Search results for: image mapping

Authors: Robson da C. Santos, Carlos Henrique de A. S. P. Coutinho, Lucas Moreira Dias, Gerson Gomes Cunha

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The article refers to the development of an augmented reality 3D display, through the control of servo motors and projection of image with aid of video projector on the model. Augmented Reality is a branch that explores multiple approaches to increase real-world view by viewing additional information along with the real scene. The article presents the broad use of electrical, electronic, mechanical and industrial automation for geospatial visualizations, applications in mathematical models with the visualization of functions and 3D surface graphics and volumetric rendering that are currently seen in 2D layers. Application as a 3D display for representation and visualization of Digital Terrain Model (DTM) and Digital Surface Models (DSM), where it can be applied in the identification of canyons in the marine area of the Campos Basin, Rio de Janeiro, Brazil. The same can execute visualization of regions subject to landslides, as in Serra do Mar - Agra dos Reis and Serranas cities both in the State of Rio de Janeiro. From the foregoing, loss of human life and leakage of oil from pipelines buried in these regions may be anticipated in advance. The physical design consists of a table consisting of a 9 x 16 matrix of servo motors, totalizing 144 servos, a mesh is used on the servo motors for visualization of the models projected by a retro projector. Each model for by an image pre-processing, is sent to a server to be converted and viewed from a software developed in C # Programming Language.

Keywords: visualization, 3D models, servo motors, C# programming language

Procedia PDF Downloads 325

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

Procedia PDF Downloads 113

Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

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Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

Procedia PDF Downloads 234

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Pradeep Kumar Singh Chauhan, Gayatri Devi, Zamir Ahmad, Komal Chauhan, Abha Mittal

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The application of geophysical methods to study the subsurface profile for site investigation is becoming popular globally. These methods are non-destructive and provide the image of subsurface at shallow depths. Seismic refraction method is one of the most common and efficient method being used for civil engineering site investigations particularly for knowing the seismic velocity of the subsurface layers. Resistivity imaging technique is a geo-electrical method used to image the subsurface, water bearing zone, bedrock and layer thickness. Integrated approach combining seismic refraction and 2-D resistivity imaging will provide a better and reliable picture of the subsurface. These are economical and less time-consuming field survey which provide high resolution image of the subsurface. Geophysical surveys carried out in this study include seismic refraction and 2D resistivity imaging method for delineation of sub-surface strata in different parts of Srinagar, Garhwal Himalaya, India. The aim of this survey was to map the shallow subsurface in terms of geological and geophysical properties mainly P-wave velocity, resistivity, layer thickness, and lithology of the area. Both sides of the river, Alaknanda which flows through the centre of the city, have been covered by taking two profiles on each side using both methods. Seismic and electrical surveys were carried out at the same locations to complement the results of each other. The seismic refraction survey was carried out using ABEM TeraLoc 24 channel Seismograph and 2D resistivity imaging was performed using ABEM Terrameter LS equipment. The results show three distinct layers on both sides of the river up to the depth of 20 m. The subsurface is divided into three distinct layers namely, alluvium extending up to, 3 m depth, conglomerate zone lying between the depth of 3 m to 15 m, and compacted pebbles and cobbles beyond 15 m. P-wave velocity in top layer is found in the range of 400 – 600 m/s, in second layer it varies from 700 – 1100 m/s and in the third layer it is 1500 – 3300 m/s. The resistivity results also show similar pattern and were in good agreement with seismic refraction results. The results obtained in this study were validated with an available exposed river scar at one site. The study established the efficacy of geophysical methods for subsurface investigations.

Keywords: 2D resistivity imaging, P-wave velocity, seismic refraction survey, subsurface

Procedia PDF Downloads 239

Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: May Abdelghani

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The present work is devoted to the study of the microstructural anisotropy in mechanical cyclic behavior of the 2017AA aluminum alloy which is widely used in the aerospace industry. The main purpose of the study is to investigate the microstructural origins of this anisotropy already confirmed in our previous work in 2017AA aluminum alloy. To do this, we have used the microstructural analysis resources such as Scanning Electron Microscope (SEM) to see the differences between breaks from different directions of cyclic loading. Another resource of investigation was used in this study is that the EBSD method, which allows us to obtain a mapping of the crystallographic texture of our material. According to the obtained results in the microscopic analysis, we are able to identify the origins of the anisotropic behavior at the macroscopic scale.

