Search results for: Jean Gray C. Achapero

Authors: Djamel Remache, Marie Semaan, Cécile Baron, Martine Pithioux, Patrick Chabrand, Jean-Marie Rossi, Jean-Louis Milan

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Cortical bone is a complex multi-scale structure. Even though several works have contributed significantly to understanding its mechanical behavior, this behavior remains poorly understood. Nanoindentation testing is one of the primary testing techniques for the mechanical characterization of bone at small scales. The purpose of this study was to provide new nanoindentation data of cortical bovine bone in different directions and at different bone microstructures (osteonal, interstitial and laminar bone), and then to identify anisotropic properties of samples with FEM (finite element method) based inverse method. Experimentally and numerical results were compared. Experimental and numerical results were compared. The results compared were in good agreement.

Keywords: mechanical behavior of bone, nanoindentation, finite element analysis, inverse optimization approach

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagallah

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Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function’s curve.

Keywords: bone scan, nuclear medicine, Matlab, image processing technique

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Authors: Sunandapriya Bhikkhu, Shimo Sraman

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The main objective of this research is to analytically study the concept of Lyotard’s different that rejects the monopoly criteria and single rule with the incommensurable, which can explain about conceptual problems of beauty in the philosophy of contemporary art. In Lyotard’s idea that basic value judgment of human should be a value like a phrase that is a small unit and an individual such as the aesthetic value that to explain the art world. From the concept of the anti-war artist that rejects the concept of the traditional aesthetic which cannot be able to explain the changing in contemporary society but emphasizes the meaning of individual beauty that is at the beginning of contemporary art today. In the analysis of the problem, the researcher supports the concept of Lyotard’s different that emphasizes the artistic expression which opens the space of perception and beyond the limitations of language process. Art is like phrase or small units that can convey a sense of humanity through the aesthetic value of the individual, not social criteria or universal. The concept of Lyotard’s different awakens and challenge us to the rejection of the single rule that is not open the social space to minorities by not accepting the monopoly criteria.

Keywords: difference, Jean-Francois Lyotard, postmodern, beauty, contemporary art

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Authors: Ahmad Rabby

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The present study emphasizing the catch trends assessment of Gray eel catfish (Plotosus canius) that was scrutinized on the basis of monthly length frequency data collected from mangrove estuary, Bangladesh during January 2017 to December 2018. A total amount of 1298 specimens were collected to estimate the total length (TL) and weight (W) of P. canius ranged from 13.3 cm to 87.4 cm and 28 g to 5200 g, respectively. The length-weight relationship was W=0.006 L2.95 with R2=0.972 for both sexes. The von Bertalanffy growth function parameters were L∞=93.25 cm and K=0.28 yr-1, hypothetical age at zero length of t0=0.059 years and goodness of the fit of Rn=0.494. The growth performances indices for L∞ and W∞ were computed as Φ'=3.386 and Φ=1.84, respectively. The size at first sexual maturity was estimated in TL as 48.8 cm for pool sexes. The natural mortality was 0.51 yr-1 at average annual water surface temperature as 22 0C. The total instantaneous mortality was 1.24 yr-1 at CI95% of 0.105–1.42 (r2=0.986). While fishing mortality was 0.73 yr-1 and the current exploitation ratio as 0.59. The recruitment was continued throughout the year with one major peak during May-June was 17.20-17.96%. The Beverton-Holt yield per recruit model was analyzed by FiSAT-II, when tc was at 1.43 yr, the Fmax was estimated as 0.6 yr-1 and F0.1 was 0.33 yr-1. Current age at the first capture was approximately 0.6 year, however Fcurrent = 0.73 yr-1 which is beyond the F0.1 indicated that the current stock of P. canius of Bangladesh was overexploited.

