Search results for: satellite images

Authors: SHWETA SRIKUMAR

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Winter stratospheric dynamics is a highly variable and spectacular field of research in middle atmosphere. It is well believed that the interaction of energetic planetary waves with mean flow causes the temperature to increase in the stratosphere and associated circulation reversal. This wave driven sudden disturbances in the polar stratosphere is defined as Sudden Stratospheric Warming. The main objective of the present work is to investigate the mid-winter major stratospheric warming events on equatorial dynamics over Peninsular India. To explore the effect of mid-winter stratospheric warming on Indian region (60oE -100oE), we have selected the winters 2003/04, 2005/06, 2008/09, 2012/13 and 2018/19. This study utilized the data from ERA-Interim Reanalysis, Outgoing Longwave Radiation (OLR) from NOAA and TRMM satellite data from NASA mission. It is observed that a sudden drop in OLR (averaged over Indian Region) occurs during the course of warming for the winters 2005/06, 2008/09 and 2018/19. But in winters 2003/04 and 2012/13, drop in OLR happens prior to the onset of major warming. Significant amplitude of planetary wave activity is observed in equatorial lower stratosphere which indicates the propagation of extra-tropical planetary waves from high latitude to equator. During the course of warming, a strong downward propagation of EP flux convergence is observed from polar to equator region. The polar westward wind reaches upto 20oN and the weak eastward wind dominates the equator during the winters 2003/04, 2005/06 and 2018/19. But in 2012/13 winter, polar westward wind reaches upto equator. The equatorial wind at 2008/09 is dominated by strong westward wind. Further detailed results will be presented in the conference.

Keywords: Equatorial dynamics, Outgoing Longwave Radiation, Sudden Stratospheric Warming, Planetary Waves

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Authors: P. Strakos, M. Jaros, T. Karasek, T. Kozubek, P. Vavra, T. Jonszta

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In medical imaging, segmentation of different areas of human body like bones, organs, tissues, etc. is an important issue. Image segmentation allows isolating the object of interest for further processing that can lead for example to 3D model reconstruction of whole organs. Difficulty of this procedure varies from trivial for bones to quite difficult for organs like liver. The liver is being considered as one of the most difficult human body organ to segment. It is mainly for its complexity, shape versatility and proximity of other organs and tissues. Due to this facts usually substantial user effort has to be applied to obtain satisfactory results of the image segmentation. Process of image segmentation then deteriorates from automatic or semi-automatic to fairly manual one. In this paper, overview of selected available software applications that can handle semi-automatic image segmentation with further 3D volume reconstruction of human liver is presented. The applications are being evaluated based on the segmentation results of several consecutive DICOM images covering the abdominal area of the human body.

Keywords: image segmentation, semi-automatic, software, 3D volumetric reconstruction

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Authors: Ali Pour Yazdanpanah, Farideh Foroozandeh Shahraki, Emma Regentova

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The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction.

Keywords: computed tomography, non-convex, sparse-view reconstruction, L1-L2 minimization, difference of convex functions

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Authors: Mohammadreza Nezamirad, Nasim Sabetpour, Azadeh Yazdi, Amirmasoud Hamedi

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In this study OpenFOAM 4.4.2 was used to investigate flow inside the coronary artery of the heart. This step is the first step of our future project, which is to include conjugate heat transfer of the heart with three main coronary arteries. Three different velocities were used as inlet boundary conditions to see the effect of velocity increase on velocity, pressure, and wall shear of the coronary artery. Also, three different fluids, namely the University of Wisconsin solution, gelatin, and blood was used to investigate the effect of different fluids on flow inside the coronary artery. A code based on Reynolds Stress Navier Stokes (RANS) equations was written and implemented with the real boundary condition that was calculated based on MRI images. In order to improve the accuracy of the current numerical scheme, hex dominant mesh is utilized. When the inlet velocity increases to 0.5 m/s, velocity, wall shear stress, and pressure increase at the narrower parts.

