Search results for: spherical images

Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

Abstract:

Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

Procedia PDF Downloads 107

Authors: Darsh N. Patel, Eric Olson

Abstract:

Fractals can be found everywhere, whether it be the shape of a leaf or a system of blood vessels. Fractals are used to help study and understand different physical and mathematical processes; however, their artistic nature is also beautiful to simply explore. This project explores fractals generated by a cubically convergent extension to Newton's method. With this iteration as a starting point, a complex plane spanning from -2 to 2 is created with a color wheel mapped onto it. Next, the polynomial whose roots the fractal will generate from is established. From the Fundamental Theorem of Algebra, it is known that any polynomial has as many roots (counted by multiplicity) as its degree. When generating the fractals, each root will receive its own color. The complex plane can then be colored to indicate the basins of attraction that converge to each root. From a computational point of view, this project’s code identifies which points converge to which roots and then obtains fractal images. A zoom path into the fractal was implemented to easily visualize the self-similar structure. This path was obtained by selecting keyframes at different magnifications through which a path is then interpolated. Using parallel processing, many images were generated and condensed into a video. This project illustrates how practical techniques used for scientific visualization can also have an artistic side.

Keywords: fractals, cubic method, Julia programming language, basin of attraction

Procedia PDF Downloads 251

Authors: Thu Nhi Tran Caliste

Abstract:

X-ray Bragg diffraction imaging (“topography”)entered into practical use when Lang designed an “easy” technical setup to characterise the defects / distortions in the high perfection crystals produced for the microelectronics industry. The use of this technique extended to all kind of high quality crystals, and deposited layers, and a series of publications explained, starting from the dynamical theory of diffraction, the contrast of the images of the defects. A quantitative version of “monochromatic topography” known as“Rocking Curve Imaging” (RCI) was implemented, by using synchrotron light and taking advantage of the dramatic improvement of the 2D-detectors and computerised image processing. The rough data is constituted by a number (~300) of images recorded along the diffraction (“rocking”) curve. If the quality of the crystal is such that a one-to-onerelation between a pixel of the detector and a voxel within the crystal can be established (this approximation is very well fulfilled if the local mosaic spread of the voxel is < 1 mradian), a software we developped provides, from the each rocking curve recorded on each of the pixels of the detector, not only the “voxel” integrated intensity (the only data provided by the previous techniques) but also its “mosaic spread” (FWHM) and peak position. We will show, based on many examples, that this new data, never recorded before, open the field to a highly enhanced characterization of the crystal and deposited layers. These examples include the characterization of dislocations and twins occurring during silicon growth, various growth features in Al203, GaNand CdTe (where the diffraction displays the Borrmannanomalous absorption, which leads to a new type of images), and the characterisation of the defects within deposited layers, or their effect on the substrate. We could also observe (due to the very high sensitivity of the setup installed on BM05, which allows revealing these faint effects) that, when dealing with very perfect crystals, the Kato’s interference fringes predicted by dynamical theory are also associated with very small modifications of the local FWHM and peak position (of the order of the µradian). This rather unexpected (at least for us) result appears to be in keeping with preliminary dynamical theory calculations.

Keywords: rocking curve imaging, X-ray diffraction, defect, distortion

Procedia PDF Downloads 131

Authors: Yi-Ting Wang, Wen-Shu Lai

Abstract:

The subject of this paper is the photography in the 1983 film Paris, Texas, directed by Wim Wenders. Wenders is well known as a film director as well as a photographer. We have found that photography is shown as a photographic element in many of his films. Some of these photographs serve as details within the films, while others play important roles that are relevant to the story. This paper aims to consider photographs in film as a specific type of text, which is the output of both still photography and the film itself. In the film Paris, Texas, three sets of important photographs appear whose symbolic meanings are as dialectical as their text types. The relationship between the existence of these photos and the storyline is both dependent and isolated. The film’s images fly by and progress into other images, while the photos in the film serve a unique narrative function by stopping the continuously flowing images thus provide the viewer a space for imagination and contemplation. They are more than just artistic forms; they also contained multiple meanings. The photographs in Paris, Texas play the role of both presence and absence according to their shifting meanings. There are references to their presence: photographs exist between film time and narrative time, so in terms of the interaction between the characters in the film, photographs are a common symbol of the beginning and end of the characters’ journeys. In terms of the audience, the film’s photographs are a link in the viewing frame structure, through which the creative motivation of the film director can be explored. Photographs also point to the absence of certain objects: the scenes in the photos represent an imaginary map of emotion. The town of Paris, Texas is therefore isolated from the physical presence of the photograph, and is far more abstract than the reality in the film. This paper embraces the ambiguous nature of photography and demonstrates its presence and absence in film with regard to the meaning of text. However, it is worth reflecting that the temporary nature of the interpretation of the film’s photographs is far greater than any other type of photographic text: the characteristics of the text cause the interpretation results to change along with the variations in the interpretation process, which makes their meaning a dynamic process. The photographs’ presence or absence in the context of Paris, Texas also demonstrates the presence and absence of the creator, time, the truth, and the imagination. The film becomes more complete as a result of the revelation of the photographs, while the intertextual connection between these two forms simultaneously provides multiple possibilities for the interpretation of the photographs in the film.

Keywords: film, Paris, Texas, photography, Wim Wenders

Procedia PDF Downloads 318

Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

Abstract:

Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

Procedia PDF Downloads 395

Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter

Procedia PDF Downloads 145

Authors: Flora Paganelli

Abstract:

The radiometric performance of remotely sensed images is important for volcanic monitoring. The Thermal Infrared Sensor (TIRS) on-board Landsat 8 was designed with specific requirements in regard to the noise-equivalent change in temperature (NEΔT) at ≤ 0.4 K at 300 K for the two thermal infrared bands B10 and B11. This study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over the volcanic activity of Kīlauea Volcano and the active East Rift Zone (Hawaii), in order to optimize the use of TIRS data. Results showed that the NEΔTs of the two bands exceeded the design specification by an order of magnitude at 300 K. Both separate bands and split window algorithm were examined to estimate the effect of NEΔT on the land surface temperature (LST) retrieval, and NEΔT contribution to the final LST error. These results were also useful in the current efforts to assess the requirements for volcanology research campaign using the Hyperspectral Infrared Imager (HyspIRI) whose airborne prototype MODIS/ASTER instruments is plan to be flown by NASA as a single campaign to the Hawaiian Islands in support of volcanology and coastal area monitoring in 2016.

Keywords: landsat 8, radiometric performance, thermal infrared sensor (TIRS), volcanology

Procedia PDF Downloads 241

Authors: Tripti Singh

Abstract:

Digital tools are merging boundaries of different mediums as endeavoured artists exploring new areas. Digital fabric printing has motivated artists to create prints by combining images acquired by photograph, scanned images, computer graphics and microscopic imaginary etc to name few, with traditional media such as hand drawing, weaving, hand printed patterns, printing making techniques and so on. It opened whole new world of possibilities for artists to search, research and combine old and contemporary mediums for their unique art prints. As artistic medium digital art fabrics have aesthetic values which have impact and influence on not only on a personality but also interiors of a living or work space. In this way it can be worn, as fashion statement and also an interior decoration. Digital art fabric prints gives opportunity to print almost everything on any fabric with long lasting prints quality. Single edition and limited editions are possible for maintaining scarcity and uniqueness of an art form. These fabric prints fulfill today’s need, as they are eco-friendly in nature and they produce less wastage compared to traditional fabric printing techniques. These prints can be used to make unique and customized curtains, quilts, clothes, bags, furniture, dolls, pillows, framed artwork, costumes, banners and much, much more. This paper will explore the procedure, process, and progress techniques of digital art fabric printing in depth with suitable pictorial examples.

