Search results for: lung computed tomography (CT) images

Authors: Mona Elsalamawy, Ashraf Ragab Mohamed, Abdellatif Elsayed Abosen

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This paper aims to evaluate the effectiveness of different crystalline coating materials concerning of chloride ions penetration. The concrete ages at the coating installation and its moisture conditions were addressed; where, these two factors may play a dominant role for the effectiveness of the used materials. Rapid chloride ions penetration test (RCPT) was conducted at different ages and moisture conditions according to the relevant standard. In addition, the contaminated area and the penetration depth of the chloride ions were investigated immediately after the RCPT test using chemical identifier, 0.1 M silver nitrate AgNO₃ solution. Results have shown that, the very low chloride ions penetrability, for the studied crystallization materials, were investigated only with the old age concrete (G1). The significant reduction in chloride ions’ penetrability was illustrated after 7 days of installing the crystalline coating layers. Using imageJ is more reliable to describe the contaminated area of chloride ions, where the distribution of aggregate and heterogeneous of cement mortar was considered in the images analysis.

Keywords: chloride permeability, contaminated area, crystalline waterproofing materials, RCPT, XRD

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Authors: A. Sohouli, A. Suleman

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This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

Procedia PDF Downloads 517

Authors: Gaurav Verma

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Organoclay loaded (0-5 weight %) polyester-urethane (PU) coatings were prepared with a branched hydroxyl-bearing polyester and an aliphatic poly-isocyanate. TEM micrographs show partial exfoliation and intercalation of clay platelets in organoclay-polyester dispersions. AFM surface images reveals that the PU hard domains tend to regularise and also self-organise into spherical shapes of sizes 50 nm (0 wt %), 60 nm (2 wt %) and 190 nm (4 wt %) respectively. IR analysis shows that PU chains have increasing tendency to interact with exfoliated clay platelets through hydrogen bonding. This interaction strengthens inter-chain linkages in PU matrix and hence improves anti-ageing properties. 1000 hours of accelerated weathering was evaluated by ATR spectroscopy, while yellowing and overall discoloration was quantified by the Δb* and ΔE* values of the CIELab colour scale. Post-weathering surface properties also showed improvement as the loss of thickness and reduction in gloss in neat PU was 25% and 42%; while it was just 3.5% and 14% respectively for the 2 wt% nanocomposite coating. This work highlights the importance of modifying surface and bulk properties of PU coatings at nanoscale, which led to improved performance in accelerated weathering conditions.

Keywords: coatings, AFM, ageing, spectroscopy

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Authors: Wei Ji

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This study was conducted in the Kansas City metropolitan area of the United States, which has experienced significant urban sprawling in recent decades. The remote sensing of land cover changes in this area spanned over four decades from 1972 through 2010. The project was implemented in two stages: the first stage focused on detection of long-term trends of urban land cover change, while the second one examined how to detect the coupled effects of human impact and climate change on urban landscapes. For the first-stage study, six Landsat images were used with a time interval of about five years for the period from 1972 through 2001. Four major land cover types, built-up land, forestland, non-forest vegetation land, and surface water, were mapped using supervised image classification techniques. The study found that over the three decades the built-up lands in the study area were more than doubled, which was mainly at the expense of non-forest vegetation lands. Surprisingly and interestingly, the area also saw a significant gain in surface water coverage. This observation raised questions: How have human activities and precipitation variation jointly impacted surface water cover during recent decades? How can we detect such coupled impacts through remote sensing analysis? These questions led to the second stage of the study, in which we designed and developed approaches to detecting fine-scale surface waters and analyzing coupled effects of human impact and precipitation variation on the waters. To effectively detect urban landscape changes that might be jointly shaped by precipitation variation, our study proposed “urban wetscapes” (loosely-defined urban wetlands) as a new indicator for remote sensing detection. The study examined whether urban wetscape dynamics was a sensitive indicator of the coupled effects of the two driving forces. To better detect this indicator, a rule-based classification algorithm was developed to identify fine-scale, hidden wetlands that could not be appropriately detected based on their spectral differentiability by a traditional image classification. Three SPOT images for years 1992, 2008, and 2010, respectively were classified with this technique to generate the four types of land cover as described above. The spatial analyses of remotely-sensed wetscape changes were implemented at the scales of metropolitan, watershed, and sub-watershed, as well as based on the size of surface water bodies in order to accurately reveal urban wetscape change trends in relation to the driving forces. The study identified that urban wetscape dynamics varied in trend and magnitude from the metropolitan, watersheds, to sub-watersheds in response to human impacts at different scales. The study also found that increased precipitation in the region in the past decades swelled larger wetlands in particular while generally smaller wetlands decreased mainly due to human development activities. These results confirm that wetscape dynamics can effectively reveal the coupled effects of human impact and climate change on urban landscapes. As such, remote sensing of this indicator provides new insights into the relationships between urban land cover changes and driving forces.