Keywords: fatigue damage, cyclic behavior, anisotropy, microstructural analysis

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Authors: Hyoseop Shin

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As design rules become smaller, semiconductor processes are becoming a new problem because defects that were not previously a problem affect yields. Recently, semiconductor fine inspection technology has been required to develop high-precision, high-efficiency technology to manage defects, and the detection capability of semiconductor inspection equipment has been improved by studying defect detection through a comprehensive understanding of semiconductor inspection equipment, semiconductor processing, and defects. The optimal test parameters were applied to actual equipment by conditional comparison results aimed at detecting 30 nm particles in low-density semiconductors, thereby improving the detection capability of particle inspection equipment. The improvement of 30 nm particle detection has been studied based on the results of image analysis and evaluation through defect simulation. Factor analysis such as wavelength polarization incident angle of semiconductor equipment parameters and acquisition of scattering signals of actual equipment has been found to have found the optimal conditions of detection power and contributed to defect detection. As a result, it was confirmed that the detection power differed significantly in the experiment of 266 nm wavelength and P incident polarization conditions using P polarization, and 30 nm particles were detected, contributing to the yield improvement.

Keywords: electronic simulation system, a semiconductor defect, Reynolds' equation, semiconductor optical measuring equipment, facility engineering

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Authors: Basiru Amuneni

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Astronomy is one domain with a rise in data. Traditional tools for data management have been employed in the quest for knowledge discovery. However, these traditional tools become limited in the face of big. One means of maximizing knowledge discovery for big data is the use of scientific visualisation. The aim of the work is to explore the possibilities offered by emerging creative technologies of Virtual Reality (VR) systems and game engines to visualize multiwavelength datasets. Game Engines are primarily used for developing video games, however their advanced graphics could be exploited for scientific visualization which provides a means to graphically illustrate scientific data to ease human comprehension. Modern astronomy is now in the era of multiwavelength data where a single galaxy for example, is captured by the telescope several times and at different electromagnetic wavelength to have a more comprehensive picture of the physical characteristics of the galaxy. Visualising this in an immersive environment would be more intuitive and natural for an observer. This work presents a standalone VR application that accesses galaxy FITS files. The application was built using the Unity Game Engine for the graphics underpinning and the OpenXR API for the VR infrastructure. The work used a methodology known as Design Science Research (DSR) which entails the act of ‘using design as a research method or technique’. The key stages of the galaxy modelling pipeline are FITS data preparation, Galaxy Modelling, Unity 3D Visualisation and VR Display. The FITS data format cannot be read by the Unity Game Engine directly. A DLL (CSHARPFITS) which provides a native support for reading and writing FITS files was used. The Galaxy modeller uses an approach that integrates cleaned FITS image pixels into the graphics pipeline of the Unity3d game Engine. The cleaned FITS images are then input to the galaxy modeller pipeline phase, which has a pre-processing script that extracts, pixel, galaxy world position, and colour maps the FITS image pixels. The user can visualise image galaxies in different light bands, control the blend of the image with similar images from different sources or fuse images for a holistic view. The framework will allow users to build tools to realise complex workflows for public outreach and possibly scientific work with increased scalability, near real time interactivity with ease of access. The application is presented in an immersive environment and can use all commercially available headset built on the OpenXR API. The user can select galaxies in the scene, teleport to the galaxy, pan, zoom in/out, and change colour gradients of the galaxy. The findings and design lessons learnt in the implementation of different use cases will contribute to the development and design of game-based visualisation tools in immersive environment by enabling informed decisions to be made.

Keywords: astronomy, visualisation, multiwavelenght dataset, virtual reality

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Authors: Rawinun Praserttaweelap, Somyot Kiatwanidvilai

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Obstacle avoidance is the one key for the robot system in unknown environment. The robots should be able to know their position and safety region. This research starts on the path planning which are SLAM and AMCL in ROS system. In addition, the best parameters of the obstacle avoidance function are required. In situation on Hard Disk Drive Manufacturing, the distance between robots and obstacles are very serious due to the manufacturing constraint. The simulations are accomplished by the SLAM and AMCL with adaptive velocity and safety region calculation.