Keywords: digital art, fabric prints, digital fabric prints, new media

Procedia PDF Downloads 515

Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

Abstract:

Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset.

Keywords: adversarial examples, attack, computer vision, image processing

Procedia PDF Downloads 193

Authors: Anahita Aris

Abstract:

Geometry always plays a key role in natural structures, which can be a source of inspiration for architects and urban designers to create spaces. By understanding formative principles in nature, a variety of options can be provided that lead to freedom of formation. The main purpose of this paper is to analyze the geometrical order found in pomegranate to find formative principles explaining its complex structure. The point is how spherical arils of pomegranate pressed together inside the fruit and filled the space as they expand in the growing process, which made a self-organized system leads to the formation of each of the arils are unique in size, topology and shape. The main challenge of this paper would be using advanced architectural modeling techniques to discover these principles.

Keywords: advanced modeling techniques, architectural modeling, computational design, the geometry of natural formation, geometrical analysis, the natural order of pomegranate, voronoi diagrams

Procedia PDF Downloads 220

Authors: Madhumita Sahu, S. S. Tiwary

Abstract:

The Radiotherapy of Carcinoma Lung has always been difficult and a matter of great concern. The significant movement due to fractional motion caused due to non-rhythmic respiratory motion poses a great challenge for the treatment of Lung cancer using Ionizing Radiation. The present study compares the accuracy in the measurement of Target Volume using Slow-CT and 4DCT Imaging in SBRT for Lung Tumor. The experimental samples were extracted from patients with Lung Cancer who underwent SBRT. Slow-CT and 4DCT images were acquired under free breathing for each patient. PTV were delineated on Slow CT images. Similarly, ITV was also delineated on each of the 4DCT volumes. Volumetric and Statistical analysis were performed for each patient by measuring corresponding PTV and ITV volumes. The study showed (1) The Maximum Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 248.58 cc. (2) The Minimum Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 5.22 cc. (3) The Mean Deviation observed between Slow-CT-based PTV and 4DCT imaging-based ITV is 63.21 cc. The present study concludes that irradiated volume ITV with 4DCT is less as compared to the PTV with Slow-CT. A better and more precise treatment could be given more accurately with 4DCT Imaging by sparing 63.21 CC of mean body volume.

Keywords: CT imaging, 4DCT imaging, lung cancer, statistical analysis

Procedia PDF Downloads 24

Authors: Mohammad F. Bostanudin, Tan S. Fei, Azwan M. Lazim

Abstract:

Toward the development of colloidal systems that are able to enhance permeation across the skin, a material combining the non-toxic and non-immunogenic of dextran with alkylglycerols permeation enhancing property has been designed. To this purpose, a range of butylglyceryl dextrans (DEX-OX4) were synthesized via functionalization with n-butylglycidyl ether and the successful functionalization was confirmed by NMR and FT-IR spectroscopies, along with GPC with a degree of modification in the range 6.3–35.7 %. A reduced viscosity and an increased molecular weight of DEX-OX4 were also recorded when compared to that of the native dextran. DEX-OX4 was further formulated into nanocarriers and loaded with α-arbutin prior to be investigated for their particle size, morphology, stability, loading ability, and release profiles. The resulting nanoparticles were found to be close-to-spherical and relatively stable at pH 5 and 7, with size 180–220 nm (ζ-potential -22 to -25 mV), and a loading degree of 11.7 %. Lack of toxicity at application-relevant concentrations and increased permeation across skin biological membrane model were demonstrated by nanoparticles in-vitro results against immortalized skin human keratinocytes cells (HaCaT).