Keywords: urban land cover, human impact, climate change, rule-based classification, across-scale analysis

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Authors: Cherifi Mehdi, Lahdir Mourad, Ameur Soltane

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Image compression is the heart of several multimedia techniques. It is used to reduce the number of bits required to represent an image. Meteosat Second Generation (MSG) satellite allows the acquisition of 12 image files every 15 minutes and that results in a large databases sizes. In this paper, a novel image compression method based on the arithmetic coding with the sorted Run Length Coding (SRLC) for MSG images is proposed. The SRLC allows us to find the occurrence of the consecutive pixels of the original image to create a sorted run. The arithmetic coding allows the encoding of the sorted data of the previous stage to retrieve a unique code word that represents a binary code stream in the sorted order to boost the compression ratio. Through this article, we show that our method can perform the best results concerning compression ratio and bit rate unlike the method based on the Run Length Coding (RLC) and the arithmetic coding. Evaluation criteria like the compression ratio and the bit rate allow the confirmation of the efficiency of our method of image compression.

Keywords: image compression, arithmetic coding, Run Length Coding, RLC, Sorted Run Length Coding, SRLC, Meteosat Second Generation, MSG

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Authors: Amit Kumar, Himanshu Singal, Arnav Bhavsar

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In this modern era of automation, most of the academic exams and competitive exams are Multiple Choice Questions (MCQ). The responses of these MCQ based exams are recorded in the Optical Mark Reader (OMR) sheet. Evaluation of the OMR sheet requires separate specialized machines for scanning and marking. The sheets used by these machines are special and costs more than a normal sheet. Available process is non-economical and dependent on paper thickness, scanning quality, paper orientation, special hardware and customized software. This study tries to tackle the problem of evaluating the OMR sheet without any special hardware and making the whole process economical. We propose an image processing based algorithm which can be used to read and evaluate the scanned OMR sheets with no special hardware required. It will eliminate the use of special OMR sheet. Responses recorded in normal sheet is enough for evaluation. The proposed system takes care of color, brightness, rotation, little imperfections in the OMR sheet images.

Keywords: OMR, image processing, hough circle trans-form, interpolation, detection, binary thresholding

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Authors: Cemre Polat, Mustafa Soyler, Bulent Yaniktepe, Coskun Ozalp

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In this study, it was aimed to control the flow actively by giving an oscillating rotational motion to a vertically placed cylinder, and flow characteristics were determined. In the study, firstly, the flow structure around the flat cylinder was investigated with dye experiments, and then the cylinders with different oscillation angles (θ = 60°, θ = 120°, and θ = 180°) and different rotation speeds (15 rpm and 30 rpm) the flow structure around it was examined. Thus, the effectiveness of oscillation and rotation speed in flow control has been investigated. In the dye experiments, the dye/water mixture obtained by mixing Rhodamine 6G in powder form with water, which shines under laser light and allows detailed observation of the flow structure, was used. During the experiments, the dye was injected into the flow with the help of a thin needle at a distance that would not affect the flow from the front of the cylinder. In dye experiments, 100 frames per second were taken with a Canon brand EOS M50 (24MP) digital mirrorless camera at a resolution of 1280 * 720 pixels. Then, the images taken were analyzed, and the pictures representing the flow structure for each experiment were obtained. As a result of the study, it was observed that no separation points were formed at 180° swing angle at 15 rpm speed, 120° and 180° swing angle at 30 rpm, and the flow was controlled according to the fixed cylinder.