Keywords: CFD, simulation, OpenFOAM, heart

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Authors: Reza Rezaeipour Honarmandzad

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Wavelet analysis is an exciting method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, etc. Wavelets allow complex information such as signals, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. In this paper a wavelet-based signal processing algorithm for airplane cable fault location is proposed. An orthogonal discrete wavelet decomposition and reconstruction algorithm is used to eliminate the noise in the aircraft cable fault signal. The experiment result has shown that the character of emission pulse and reflect pulse used to test the aircraft cable fault point are reserved and the high-frequency noise are eliminated by means of the proposed algorithm in this paper.

Keywords: wavelet analysis, signal processing, orthogonal discrete wavelet, noise, aircraft cable fault signal

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Authors: Tsai-chun Huang, Xinyan Liu

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This paper presents an innovative targeted zoning design method for the back of gym wear, based on the visual mapping method for superficial skin deformation of back muscles discussed in Back Skin Deformation during Anaerobic Exercises for Ergonomics Application. The method involves developing a knitted self-pleating structure for areas of greater skin deformation, designed to match the range of superficial skin deformation of the back muscles during large-scale movements. Current research on the functional zoning design of gym wear primarily concentrates on the human sweat map, fat distribution map, and pressure distribution map, with a particular emphasis on the lower body. However, there remains a gap in research on targeted zoning design specifically addressing the superficial skin deformation on the back. Based on the visual muscle deformation images of three back resistance training movements discussed previously, the two images with the greatest overall color differences for each muscle visualization, indicating the highest degree of skin deformation, were overlaid in Photoshop to analyze the color depth distribution. The results show that the darkest areas are concentrated on the upper edge of the trapezius muscle and the upper edge of the latissimus dorsi muscle, suggesting that these regions experience the most significant superficial skin deformation during exercises. To ensure comfort and flexibility during exercise, the entire area of these two regions is preserved to prevent seams from intersecting the regions of greatest skin deformation, thereby reducing skin friction. The heat map indicates that the skin deformation range at the upper edge of the trapezius muscle is from -25% to 15%, while the upper edge of the latissimus dorsi muscle shows a deformation range of -25% to 25%. Based on these findings, the fabric structure and stretch range of the knitted self-pleating structure were developed and adjusted accordingly. According to the tensile test, the developed weft-knitted self-pleating structure has a stretch rate of -46.6% to 50%, which covers the stretch range of the main muscle groups in the back, indicating that the structure can be used to wrap the muscles in this area. For the remaining areas with skin deformation ranging from -15% to 15%, elastic knitted fabrics with spandex content were utilized to accommodate this range. Based on the skin deformation data, a partitioned gym vest prototype was designed and made. The significance of this study lies in providing an innovative methodology for gym wear design, particularly for gym wear involving a large range of motion and significant skin deformation. A distinctively developed knitted self-pleating structure is utilized in areas of extensive deformation, while a knitted fabric containing spandex is employed in areas with less deformation. This zoning design method enhances adaptability to the dynamic changes of human movement, allowing designers to more precisely select and adjust materials and structures. This approach not only improves athletes’ comfort and flexibility but also effectively reduces friction and binding of fabric on the skin during exercise, providing valuable insights for designers to create more reasonable and effective back area solutions for gym wear.

Keywords: zoning design, skin deformation, self-pleating structures, gym wear design, back muscle

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: K. Kanja, M. Mweemba, K. Malungwa

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Due to the rampant population growth coupled with retrenchments currently going on in the Copper mines in Zambia, a number of people are resorting to land clearing for agriculture, illegal settlements as well as charcoal production among other vices. This study aims at assessing the rate of expansion of agricultural fields and illegal settlements in protected areas using remote sensing and Geographic Information System. Zambia’s Mwekera National Forest Reserve was used as a case study. Iterative Self-Organizing Data Analysis Technique (ISODATA), as well as maximum likelihood, supervised classification on four Landsat images as well as an accuracy assessment of the classifications was performed. Over the period under observation, results indicate annual percentage changes to be -0.03, -0.49 and 1.26 for agriculture, forests and settlement respectively indicating a higher conversion of forests into human settlements and agriculture.

