Handwritten Devanagari numeral recognition

Optical character recognition (OCR) plays a very vital role in today’s modern world. OCR can be useful for solving many complex problems and thus making human’s job easier. In OCR we give a scanned digital image or handwritten text as the input to the system. OCR can be used in postal department for sorting of the mails and in other offices. Much work has been done for English alphabets but now a day’s Indian script is an active area of interest for the researchers. Devanagari is on such Indian script. Research is going on for the recognition of alphabets but much less concentration is given on numerals. Here an attempt was made for the recognition of Devanagari numerals. The main part of any OCR system is the feature extraction part because more the features extracted more is the accuracy. Here two methods were used for the process of feature extraction. One of the method was moment based method. There are many moment based methods but we have preferred the Tchebichef moment. Tchebichef moment was preferred because of its better image representation capability. The second method was based on the contour curvature. Contour is a very important boundary feature used for finding similarity between shapes. After the process of feature extraction, the extracted feature has to be classified and for the same Artificial Neural Network (ANN) was used. There are many classifier but we preferred ANN because it is easy to handle and less error prone and apart from that its accuracy is much higher compared to other classifier. The classification was done individually with the two extracted features and finally the features were cascaded to increase the accuracy

Imaging and discrete modelling of sand shape

The shape of particles is known to play an important role in soil behaviour, with significant effects of engineering responses. Investigating how the shape of particles can be measured and quantified is therefore considered increasingly important in modern soil mechanics. This is propelled by the advent of computer based image-analyses and discrete modelling algorithms, which have opened new ways to tackle this problem. This work demonstrates how these two techniques can be made to work together. Image analyses are performed on x-rays micro-tomographs (µ-CT) of triaxial sand specimens, focusing on the characterisation and quantification of particle shapes. Two with very different particle shape sands are studied in details: Caicos ooids (rounded) and Hostun sand (angular). A discrete Digital Volume Correlation (DVC) algorithm is then used to track the kinematics of individual grains (around 50000 for each sand specimen) during the triaxial test and measure, with good precision, their cumulated displacements and rotations. Joint analysis of the shape and kinematic databases acquired is performed to find how particle shape descriptors are related to observed kinematics at the microscale level. It appears that true sphericity is a good predictor of upper bound rotational restraint. Modelling is based on the Discrete Element Method (DEM). Models that introduce rolling resistance at the contact are widely employed in DEM simulations, these approaches offer substantial computational benefits at the prize of increased calibration complexity. In this work, the values of true sphericity obtained by image analysis of the grains, either directly by 3D acquisition or by correlation with simpler to obtain 2D shape measures, are used to establish mechanically equivalent rotational restrictions. An empirical relation between a contact parameter (rolling friction) and a 3D grain shape descriptor (true sphericity is first calibrated - using both specimen-scale and grain scale results from two triaxial tests in Hostun sand and Caicos ooids. It is then validated by simulating other triaxial tests (1) with the same sands, but in different conditions (2) with Ottawa sand, for which 3D grain images were also available for examination, and (3) with Ticino sand, for which only 2D grain images were available. Finally, results of large-scale DEM simulations on the Cone Penetration Test (CPT) - exploiting the new proposed contact model - are presented. Experimental data on the CPT performed in a Calibration Chamber (CC) comprised of Ticino sand are successfully fitted by the numerical penetration curves at different confining pressures and conditions. A parametric study about the influence of particle shape and particle shape variability put in evidence the strong-coupled effects of rolling and frictional resistances at the particles contacts. The work described in this thesis will ease the use of DEM for large-scale simulations of