Keywords: Plotosus canius, mangrove estuary, asymptotic length, FiSAT-II

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Authors: Tahani Farah, Hanène Souli, Jean-Marie Fleureau, Guillaume Kermouche, Jean-Jacques Fry, Benjamin Girard, Denis Aelbrecht

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In this work, the bio-clogging of two soils with different granulometries is presented. The durability of the clogging is also studied under cycles of hydraulic head and under cycles of desaturation- restauration. The studied materials present continuous grain size distributions. The first one corresponding to the "material 1", presents grain sizes between 0.4 and 4 mm. The second material called "material 2" is composed of grains with size varying between 1 and 10 mm. The results show that clogging occurs very quickly after the injection of nutrition and an outlet flow near to 0 is observed. The critical hydraulic head is equal to 0.76 for "material 1", and 0.076 for "material 2". The durability tests show a good resistance to unclogging under cycles of hydraulic head and desaturation-restauration for the "material 1". Indeed, the flow after the cycles is very low. In contrast, "material 2", shows a very bad resistance, especially under the hydraulic head cycles. The resistance under the cycles of desaturation-resaturation is better but an important increase of the flow is observed. The difference of behavior is due to the granulometry of the materials. Indeed, the large grain size contributes to the reduction of the efficiency of the bio-clogging treatment in this material.

Keywords: bio-clogging, granulometry, permeability, nutrition

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Authors: Azza Taha Zaki

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Modern drama was characterised by realism and naturalism as dominant literary movements that focused on contemporary people and their issues to reflect the status of modern man and his environment. However, some modern dramatists have often fallen on classical mythology in ancient Greek tragedies to create a sense of the universality of the human experience. The tragic overtones of classical myths have helped modern dramatists in their attempts to create an enduring piece by evoking the majestic grandeur of the ancient myths and the heroic struggle of man against forces he cannot fight. Myths have continued to appeal to modern playwrights not only for the plot and narrative material but also for the vision and insight into the human experience and human condition. This paper intends to study how the reworking of Sophocles’ Antigone by Jean Anouilh in his Antigone, written in 1942 at the height of the Second World War and during the German occupation of his country, France, fits his own purpose and his own time. The paper will also offer an analysis of the vision in both plays to show how Anouilh has used the classical Antigone freely to produce a modern vision of the dilemma of man when faced by personal and national conflicts.

Keywords: Anouilh, Antigone, drama, Greek tragedy, modern, myth, sophocles

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Authors: Jean Marie Ntaganda, Froduald Minani, Wellars Banzi, Lydie Mpinganzima, Japhet Niyobuhungiro, Jean Bosco Gahutu, Vincent Dusabejambo, Immaculate Kambutse

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In this paper, we present a nonlinear dynamic model for the interactive mechanism of the cardiovascular and respiratory system. The model is designed and analyzed for human during physical exercises. In order to verify the adequacy of the designed model, data collected in Rwanda are used for validation. We have simulated the impact of heart rate and alveolar ventilation as controls of cardiovascular and respiratory system respectively to steady state response of the main cardiovascular hemodynamic quantities i.e., systemic arterial and venous blood pressures, arterial oxygen partial pressure and arterial carbon dioxide partial pressure, to the stabilised values of controls. We used data collected in Rwanda for both male and female during physical activities. We obtained a good agreement with physiological data in the literature. The model may represent an important tool to improve the understanding of exercise physiology.

Keywords: exercise, cardiovascular/respiratory, hemodynamic quantities, numerical simulation, physical activity, sportsmen in Rwanda, system

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Authors: Enola Lamy, Jean Paul Mereaux, Jean Claude Lopez

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In the face of new regulations related to waste management, it has become essential to understand the organizational process that accompanies this change. Through an experiment on the sorting of food waste in the community of Grand Reims, this research explores the acceptability, behavior, and tools needed to manage the change. Our position within a private company, SUEZ, a key player in the waste management sector, has allowed us to set up a driven team with concerned public organizations. The research was conducted through a theoretical study combined with semi-structured interviews. This qualitative method allowed us to conduct exchanges with users to assess the motivations and obstacles linked to the sorting of bio-waste. The results revealed the action levers necessary for the project's sustainability. Making the sorting gestures accessible and simplified makes it possible to target all populations. Playful communication adapted to each type of persona allows the user and stakeholders to be placed at the heart of the strategy. These recommendations are spotlighted thanks to the combination of theoretical and operational contributions, with the aim of facilitating the new public management and inducing the notion of performance while providing an example of added value.