Keywords: active flow control, cylinder, flow visualization rotationally oscillating

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Authors: Caren Hilger, Silke Burkert, Friederike Kendel

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Men with localized prostate cancer (PCa) have to choose among different treatment options, such as active surveillance (AS) and radical prostatectomy (RP). All available treatment options may be accompanied by specific psychological or physiological side effects. Depending on the nature and extent of these side effects, patients are more or less likely to be satisfied or to struggle with their treatment decision in the long term. Therefore, the aim of this study was to assess and explain decisional regret in men with localized PCa. The role of erectile functioning as one of the main physiological side effects of invasive PCa treatment, depressive symptoms as a common psychological side effect, and the association of erectile functioning and depressive symptoms with decisional regret were investigated. Men with localized PCa initially managed with AS or RP (N=292) were matched according to length of therapy (mean 47.9±15.4 months). Subjects completed mailed questionnaires assessing decisional regret, changes in erectile functioning, depressive symptoms, and sociodemographic variables. Clinical data were obtained from case report forms. Differences among the two treatment groups (AS and RP) were calculated using t-tests and χ²-tests, relationships of decisional regret with erectile functioning and depressive symptoms were computed using multiple regression. Men were on average 70±7.2 years old. The two treatment groups differed markedly regarding decisional regret (p<.001, d=.50), changes in erectile functioning (p<.001, d=1.2), and depressive symptoms (p=.01, d=.30), with men after RP reporting higher values, respectively. Regression analyses showed that after adjustment for age, tumor risk category, and changes in erectile functioning, depressive symptoms were still significantly associated with decisional regret (B=0.52, p<.001). Additionally, when predicting decisional regret, the interaction of changes in erectile functioning and depressive symptoms reached significance for men after RP (B=0.52, p<.001), but not for men under AS (B=-0.16, p=.14). With increased changes in erectile functioning, the association of depressive symptoms with decisional regret became stronger in men after RP. Decisional regret is a phenomenon more prominent in men after RP than in men under AS. Erectile functioning and depressive symptoms interact in their prediction of decisional regret. Screening and treating depressive symptoms might constitute a starting point for interventions aiming to reduce decisional regret in this target group.

Keywords: active surveillance, decisional regret, depressive symptoms, erectile functioning, prostate cancer, radical prostatectomy

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Authors: Samira Taleb, Sakina Aoun, Slimane Ziani, Zoheir Mentouri, Adel Boudiaf

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Rolling is a pressure treatment technique that modifies the shape of steel ingots or billets between rotating rollers. During this process, defects may form on the surface of the rolled sheets and are likely to affect the performance and quality of the finished product. In our study, we developed a method for detecting surface hole defects using a pixel classification approach. This work includes several steps. First, we performed image preprocessing to delimit areas with and without hole defects on the sheet image. Then, we developed the histograms of each area to generate the gray level membership intervals of the pixels that characterize each area. As we noticed an intersection between the characteristics of the gray level intervals of the images of the two areas, we finally performed a learning step based on a series of detection tests to refine the membership intervals of each area, and to choose the defect detection criterion in order to optimize the recognition of the surface hole.

Keywords: classification, defect, surface, detection, hole

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Authors: Lin Chang

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Birmingham, England was a town in the late-eighteenth century and became the nation’s second largest city in the late nineteenth century. The city expanded rapidly in terms of its population and size. Three generations of artists from a local family, the Lines, made a large number of drawings and paintings depicting the growth and changes of their city. At first sight, the meaning of the pictures seems straight-forward: providing records of what were torn down and newly-built. However, except for being read as maps, the pictures reveal a struggle in vision as to whether unsightly manufactories and their smoking chimneys should be visualized and how far the borders of the town should have been positioned and understood as they continued to grow and encroached upon its immediate countryside. This art-historic paper examines some topographic views by the Lines family and explores how they, through unusual depiction of rural and urban scenery, manage to give form to the borderlands between the country and the city. This paper argues that while the idea of the country and the city seems to be common sense, the two realms actually pose difficulty for visual representation as to where exactly their borders are and the idea itself has dichotomized the way people consider landscape imageries to be.

