Search results for: fixed live camera images

Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

Procedia PDF Downloads 138

Authors: Muhammad Heikal Ismail, Law Chung Lim, Hii Ching Lik

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In maintaining food quality and shelf life, drying is employed in food industry as the most reliable perseverance technique. In this way, heat pump drying and hot air drying of fresh rice noodles was deduced to freeze drying in achieving quality attributes of oil content Scanning Electron Microscope (SEM) images, texture, and colour. Soxthlet analysis shows freeze dried noodles contain more than 10 times oil content, distinct pores of SEM images, higher hardness by more than three times, and wider colour changes by average more than two times to both methods to explain the less transparency physical outlook of freeze dried samples.

Keywords: freeze drying, heat pump drying, noodles, Soxthlet

Procedia PDF Downloads 466

Authors: Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, Goutam Kumar Ghorai, Gautam Sarkar, Ashis K. Dhara

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Accurate segmentation of the optic disc is very important for computer-aided diagnosis of several ocular diseases such as glaucoma, diabetic retinopathy, and hypertensive retinopathy. The paper presents an accurate and fast optic disc detection and segmentation method using an attention based fully convolutional network. The network is trained from scratch using the fundus images of extended MESSIDOR database and the trained model is used for segmentation of optic disc. The false positives are removed based on morphological operation and shape features. The result is evaluated using three-fold cross-validation on six public fundus image databases such as DIARETDB0, DIARETDB1, DRIVE, AV-INSPIRE, CHASE DB1 and MESSIDOR. The attention based fully convolutional network is robust and effective for detection and segmentation of optic disc in the images affected by diabetic retinopathy and it outperforms existing techniques.

Keywords: attention-based fully convolutional network, optic disc detection and segmentation, retinal fundus image, screening of ocular diseases

Procedia PDF Downloads 120

Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

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Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: dynamic time warping, glottal area waveform, linear predictive coding, high-speed laryngeal images, Hilbert transform

Procedia PDF Downloads 227

Authors: Changsoo Chon, Sangkuy Han, Donghyun Seo, Jihyung Park, Bokku Kang, Hansung Kim, Keyoungjin Chun, Cheolwoong Ko

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The purpose of the study is to develop a finite element model based on 3D bone structural images of Micro-CT and to analyze the stress distribution for the osteoporosis mouse femora. In this study, results of finite element analysis show that the early osteoporosis of mouse model decreased a bone density in trabecular region; however, the bone density in cortical region increased.

Keywords: micro-CT, finite element analysis, osteoporosis, bone strength

Procedia PDF Downloads 345

Authors: Damith Tennakoon, Mojgan Jadidi, Seyedreza Razavialavi

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With recent advancements of automation in robotics, from self-driving cars to autonomous 4-legged quadrupeds, one industry that has been stagnant is the construction industry. The methodologies used in a modern-day construction site consist of arduous physical labor and the use of heavy machinery, which has not changed over the past few decades. The dangers of a modern-day construction site affect the health and safety of the workers due to performing tasks such as lifting and moving heavy objects and having to maintain unhealthy posture to complete repetitive tasks such as painting, installing drywall, and laying bricks. Further, training for heavy machinery is costly and requires a lot of time due to their complex control inputs. The main focus of this research is using immersive wearable technology and robotic arms to perform the complex and intricate skills of modern-day construction workers while alleviating the physical labor requirements to perform their day-to-day tasks. The methodology consists of mounting a stereo vision camera, the ZED Mini by Stereolabs, onto the end effector of an industrial grade robotic arm, streaming the video feed into the Virtual Reality (VR) Meta Quest 2 (Quest 2) head-mounted display (HMD). Due to the nature of stereo vision, and the similar field-of-views between the stereo camera and the Quest 2, human-vision can be replicated on the HMD. The main advantage this type of camera provides over a traditional monocular camera is it gives the user wearing the HMD a sense of the depth of the camera scene, specifically, a first-person view of the robotic arm’s end effector. Utilizing the built-in cameras of the Quest 2 HMD, open-source hand-tracking libraries from OpenXR can be implemented to track the user’s hands in real-time. A mixed-reality (XR) Unity application can be developed to localize the operator's physical hand motions with the end-effector of the robotic arm. Implementing gesture controls will enable the user to move the robotic arm and control its end-effector by moving the operator’s arm and providing gesture inputs from a distant location. Given that the end effector of the robotic arm is a gripper tool, gripping and opening the operator’s hand will translate to the gripper of the robot arm grabbing or releasing an object. This human-robot interaction approach provides many benefits within the construction industry. First, the operator’s safety will be increased substantially as they can be away from the site-location while still being able perform complex tasks such as moving heavy objects from place to place or performing repetitive tasks such as painting walls and laying bricks. The immersive interface enables precision robotic arm control and requires minimal training and knowledge of robotic arm manipulation, which lowers the cost for operator training. This human-robot interface can be extended to many applications, such as handling nuclear accident/waste cleanup, underwater repairs, deep space missions, and manufacturing and fabrication within factories. Further, the robotic arm can be mounted onto existing mobile robots to provide access to hazardous environments, including power plants, burning buildings, and high-altitude repair sites.