Keywords: bio-waste, CSR approach, stakeholders, users, perception

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Authors: Meshal Alsaiari

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Fused Deposition Modeling is one of the additive manufacturing technologies the industry is shifting to nowadays due to its simplicity and low affordable cost. The fabrication processing parameters predominantly influence FDM part strength and mechanical properties. This presentation will demonstrate the influences of the two manufacturing parameters on the tensile testing evaluation indexes, infill density, and Printing Orientation, which were analyzed to create a piping spacer suitable for oil and gas applications. The tensile specimens are made of two polymers, Acrylonitrile Styrene Acrylate (ASA) and High high-impact polystyrene (HIPS), to characterize the mechanical properties performance for creating the final product. The mechanical testing was carried out per the ASTM D638 testing standard, following Type IV requirements. Taguchi's experiment design using an L-9 orthogonal array was used to evaluate the performance output and identify the optimal manufacturing factors. The experimental results demonstrate that the tensile test is more pronounced with 100% infill for ASA and HIPS samples. However, the printing orientations varied in reactions; ASA is maximum at 0 degrees while HIPS shows almost similar percentages between 45 and 90 degrees. Taguchi-Gray integrated methodology was adopted to minimize the response and recognize optimal fabrication factors combinations.

Keywords: FDM, ASTM D638, tensile testing, acrylonitrile styrene acrylate

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Authors: L. I. Izhar, T. Stathaki, K. Howell

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Analyzing and interpreting of thermograms have been increasingly employed in the diagnosis and monitoring of diseases thanks to its non-invasive, non-harmful nature and low cost. In this paper, a novel system is proposed to improve diagnosis and monitoring of morphoea skin disorder based on integration with the published lines of Blaschko. In the proposed system, image registration based on global and local registration methods are found inevitable. This paper presents a modified normalized gradient cross-correlation (NGC) method to reduce large geometrical differences between two multimodal images that are represented by smooth gray edge maps is proposed for the global registration approach. This method is improved further by incorporating an iterative-based normalized cross-correlation coefficient (NCC) method. It is found that by replacing the final registration part of the NGC method where translational differences are solved in the spatial Fourier domain with the NCC method performed in the spatial domain, the performance and robustness of the NGC method can be greatly improved. It is shown in this paper that the hybrid NGC method not only outperforms phase correlation (PC) method but also improved misregistration due to translation, suffered by the modified NGC method alone for thermograms with ill-defined jawline. This also demonstrates that by using the gradients of the gray edge maps and a hybrid technique, the performance of the PC based image registration method can be greatly improved.

Keywords: Blaschko’s lines, image registration, morphoea, thermal imaging

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Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Djamel Remache, Serge Dos Santos, Michael Cliez, Michel Gratton, Patrick Chabrand, Jean-Marie Rossi, Jean-Louis Milan

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Skin tissue is an inhomogeneous and anisotropic material. Uniaxial tensile testing is one of the primary testing techniques for the mechanical characterization of skin at large scales. In order to predict the mechanical behavior of materials, the direct or inverse analytical approaches are often used. However, in case of an inhomogeneous and anisotropic material as skin tissue, analytical approaches are not able to provide solutions. The numerical simulation is thus necessary. In this work, the uniaxial tensile test and the FEM (finite element method) based inverse method were used to identify the anisotropic mechanical properties of porcine skin tissue. The uniaxial tensile experiments were performed using Instron 8800 tensile machine®. The uniaxial tensile test was simulated with FEM, and then the inverse optimization approach (or the inverse calibration) was used for the identification of mechanical properties of the samples. Experimentally results were compared to finite element solutions. The results showed that the finite element model predictions of the mechanical behavior of the tested skin samples were well correlated with experimental results.

