Search results for: probabilistic matching model for binary images

Authors: Tun Myat Aung, Ni Ni Hla

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This paper begins by describing basic properties of finite field and elliptic curve cryptography over prime field and binary field. Then we discuss the discrete logarithm problem for elliptic curves and its properties. We study the general common attacks on elliptic curve discrete logarithm problem such as the Baby Step, Giant Step method, Pollard’s rho method and Pohlig-Hellman method, and describe in detail experiments of these attacks over prime field and binary field. The paper finishes by describing expected running time of the attacks and suggesting strong elliptic curves that are not susceptible to these attacks.c

Keywords: discrete logarithm problem, general attacks, elliptic curve, prime field, binary field

Procedia PDF Downloads 198

Authors: Ebrahim Ghanbari, Mohammad Reza Nematollahi

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Integrating deterministic and probabilistic safety assessment (IDPSA) is one of the most commonly used issues in the field of safety analysis of power plant accident. It has also been recognized today that the role of human error in creating these accidents is not less than systemic errors, so the human interference and system errors in fault and event sequences are necessary. The integration of these analytical topics will be reflected in the frequency of core damage and also the study of the use of water resources in an accident such as the loss of all electrical power of the plant. In this regard, the SBO accident was simulated for the pressurized water reactor in the deterministic analysis issue, and by analyzing the operator's behavior in controlling the accident, the results of the combination of deterministic and probabilistic assessment were identified. The results showed that the best performance of the plant operator would reduce the risk of an accident by 10%, as well as a decrease of 6.82 liters/second of the water sources of the plant.

Keywords: IDPSA, human error, SBO, risk

Procedia PDF Downloads 102

Authors: Zviad Ghadua, Biswa Bhattacharya

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The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina

Procedia PDF Downloads 106

Authors: Dojoon Jung, Kiwoong Moon, Joong Lee

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The height of criminals is one of the important clues that can determine the scope of the suspect's search or exclude the suspect from the search target. Although measuring the height of criminals by video alone is limited by various reasons, the 3D data of the scene and the Closed Circuit Television (CCTV) footage are matched, the height of the criminal can be measured. However, it is still difficult to measure the height of CCTV footage in the non-contact type measurement method because of variables such as position, posture, and head shape of criminals. In this paper, we propose a method of matching the CCTV footage with the 3D data on the crime scene and measuring the height of the person using the 3D human body model in the matched data. In the proposed method, the height is measured by using 3D human model in various scenes of the person in the CCTV footage, and the measurement value of the target person is corrected by the measurement error of the replay CCTV footage of the reference person. We tested for 20 people's walking CCTV footage captured from an indoor and an outdoor and corrected the measurement values with 5 reference persons. Experimental results show that the measurement error (true value-measured value) average is 0.45 cm, and this method is effective for the measurement of the person's height in CCTV footage.

Keywords: human height, CCTV footage, 2D/3D matching, 3D human body model

Procedia PDF Downloads 223

Authors: Anastasia Rita Widiarti, Agus Harjoko, Marsono, Sri Hartati

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This paper presents the results of a study to test whether the Javanese character manuscript image preprocessing model that have been more widely applied, can also be applied to segment of the Batak characters manuscripts. The treatment process begins by converting the input image into a binary image. After the binary image is cleaned of noise, then the segmentation lines using projection profile is conducted. If unclear histogram projection is found, then the smoothing process before production indexes line segments is conducted. For each line image which has been produced, then the segmentation scripts in the line is applied, with regard of the connectivity between pixels which making up the letters that there is no characters are truncated. From the results of manuscript preprocessing system prototype testing, it is obtained the information about the system truth percentage value on pieces of Pustaka Batak Podani Ma AjiMamisinon manuscript ranged from 65% to 87.68% with a confidence level of 95%. The value indicates the truth percentage shown the initial processing model in Javanese characters manuscript image can be applied also to the image of the Batak characters manuscript.

Keywords: connected component, preprocessing, manuscript image, projection profiles

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Authors: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Mohamed Jama Madar, Oso Wilis

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E-learning is the application of information technology in the teaching and learning process. This paper presents the Funnel model as a solution for the problems of implementation of e-learning in tertiary education institutions. While existing models such as TAM, theory-based e-learning and pedagogical model have been used over time, they have generally been found to be inadequate because of their tendencies to treat materials development, instructional design, technology, delivery and governance as separate and isolated entities. Yet it is matching components that bring framework of e-learning strategic implementation. The Funnel model enhances all these into one and applies synchronously and asynchronously to e-learning implementation where the only difference is modalities. Such a model for e-learning implementation has been lacking. The proposed Funnel model avoids ad-ad-hoc approach which has made other systems unused or inefficient, and compromised educational quality. Therefore, the proposed Funnel model should help tertiary education institutions adopt and develop effective and efficient e-learning system which meets users’ requirements.