Keywords: construction automation, human-robot interaction, hand-tracking, mixed reality

Procedia PDF Downloads 64

Authors: Ouafa Benaida, Abdelhamid Loukil, Adda Ali Pacha

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In recent years, the growing interest of scientists in the field of image processing and analysis of underwater images and videos has been strengthened following the emergence of new underwater exploration techniques, such as the emergence of autonomous underwater vehicles and the use of underwater image sensors facilitating the exploration of underwater mineral resources as well as the search for new species of aquatic life by biologists. Indeed, underwater images and videos have several defects and must be preprocessed before their analysis. Underwater landscapes are usually darkened due to the interaction of light with the marine environment: light is absorbed as it travels through deep waters depending on its wavelength. Additionally, light does not follow a linear direction but is scattered due to its interaction with microparticles in water, resulting in low contrast, low brightness, color distortion, and restricted visibility. The improvement of the underwater image is, therefore, more than necessary in order to facilitate its analysis. The research presented in this paper aims to implement and evaluate a set of classical techniques used in the field of improving the quality of underwater images in several color representation spaces. These methods have the particularity of being simple to implement and do not require prior knowledge of the physical model at the origin of the degradation.

Keywords: underwater image enhancement, histogram normalization, histogram equalization, contrast limited adaptive histogram equalization, single-scale retinex

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 99

Authors: Yogesh Karunakar, Praveen Kandaswamy

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Mother’s milk provides the most superior source of nutrition to a child. There is no other substitute to the mother’s milk. Postpartum secretions like breast milk can be analyzed on the go for testing the presence of any harmful pathogen before a mother can feed the child or donate the milk for the milk bank. Since breast feeding is one of the main causes for transmission of diseases to the newborn, it is mandatory to test the secretions. In this paper, we describe the detection of pathogens like E-coli, Human Immunodeficiency Virus (HIV), Hepatitis B (HBV), Hepatitis C (HCV), Cytomegalovirus (CMV), Zika and Ebola virus through an innovative method, in which we are developing a unique chip for testing the mother’s milk sample. The chip will contain an antibody specific to the target pathogen that will show a color change if there are enough pathogens present in the fluid that will be considered dangerous. A smart-phone camera will then be acquiring the image of the strip and using various image processing techniques we will detect the color development due to antigen antibody interaction within 5 minutes, thereby not adding to any delay, before the newborn is fed or prior to the collection of the milk for the milk bank. If the target pathogen comes positive through this method, then the health care provider can provide adequate treatment to bring down the number of pathogens. This will reduce the postpartum related mortality and morbidity which arises due to feeding infectious breast milk to own child.