Keywords: Birmingham, suburb, urban fringes, landscape

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Authors: Leah Ning

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This paper discusses the development of a machine learning algorithm that accurately detects metastatic breast cancer (cancer has spread elsewhere from its origin part) in selected images that come from pathology scans of lymph node sections. Being able to develop an accurate artificial intelligence (AI) algorithm would help significantly in breast cancer diagnosis since manual examination of lymph node scans is both tedious and oftentimes highly subjective. The usage of AI in the diagnosis process provides a much more straightforward, reliable, and efficient method for medical professionals and would enable faster diagnosis and, therefore, more immediate treatment. The overall approach used was to train a convolution neural network (CNN) based on a set of pathology scan data and use the trained model to binarily classify if a new scan were benign or malignant, outputting a 0 or a 1, respectively. The final model’s prediction accuracy is very high, with 100% for the train set and over 70% for the test set. Being able to have such high accuracy using an AI model is monumental in regard to medical pathology and cancer detection. Having AI as a new tool capable of quick detection will significantly help medical professionals and patients suffering from cancer.

Keywords: breast cancer detection, AI, machine learning, algorithm

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Authors: Janella Salamania, Marcedon Fernandez, Matthew Villanueva Henry Ramos

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Titanium nitrite (TiN) a popular functional and decorative coating because of its golden yellow color, high hardness and superior wear resistance. It is also being studied as a diffusion barrier in integrated circuits due to its known chemical stability and low resistivity. While there have been numerous deposition methods done for TiN, most required the heating of substrates at high temperatures. In this work, TiN films are deposited on silicon (111) and (100) substrates without substrate heating using a patented magnetized sheet plasma source. Films were successfully deposited without substrate heating at various target bias, while maintaining a constant 25% N2 to Ar ratio, and deposition of time of 30 minutes. The resulting films exhibited a golden yellow color which is characteristic of TiN. X-ray diffraction patterns show the formation of TiN predominantly oriented in the (111) direction regardless of substrate used. EDX data also confirms the 1:1 stoichiometry of titanium an nitrogen. Ellipsometry measurements estimate the thickness to range from 28 nm to 33 nm. SEM images were also taken to observe the morphology of the film.

Keywords: coatings, nitrides, coatings, reactive magnetron sputtering, thin films

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Authors: Yanwen Li, Shuguo Xie

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In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: gradient image, segmentation and extract, mean-shift algorithm, dictionary iearning

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Sapora Sipon, Mohd Fo’ad Sakdan, Che Su Mustaffa, Najib Ahmad Marzuki, Mohamad Sukeri Khalid, Mohd Taib Ariffin, Husni Mohd Radzi, Salhah Abdullah

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Flood disasters occur throughout the world including Malaysia. The major flood disaster that hit Malaysia in the 2014-2015 episodes proved the psychosocial and mental health consequences such as vivid images of destruction, upheaval, death and loss of lives. Flood, flood survivors reported that flood has changed one looks at their religious belief. The main objective of this paper is to investigate the changes in religious belief after the 2014-2015 Malaysia flood disaster. The total population of 1300 respondents who experienced the 2014-2015 Malaysia flood were surveyed a month after the disaster. The questionnaires were used to measure religiosity and stress. The results provide compelling evidence that religion played an important role in the lives of Malaysia flood disasters’ survivor where more than half of the respondents (>75%) experiencing the strengthening of their religious belief. It was also reported the victims’ strengthening of their religious belief proved to be a powerful factor in reducing stress in the aftermath of the flood.

Keywords: religious belief, flood disaster, humanity, society

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Authors: William E. Bauta, Jennifer Rebeles, Reggie Jacob