Keywords: e-learning, pedagogical, technology, strategy

Procedia PDF Downloads 426

Authors: Pedro M. A. Vitoriano, Tito. G. Amaral

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Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The correct extraction of the component for posterior analysis is a critical step of the AOI process. Nowadays, the Pattern Matching Algorithm is commonly used, although this algorithm requires extensive calculations and is time consuming. This paper will present an improved algorithm for the component localization process, with the capability of implementation in a parallel execution system.

Keywords: AOI, automated optical inspection, SMD, surface mounting devices, pattern matching, parallel execution

Procedia PDF Downloads 277

Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: F. Amireche, I. Mokbel, J. Jose, B. F. Belaribi

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The experimental vapour-liquid equilibria (VLE) at isothermal conditions and excess molar Gibbs energies GE are carried out for the three binary mixtures: 1, 2- dichloroethane + ethylene glycol dimethyl ether, + diethylene glycol dimethyl ether or + diethylene glycol diethyl ether, at ten temperatures ranging from 273 to 353.15 K. A good static device was employed for these measurements. The VLE data were reduced using the Redlich-Kister equation by taking into consideration the vapour pressure non-ideality in terms of the second molar virial coefficient. The experimental data were compared to the results predicted with the DISQUAC and Dortmund UNIFAC group contribution models for the total pressures P, the excess molar Gibbs energies GE and the excess molar enthalpies HE.

Keywords: Disquac model, Dortmund UNIFAC model, 1, 2- dichloroethane, excess molar Gibbs energies GE, polyethers, VLE

Procedia PDF Downloads 240

Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

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In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition

Procedia PDF Downloads 197

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

Procedia PDF Downloads 406

Authors: Satyam Raikwar, Thomas Herlitzius, Jens Fehrmann

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In recent years, the autonomous navigation of orchard and field robots is an emerging technology of the mobile robotics in agriculture. One of the core aspects of autonomous navigation builds upon path planning, which is still a crucial issue. Generally, for simple representation, the path planning for a mobile robot is performed in a two-dimensional space, which creates a path between the start and goal point. This paper presents the automatic path planning approach for robots used in orchards and vineyards using occupancy grid maps with field consideration. The orchards and vineyards are usually structured environment and their topology is assumed to be constant over time; therefore, in this approach, an RGB image of a field is used as a working environment. These images undergone different image processing operations and then discretized into two-dimensional grid matrices. The individual grid or cell of these grid matrices represents the occupancy of the space, whether it is free or occupied. The grid matrix represents the robot workspace for motion and path planning. After the grid matrix is described, a probabilistic roadmap (PRM) path algorithm is used to create the obstacle-free path over these occupancy grids. The path created by this method was successfully verified in the test area. Furthermore, this approach is used in the navigation of the orchard robot.

Keywords: orchard robots, automatic path planning, occupancy grid, probabilistic roadmap

Procedia PDF Downloads 130

Authors: G. Balakrishnan

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Stereo Correspondence plays a major role in estimation of distance of an object from the stereo camera pair for various applications. In this paper, a stereo correspondence algorithm based on block-matching technique is presented. Initially, an energy matrix is calculated for every disparity obtained using modified Sum of Absolute Difference (SAD). Higher energy matrix errors are removed by using threshold value in order to reduce the mismatch errors. A smoothening filter is applied to eliminate unreliable disparity estimate across the object boundaries. The purpose is to improve the reliability of calculation of disparity map. The experimental results obtained shows that the final depth map produce better results and can be used to all the applications using stereo cameras.

Keywords: stereo matching, filters, energy matrix, disparity

Procedia PDF Downloads 177

Authors: Boukelkoul Lahcen

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The cumulative costs for O&M may represent as much as 65%-90% of the turbine's investment cost. Nowadays the cost effectiveness concept becomes a decision-making and technology evaluation metric. The cost of energy metric accounts for the effect replacement cost and unscheduled maintenance cost parameters. One key of the proposed approach is the idea of maintaining the WTs which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating the cost of O&M is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the cost according to various options of maintenance.

Keywords: cost, finite state, Markov model, operation and maintenance

Procedia PDF Downloads 499

Authors: Seon Soon Choi

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The principal purpose of this paper is to find the influence of maximum fatigue load on the probabilistic aspect of fatigue crack propagation life at a specified grown crack in magnesium alloys. The experiments of fatigue crack propagation are carried out in laboratory air under different conditions of the maximum fatigue loads to obtain the fatigue crack propagation data for the statistical analysis. In order to analyze the probabilistic aspect of fatigue crack propagation life, the goodness-of fit test for probability distribution of the fatigue crack propagation life at a specified grown crack is implemented through Anderson-Darling test. The good probability distribution of the fatigue crack propagation life is also verified under the conditions of the maximum fatigue loads.