Keywords: mechanical skin tissue behavior, uniaxial tensile test, finite element analysis, inverse optimization approach

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Raveendranath Veeramani, Sunil Jonathan Holla, Parkash Chand, Sunil Chumber

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Gray’s Anatomy for Students has been a well-appreciated book among undergraduate students of anatomy in Asia. However, the current curricular requirements of anatomy require a more focused and organized approach. The editors of the first South Asia edition of Gray’s Anatomy for Students hereby highlight the modifications and importance of this edition. There is an emphasis on active learning by making the clinical relevance of anatomy explicit. Learning anatomy in context has been fostered by the association between anatomists and clinicians in keeping with the emerging integrated curriculum of the 21st century. The language has been simplified to aid students who have studied in the vernacular. The original illustrations have been retained, and few illustrations have been added. There are more figure numbers mentioned in the text to encourage students to refer to the illustrations while learning. The text has been made more student-friendly by adding generalizations, classifications and summaries. There are useful review materials at the beginning of the chapters which include digital resources for self-study. There are updates on imaging techniques to encourage students to appreciate the importance of essential knowledge of the relevant anatomy to interpret images, due emphasis has been laid on dissection. Additional importance has been given to the cranial nerves, by describing their relevant details with several additional illustrations and flowcharts. This new edition includes innovative features such as set inductions, outlines for subchapters and flowcharts to facilitate learning. Set inductions are mostly clinical scenarios to create interest in the need to study anatomy for healthcare professions. The outlines are a modern multimodal facilitating approach towards various topics to empower students to explore content and direct their learning and include learning objectives and material for review. The components of the outline encourage the student to be aware of the need to create solutions to clinical problems. The outlines help students direct their learning to recall facts, demonstrate and analyze relationships, use reason to explain concepts, appreciate the significance of structures and their relationships and apply anatomical knowledge. The 'structures to be identified in a dissection' are given as Level I, II and III which represent the 'must know, desirable to know and nice to know' content respectively. The flowcharts have been added to get an overview of the course of a structure, recapitulate important details about structures, and as an aid to recall. There has been a great effort to balance the need to have content that would enable students to understand concepts as well as get the basic material for the current condensed curriculum.

Keywords: Grays anatomy, South Asia, human anatomy, students anatomy

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Authors: Dumont Maxime, Correia Carlos, Sauvage Jean-François, Schwartz Noah, Gray Morgan

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Context: The concept of the segmented telescope is unavoidable to build extremely large telescopes (ELT) in the quest for spatial resolution, but it also allows one to fit a large telescope within a reduced volume of space (JWST) or into an even smaller volume (Standard Cubesat). Cubesats have tight constraints on the computational burden available and the small payload volume allowed. At the same time, they undergo thermal gradients leading to large and evolving optical aberrations. The pupil segmentation comes nevertheless with an obvious difficulty: to co-phase the different segments. The CubeSat constraints prevent the use of a dedicated wavefront sensor (WFS), making the focal-plane images acquired by the science detector the most practical alternative. Yet, one of the challenges for the wavefront sensing is the non-linearity between the image intensity and the phase aberrations. Plus, for Earth observation, the object is unknown and unrepeatable. Recently, several studies have suggested Neural Networks (NN) for wavefront sensing; especially convolutional NN, which are well known for being non-linear and image-friendly problem solvers. Aims: We study in this paper the prospect of using NN to measure the phasing aberrations of a segmented pupil from the focal-plane image directly without a dedicated wavefront sensing. Methods: In our application, we take the case of a deployable telescope fitting in a CubeSat for Earth observations which triples the aperture size (compared to the 10cm CubeSat standard) and therefore triples the angular resolution capacity. In order to reach the diffraction-limited regime in the visible wavelength, typically, a wavefront error below lambda/50 is required. The telescope focal-plane detector, used for imaging, will be used as a wavefront-sensor. In this work, we study a point source, i.e. the Point Spread Function [PSF] of the optical system as an input of a VGG-net neural network, an architecture designed for image regression/classification. Results: This approach shows some promising results (about 2nm RMS, which is sub lambda/50 of residual WFE with 40-100nm RMS of input WFE) using a relatively fast computational time less than 30 ms which translates a small computation burder. These results allow one further study for higher aberrations and noise.