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Porphyrins are noteworthy in biomedical science for their cancer tissue accumulation and photophysical properties. The preferential accumulation of some porphyrins in cancerous tissue has been known for many years. This, combined with their characteristic photophysical and photochemical properties, including their strong fluorescence and their ability to generate reactive oxygen species in vivo upon laser irradiation, has led to much research into the application of porphyrins as cancer diagnostic and therapeutic agents. Porphyrins have been used as dyes to detect cancer cells both in vivo and, less commonly, in vitro. In one example, human sputum samples from lung cancer patients and patients without the disease were dissociated and stained with the porphyrin TCPP (5,10,15,20-tetrakis-(4-carboxyphenyl)-porphine). Cells were analyzed by flow cytometry. Cancer samples were identified by their higher TCPP fluorescence intensity relative to the no-cancer controls. However, quantitative analysis of fluorescence in cell suspensions stained with multiple fluorophores requires particles stained with each of the individual fluorophores as controls. Fluorescent control particles must be compatible in size with flow cytometer fluidics and have favorable hydrodynamic properties in suspension. They must also display fluorescence comparable to the cells of interest and be stable upon storage amine-functionalized spherical polystyrene beads in the 5 to 20-micron diameter range that was reacted with TCPP and EDC in aqueous pH six buffer overnight to form amide bonds. Beads were isolated by centrifugation and tested by flow cytometry. The 10-micron amine-functionalized beads displayed the best combination of fluorescence intensity and hydrodynamic properties, such as lack of clumping and remaining in suspension during the experiment. These beads were further optimized by varying the stoichiometry of EDC and TCPP relative to the amine. The reaction was accompanied by the formation of a TCPP-related particulate, which was removed, after bead centrifugation, using a microfiltration process. The resultant TCPP-functionalized beads were compatible with flow cytometry conditions and displayed a fluorescence comparable to that of stained cells, which allowed their use as fluorescence standards. The beads were stable in refrigerated storage in the dark for more than eight months. This work demonstrates the first preparation of porphyrin-functionalized flow cytometry control beads.

Keywords: tetraaryl porphyrin, polystyrene beads, flow cytometry, peptide coupling

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Authors: Guankai Lin, Wei Tong, Hong Zhu

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The electric current induced Joule heating effects have been investigated in La₀.₈Ba₀.₂MnO₃ ultrathin films deposited on LaAlO₃(001) single crystal substrate with smaller lattice constant by using the sol-gel method. By applying moderate bias currents (~ 10 mA), it is found that Joule self-heating simply gives rise to a temperature deviation between the thermostat and the test sample, but the intrinsic ρ(T) relationship measured at a low current (0.1 mA) changes little. However, it is noteworthy that the low-temperature transport behavior degrades from metallic to insulating state after applying higher bias currents ( > 31 mA) in a vacuum. Furthermore, metallic transport can be recovered by placing the degraded film in air. The results clearly suggest that the oxygen vacancy in the La₀.₈Ba₀.₂MnO₃ films is controllable in different atmospheres, particularly with the aid of the Joule self-heating. According to the SEM images, we attribute the controlled oxygen vacancy to the nano-sized labyrinth pattern of the films, where the large surface-to-volume ratio plays a curial role.

Keywords: controlling oxygen vacancy, joule self-heating, manganite, sol-gel method

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Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

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Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

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Authors: Laith K. Abbas, Xiaoting Rui, Pier Marzocca

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Aerospace systems, generally speaking, are inherently nonlinear. These nonlinearities may modify the behavior of the system. However, nonlinearities in an aeroelastic system can be divided into structural and aerodynamic. Structural nonlinearities can be subdivided into distributed and concentrated ones. Distributed nonlinearities are spread over the whole structure representing the characteristic of materials and large motions. Concentrated nonlinearities act locally, representing loose of attachments, worn hinges of control surfaces, and the presence of external stores. The concentrated nonlinearities can be approximated by one of the classical structural nonlinearities, namely, cubic, free-play and hysteresis, or by a combination of these, for example, a free-play and a cubic one. Compressibility, aerodynamic heating, separated flows and turbulence effects are important aspects that result in nonlinear aerodynamic behavior. An issue related to the low-speed flutter and its catastrophic/benign character represented by Limit Cycle Oscillation (LCO) of all-movable fin, as well to their control is addressed in the present work. To the approach of this issue: (1) Quasi-Steady (QS) Theory and Computational Fluid Dynamics (CFD) of subsonic flow are implemented, (2) Flutter motion equations of a two-dimensional typical section with cubic nonlinear stiffness in the pitching direction and free play gap are established, (3) Uncoupled bending/torsion frequencies of the selected fin are computed using recently developed Transfer Matrix Method of Multibody System Dynamics (MSTMM), and (4) Time simulations are carried out to study the bifurcation behavior of the aeroelastic system. The main objective of this study is to investigate how the LCO and chaotic behavior are influenced by the coupled aeroelastic nonlinearities and intend to implement a control capability enabling one to control both the flutter boundary and its character. By this way, it may expand the operational envelop of the aerospace vehicle without failure.

