 and temperature 20°, 30°, and 40°C were 1.82, 1.28, 1.29, 1.62, 1.36, 1.42, 1.36, 1.44, and 1.28, respectively. After comparison of fractal dimension, radius of oil droplets captured, and floc length in each oil type, the optimal flocculation temperature was determined to be 30°C. ^

Estudo de arranjos de antenas de microfita com Patch quase-fractal para comunicações sem fio

In this dissertation, are presented two microstrip antennas and two arrays for applications in wireless communication systems multiband. Initially, we studied an antenna and a linear array consisting of two elements identical to the patch antenna isolated. The shape of the patch used in both structures is based on fractal geometry and has multiband behavior. Next a new antenna is analyzed and a new array such as initial structure, but with the truncated ground plane, in order to obtain better bandwidths and return loss. For feeding the structures, we used microstrip transmission line. In the design of planar structures, was used HFSS software for the simulation. Next were built and measures electromagnetic parameters such as input impedance and return loss, using vector network analyzer in the telecommunications laboratory of Federal University of Rio Grande do Norte. The experimental results were compared with the simulated and showed improved return loss for the first array and also appeared a fourth band and increased directivity compared with the isolated antenna. The first two benefits are not commonly found in the literature. For structures with a truncated ground planes, the technique improved impedance matching, bandwidth and return loss when compared to the initial structure with filled ground planes. Moreover, these structures exhibited a better distribution of frequency, facilitating the adjustment of frequencies. Thus, it is expected that the planar structures presented in this study, particularly arrays may be suitable for specific applications in wireless communication systems when frequency multiband and wideband transmission signals are required.

Fluxo de fluído através de um meio poroso fractal desordenado. Análise das tensões de cisalhamento e efeito de escala na estimativa das forças viscosas

In this work we have investigated some aspects of the two-dimensional flow of a viscous Newtonian fluid through a disordered porous medium modeled by a random fractal system similar to the Sierpinski carpet. This fractal is formed by obstacles of various sizes, whose distribution function follows a power law. They are randomly disposed in a rectangular channel. The velocity field and other details of fluid dynamics are obtained by solving numerically of the Navier-Stokes and continuity equations at the pore level, where occurs actually the flow of fluids in porous media. The results of numerical simulations allowed us to analyze the distribution of shear stresses developed in the solid-fluid interfaces, and find algebraic relations between the viscous forces or of friction with the geometric parameters of the model, including its fractal dimension. Based on the numerical results, we proposed scaling relations involving the relevant parameters of the phenomenon, allowing quantifying the fractions of these forces with respect to size classes of obstacles. Finally, it was also possible to make inferences about the fluctuations in the form of the distribution of viscous stresses developed on the surface of obstacles.

A methodology for hierarchical image segmentation evaluation

This paper proposes a method to evaluate hierarchical image segmentation procedures, in order to enable comparisons between different hierarchical algorithms and of these with other (non-hierarchical) segmentation techniques (as well as with edge detectors) to be made. The proposed method builds up on the edge-based segmentation evaluation approach by considering a set of reference human segmentations as a sample drawn from the population of different levels of detail that may be used in segmenting an image. Our main point is that, since a hierarchical sequence of segmentations approximates such population, those segmentations in the sequence that best capture each human segmentation level of detail should provide the basis for the evaluation of the hierarchical sequence as a whole. A small computational experiment is carried out to show the feasibility of our approach.