Keywords: fatigue crack propagation life, magnesium alloys, maximum fatigue load, probability

Procedia PDF Downloads 358

Authors: Ahmed Elaksher

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Over the last few years, significant progresses have been made and new approaches have been proposed for efficient collection of 3D spatial data from Unmanned aerial vehicles (UAVs) with reduced costs compared to imagery from satellite or manned aircraft. In these systems, a low-cost GPS unit provides the position, velocity of the vehicle, a low-quality inertial measurement unit (IMU) determines its orientation, and off-the-shelf cameras capture the images. Structure from Motion (SfM) and photogrammetry are the main tools for 3D surface reconstruction from images collected by these systems. Unlike traditional techniques, SfM allows the computation of calibration parameters using point correspondences across images without performing a rigorous laboratory or field calibration process and it is more flexible in that it does not require consistent image overlap or same rotation angles between successive photos. These benefits make SfM ideal for UAVs aerial mapping. In this paper, a direct comparison between SfM Digital Elevation Models (DEM) and those generated through traditional photogrammetric techniques was performed. Data was collected by a 3DR IRIS+ Quadcopter with a Canon PowerShot S100 digital camera. Twenty ground control points were randomly distributed on the ground and surveyed with a total station in a local coordinate system. Images were collected from an altitude of 30 meters with a ground resolution of nine mm/pixel. Data was processed with PhotoScan, VisualSFM, Imagine Photogrammetry, and a photogrammetric algorithm developed by the author. The algorithm starts with performing a laboratory camera calibration then the acquired imagery undergoes an orientation procedure to determine the cameras’ positions and orientations. After the orientation is attained, correlation based image matching is conducted to automatically generate three-dimensional surface models followed by a refining step using sub-pixel image information for high matching accuracy. Tests with different number and configurations of the control points were conducted. Camera calibration parameters estimated from commercial software and those obtained with laboratory procedures were comparable. Exposure station positions were within less than few centimeters and insignificant differences, within less than three seconds, among orientation angles were found. DEM differencing was performed between generated DEMs and few centimeters vertical shifts were found.

Keywords: UAV, photogrammetry, SfM, DEM

Procedia PDF Downloads 263

Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

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In order to effectively detect aerial targets over long distances, an optimal matching design method of space-based optical payload is proposed. Firstly, main factors affecting optical detectability of small targets under complex environment are analyzed based on the full link of a detection system, including band center, band width and spatial resolution. Then a performance characterization model representing the relationship between image signal-to-noise ratio (SCR) and the above influencing factors is established to describe a detection system. Finally, an optimal matching design example is demonstrated for a typical aerial target by simulating and analyzing its SCR under different scene clutter coupling with multi-scale characteristics, and the optimized detection band and spatial resolution are presented. The method can provide theoretical basis and scientific guidance for space-based detection system design, payload specification demonstration and information processing algorithm optimization.

Keywords: space-based detection, aerial targets, optical system design, detectability characterization

Procedia PDF Downloads 142

Authors: Arnold M. Brener, Lesbek Tashimov, Ablakim S. Muratov

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The paper deals with the special model for coagulation kernels which represents new control parameters in the Smoluchowski equation for binary aggregation. On the base of the model the new approach to evaluating aggregative stability of disperse systems has been submitted. With the help of this approach the simple estimates for aggregative stability of various types of hydrophilic nano-suspensions have been obtained.

Keywords: aggregative stability, coagulation kernels, disperse systems, mathematical model

Procedia PDF Downloads 286

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Maher un Nisa, Ahsan Khawaja

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Retinal imaging with Ultra-WideField (UWF) view technology has opened up new avenues in the field of retinal pathology detection. Recent developments in retinal imaging such as Optos California Imaging Device helps in acquiring high resolution images of the retina to help the Ophthalmologists in diagnosing and analyzing eye related pathologies more accurately. This paper investigates the acquired retinal details by comparing vessel detection in standard 450 color fundus images with the state of the art 2000 UWF retinal images.