Keywords: aeroelasticity, CFD, MSTMM, flutter, freeplay, fin

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Authors: Blessing Mavhuru, Nthaduleni Nethengwe, Hector Chikoore, Onyango Beneah Daniel Odhiambo

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The main objective of this study was to classify land use/cover and how it has changed in Nzhelele Valley Limpopo Province, South Africa. The study aimed to identify and analyse the types of land use/cover in the years 2005, 2010, and 2015 with a view to assess the impact on soil erosion over time. Using GIS, the changes within land use/cover were assessed through the classification of satellite images. The study area was classified into four major land cover/use classes, which are vegetation, gravel road, built up land and agricultural fields. Over the period 2005-2015 the resultant land use/cover demonstrated (i) a significant increase (12%) for vegetation cover, (ii) a significant decrease in agriculture (16%) land use/cover, (iii) increase in built-up land (1%), as well as (iv) an increase in gravel roads (3%). This study envisages assisting policy makers in decision making on land use management for Nzhelele Valley.

Keywords: land use, land cover, change, soil erosion

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

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Authors: Mohammadjavad Sotoudeheian

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COVID-19, which began in December 2019, uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter and spread through the cells. ACE2 mRNA is present in almost every organ, including nasopharynx, lung, as well as the brain. Ports of entry of SARS-CoV-2 into the central nervous system (CNS) may include arterial circulation, while viremia is remarkable. However, it is imperious to develop neurological symptoms evaluation CSF analysis in patients with COVID-19, but theoretically, ACE2 receptors are expressed in cerebellar cells and may be a target for SARS-CoV-2 infection in the brain. Recent evidence agrees that SARS-CoV-2 can impact the brain through direct and indirect injury. Two biomarkers for CNS injury, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL) detected in the plasma of patients with COVID-19. NFL, an axonal protein expressed in neurons, is related to axonal neurodegeneration, and GFAP is over-expressed in CNS inflammation. GFAP cytoplasmic accumulation causes Schwan cells to misfunction, so affects myelin generation, reduces neuroskeletal support over NfLs during CNS inflammation, and leads to axonal degeneration. Interleukin-6 (IL-6), which extensively over-express due to interleukin storm during COVID-19 inflammation, regulates gene expression, as well as GFAP through STAT molecular pathway. IL-6 also impresses the phosphoinositide 3-kinase (PI3K)/STAT/smads pathway. The PI3K/ protein kinase B (Akt) pathway is the main modulator upstream of the mammalian target of rapamycin (mTOR), and alterations in this pathway are common in neurodegenerative diseases. Most neurodegenerative diseases show a disruption of autophagic function and display an abnormal increase in protein aggregation that promotes cellular death. Therefore, induction of autophagy has been recommended as a rational approach to help neurons clear abnormal protein aggregates and survive. The mTOR is a major regulator of the autophagic process and is regulated by cellular stressors. The mTORC1 pathway and mTORC2, as complementary and important elements in mTORC1 signaling, have become relevant in the regulation of the autophagic process and cellular survival through the extracellular signal-regulated kinase (ERK) pathway.

Keywords: mTORC1, COVID-19, PI3K, autophagy, neurodegeneration

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Authors: Vineet Gairola

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In many cultures, ethnographers have tried to set up a tight link between music and possession. However, they rarely informed us about the psychology of interactions between music and the possessed. Ancient myths and the archetypal find expression through the rituals practiced in Uttarakhand. In Uttarakhand (a part of the Central Himalayan region), an intriguing archetypal healing mechanism takes place. Some people get 'possessed' by a deity and shower blessings onto people gathered for a puja in a temple, where invocation of deity takes place through two archetypal drumming instruments played together named dhol-damaun. There is devi-doli (palanquin of the goddess) worship, which is carried on the shoulders of two people and is said to be tilting and shaking on its own. Archetypal in the modern mind survives effortlessly. The 'oceanic' of religious feelings are explored through an oral text of Dholsagar. The method of ethnography along with case-studies has been used. A substantial part of fieldwork was carried out in Rudraprayag, Uttarakhand. The research suggests that the collective unconscious is also sonic in nature, which is characterized by sounds and rhythms—not only symbols and images, as Dr. Jung suggested.