Keywords: geographic information system, land cover change, Landsat TM and ETM+, Mwekera forest reserve, remote sensing

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Authors: R. Rama Kishore, Sunesh Malik

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Development in the usage of internet for different purposes in recent times creates great threat for the copy right protection of the digital images. Digital watermarking is the best way to rescue from the said problem. This paper presents detailed review of the different watermarking techniques, latest trends in the field and categorized like spatial and transform domain, blind and non-blind methods, visible and non visible techniques etc. It also discusses the different optimization techniques used in the field of watermarking in order to improve the robustness and imperceptibility of the method. Different measures are discussed to evaluate the performance of the watermarking algorithm. At the end, this paper proposes a watermarking algorithm using (k.n) shares of halftone visual cryptography (HVC) instead of (2, 2) share cryptography. (k,n) shares visual cryptography improves the security of the watermark. As halftone is a method of reprographic, it helps in improving the visual quality of watermark image. The proposed method uses fractional transformation to improve the robustness of the copyright protection of the method.

Keywords: digital watermarking, fractional transform, halftone, visual cryptography

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Ismail Celik, Ismail Sahin, Fetah Eren

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The computer, among the most important inventions of the twentieth century, has become an increasingly important component in our everyday lives. Computer games also have become increasingly popular among people day-by-day, owing to their features based on realistic virtual environments, audio and visual features, and the roles they offer players. In the present study, the metaphors students have for computer games are investigated, as well as an effort to fill the gap in the literature. Students were asked to complete the sentence—‘Computer game is like/similar to….because….’— to determine the middle school students’ metaphorical images of the concept for ‘computer game’. The metaphors created by the students were grouped in six categories, based on the source of the metaphor. These categories were ordered as ‘computer game as a means of entertainment’, ‘computer game as a beneficial means’, ‘computer game as a basic need’, ‘computer game as a source of evil’, ‘computer game as a means of withdrawal’, and ‘computer game as a source of addiction’, according to the number of metaphors they included.

Keywords: computer game, metaphor, middle school students, virtual environments

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Authors: Sneha Noble, Rahul Krishnan, Uma G., D. K. Vijaykumar

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Breast-Cancer related Lymphedema is a major problem that affects many women. Lymphedema is the swelling that generally occurs in the arms or legs caused by the removal of or damage to lymph nodes as a part of cancer treatment. Treating it at the earliest possible stage is the best way to manage the condition and prevent it from leading to pain, recurrent infection, reduced mobility, and impaired function. So, this project aims to focus on the multi-modal approaches to identify the risks of Lymphedema in post-surgical oncology patients and prevent it at the earliest. The Kinect IR Sensor is utilized to capture the images of the body and after image processing techniques, the region of interest is obtained. Then, performing the voxelization method will provide volume measurements in pre-operative and post-operative periods in patients. The formation of a mathematical model will help in the comparison of values. Clinical pathological data of patients will be investigated to assess the factors responsible for the development of lymphedema and its risks.

Keywords: Kinect IR sensor, Lymphedema, voxelization, lymph nodes

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Authors: Ilenia Lanni, Claudia Del Gatto, Allegra Indraccolo, Riccardo Brunetti