geotechnical engineering problems.Se sabe que la forma de las partículas juega un papel importante en el comportamiento del suelo, con efectos significativos de las respuestas mecánicas relevantes en ingeniería geotécnica. Por lo tanto, investigar cómo se puede medir y cuantificar la forma de las partículas se considera cada vez más importante en la mecánica del suelo moderna. Esto se acrecienta debido a las técnicas de análisis computacionales de imágenes y algoritmos de modelado discreto (DEM), que han abierto nuevas formas de abordar este problema. Este trabajo demuestra cómo se pueden hacer que estas dos técnicas funcionen juntas. Los análisis de imagen se realizan sobre micro-tomografías de rayos X (µ-CT) de muestras de arena en celdas triaxiales, centrándose en la caracterización y cuantificación de la forma de las partículas. Se estudian en detalle dos arenas con la forma de sus partículas muy diferentes: Caicos ooids (redondeados) y Hostun sand (angular). Luego se utiliza un algoritmo discreto de correlación de volumen digital (DVC) para rastrear la cinemática de granos individuales (alrededor de 50000 por cada muestra de arena) durante la prueba triaxial y medir, con buena precisión, sus desplazamientos y rotaciones acumulados. El análisis conjunto de la forma y las bases de datos cinemáticas adquiridas se realiza para encontrar cómo los descriptores de forma de partículas se relacionan con la cinemática observada a nivel de micro-escala. Resulta que la esfericidad verdadera predice bien el límite superior de rotación de una partícula. La modelización numérica se basa en el Método de Elementos Discretos (DEM). Los modelos que introducen resistencia a la rotación en el contacto se emplean ampliamente en simulaciones DEM, estos enfoques ofrecen beneficios computacionales sustanciales a costa de una mayor complejidad de calibración. En este trabajo, los valores de esfericidad verdadera (i.e., true sphericity) obtenidos mediante análisis de imagen de los granos, ya sea directamente por adquisición 3D o por correlación con medidas de forma 2D más simples, se utilizan para establecer restricciones de rotación mecánicamente equivalentes. Una relación empírica entre un parámetro de contacto (rolling friction) y un descriptor de forma de grano 3D (la esfericidad verdadera) se calibra primero, utilizando los resultados de la escala de muestras y de la escala de granos de dos pruebas triaxiales en las arenas de Hostun y de Caicos. Luego se valida simulando otras pruebas triaxiales (1) con las mismas arenas, pero en diferentes condiciones (2) con arena de Ottawa, para la que también estaban disponibles imágenes 3D de granos para su examen, y (3) con arena de Ticino, para la cual solo estaban disponibles imágenes 2D de los granos. Finalmente, se presentan resultados de simulaciones DEM a gran escala de la prueba de penetración de cono (CPT), aprovechando el nuevo modelo de contacto propuesto. Los datos experimentales del CPT realizado en una cámara de calibración (CC) sobre arena de Ticino se ajustan con éxito por las curvas de penetración numérica a diferentes presiones y condiciones de confinamiento. Un estudio paramétrico sobre la influencia de la forma de las partículas y la variabilidad de las formas de las partículas puso de manifiesto los efectos fuertemente acoplados de las resistencias rotacional y friccional en los contactos entre partículas. El trabajo descrito en esta tesis facilitará el uso de DEM para simulaciones a gran escala en problemas de ingeniería geotécnica.Postprint (published version

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Design/cost tradeoff studies. Appendix A. Supporting analyses and tradeoffs, book 2. Earth Observatory Satellite system definition study (EOS)

Attitude reference systems for use with the Earth Observatory Satellite (EOS) are described. The systems considered are fixed and gimbaled star trackers, star mappers, and digital sun sensors. Covariance analyses were performed to determine performance for the most promising candidate in low altitude and synchronous orbits. The performance of attitude estimators that employ gyroscopes which are periodically updated by a star sensor is established by a single axis covariance analysis. The other systems considered are: (1) the propulsion system design, (2) electric power and electrical integration, (3) thermal control, (4) ground data processing, and (5) the test plan and cost reduction aspects of observatory integration and test

Computerized cancer malignancy grading of fine needle aspirates

According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac

Generation of a Land Cover Atlas of environmental critic zones using unconventional tools