Keywords: archetypal, music, myth, mysticism, possession, sonic collective unconscious

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Authors: Y. Zhang, J. Perez, S. Marnier

Abstract:

To date, no study on goaltending butterfly motion has been completed in real conditions, during an ice hockey game or training practice, to the author's best knowledge. This motion, performed to save score, is unnatural, intense, and repeated. The target of this research activity is to identify representative biomechanical criteria for this goaltender-specific movement pattern. Determining specific physical parameters may allow to will identify the risk of hip and groin injuries sustained by goaltenders. Four professional or academic goalies were instrumented during ice hockey training practices with five inertial measurement units. These devices were inserted in dedicated pockets located on each thigh and shank, and the fifth on the lumbar spine. A camera was also installed close to the ice to observe and record the goaltenders' activities, especially the butterfly motions, in order to synchronize the captured data and the behavior of the goaltender. Each data recorded began with a calibration of the inertial units and a calibration of the fully equipped goaltender on the ice. Three butterfly motions were recorded out of the training practice to define referential individual butterfly motions. Then, a data processing algorithm based on the Madgwick filter computed hip and knee joints joint range of motion as well as angular specific angular velocities. The developed algorithm software automatically identified and analyzed all the butterfly motions executed by the four different goaltenders. To date, it is still too early to show that the analyzed criteria are representative of the trauma generated by the butterfly motion as the research is only at its beginning. However, this descriptive research activity is promising in its ecological assessment, and once the criteria are found, the tools and protocols defined will allow the prevention of as many injuries as possible. It will thus be possible to build a specific training program for each goalie.

Keywords: biomechanics, butterfly motion, human motion analysis, ice hockey, inertial measurement unit

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Authors: M. V. Chidananda Murthy, M. Z. Kurian, H. S. Guruprasad

Abstract:

Image restoration in the process of communication is one of the emerging fields in the image processing. The motion analysis processing is the simplest case to detect motion in an image. Applications of motion analysis widely spread in many areas such as surveillance, remote sensing, film industry, navigation of autonomous vehicles, etc. The scene may contain multiple moving objects, by using motion analysis techniques the blur caused by the movement of the objects can be enhanced by filling-in occluded regions and reconstruction of transparent objects, and it also removes the motion blurring. This paper presents the design and comparison of various motion detection and enhancement filters. Median filter, Linear image deconvolution, Inverse filter, Pseudoinverse filter, Wiener filter, Lucy Richardson filter and Blind deconvolution filters are used to remove the blur. In this work, we have considered different types and different amount of blur for the analysis. Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the performance of the filters. The designed system has been implemented in Matlab software and tested for synthetic and real-time images.

Keywords: image enhancement, motion analysis, motion detection, motion estimation

Procedia PDF Downloads 285

Authors: Marcio Mesquita, Diogo Henrique Morato de Moraes, Henrique Fonseca Elias de Oliveira, Rilner Alves Flores, Mateus Rodrigues Ferreira, Dalva Graciano Ribeiro

Abstract:

This study aimed to analyze the movement of water in irrigated and non-irrigated rice (Oryza sativa L.) leaves, from the xylem to the stomata, through numerical simulations. Through three-dimensional modeling, it was possible to determine how the spacing of parenchyma cells and the permeability of these cells influence the apoplastic flow and the opening of the stomata. The thickness of the cuticle and the number of vascular bundles are greater in plants subjected to water stress, indicating an adaptive response of plants to environments with water deficit. In addition, numerical simulations revealed that the opening of the stomata, the permeability of the parenchyma cells and the cell spacing have significant impacts on the energy loss and the speed of water movement. It was observed that a more open stoma facilitates water flow, decreasing the resistance and energy required for transport, while higher levels of permeability reduce energy loss, indicating that a more permeable tissue allows for more efficient water transport. Furthermore, it was possible to note that stomatal aperture, parenchyma permeability and cell spacing are crucial factors in the efficient water management of plants, especially under water stress conditions. These insights are essential for the development of more effective agricultural management strategies and for the breeding of plant varieties that are more resistant to adverse growing conditions. Computed fluid dynamics has allowed us to overcome the limitations of conventional techniques by providing a means to visualize and understand the complex hydrodynamic processes within the vascular system of plants.