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Empathy represents a multifaceted construct encompassing affective and cognitive components. Among these, empathic accuracy—defined as the ability to accurately infer another person’s emotions or mental state—plays a pivotal role in fostering empathetic understanding. Emotional contagion, the automatic process through which individuals mimic and synchronize facial expressions, vocalizations, and postures, is considered a foundational mechanism for empathy. This embodied simulation enables shared emotional experiences and facilitates the recognition of others’ emotional states, forming the basis of empathic accuracy. Facial mimicry, an integral part of emotional contagion, creates a physical and emotional resonance with others, underscoring its potential role in enhancing empathic understanding. Building on these findings, the present study explores how manipulating emotional contagion through the enfacement illusion impacts empathic accuracy, particularly in the recognition of complex emotional expressions. The enfacement illusion was implemented as a visuo-tactile multisensory manipulation, during which participants experienced synchronous and spatially congruent tactile stimulation on their own face while observing the same stimulation being applied to another person’s face. This manipulation enhances facial mimicry, which is hypothesized to play a key role in improving empathic accuracy. Following the enfacement illusion, participants completed a modified version of the Diagnostic Analysis of Nonverbal Accuracy–Form 2 (DANVA2-AF). The task included 48 images of adult faces expressing happiness, sadness, or morphed emotions blending neutral with happiness or sadness to increase recognition difficulty. These images featured both familiar and unfamiliar faces, with familiar faces belonging to the actors involved in the prior visuo-tactile stimulation. Participants were required to identify the target’s emotional state as either "happy" or "sad," with response accuracy and reaction times recorded. Results from this study indicate that emotional contagion, as manipulated through the enfacement illusion, significantly enhances empathic accuracy, particularly for the recognition of happiness. Participants demonstrated greater accuracy and faster response times in identifying happiness when viewing familiar faces compared to unfamiliar ones. These findings suggest that the enfacement illusion strengthens emotional resonance and facilitates the processing of positive emotions, which are inherently more likely to be shared and mimicked. Conversely, for the recognition of sadness, an opposite but non-significant trend was observed. Specifically, participants were slightly faster at recognizing sadness in unfamiliar faces compared to familiar ones. This pattern suggests potential differences in how positive and negative emotions are processed within the context of facial mimicry and emotional contagion, warranting further investigation. These results provide insights into the role of facial mimicry in emotional contagion and its selective impact on empathic accuracy. This study highlights how the enfacement illusion can precisely modulate the recognition of specific emotions, offering a deeper understanding of the mechanisms underlying empathy.

Keywords: empathy, emotional contagion, enfacement illusion, emotion recognition

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Authors: Simbarashe Nzvere

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In an era dominated by digital communication, social media has become a powerful tool for political leaders to connect with the public, shape their narratives, and influence perceptions. With this much performance from political leaders, this paper will explore the dichotomy between authenticity and performance in the digital personas of political leaders. By examining how leaders craft their image on platforms such as X (Formerly Twitter), Facebook, Instagram, and Linked In, this study investigates whether these portrayals align with their true character or represent a strategic facade designed to resonate with target audiences. Utilizing case studies and content analysis, the research delves into the methods leaders employ to construct their online identities, the role of digital marketing teams in shaping these images, and the implications for public trust and political engagement. The findings highlight the complex interplay between genuine representation and strategic branding, offering insights into how social media reshapes political leadership in the 21st century.

Keywords: political leadership, social media, authenticity, public perceptions, digital persona

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Authors: M. A. Alavianmehr, A. Tashk, A. Sodagaran

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Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method.

Keywords: image processing, background models, video surveillance, foreground detection, Gaussian mixture model

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Authors: Navab Karimi, Tohid Alizadeh

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An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively.

Keywords: sun-dried organic raisin, genetic algorithm, feature extraction, ann regression, linear regression, support vector machine, south azerbaijan.

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Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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Authors: Chad Goldsworthy, B. Rajeswari Matam

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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.

Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation

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Authors: Seung Hun Lee, Sang Yeoup Lee

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Objectives: To investigate associations between degrees of obesity using correlations between obesity indices and cardiometabolic risk factors. Methods: BMI, waist circumference (WC), fasting insulin, fasting glucose, lipids, and visceral adipose tissue (VAT) area using computed tomographic images were measured in 113 obese female without cardiovascular disease (CVD). Correlations between obesity indices and cardiometabolic risk factors were analyzed in obese subgroups defined using sequential obesity indices. Results: Mean BMI and WC were 29.6 kg/m2 and 92.8 cm. BMI showed significant correlations with all five cardiometabolic risk factors until the BMI cut-off point reached 27 kg/m2, but when it exceeded 30 kg/m2, correlations no longer existed. WC was significantly correlated with all five cardiometabolic risk factors up to a value of 85 cm, but when WC exceeded 90 cm, correlations no longer existed. Conclusions: Our data suggest that moderate weight-loss goals may not be enough to ameliorate cardiometabolic markers in severely obese patients. Therefore, individualized weight-loss goals should be recommended to such patients to improve health benefits.