Keywords: postpartum, fluids, camera, HIV, HCV, CMV, Zika, Ebola, smart-phones, breast milk, pathogens, image processing techniques

Procedia PDF Downloads 206

Authors: Lee Feng Koo, Tze Jin Wong, Pang Hung Yiu, Nik Mohd Asri Nik Long

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Greater common divisor (GCD) attack is an attack that relies on the polynomial structure of the cryptosystem. This attack required two plaintexts differ from a fixed number and encrypted under same modulus. This paper reports a security reaction of Lucas Based El-Gamal Cryptosystem in the Elliptic Curve group over finite field under GCD attack. Lucas Based El-Gamal Cryptosystem in the Elliptic Curve group over finite field was exposed mathematically to the GCD attack using GCD and Dickson polynomial. The result shows that the cryptanalyst is able to get the plaintext without decryption by using GCD attack. Thus, the study concluded that it is highly perilous when two plaintexts have a slight difference from a fixed number in the same Elliptic curve group over finite field.

Keywords: decryption, encryption, elliptic curve, greater common divisor

Procedia PDF Downloads 233

Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

Procedia PDF Downloads 242

Authors: S. M. Ishtiaque, V. K. Yadav, S. D. Joshi, J. K. Chatterjee

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The DIAMETRIC FAULTS system has been developed that captures a bi-directional image of yarn continuously in sequentially manner and provides the detailed classification of faults. A novel mathematical framework developed on the acquired bi-directional images forms the basis of fault classification in four broad categories, namely, Thick1, Thick2, Thin and Normal Yarn. A discretised version of Radon transformation has been used to convert the bi-directional images into one-dimensional signals. Images were divided into training and test sample sets. Karhunen–Loève Transformation (KLT) basis is computed for the signals from the images in training set for each fault class taking top six highest energy eigen vectors. The fault class of the test image is identified by taking the Euclidean distance of its signal from its projection on the KLT basis for each sample realization and fault class in the training set. Euclidean distance applied using various techniques is used for classifying an unknown fault class. An accuracy of about 90% is achieved in detecting the correct fault class using the various techniques. The four broad fault classes were further sub classified in four sub groups based on the user set boundary limits for fault length and fault volume. The fault cross-sectional area and the fault length defines the total volume of fault. A distinct distribution of faults is found in terms of their volume and physical dimensions which can be used for monitoring the yarn faults. It has been shown from the configurational based characterization and classification that the spun yarn faults arising out of mass variation, exhibit distinct characteristics in terms of their contours, sizes and shapes apart from their frequency of occurrences.

Keywords: Euclidean distance, fault classification, KLT, Radon Transform

Procedia PDF Downloads 249

Authors: Vahdetti̇n Sariyel, Bi̇rol Dağ

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In this research, growth and development parameters in Konya, a private company in Saanen kids reared in intensive conditions in the province were examined. Average birth weights were 3.42, 2.96, 3.57, 3.23 and 2.77 kg for male, female, single, twins and triplets kids. Average weaning weights (three months of age) were 12.65, 12.09, 12.80, 12.65 and 11.68 kg for male, female, single, twins and triplets kids. Average body weights at seven months of age were 20.55, 18.98, 20.12, 20.12 and 19.05 kg for male, female, single, twins and triplets kids respectively. Considering the gender of the live weight factors birth control and rule in favor of the first en ( P <0.01), the second control finally it disappeared statistically significant ( P> 0.05). Main age and the effect of birth weight in the first month, while significant (P < 0.01); The effect of the second month following the live weight of the kid was not significant.