Keywords: color fundus, retinal images, ultra-widefield, vessel detection

Procedia PDF Downloads 424

Authors: Misganaw Chere Siferih

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This study assessed the impact of crossbreed dairy cows on household income and food security. The study area is found in Dejen Woreda, East Gojam Zone, and Amhara region of Ethiopia. Random sampling technique was used to obtain a sample of 80 crossbreed dairy cow owners and 176 indigenous dairy cow owners. The study employed food consumption score analytical framework to measure food security status of the household. No Statistical significant mean difference is found between crossbreed owners and indigenous owners. Logistic regression was employed to investigate crossbreed dairy cow adoption determinants , the result indicates that gender, education, labor number, land size cultivated, dairy cooperatives membership, net income and food security status of the household are statistically significant independent variables, which explained the binary dependent variable, crossbreed dairy cow adoption. Propensity score matching (PSM) was employed to analyze the impact of crossbreed dairy cow owners on farmers’ income and food security. The average net income of crossbreed dairy cow owners was found to be significantly higher than indigenous dairy cow owners. Estimates of average treatment effect of the treated (ATT) indicated that crossbreed dairy cow is able to impact households’ net income by 42%, 38.5%, 30.8% and 44.5% higher in kernel, radius, nearest neighborhood and stratification matching algorithms respectively as compared to indigenous dairy cow owners. However, estimates of average treatment of the treated (ATT) suggest that being an owner of crossbreed dairy cow is not able to affect food security significantly. Thus, crossbreed dairy cow enables farmers to increase income but not their food security in the study area. Finally, the study recommended establishing dairy cooperatives and advice farmers to become a member of them, attention to promoting the impact of crossbreed dairy cows and promotion of nutrition focus projects.

Keywords: crossbreed dairy cow, net income, food security, propensity score matching

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Authors: Said Ettaieb, Kamel Hamrouni, Su Ruan

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This paper presents a new method based both on Active Shape Model and a priori knowledge about the spatio-temporal shape variation for tracking deformable structures in medical imaging. The main idea is to exploit the a priori knowledge of shape that exists in ASM and introduce new knowledge about the shape variation over time. The aim is to define a new more stable method, allowing the reliable detection of structures whose shape changes considerably in time. This method can also be used for the three-dimensional segmentation by replacing the temporal component by the third spatial axis (z). The proposed method is applied for the functional and morphological study of the heart pump. The functional aspect was studied through temporal sequences of scintigraphic images and morphology was studied through MRI volumes. The obtained results are encouraging and show the performance of the proposed method.

Keywords: active shape model, a priori knowledge, spatiotemporal shape variation, deformable structures, medical images

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Authors: Issam Ben Jami

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Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results.

Keywords: feature matching, historical documents, pattern recognition, word spotting

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Authors: H. Nabaei, M. Joghataie

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In this article, we study the design methods of matched filter with commercial software including CST Studio and ADS in specific frequency: 900 MHz. At first, we select two amounts of impedance for studying matching of them. Then, using by matched filter utility tool in ADS software, we simulate and deviate the elements of matched filters. In the following, we implement matched filter in CST STUDIO software. The simulated results show the great conformity in this field. Also, we peruse scattering and Impedance parameters in the Derivative structure. Finally, the layout of matched filter is obtained by the schematic tool of CST STUDIO. In fact, here, we present the design process of matched filters in the specific frequency.

Keywords: impedance matching, lumped element, transmission line, maximum power transmission, 3D layout

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Yousif Mohamed Y. Abdallah, Razan Manofely, Rajab M. Ben Yousef

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X-Ray images are very popular as a first tool for diagnosis. Automating the process of analysis of such images is important in order to help physician procedures. In this practice, teeth segmentation from the radiographic images and feature extraction are essential steps. The main objective of this study was to study correction preprocessing of x-rays images using local adaptive filters in order to evaluate contrast enhancement pattern in different x-rays images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in x-rays images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were contrast enhancement filtering and deblurring images using the blind deconvolution algorithm. In this paper, prominent constraints are firstly preservation of image's overall look; secondly, preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image.

Keywords: enhancement, x-rays, pixel intensity values, MatLab

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Ahd Aljarf, Saad Amin

Abstract:

Images are important source of information used as evidence during any investigation process. Their clarity and accuracy is essential and of the utmost importance for any investigation. Images are vulnerable to losing blocks and having noise added to them either after alteration or when the image was taken initially, therefore, having a high performance image processing system and it is implementation is very important in a forensic point of view. This paper focuses on improving the quality of the forensic images. For different reasons packets that store data can be affected, harmed or even lost because of noise. For example, sending the image through a wireless channel can cause loss of bits. These types of errors might give difficulties generally for the visual display quality of the forensic images. Two of the images problems: noise and losing blocks are covered. However, information which gets transmitted through any way of communication may suffer alteration from its original state or even lose important data due to the channel noise. Therefore, a developed system is introduced to improve the quality and clarity of the forensic images.

Keywords: image filtering, image reconstruction, image processing, forensic images

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