Keywords: wavefront sensing, deep learning, deployable telescope, space telescope

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Authors: S. Sushma, S. Balasubramanian, K. C. Latha

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The mammographic image of breast cancer compressed and synthesized to get co-efficient values which will be converted (5x5) matrix to get ROI image where we get the highest value of effected region and with the same ideology the technique has been extended to differentiate between Calcification and normal cell image using mean value derived from 5x5 matrix values

Keywords: texture analysis, mammographic image, partitioned gray scale co-oocurance matrix, co-efficient

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Authors: Sylvain Amailland, Jean-Hugh Thomas, Charles Pézerat, Romuald Boucheron, Jean-Claude Pascal

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The noise requirements for naval and research vessels have seen an increasing demand for quieter ships in order to fulfil current regulations and to reduce the effects on marine life. Hence, new methods dedicated to the characterization of propeller noise, which is the main source of noise in the far-field, are needed. The study of cavitating propellers in closed-section is interesting for analyzing hydrodynamic performance but could involve significant difficulties for hydroacoustic study, especially due to reverberation and boundary layer noise in the tunnel. The aim of this paper is to present a numerical methodology for the identification of hydroacoustic sources on marine propellers using hydrophone arrays in a large hydrodynamic tunnel. The main difficulties are linked to the reverberation of the tunnel and the boundary layer noise that strongly reduce the signal-to-noise ratio. In this paper it is proposed to estimate the reflection coefficients using an inverse method and some reference transfer functions measured in the tunnel. This approach allows to reduce the uncertainties of the propagation model used in the inverse problem. In order to reduce the boundary layer noise, a cleaning algorithm taking advantage of the low rank and sparse structure of the cross-spectrum matrices of the acoustic and the boundary layer noise is presented. This approach allows to recover the acoustic signal even well under the boundary layer noise. The improvement brought by this method is visible on acoustic maps resulting from beamforming and DAMAS algorithms.

Keywords: acoustic imaging, boundary layer noise denoising, inverse problems, model adaptation

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

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Authors: Donatella Giuliani

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In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs.

Keywords: clustering images, firefly algorithm, Gaussian mixture model, meta heuristic algorithm, image segmentation

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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Authors: Jean-François Caron

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The current attempts to develop what we can call 'super soldiers' are problematic in many regards. This is what this text will try to explore by concentrating primarily on the repercussions of this technology and medical research on the physical and psychological integrity of soldiers. It argues that medicines or technologies may affect soldiers’ psychological and mental features and deprive them of their capacity to reflect upon their actions as autonomous subjects and that such a possibility entails serious moral as well as judicial consequences.

Keywords: military research, super soldiers, involuntary intoxication, criminal responsibility

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Authors: Jean-Francois Caron

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This text explores the value of moral acts performed by war criminals, and the extent to which they should alleviate the punishment these individuals ought to receive for violating the rules of war. Without neglecting the necessity of retribution in war crimes cases, it argues from an ethical perspective that we should not rule out the possibility of considering lesser punishments for war criminals who decide to perform a moral act, as it might produce significant positive moral outcomes. This text also analyzes how such a norm could be justified from a moral perspective.