Keywords: Saanen kids, growth, development, body weight

Procedia PDF Downloads 258

Authors: B. Farahani, R. Fadil, A. Aboonabi, B. Hoffmann, J. Loscheider, K. Tavakolian, S. Arzanpour

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Wheelchair users spend long hours in a sitting position, and selecting the right cushion is highly critical in preventing pressure ulcers in that demographic. Pressure mapping systems (PMS) are typically used in clinical settings by therapists to identify the sitting profile and pressure points in the sitting area to select the cushion that fits the best for the users. A PMS is a flexible mat composed of arrays of distributed networks of flexible sensors. The output of the PMS systems is a color-coded image that shows the intensity of the pressure concentration. Therapists use the PMS images to compare different cushions fit for each user. This process is highly subjective and requires good visual memory for the best outcome. This paper aims to develop an image processing technique to analyze the images of PMS and provide an objective measure to assess the cushions based on their pressure distribution mappings. In this paper, we first reviewed the skeletal anatomy of the human sitting area and its relation to the PMS image. This knowledge is then used to identify the important features that must be considered in image processing. We then developed an algorithm based on those features to analyze the images and rank them according to their fit to the users' needs.

Keywords: dynamic cushion, image processing, pressure mapping system, wheelchair

Procedia PDF Downloads 150

Authors: F. R. M. Nascimento, E. E. S. Lora, J. C. E. Palácio

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A mathematical model to investigate the performance of a two stage fixed bed downdraft gasifier operating with air, steam and oxygen mixtures as the gasifying fluid has been developed. The various conditions of mixtures for a double stage fluid entry, have been performed. The model has been validated through a series of experimental tests performed by NEST – The Excellence Group in Thermal and Distributed Generation of the Federal University of Itajubá. Influence of mixtures are analyzed through the Steam to Biomass (SB), Equivalence Ratio (ER) and the Oxygen Concentration (OP) parameters in order to predict the best operating conditions to obtain adequate output gas quality, once is a key parameter for subsequent gas processing in the synthesis of biofuels, heat and electricity generation. Results show that there is an optimal combination in the steam and oxygen content of the gasifying fluid which allows the user find the best conditions to design and operate the equipment according to the desired application.

Keywords: air, equilibrium, downdraft, fixed bed gasification, mathematical modeling, mixtures, oxygen steam

Procedia PDF Downloads 465

Authors: Shahriar Farzam, Maryam Rastgarpour

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Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME).

Keywords: curvelet transform, CBCT, image enhancement, image denoising

Procedia PDF Downloads 279

Authors: Reza Hadjiaghaie Vafaie, Sevda Givtaj

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Automation and control of biological samples and solutions at the microscale is a major advantage for biochemistry analysis and biological diagnostics. Despite the known potential of miniaturization in biochemistry and biomedical applications, comparatively little is known about fluid automation and control at the microscale. Here, we study the electric field effect inside a fluidic channel and proper electrode structures with different patterns proposed to form forward, reversal, and rotational flows inside the channel. The simulation results confirmed that the ac electro-thermal flow is efficient for the control and automation of high-conductive solutions. In this research, the fluid pumping and mixing effects were numerically studied by solving physic-coupled electric, temperature, hydrodynamic, and concentration fields inside a microchannel. From an experimental point of view, the electrode structures are deposited on a silicon substrate and bonded to a PDMS microchannel to form a microfluidic chip. The motions of fluorescent particles in pumping and mixing modes were captured by using a CCD camera. By measuring the frequency response of the fluid and exciting the electrodes with the proper voltage, the fluid motions (including pumping and mixing effects) are observed inside the channel through the CCD camera. Based on the results, there is good agreement between the experimental and simulation studies.

Keywords: microfluidic, nano/micro actuator, AC electrothermal, Reynolds number, micropump, micromixer, microfabrication, mass transfer, biomedical applications