Keywords: obstacle avoidance, OA, Simultaneous Localization and Mapping, SLAM, Adaptive Monte Carlo Localization, AMCL, KLD sampling, KLD

Procedia PDF Downloads 182

Authors: Inigo Beckett

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In forensic investigations, it is often the case that the most potentially useful recorded evidence derives from coincidental imagery, recorded immediately before or during an incident, and that during the incident (e.g. a ‘failure’ or fire event), the evidence is changed or destroyed. To an image analysis expert involved in photogrammetric analysis for Civil or Criminal Proceedings, traditional computer vision methods involving calibrated cameras is often not appropriate because image metadata cannot be relied upon. This paper presents an approach for resolving this problem, considering in particular and by way of a case study, the angle of a simple ladder shown in a photograph. The UK Health and Safety Executive (HSE) guidance document published in 2014 (INDG455) advises that a leaning ladder should be erected at 75 degrees to the horizontal axis. Personal injury cases can arise in the construction industry because a ladder is too steep or too shallow. Ad-hoc photographs of such ladders in their incident position provide a basis for analysis of their angle. This paper presents a direct approach for ascertaining the position of the camera and the angle of the ladder simultaneously from the photograph(s) by way of a workflow that encompasses a novel application of projective geometry and matrix analysis. Mathematical analysis shows that for a given pixel ratio of directly measured collinear points (i.e. features that lie on the same line segment) from the 2D digital photograph with respect to a given viewing point, we can constrain the 3D camera position to a surface of a sphere in the scene. Depending on what we know about the ladder, we can enforce another independent constraint on the possible camera positions which enables us to constrain the possible positions even further. Experiments were conducted using synthetic and real-world data. The synthetic data modeled a vertical plane with a ladder on a horizontally flat plane resting against a vertical wall. The real-world data was captured using an Apple iPhone 13 Pro and 3D laser scan survey data whereby a ladder was placed in a known location and angle to the vertical axis. For each case, we calculated camera positions and the ladder angles using this method and cross-compared them against their respective ‘true’ values.

Keywords: image analysis, projective geometry, homography, photogrammetry, ladders, Forensics, Mathematical modeling, planar geometry, matrix analysis, collinear, cameras, photographs

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Lauren Schuldt Wilson

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Felicia Hemans’ memorializing of the unwritten lives of women and the consequent allowance for marginalized voices to remember and be remembered has been considered by many critics in terms of ekphrasis and elegy, terms which privilege the question of whether Hemans’ poeticizing can represent lost voices of history or only her poetic expression. Amy Gates, Brian Elliott, and others point out Hemans’ acknowledgement of the self-projection necessary for imaginatively filling the absences of unrecorded histories. Yet, few have examined the complex temporal positioning Hemans inscribes in these moments of self-projection and imaginative historicizing. In poems like ‘The Image in Lava,’ Hemans maps not only a lost past, but also a lost potential future onto the image of a dead infant in its mother’s arms, the discovery and consideration of which moves the imagined viewer to recover and incorporate the ‘hope’ encapsulated in the figure of the infant into a reevaluation of national time embodied by the ‘relics / Left by the pomps of old.’ By examining Hemans’ acknowledgement and response to Percy Bysshe Shelley’s ‘Ozymandias,’ this essay explores how Hemans’ depictions of imaginative historicizing open new horizons of possibility and reevaluate temporal value structures by imagining previously undiscovered or unexplored potentialities of the past. Where Shelley’s poem mocks the futility of national power and time, this essay outlines Hemans’ suggestion of alternative threads of identity and temporal meaning-making which, regardless of historical veracity, exist outside of and against the structures Shelley challenges. Counter to previous readings of Hemans’ poem as celebration of either recovered or poetically constructed maternal love, this essay argues that Hemans offers a meditation on sites of reproduction—both of personal reproductive futurity and of national reproduction of power. This meditation culminates in Hemans’ gesturing towards a method of historicism by which the imagined viewer reinvigorates the sterile, ‘shattered visage’ of national time by forming temporal identity through the imagining of trans-historical hope inscribed on the infant body of the universal, individual subject rather than the broken monument of the king.

Keywords: futurity, national temporalities, reproduction, revisionary histories

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Authors: Azimollah Aleshzadeh, Enver Vural Yavuz