Keywords: butylglycerols, dextran, nanoparticles, transdermal

Procedia PDF Downloads 123

Authors: Gang Li, Ghulam Mubashar Hassan, Arcady Dyskin, Cara MacNish

Abstract:

Digital image correlation (DIC) is a contactless full-field displacement and strain reconstruction technique commonly used in the field of experimental mechanics. Comparing with physical measuring devices, such as strain gauges, which only provide very restricted coverage and are expensive to deploy widely, the DIC technique provides the result with full-field coverage and relative high accuracy using an inexpensive and simple experimental setup. It is very important to study the natural patterns effect on the DIC technique because the preparation of the artificial patterns is time consuming and hectic process. The objective of this research is to study the effect of using images having natural pattern on the performance of DIC. A systematical simulation method is used to build simulated deformed images used in DIC. A parameter (subset size) used in DIC can have an effect on the processing and accuracy of DIC and even cause DIC to failure. Regarding to the picture parameters (correlation coefficient), the higher similarity of two subset can lead the DIC process to fail and make the result more inaccurate. The pictures with good and bad quality for DIC methods have been presented and more importantly, it is a systematic way to evaluate the quality of the picture with natural patterns before they install the measurement devices.

Keywords: Digital Image Correlation (DIC), deformation simulation, natural pattern, subset size

Procedia PDF Downloads 419

Authors: Berhanu Keno Terfa

Abstract:

To understand the consequences of urbanization, accurate, and long-term representation of urban dynamics is essential. Remote sensing data from various multi-temporal satellite images viz., TM (1987), TM (1995), ETM+ (2005) and OLI (2017) were used. An integrated method, landscape metrics, built-up density, and urban growth type analysis were employed to analyze the pattern, process, and overall growth status in the city. The result showed that the built-up area had increased by 541.3% between 1987 and 2017, at an average annual increment of 8.9%. The area of urban expansion in a city has tripled during the 2005-2017 period as compared to 187- 1995. The major growth took place in the east and southeast directions during 1987–1995 period, whereas predominant built-up development was observed in south and southeast direction during 1995–2017 period. The analysis using landscape metrics and urban typologies showed that Hawassa experienced a fragmented and irregular spatiotemporal urban growth patterns, mostly by extension, suggesting a strong tendency towards sprawl in the past three decades.

Keywords: Hawassa, spatial patterns, remote sensing, multi-temporal, urban sprawl

Procedia PDF Downloads 148

Authors: Wei Zhang, Yan He, Yan Wang, Yufeng Li, Chuanpeng Hao

Abstract:

Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness.

Keywords: feature analysis, machine vision, PCA, surface roughness, SVM

Procedia PDF Downloads 212

Authors: Shinsuke Udagawa, Masato Yamagishi, Masanori Ota

Abstract:

The Schlieren method, which has been conventionally used to visualize high-speed flows, has disadvantages such as the complexity of the experimental setup and the inability to quantitatively analyze the amount of refraction of light. The Background Oriented Schlieren (BOS) method proposed by Meier is one of the measurement methods that solves the problems, as mentioned above. The refraction of light is used for BOS method same as the Schlieren method. The BOS method is characterized using a digital camera to capture the images of the background behind the observation area. The images are later analyzed by a computer to quantitatively detect the amount of shift of the background image. The experimental setup for BOS does not require concave mirrors, pinholes, or color filters, which are necessary in the conventional Schlieren method, thus simplifying the experimental setup. However, the defocusing of the observation results is caused in case of using BOS method. Since the focus of camera on the background image leads to defocusing of the observed object. The defocusing of object becomes greater with increasing the distance between the background and the object. On the other hand, the higher sensitivity can be obtained. Therefore, it is necessary to adjust the distance between the background and the object to be appropriate for the experiment, considering the relation between the defocus and the sensitivity. The purpose of this study is to experimentally clarify the effect of defocus on density field reconstruction. In this study, the visualization experiment of underexpanded jet using BOS measurement system with ronchi ruling as the background that we constructed, have been performed. The reservoir pressure of the jet and the distance between camera and axis of jet is fixed, and the distance between background and axis of jet has been changed as the parameter. The images have been later analyzed by using personal computer to quantitatively detect the amount of shift of the background image from the comparison between the background pattern and the captured image of underexpanded jet. The quantitatively measured amount of shift have been reconstructed into a density flow field using the Abel transformation and the Gradstone-Dale equation. From the experimental results, it is found that the reconstructed density image becomes blurring, and noise becomes decreasing with increasing the distance between background and axis of underexpanded jet. Consequently, it is cralified that the sensitivity constant should be greater than 20, and the circle of confusion diameter should be less than 2.7mm at least in this experimental setup.