Procedia PDF Downloads 41

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 96

Authors: Sameh El Mahdy

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Recording and documenting archaeological collections, especially photographic documentation, is considered one of the very important matters that museums care about and give great priority, as photographic documentation is of great importance. We monitor some of them for example, Photographs of collectibles are considered evidence and an archival record that proves the condition of the collectibles at various stages. A photo of the possessions is placed on the paper record of the possessions registration. These photos are used in inventorying archaeological collections. These pictures are viewed by researchers and scholars interested in studying these collections. These images are used in advertising campaigns for museum displays of archaeological collections. The Grand Egyptian Museum is considered one of the museums that is a unique model in terms of establishing a specific system that is used when photographing archaeological collections. The Grand Egyptian Museum sets standards for the photos that are taken inside the Grand Egyptian Museum. We mention some of them for example, Pictures must be of high quality. It is necessary to set a color scale for the drawing in order to clarify the dimensions of the collectibles in the picture and also in order to clarify the natural colors of the collectibles without any additions. Putting the numbers of the collectibles in the pictures, especially the number of the Grand Egyptian Museum. To take a good photo of the artifacts in the Grand Egyptian Museum, there are many steps: (1) Create a good location, (2) How to handle the Artifacts. (3) Choose the best position for the artifact, (4) Make the light to create a good photo without shadows to make the photo represent all the artifact details. (5) Be sure of the camera settings, and their quality. All of these steps and other ones are the best criteria for taking the best photo, which helps us in the database to represent the details of the artifact in our interface.

Keywords: grand egyptian museum, photographing, museum collections, registration and documentation

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Authors: Chee Sun Won

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This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods.

Keywords: image compression, image matching, key-point detection and description, machine-to-machine communication

Procedia PDF Downloads 400

Authors: Sri Ratna Rahayu, Oktia Woro Kasmini Handayani, Lourensiana Y. S. Ngaga, Imade Sudana, Irwan Budiono

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Malaria is an infectious disease that still cannot be solved in Kori village, West Nusa Tenggara, Indonesia, where the most of people live in rumah panggung (Stilts House). The purpose of this study was to know whether there were the effects of rumah panggung environment, social culture, and behavior on malaria incidence in the Kori village. A cross-sectional study was performed to explore the effects of rumah panggung environment, social culture and behavior on malaria incidence. This study recruited 280 respondents, who live in the rumah panggung, permanent residents in Kori village, were age above 17 years old, and suffered from malaria in the past year. The collected data were analyzed with path analysis. The results of this study showed that the environment of rumah panggung and behavior have a direct effect on the incidence of malaria (p < 0.05). It could be concluded that improvement of environmental conditions of rumah panggung, sociocultural, and behavioral changes to maintain a healthy environment are needed to reduce the malaria incidence.

Keywords: Rumah panggung, socio-cultural, behavior, Malaria

Procedia PDF Downloads 204

Authors: Kamel Al-Khaled

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A comparative study of the Sinc-Galerkin and Sinc-Collocation methods for solving the Kuramoto-Sivashinsky equation is given. Both approaches depend on using Sinc basis functions. Firstly, a numerical scheme using Sinc-Galerkin method is developed to approximate the solution of Kuramoto-Sivashinsky equation. Sinc approximations to both derivatives and indefinite integrals reduces the solution to an explicit system of algebraic equations. The error in the solution is shown to converge to the exact solution at an exponential. The convergence proof of the solution for the discrete system is given using fixed-point iteration. Secondly, a combination of a Crank-Nicolson formula in the time direction, with the Sinc-collocation in the space direction is presented, where the derivatives in the space variable are replaced by the necessary matrices to produce a system of algebraic equations. The methods are tested on two examples. The demonstrated results show that both of the presented methods more or less have the same accuracy.

Keywords: Sinc-Collocation, nonlinear PDEs, numerical methods, fixed-point

Procedia PDF Downloads 459

Authors: S. S. Patil, Sachidanand Kini

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Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Mahalanobis distance, minimum distance, supervised, unsupervised, user classification accuracy, producer's classification accuracy, maximum likelihood, kappa coefficient

Procedia PDF Downloads 164

Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux, Mariana Kuras, Vincent Récamier, Martial Piotto, Karim Elbayed, François Proust, Izzie Namer