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The main goal of this study is to produce landslide susceptibility maps using different data-driven bivariate statistical approaches; namely, entropy weight method (EWM), evidence belief function (EBF), and information content model (ICM), at Uzundere county, Erzurum province, in the north-eastern part of Turkey. Past landslide occurrences were identified and mapped from an interpretation of high-resolution satellite images, and earlier reports as well as by carrying out field surveys. In total, 42 landslide incidence polygons were mapped using ArcGIS 10.4.1 software and randomly split into a construction dataset 70 % (30 landslide incidences) for building the EWM, EBF, and ICM models and the remaining 30 % (12 landslides incidences) were used for verification purposes. Twelve layers of landslide-predisposing parameters were prepared, including total surface radiation, maximum relief, soil groups, standard curvature, distance to stream/river sites, distance to the road network, surface roughness, land use pattern, engineering geological rock group, topographical elevation, the orientation of slope, and terrain slope gradient. The relationships between the landslide-predisposing parameters and the landslide inventory map were determined using different statistical models (EWM, EBF, and ICM). The model results were validated with landslide incidences, which were not used during the model construction. In addition, receiver operating characteristic curves were applied, and the area under the curve (AUC) was determined for the different susceptibility maps using the success (construction data) and prediction (verification data) rate curves. The results revealed that the AUC for success rates are 0.7055, 0.7221, and 0.7368, while the prediction rates are 0.6811, 0.6997, and 0.7105 for EWM, EBF, and ICM models, respectively. Consequently, landslide susceptibility maps were classified into five susceptibility classes, including very low, low, moderate, high, and very high. Additionally, the portion of construction and verification landslides incidences in high and very high landslide susceptibility classes in each map was determined. The results showed that the EWM, EBF, and ICM models produced satisfactory accuracy. The obtained landslide susceptibility maps may be useful for future natural hazard mitigation studies and planning purposes for environmental protection.

Keywords: entropy weight method, evidence belief function, information content model, landslide susceptibility mapping

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Authors: Khalid A. Alsaif, Mosaad A. Foda

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The primary objective of this study is to develop the first round of road traffic noise maps for Riyadh City using Geographical Information Systems (GIS) and software LimA 7810 predictor. The road traffic data were measured or estimated as accurate as possible in order to obtain reliable noise maps. Meanwhile, the attributes of the roads and buildings are automatically exported from GIS. The simulation results at some chosen locations are validated by actual field measurements, which are obtained by a system that consists of a sound level meter, a GPS receiver and a database to manage the measured data. The results show that the average error between the predicted and measured noise levels is below 3.0 dB.

Keywords: noise pollution, road traffic noise, LimA predictor, GIS

Procedia PDF Downloads 387

Authors: Guanqiao Wang, Hongyang Yu

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There is a lot of repetitive work in the traditional construction industry. These repetitive tasks can significantly improve production efficiency by replacing manual tasks with robots. There- fore, robots appear more and more frequently in the construction industry. Navigation and positioning are very important tasks for construction robots, and the requirements for accuracy of positioning are very high. Traditional indoor robots mainly use radiofrequency or vision methods for positioning. Compared with ordinary robots, the indoor plastering robot needs to be positioned closer to the wall for wall plastering, so the requirements for construction positioning accuracy are higher, and the traditional navigation positioning method has a large error, which will cause the robot to move. Without the exact position, the wall cannot be plastered, or the error of plastering the wall is large. A new positioning method is proposed, which is assisted by line lasers and uses image processing-based positioning to perform more accurate positioning on the traditional positioning work. In actual work, filter, edge detection, Hough transform and other operations are performed on the images captured by the camera. Each time the position of the laser line is found, it is compared with the standard value, and the position of the robot is moved or rotated to complete the positioning work. The experimental results show that the actual positioning error is reduced to less than 0.5 mm by this accurate positioning method.

Keywords: indoor plastering robot, navigation, precise positioning, line laser, image processing

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Authors: M. Bouinoun, M. Bouhadef

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The present study is concerned with the problem of determining the shape of the free surface flow in a hydraulic channel which has an uneven bottom. For the mathematical formulation of the problem, the fluid of the two-dimensional irrotational steady flow in water is assumed inviscid and incompressible. The solutions of the nonlinear problem are obtained by using the usual conformal mapping theory and Hilbert’s technique. An experimental study, for comparing the obtained results, has been conducted in a hydraulic channel (subcritical regime and supercritical regime).

Keywords: free-surface flow, experiments, numerical method, uneven bottom, supercritical regime, subcritical regime

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Authors: N. Reppas, Y. L. Gui, B. Wetenhall, C. T. Davie, J. Ma

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A theoretical constitutive model describing the stress-strain behavior of rock subjected to different confining pressures is presented. A bounding surface plastic model with hardening effects is proposed which includes the effect of temperature drop. The bounding surface is based on a mapping rule and the temperature effect on rock is controlled by Poisson’s ratio. Validation of the results against available experimental data is also presented. The relation of deviatoric stress and axial strain is illustrated at different temperatures to analyze the effect of temperature decrease in terms of stiffness of the material.