Keywords: BOS method, underexpanded jet, abel transformation, density field visualization

Procedia PDF Downloads 78

Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

Abstract:

Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

Procedia PDF Downloads 137

Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

Abstract:

Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

Procedia PDF Downloads 48

Authors: Khondaker Sakil Ahmed, Ang Kok Keng, Shah Md Muniruzzaman

Abstract:

Spherical and Berkovich indentation tests are carried out numerically using finite element method for uniformly dispersed Carbon Nanotube (CNT) in the polymer matrix in which perfectly bonded CNT/matrix interface is considered. The Large strain elasto-plastic analysis is performed to investigate the actual scenario of nanoindentation test. This study investigates how the addition of CNT in polymer matrix influences the mechanical properties like hardness, elastic modulus of the nanocomposite. Since the wall thickness to radius ratio (t/r) is significantly small for SWCNT there is a huge possibility of lateral buckling which is a function of the location of indentation tip as well as the mechanical properties of matrix. Separate finite element models are constructed to compare the result with Berkovich indentation. This study also investigates the buckling behavior of different nanotube in a different polymer matrix.

Keywords: carbon nanotube, elasto-plastic, finite element model, nano-indentation

Procedia PDF Downloads 389

Authors: Ozgun Oktar, Sevilay Can, Cengiz V. Ekici

Abstract:

The oil spill is a form of pollution caused by releasing of a liquid petroleum hydrocarbon into the marine environment. Considering the growth of ship traffic, increasing of off-shore oil drilling and seaside refineries affect the risk of oil spill upward. The oil spill is easy to spread to large areas when occurs especially on the sea surface. Remote sensing technology offers the easiest way to control/monitor the area of the oil spill in a large region. It’s usually easy to detect pollution when occurs by the ship accidents, however monitoring non-accidental pollution could be possible by remote sensing. It is also needed to observe specific regions daily and continuously by satellite solutions. Remote sensing satellites mostly and effectively used for monitoring oil pollution are RADARSAT, ENVISAT and MODIS. Spectral coverage and transition period of these satellites are not proper to monitor Marmara Sea and Istanbul Strait continuously. In this study, RASAT and GOKTURK-2 are suggested to use for monitoring Marmara Sea and Istanbul Strait. RASAT, with spectral resolution 420 – 730 nm, is the first Turkish-built satellite. GOKTURK-2’s resolution can reach up to 2,5 meters. This study aims to analyze the images from both satellites and produce maps to show the regions which have potentially affected by spills from shipping traffic.

Keywords: Marmara Sea, monitoring, oil spill, satellite remote sensing

Procedia PDF Downloads 423

Authors: Brighton Chamunorwa

Abstract:

The current remote sensing of water quality procedures does not provide a step representing physical visualisation of the monitored dam. The application of the remote sensing of water quality techniques may benefit from use of mathematical morphology operators for shape identification. Given an input of dam outline, morphological operators such as the hit or miss transform identifies if the water body is present on input remotely sensed images. This study seeks to determine the accuracy of the hit or miss transform to identify dams monitored by the water resources authorities in South Africa on satellite images. To achieve this objective the study download a Landsat image acquired in winter and tested the capability of the hit or miss transform using shapefile boundaries of dams in the crocodile marico catchment. The results of the experiment show that it is possible to detect most dams on the Landsat image after the adjusting the erosion operator to detect pixel matching a percentage similarity of 80% and above. Successfully implementation of the current study contributes towards optimisation of mathematical morphology image operators. Additionally, the effort helps develop remote sensing of water quality monitoring with improved simulation of the conventional procedures.