Keywords: correlation, cardiovascular disease, risk factors, obesity

Procedia PDF Downloads 357

Authors: J. Urban, Á. S. Botella, L. E. Robaina, A. Bárta, P. Souček, P. Císař, Š. Papáček, L. M. Domínguez

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Skin coloration in fish is of great physiological, behavioral and ecological importance and can be considered as an index of animal welfare in aquaculture as well as an important quality factor in the retail value. Currently, in order to compare color in animals fed on different diets, biochemical analysis, and colorimetry of fished, mildly anesthetized or dead body, are very accurate and meaningful measurements. The noninvasive method using digital images of the fish body was developed as a standalone application. This application deals with the computation burden and memory consumption of large input files, optimizing piece wise processing and analysis with the memory/computation time ratio. For the comparison of color distributions of various experiments and different color spaces (RGB, CIE L*a*b*) the comparable semi-equidistant binning of multi channels representation is introduced. It is derived from the knowledge of quantization levels and Freedman-Diaconis rule. The color calibrations and camera responsivity function were necessary part of the measurement process.

Keywords: color distribution, fish skin color, piecewise transformation, object to background segmentation

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Authors: H. Farokhi, A. Bahadoran

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This paper is focused on the absorbance and magnetic properties of a novel nanocomposite based on conducting polymer, carbon black and barium hexaferrite in epoxy resin on the E-glass fibre substrate. The highly conductive nanocomposite was provided by in-situ polymerization of aniline in the presence of carbon black (C) and barium hexaferrite (BaFe12O19) as electromagnetic absorbance material. The structure, morphology, and magnetic properties of samples were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM) and vibrating sample magnetometer (VSM). SEM images showed the uniformly coated PAni on the surface of carbon black and barium hexaferrite. XRD peaks also verified the presence of carbon black and barium hexaferrite in the nanocomposite. The microwave characteristics determined from the magnetic and dielectric properties of the elastomeric composites obtained from scattering data by fitting the samples in a waveguide, where measured in the frequency in X-band frequency range, the range of 8 to 12 GHz. The reflection losses were evaluated to be less than −5dB over the whole X-band frequency (8–12 GHz) for the thickness of 1.4mm.

Keywords: conductive polymer, magnetic materials, capacitance, electrochemical cell

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Authors: DEVASHISH KHULBE, MANU PATHAK

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The rise in popularity of major social media platforms have enabled people to share photos and textual information about their daily life. One of the popular topics about which information is shared is food. Since a lot of media about food are attributed to particular locations and restaurants, information like spatio-temporal popularity of various cuisines can be analyzed. Tracking the popularity of food types and retail locations across space and time can also be useful for business owners and restaurant investors. In this work, we present an approach using off-the shelf machine learning techniques to identify trends and popularity of cuisine types in an area using geo-tagged data from social media, Google images and Yelp. After adjusting for time, we use the Kernel Density Estimation to get hot spots across the location and model the dependencies among food cuisines popularity using Bayesian Networks. We consider the Manhattan borough of New York City as the location for our analyses but the approach can be used for any area with social media data and information about retail businesses.

Keywords: Web Mining, Geographic Information Systems, Business popularity, Spatial Data Analyses

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Authors: Amal Khalifa, Nicolas Vana Santos

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Steganography has been known for centuries as an efficient approach for covert communication. Due to its popularity and ease of access, image steganography has attracted researchers to find secure techniques for hiding information within an innocent looking cover image. In this research, we propose a novel deep-learning approach to digital image steganography. The proposed method, DeepWaveletFusion, uses convolutional neural networks (CNN) to hide a secret image into a cover image of the same size. Two CNNs are trained back-to-back to merge the Discrete Wavelet Transform (DWT) of both colored images and eventually be able to blindly extract the hidden image. Based on two different image similarity metrics, a weighted gain function is used to guide the learning process and maximize the quality of the retrieved secret image and yet maintaining acceptable imperceptibility. Experimental results verified the high recoverability of DeepWaveletFusion which outperformed similar deep-learning-based methods.