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This paper proposes an augmented reality system dedicated to neurosurgery in order to assist the surgeon during an operation. This work is part of the ExtempoRMN project (Funded by Bpifrance) which aims at analyzing during a surgical operation the metabolic content of tumoral brain biopsy specimens by HRMAS NMR. Patients affected with a brain tumor (gliomas) frequently need to undergo an operation in order to remove the tumoral mass. During the operation, the neurosurgeon removes biopsy specimens using image-guided surgery. The biopsy specimens removed are then sent for HRMAS NMR analysis in order to obtain a better diagnosis and prognosis. Image-guided refers to the use of MRI images and a computer to precisely locate and target a lesion (abnormal tissue) within the brain. This is performed using preoperative MRI images and the BrainLab neuro-navigation system. With the patient MRI images loaded on the Brainlab Cranial neuro-navigation system in the operating theater, surgeons can better identify their approach before making an incision. The Brainlab neuro-navigation tool tracks in real time the position of the instruments and displays their position on the patient MRI data. The results of the biopsy analysis by 1H HRMAS NMR are then sent back to the operating theater and superimposed on the 3D localization system directly on the MRI images. The method we have developed to communicate between the HRMAS NMR analysis software and Brainlab makes use of a combination of C++, VTK and the Insight Toolkit using OpenIGTLink protocol.

Keywords: neuro-navigation, augmented reality, biopsy, BrainLab, HR-MAS NMR

Procedia PDF Downloads 351

Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

Procedia PDF Downloads 168

Authors: Saarah Juman, Ilona Johnson, Stephen Richmond, Brett Duane, Sheelagh Rogers

Abstract:

Introduction: Researchers suggest that within 50 years or less, the available supply of a range of metals will be exhausted, potentially leading to increases in resource conflict and largescale production shortages. The healthcare, dental and orthodontic sectors will undoubtedly be affected as stainless steel instruments are generally heavily relied on. Although changing orthodontic archwires are unavoidable and necessary to allow orthodontic tooth movement through the progression of an archwire sequence with fixed appliances, they are thought to be manufactured in excess of what is needed. Furthermore, orthodontic archwires require trimming extraorally to allow safe intraoral insertion, thus contributing to unnecessary waste of natural resources. Currently, there is no evidence to support the optimisation of archwire length according to orthodontic fixed appliance stage. As such, this study aims to quantify archwire excess (extraoral archwire trimmings) for different stages of orthodontic fixed appliance treatment. Methodology: This prospective, observational, quantitative study observed trimmings made extraorally against pre-treatment study models by clinicians over a 3-month period. Archwires were categorised into one of three categories (initial aligning, sequence, working/finishing arcwhires) within the orthodontic fixed appliance archwire sequence. Data collection included archwire material composition and the corresponding length and weight of excess archwire. Data was entered using a Microsoft Excel spreadsheet and imported into statistical software to obtain simple descriptive statistics. Results: Measurements were obtained for a total of 144 archwires. Archwire materials included nickel titanium and stainless steel. All archwires observed required extraorally trimming to allow safe intraoral insertion. The manufactured lengths of orthodontic initial aligning, sequence, and working/finishing arcwhires were at least 31%, 26%, and 39% in excess, respectively. Conclusions: Orthodontic archwires are manufactured to be excessively long at all orthodontic archwire sequence stages. To conserve natural resources, this study’s findings support the optimisation of orthodontic archwire lengths by manufacturers according to the typical stages of an orthodontic archwire sequence.

Keywords: archwire, orthodontics, sustainability, waste

Procedia PDF Downloads 172

Authors: Rajkumar N. Ingle

Abstract:

In this paper, the existence of a solution of nonlinear functional random differential equations of the first order is proved under caratheodory condition. The study of the functional random differential equation has got importance in the random analysis of the dynamical systems of universal phenomena. Objectives: Nonlinear functional random differential equation is useful to the scientists, engineers, and mathematicians, who are engaged in N.F.R.D.E. analyzing a universal random phenomenon, govern by nonlinear random initial value problems of D.E. Applications of this in the theory of diffusion or heat conduction. Methodology: Using the concepts of probability theory, functional analysis, generally the existence theorems for the nonlinear F.R.D.E. are prove by using some tools such as fixed point theorem. The significance of the study: Our contribution will be the generalization of some well-known results in the theory of Nonlinear F.R.D.E.s. Further, it seems that our study will be useful to scientist, engineers, economists and mathematicians in their endeavors to analyses the nonlinear random problems of the universe in a better way.