Keywords: hit or miss transform, mathematical morphology, remote sensing, water quality monitoring

Procedia PDF Downloads 153

Authors: Evan Lowhorn, Rocio Alba-Flores

Abstract:

The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

Procedia PDF Downloads 210

Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

Abstract:

The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors.

Keywords: Landsat ETM+, EO1, data fusion, vegetation, Tassili, Algeria

Procedia PDF Downloads 433

Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

Abstract:

The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

Procedia PDF Downloads 116

Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

Abstract:

In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

Procedia PDF Downloads 140

Authors: N. Moudir, N. Moulai-Mostefa, Y. Boukennous, I. Bozetine, N. Kamel, D. Moudir

Abstract:

the study focuses on a novel process of silver powders synthesis for the preparation of conductive pastes used for solar cells metalization. Butyl-Carbitol and butyl-carbitol Acetate have been used as solvents and reducing agents of silver nitrate (AgNO3) as precursor to get silver powders. XRD characterization revealed silver powders with a cubic crystal system. SEM micro graphs showed spherical morphology of the particles. Laser granulometer gives similar particles distribution for the two agents. Using same glass frit and organic vehicle for comparative purposes, two conductive pastes were prepared with the synthesized silver powders for the front-side metalization of multi-crystalline cells. The pastes provided acceptable fill factor of 59.5 % and 60.8 % respectively.

Keywords: chemical reduction, conductive paste, silver nitrate, solar cell

Procedia PDF Downloads 304

Authors: Eric Asare, Jamie Evans, Mark Newton, Emiliano Bilotti

Abstract:

Two types of filler shapes (sphere and flakes) and three different sizes are employed to study the size effect on PTC. The composite is prepared using a mini-extruder with high-density polyethylene (HDPE) as the matrix. A computer modelling is used to fit the experimental results. The percolation threshold decreases with decreasing filler size and this was observed for both the spherical particles as well as the flakes. This was caused by the decrease in interparticle distance with decreasing filler size. The 100 µm particles showed a larger PTC intensity compared to the 5 µm particles for the metal coated glass sphere and flake. The small particles have a large surface area and agglomeration and this makes it difficult for the conductive network to e disturbed. Increasing the filler content decreased the PTC intensity and this is due to an increase in the conductive network within the polymer matrix hence more energy is needed to disrupt the network.

Keywords: positive temperature coefficient (PTC) effect, conductive polymer composite (CPC), electrical conductivity

Procedia PDF Downloads 427

Authors: Ellen Nhedzi Gozo

Abstract:

Unavailability of adequate hydro-meteorological data has always limited the analysis and understanding of hydrological behaviour of several dam catchments including Mazowe Dam in Zimbabwe. The problem of insufficient data for Mazowe Dam catchment analysis was solved by extracting catchment characteristics and aerial hydro-meteorological data from ASTER, LANDSAT, Shuttle Radar Topographic Mission SRTM remote sensing (RS) images using ILWIS, ArcGIS and ERDAS Imagine geographic information systems (GIS) software. Available observed hydrological as well as meteorological data complemented the use of the remotely sensed information. Ground truth land cover was mapped using a Garmin Etrex global positioning system (GPS) system. This information was then used to validate land cover classification detail that was obtained from remote sensing images. A bathymetry survey was conducted using a SONAR system connected to GPS. Hydrological modelling using the HBV model was then performed to simulate the hydrological process of the catchment in an effort to verify the reliability of the derived parameters. The model output shows a high Nash-Sutcliffe Coefficient that is close to 1 indicating that the parameters derived from remote sensing and GIS can be applied with confidence in the analysis of Mazowe Dam catchment.