Keywords: deep learning, steganography, image, discrete wavelet transform, fusion

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

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Authors: Supaporn Wimonchailerk

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This research aims to study how to design and create the media to promote Thai cuisine. The study used qualitative research methods by using in-depth interview 3 key informants who have experienced in the production of food or cooking shows in television programs with an aspect of acknowledging Thai foods. The results showed that visual presentation is divided into four categories. First, the light meals should be presented in details via the close-up camera with lighting to make the food look more delicious. Then the curry presentation should be arranged a clear and crisp light focus on a colorful curry paste. Besides the vision of hot steam floating from the plate and a view of curry spread on steamed rice can call great attentions. Third, delivering good appearances of the fried or spicy foods, the images must allow the audiences to see the shine of the coat covering the texture of the food and the colorful of the ingredients. Fourth, the presentation of sweets is recommended to focus on details of food design, composition, and layout.

Keywords: media production, television, promote, Thai cuisine

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

Abstract:

Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Imana Shahrin Tania, Mohammad Ali

Abstract:

The present study reports the synthesis, characterization and application of nano-sized zinc-oxide (ZnO) particles on a cotton fabric surface. The aim of the investigations is to impart the antimicrobial activity on textile cloth. Nanoparticle is synthesized by wet chemical method from zinc sulphate and sodium hydroxide. SEM (scanning electron micrograph) images are taken to demonstrate the surface morphology of nanoparticles. XRD analysis is done to determine the crystal size of the nanoparticle. With the conformation of nanoformation, the cotton woven fabric is treated with ZnO nanoparticle by mechanical thermo-fixation (pad-dry-cure) technique. To increase the wash durability of nano treated fabric, an acrylic binder is used as a fixing agent. The treated fabric shows up to 90% bacterial reduction for S. aureus (Staphylococcus aureus) and 87% for E. coli (Escherichia coli) which is appreciable for bacteria protective clothing.

Keywords: nanoparticle, zinc oxide, cotton fabric, antibacterial activity, binder

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Authors: Ahmad Zaini, F. Muhammad Reza Hadafi, Surya Sumpeno, Muhtadin, Mochamad Hariadi

Abstract:

The lack of information about museum’s collection reduces the number of visits of museum. Museum’s revitalization is an urgent activity to increase the number of visits. The research's roadmap is building a web-based application that visualizes museum in the virtual form including museum's statue reconstruction in the form of 3D. This paper describes implementation of three-dimensional model reconstruction method based on light-strip pattern on the museum statue using 3D scanner. Noise removal, alignment, meshing and refinement model's processes is implemented to get a better 3D object reconstruction. Model’s texture derives from surface texture mapping between object's images with reconstructed 3D model. Accuracy test of dimension of the model is measured by calculating relative error of virtual model dimension compared against the original object. The result is realistic three-dimensional model textured with relative error around 4.3% to 5.8%.

Keywords: 3D reconstruction, light pattern structure, texture mapping, museum

Procedia PDF Downloads 468

Authors: Celso Afonso Klein Jr., Henrique Cantarelli, Fernando Portella, Keiichi Hosaka, Eduardo Reston, Fabricio Collares, Roberto Zimmer

Abstract:

TTo evaluate the influence of preheating self-adhesive cement at 39ºC on cell migration, cytotoxicity and degree of conversion. RelyX U200, Set PP and MaxCem Elite were subjected to a degree of conversion analysis (FTIR-ATR). For the cytotoxicity analysis, extracts (24 h and 7 days) were placed in contact with NIH/3T3 cells. For cell migration, images were captured of each sample until the possible closure of the cleft occurred. In the results of the degree of conversion, preheating did not improve the conversion of cement. For the MTT, preheating did not improve the results within 24 hours. However, it generated positive results within 7 days for the Set PP resin cement. For cell migration, high rates of cell death were found in all groups. It is concluded that preheating at 39ºC caused a positive effect only in increasing the cell viability of the Set PP resin cement and that both materials analyzed are highly cytotoxic.

Keywords: dental cements, resin cements, degree of conversion, cytotoxicity, cell migration assays

Procedia PDF Downloads 74