Keywords: numerical modeling, vascular anatomy, vascular hydrodynamics, xylem, Oryza sativa L.

Procedia PDF Downloads 15

Authors: Mohamad Ammar Ayass, Natalya Griko, Victor Pashkov, Wanying Cao, Kevin Zhu, Jin Zhang, Lina Abi Mosleh

Abstract:

Respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for coronavirus disease 2019 (COVID-19). Early evidence pointed at the angiotensin-converting enzyme 2 (ACE-2) expressed on the epithelial cells of the lung as the main entry point of SARS-CoV-2 into the cells. The viral entry is mediated by the binding of the Receptor Binding Domain (RBD) of the spike protein that is expressed on the surface of the virus to the ACE-2 receptor. As the number of SARS-CoV-2 variants continues to increase, mutations arising in the RBD of SARS-CoV-2 may lead to the ineffectiveness of RBD targeted neutralizing antibodies. To address this limitation, the objective of this study is to develop a combination of aptamers that target different regions of the RBD, preventing the binding of the spike protein to ACE-2 receptor and subsequent viral entry and replication. A safe and innovative biomedical tool was developed to inhibit viral infection and reduce the harms of COVID-19. In the present study, DNA aptamers were developed against a recombinant trimer S protein using the Systematic Evolution of Ligands by Exponential enrichment (SELEX). Negative selection was introduced at round number 7 to select for aptamers that bind specifically to the RBD domain. A series of 9 aptamers (ADI2010, ADI2011, ADI201L, ADI203L, ADI205L, ADIR68, ADIR74, ADIR80, ADIR83) were selected and characterized with high binding affinity and specificity to the RBD of the spike protein. Aptamers (ADI25, ADI2009, ADI203L) were able to bind and pull down endogenous spike protein expressed on the surface of SARS-CoV-2 virus in COVID-19 positive patient samples and determined by liquid chromatography- tandem mass spectrometry analysis (LC-MS/MS). LC-MS/MS data confirmed that aptamers can bind to the RBD of the spike protein. Furthermore, results indicated that the combination of the 9 best aptamers inhibited the binding of the purified trimer spike protein to the ACE-2 receptor found on the surface of Vero E6 cells. In the same experiment, the combined aptamers displayed a better neutralizing effect than antibodies. The data suggests that the selected aptamers could be used in therapy to neutralize the effect of the SARS-CoV-2 virus by inhibiting the interaction between the RBD and ACE-2 receptor, preventing viral entry into target cells and therefore blocking viral replication.

Keywords: aptamer, ACE-2 receptor, binding inhibitor, COVID-19, spike protein, SARS-CoV-2, treatment

Procedia PDF Downloads 183

Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

Abstract:

It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

Procedia PDF Downloads 337

Authors: Josef Wolfartsberger, Michael Heiml, Georg Schwarz, Sabrina Egger

Abstract:

Growing individualization and higher numbers of variants in industrial assembly products raise the complexity of manufacturing processes. Technical assistance systems considering both procedural and human factors allow for an increase in product quality and a decrease in required learning times by supporting workers with precise working instructions. Due to varying needs of workers, the presentation of working instructions leads to several challenges. This paper presents an approach for a multi-modal visualization application to support assembly work of complex parts. Our approach is integrated within an interconnected assistance system network and supports the presentation of cloud-streamed textual instructions, images, videos, 3D animations and audio files along with multi-modal user interaction, customizable UI, multi-platform support (e.g. tablet-PC, TV screen, smartphone or Augmented Reality devices), automated text translation and speech synthesis. The worker benefits from more accessible and up-to-date instructions presented in an easy-to-read way.

Keywords: assembly, assistive technologies, augmented reality, manufacturing, visualization

Procedia PDF Downloads 164