Keywords: bounding surface, cooling of rock, plasticity model, rock deformation, elasto-plastic behavior

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Authors: Alfi Al Fahreizy

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Yogyakarta is one of the provinces in Indonesia that often get a power outage because of high load electrical consumption. The authors mapped the electrical energy consumption [GWh] for the province of Yogyakarta in 2014-2025 using LEAP (Long-range Energy Alternatives Planning system) software. This paper use BAU (Business As Usual) scenario. BAU scenario in which the projection is based on the assumption that growth in electricity consumption will run as normally as before. The goal is to be able to see the electrical energy consumption in the household sector, industry , business, social, government office building, and street lighting. The data is the data projected statistical population and consumption data electricity [GWh] 2010, 2011, 2012 in Yogyakarta province.

Keywords: LEAP, energy consumption, Yogyakarta, BAU

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Authors: Chen-Chang Lee, Po-Chou Chen, Jo-Chi Jao, Chun-Chung Lui, Leung-Chit Tsang, Lain-Chyr Hwang

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Fetal Magnetic Resonance Imaging (MRI) is a challenge task because the fetal movements could cause motion artifact in MR images. The remedy to overcome this problem is to use fast scanning pulse sequences. The Single-Shot Fast Spin-Echo (SSFSE) T2-weighted imaging technique is routinely performed and often used as a gold standard in clinical examinations. Fast spoiled gradient-echo (FSPGR) T1-Weighted Imaging (T1WI) is often used to identify fat, calcification and hemorrhage. Fast Imaging Employing Steady-State Acquisition (FIESTA) is commonly used to identify fetal structures as well as the heart and vessels. The contrast of FIESTA image is related to T1/T2 and is different from that of SSFSE. The advantages and disadvantages of these two scanning sequences for fetal imaging have not been clearly demonstrated yet. This study aimed to compare these three rapid MRI techniques (SSFSE, FIESTA, and FSPGR) for fetal MRI examinations. The image qualities and influencing factors among these three techniques were explored. A 1.5T GE Discovery 450 clinical MR scanner with an eight-channel high-resolution abdominal coil was used in this study. Twenty-five pregnant women were recruited to enroll fetal MRI examination with SSFSE, FIESTA and FSPGR scanning. Multi-oriented and multi-slice images were acquired. Afterwards, MR images were interpreted and scored by two senior radiologists. The results showed that both SSFSE and T2W-FIESTA can provide good image quality among these three rapid imaging techniques. Vessel signals on FIESTA images are higher than those on SSFSE images. The Specific Absorption Rate (SAR) of FIESTA is lower than that of the others two techniques, but it is prone to cause banding artifacts. FSPGR-T1WI renders lower Signal-to-Noise Ratio (SNR) because it severely suffers from the impact of maternal and fetal movements. The scan times for these three scanning sequences were 25 sec (T2W-SSFSE), 20 sec (FIESTA) and 18 sec (FSPGR). In conclusion, all these three rapid MR scanning sequences can produce high contrast and high spatial resolution images. The scan time can be shortened by incorporating parallel imaging techniques so that the motion artifacts caused by fetal movements can be reduced. Having good understanding of the characteristics of these three rapid MRI techniques is helpful for technologists to obtain reproducible fetal anatomy images with high quality for prenatal diagnosis.

Keywords: fetal MRI, FIESTA, FSPGR, motion artifact, SSFSE

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Authors: Delara Kazempour, Mashallah Abasi Dezfuli, Reza Javidan

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Video compression used for channels with limited bandwidth and storage devices has limited storage capabilities. One of the most popular approaches in video compression is the usage of different transforms. Discrete cosine transform is one of the video compression methods that have some problems such as blocking, noising and high distortion inappropriate effect in compression ratio. wavelet transform is another approach is better than cosine transforms in balancing of compression and quality but the recognizing of curve curvature is so limit. Because of the importance of the compression and problems of the cosine and wavelet transforms, the contourlet transform is most popular in video compression. In the new proposed method, we used contourlet transform in video image compression. Contourlet transform can save details of the image better than the previous transforms because this transform is multi-scale and oriented. This transform can recognize discontinuity such as edges. In this approach we lost data less than previous approaches. Contourlet transform finds discrete space structure. This transform is useful for represented of two dimension smooth images. This transform, produces compressed images with high compression ratio along with texture and edge preservation. Finally, the results show that the majority of the images, the parameters of the mean square error and maximum signal-to-noise ratio of the new method based contourlet transform compared to wavelet transform are improved but in most of the images, the parameters of the mean square error and maximum signal-to-noise ratio in the cosine transform is better than the method based on contourlet transform.