Keywords: Random Fixed Point Theorem, functional random differential equation, N.F.R.D.E., universal random phenomenon

Procedia PDF Downloads 474

Authors: Parya Tabei, Maryam Habibifar

Abstract:

Decision-making is an important cognitive function that can be defined as the process of choosing an option among available options to achieve a specific goal. Consumer ‘need’ is the main reason for purchasing decisions. Human decision-making while buying products online is subject to various factors, one of which is the quality and effect of advertising photos. Advertising photo editing can have a significant impact on people's virtual purchase decisions. This technique helps improve the quality and overall appearance of photos by adjusting various aspects such as brightness, contrast, colors, cropping, resizing, and adding filters. This study, by examining the effect of editing advertising photos on the virtual purchase decision using EEG data, tries to investigate the effect of edited images on the decision-making of customers. A group of 30 participants were asked to react to 24 edited and unedited images while their EEG was recorded. Analysis of the EEG data revealed increased alpha wave activity in the occipital regions (O1, O2) for both edited and unedited images, which is related to visual processing and attention. Additionally, there was an increase in beta wave activity in the frontal regions (FP1, FP2, F4, F8) when participants viewed edited images, suggesting involvement in cognitive processes such as decision-making and evaluating advertising content. Gamma wave activity also increased in various regions, especially the frontal and parietal regions, which are associated with higher cognitive functions, such as attention, memory, and perception, when viewing the edited images. While the visual processing reflected by alpha waves remained consistent across different visual conditions, editing advertising photos appeared to boost neural activity in frontal and parietal regions associated with decision-making processes. These Findings suggest that photo editing could potentially influence consumer perceptions during virtual shopping experiences by modulating brain activity related to product assessment and purchase decisions.

Keywords: virtual purchase decision, advertising photo, EEG parameters, decision Making

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Authors: Gaokai Liu

Abstract:

The distribution of material grain sizes reflects the strength, fracture, corrosion and other properties, and the grain size can be acquired via the grain boundary. In recent years, the automatic grain boundary detection is widely required instead of complex experimental operations. In this paper, an effective solution is applied to acquire the grain boundary of material images. First, the initial superpixel segmentation result is obtained via a superpixel approach. Then, a region merging method is employed to merge adjacent regions based on certain similarity criterions, the experimental results show that the merging strategy improves the superpixel segmentation result on material datasets.

Keywords: grain boundary detection, image segmentation, material images, region merging

Procedia PDF Downloads 151

Authors: Andrii Natochii

Abstract:

We are currently presenting the recent results from the CERN accelerator facilities obtained in the frame of the UA9 Collaboration. The UA9 experiment investigates how a tiny silicon bent crystal (few millimeters long) can be used for various high-energy physics applications. Due to the huge electrostatic field (tens of GV/cm) between crystalline planes, there is a probability for charged particles, impinging the crystal, to be trapped in the channeling regime. It gives a possibility to steer a high intensity and momentum beam by bending the crystal: channeled particles will follow the crystal curvature and deflect on the certain angle (from tens microradians for LHC to few milliradians for SPS energy ranges). The measurements at SPS, performed in 2017 and 2018, confirmed that the protons deflected by the first crystal, inserted in the primary beam halo, can be caught and channeled by the second crystal. In this configuration, we measure the single pass deflection efficiency of the second crystal and prove our opportunity to perform the fixed target experiment at SPS accelerator (LHC in the future).

Keywords: channeling, double-crystal setup, fixed target experiment, Timepix detector

Procedia PDF Downloads 135