Keywords: war criminals, pardon, amnesty, retribution

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Authors: Sandra Baez, Mariana Pino, Mildred Berrio, Hernando Santamaria-Garcia, Lucas Sedeno, Adolfo Garcia, Sol Fittipaldi, Agustin Ibanez

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Schadenfreude refers to the perceiver’s experience of pleasure at another’s misfortune. This is a multidetermined emotion which can be evoked by hostile feelings and envy. The experience of Schadenfreude engages mechanisms implicated in diverse social cognitive processes. For instance, Schadenfreude involves heightened reward processing, accompanied by increased striatal engagement and it interacts with mentalizing and perspective-taking abilities. Patients with Huntington's disease (HD) exhibit reductions of Schadenfreude experience, suggesting a role of striatal degeneration in such an impairment. However, no study has directly assessed the relationship between regional brain atrophy in HD and reduced Schadenfreude. This study investigated whether gray matter (GM) atrophy in HD patients correlates with ratings of Schadenfreude. First, we compared the performance of 20 HD patients and 23 controls on an experimental task designed to trigger Schadenfreude and envy (another social emotion acting as a control condition). Second, we compared GM volume between groups. Third, we examined brain regions where atrophy might be associated with specific impairments in the patients. Results showed that while both groups showed similar ratings of envy, HD patients reported lower Schadenfreude. The latter pattern was related to atrophy in regions of the reward system (ventral striatum) and the mentalizing network (precuneus and superior parietal lobule). Our results shed light on the intertwining of reward and socioemotional processes in Schadenfreude, while offering novel evidence about their neural correlates. In addition, our results open the door to future studies investigating social emotion processing in other clinical populations characterized by striatal or mentalizing network impairments (e.g., Parkinson’s disease, schizophrenia, autism spectrum disorders).

Keywords: envy, Gray matter atrophy, Huntigton's disease, Schadenfreude, social emotions

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Authors: Houssam Hijazi, Laurent Cantrel, Jean-François Paul

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Periodic DFT calculations were performed to study the chemistry of CsI particles and the possible release of volatile iodine from CsI surfaces for nuclear safety interest. The results show that water adsorbs at low temperature associatively on the (011) surface of CsI, while water desorbs at higher temperatures. On the other hand, removing iodine species from the surface requires oxidizing the surface one time for each removed iodide atom. The activation energy of removing I₂ from the surface in the presence of two OH is 1,2 eV.

Keywords: aerosols, CSI, reactivity, DFT, water adsorption

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Authors: Jyoti Bharti, Navneet Manjhi, M. K.Gupta, Bimi Jain

Abstract:

A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a simple method for gait identification which is based on moments. Moment values are extracted on different number of frames of gray scale and silhouette images of CASIA database. These moment values are considered as feature values. Fuzzy logic and nearest neighbour classifier are used for classification. Both achieved higher recognition.

Keywords: gait, fuzzy logic, nearest neighbour, recognition rate, moments

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

Abstract:

Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Emine Yılmaz Bolat

Abstract:

Early childhood is a very important period in terms of identifying and developing early skills and abilities. It is likely that the child's development will be in the same direction in the future. This study was conducted with 26 children for the purpose of examining pictures of children of five years old. In the survey, children were asked to draw a picture with pastel dyes. The drawings were collected and evaluated by the researcher. At the end of the research, it was found that the children used the yellow color (N = 17, 16,34%) and the least gray color (N = 1, 0,96%). When the features of children's pictures are examined, the children's paintings have been found to have hierarchy, transparency, completion, the use of vivid colors, and the presence of vertical and horizontal painting lines.

Keywords: early childhood, kindergarten, pictures of children, features of pictures

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Authors: Thiago M. R Dias, Gray F. Moita

Abstract:

The analysis of scientific collaboration networks has contributed significantly to improving the understanding of how does the process of collaboration between researchers and also to understand how the evolution of scientific production of researchers or research groups occurs. However, the identification of collaborations in large scientific databases is not a trivial task given the high computational cost of the methods commonly used. This paper proposes a method for identifying collaboration in large data base of curriculum researchers. The proposed method has low computational cost with satisfactory results, proving to be an interesting alternative for the modeling and characterization of large scientific collaboration networks.

Keywords: extraction, data integration, information retrieval, scientific collaboration

Procedia PDF Downloads 361