Keywords: geographic information systems, hydrological modelling, remote sensing, water resources management

Procedia PDF Downloads 336

Authors: Somayeh Komeylian

Abstract:

The antenna arrays for the entire 3D spherical coverage have been developed for their potential use in variety of applications such as radars and body-worn devices of the body area networks. In this study, we have rigorously revamped the hybrid antenna array using the optimum geometry of bowtie elements for achieving a significant improvement in the angular discrimination capability as well as in separating two adjacent targets. In this scenario, we have analogously investigated the effectiveness of increasing the virtual array length in fostering and enhancing the directivity and angular resolution in the 10 GHz frequency. The simulation results have extensively verified that the proposed antenna array represents a drastic enhancement in terms of size, directivity, side lobe level (SLL) and, especially resolution compared with the other available geometries. We have also verified that the maximum directivities of the proposed hybrid antenna array represent the robustness to the all  variations, which is accompanied by the uniform 3D scanning characteristic.

Keywords: bowtie antenna, hybrid antenna array, array signal processing, body area networks

Procedia PDF Downloads 153

Authors: Kyong-Ku Yun, Dae-Ae Kim, Kyeo-Re Lee, Kyong Namkung, Seung-Yeon Han

Abstract:

Micro silica is collected as a by-product of the silicon and ferrosilicon alloy production in electric arc furnace using highly pure quartz, wood chips, coke and the like. It consists of about 85% of silicon which has spherical particles with an average particle size of 150 μm. The bulk density of micro silica varies from 150 to 700kg/m^3 and the fineness ranges from 150,000 to 300,000cm^2/g. An amorphous structure with a high silicon oxide content of micro silica induces an active reaction with calcium hydroxide (Ca(OH)₂) generated by the cement hydrate of a large surface area (about 20 m^² / g), and they are also known to form calcium, silicate, hydrate conjugate (C-S-H). Micro silica tends to act as a filler because of the fine particles and the spherical shape. These particles do not get covered by water and they fit well in the space between the relatively rough cement grains which does not freely fluidize concrete. On the contrary, water demand increases since micro silica particles have a tendency to absorb water because of the large surface area. The overall effect of micro silica depends on the amount of micro silica added with other parameters in the water-(cement + micro silica) ratio, and the availability of superplasticizer. In this research, it was studied on cellular sprayed concrete. This method involves a direct re-production of ready mixed concrete into a high performance at a job site. It could reduce the cost of construction by an adding a cellular and a micro silica into a ready mixed concrete truck in a field. Also, micro silica which is difficult with mixing due to high fineness in the field can be added and dispersed in concrete by increasing the fluidity of ready mixed concrete through the surface activity of cellular. Increased air content is converged to a certain level of air content by spraying and it also produces high-performance concrete by remixing of powders in the process of spraying. As it does not use a field mixing equipment the cost of construction decrease and it can be constructed after installing special spray machine in a commercial pump car. Therefore, use of special equipment is minimized, providing economic feasibility through the utilization of existing equipment. This study was carried out to evaluate a highly reliable method of confirming dispersion through a high performance cellular sprayed concrete. A mixture of 25mm coarse aggregate and river sand was applied to the concrete. In addition, by applying silica fume and foam, silica fume dispersion is confirmed in accordance with foam mixing, and the mean and standard deviation is obtained. Then variation coefficient is calculated to finally evaluate the dispersion. Comparison and analysis of before and after spraying were conducted on the experiment variables of 21L, 35L foam for each 7%, 14% silica fume respectively. Taking foam and silica fume as variables, the experiment proceed. Casting a specimen for each variable, a five-day sample is taken from each specimen for EDS test. In this study, it was examined by an experiment materials, plan and mix design, test methods, and equipment, for the evaluation of dispersion in accordance with micro silica and foam.

Keywords: micro silica, distribution, ready mixed concrete, foam

Procedia PDF Downloads 218