Keywords: video compression, contourlet transform, discrete cosine transform, wavelet transform

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Authors: Tereza Bartosikova

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This paper describes the values of a specific type of industrial heritage-aluminum factories. It is an especially endangered kind of industrial heritage with only a little attention paid. The paper aims to highlight the uniqueness of these grounds and to specify several options for revitalizations. The research is based on complex aluminum factories mapping in Europe from archives and bibliographic sources and on site. There is analyzed gained information that could offer a new view on the aluminum grounds. Primarily, the data are described according to the works in Žiar nad Hronom, Slovakia. More than a half aluminum grounds have ended up the production, although they can go on further. They are closely connected with some areas identity and their presence has left striking footsteps in the environment. By saving them, the historical continuity, cultural identity of population and also the economic stability of region would be supported.

Keywords: aluminum, industrial heritage, regeneration, values

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Dali Nikolaishvili, Nino Khareba, Mariam Tsitsagi

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Tbilisi has been a capital of Georgia since the 5ᵗʰ century. City area was covered by forest in historical past. Nowadays the situation has been changing dramatically. Dozens of problems are caused by damages/destruction of green cover and solution, at one glance, seems to be uncomplicated (planting trees and creating green quarters), but on the other hand, according to the increasing tendency, the built up of areas still remains unsolved. Finding out the ways to overcome such obstacles is important even for protecting the health of society. Making of Retrospective cartography of the forest area of Tbilisi with use of GIS technology and remote sensing was the main aim of the research. Research about the dynamic of forest-cover in Tbilisi and its surroundings included the following steps: assessment of the dynamic of forest in Tbilisi and its surroundings. The survey was mainly based on the retrospective mapping method. Using of GIS technology, studying, comparing and identifying the narrative sources was the next step. And the last one was analyzed of the changes from the 80s to the present days on the basis of decryption of remotely sensed images. After creating a unified cartographic basis, the mapping and plans of different periods have been linked to this geodatabase. Data about green parks, individual old plants existing in the private yards and respondents' Information (according to a questionnaire created in advance) was added to the basic database, the general plan of Tbilisi and Scientific works as well. On the basis of analysis of historic, including cartographic sources, forest-cover maps for different periods of time were made. In addition, was made the catalog of individual green parks (location, area, typical composition, name and so on), which was the basis of creating several thematic maps. Areas with a high rate of green area degradation were identified. Several maps depicting the dynamics of forest cover of Tbilisi were created and analyzed. The methods of linking the data of the old cartographic sources to the modern basis were developed too, the result of which may be used in Urban Planning of Tbilisi. Understanding, perceiving and analyzing the real condition of green cover in Tbilisi and its problems, in turn, will help to take appropriate measures for the maintenance of ancient plants, to develop forests and to plan properly parks, squares, and recreational sites. Because the healthy environment is the main condition of human health and implies to the rational development of the city.

Keywords: catalogue of green area, GIS, historical cartography, cartography, remote sensing, Tbilisi

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Authors: Marine Kobalava

Abstract:

Introduction. The paper substantiates the crucial role of anti-crisis PR in managing the image and reputation of companies. The critical situation caused by the Covid-19 virus in various countries of the world and the actions taken have had a significant negative impact on the image of companies and public groups. The mentioned circumstance has caused some problems for companies’ products in terms of customer demand. Accordingly, the main goal of PR has become to achieve the optimal relationship between companies and society with effective management. It should also be taken into account that the range of action of PR in crisis situations is much wider than that of advertising. In the paper, Public Relations is evaluated as a determining factor of the companies' prestige, its reliability, which has a decisive effect on the goodwill, trust, and general reputation of the public towards the company. The purpose of the study is to reveal the challenges of anti-crisis PR in Georgian companies and to develop recommendations on effective management mechanisms. Methodologies. Analysis, induction, synthesis, and other methods are used in the paper; Matrix and SWOT analysis are constructed. Ways of establishing and implementing an anti-crisis PR system in companies are proposed. The main aspects of anti-crisis management are identified by using the matrix of the choice of diversification strategy of the companies' activities, the possibilities of making adequate decisions using PR are studied according to the characteristics of the companies' activities and priority directions. Conclusion. The paper draws conclusions on modern problems of anti-crisis PR, offers recommendations on ways to solve it through PR strategies.

Keywords: anti-crisis PR, effective management, company, PR strategy

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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