L'abstract è presente nell'allegato / the abstract is in the attachmen

Detection of Anomalous Vehicle Loading

Determining the mass of a vehicle from ground based passive sensor data is important for many security and traffic safety reasons. A vehicle consists of multiple dependent and independent systems that each respond differently to changes in vehicle mass. In some cases, the responses of these vehicle systems can be measured remotely. If these remotely sensed system responses are correlated to the vehicle\u27s mass, and the required vehicle parameters were known, it would be possible to calculate the mass of the vehicle as a function of these responses. The research described here investigates multiple vehicle phenomenologies and their correlation to vehicle load. Brake temperature, engine acoustics, exhaust output, tire temperature, tire deformation, vehicle induced ground vibration, suspension response, and engine torque induced frame twist were all evaluated and assessed as potential methods of remotely measuring a vehicle\u27s mass. Extensive field experiments were designed and carried out using multiple sensors of various types; including microphones, accelerometers, high-speed video cameras, high-resolution video cameras, LiDAR, and thermal imagers. These experiments were executed at multiple locations and employed passenger vehicles, and commercial trucks with loads ranging from zero to beyond the recommended load capacity of the vehicle. The results of these experiments were used to determine if the signature for each phenomenology could be accurately observed remotely, and if so, how well they correlated to vehicle mass. The suspension response and engine torque induced frame twist phenomenologies were found to have the best correlation to vehicle mass of the phenomenologies considered, with correlation values of 90.5% and 97.7%, respectively. Physics-based models were built for both the suspension response, and the engine torque induced frame twist phenomenologies. These models detailed the relationship between each phenomenology and the mass of the vehicle. Full-scale field testing was done using improved remote detection methods, and the results were used to validate the physics-based models. The results of the full-scale field testing showed that both phenomenology could accurately calculate the mass of the vehicle remotely, given that certain vehicle parameters were accurately known. The engine torque induced frame twist phenomenology was able to find the mass of the test vehicle to within 10% of the true mass. Using the suspension response phenomenology the mass was accurately predicted as a function of its location on the vehicle. For either phenomenology to be effective, certain vehicle parameters must be known accurately; specifically the spring constant and damping coefficients of the vehicle\u27s suspension, the unloaded mass, the unloaded center of gravity, and the unloaded moment of inertia of the vehicle. The models were also used to propagate measurement and parameter uncertainty through the vehicle mass calculation to arrive at the uncertainty in the mass estimation. Finally, the results of both the phenomenologies were combined into a single vehicle mass estimate with a smaller uncertainty than the individual vehicle system estimations taken alone

Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Feature Extraction Methods for Character Recognition

Not Include

LightGWAS: A Novel Genome-Wide Association Study Procedure

This dissertation proposes LightGWAS, a novel machine learning procedure for genome-wide association study (GWAS) based on LightGBM and k-fold cross-validation. The conducted literature review identified that the currently available GWAS implementations rely on massive manual quality control steps to address statistical issues, such as controlling for false-positive inflation and power reduction. It also showed they demand a specific GWAS method for each type of genomic dataset morphology, which consequently increases the human dependency and open margins for misleadings. LightGWAS is a potential single, resilient, autonomous and scalable solution to address such concerns. Through this research, LightGWAS was contrasted against the current state-of-the-art for GWAS throughout secondary research method. It has been compared with a GWAS implementation based on general linear model (GLM) with support to Firth regularisation. Quantitative empirical tests and deductive reasoning have been employed to reach and evaluate the results. The models were submitted to balanced (case:control=1:1), imbalanced (case:control=1:10), and high-imbalanced (case:control=1:100) genomic datasets of binary phenotypes. The results from statistical tests denoted that LightGWAS performs equivalently to the compared GLM method for balanced dataset scenarios, and outperforms for imbalanced and high-imbalanced datasets. The assessed metrics were weighted average of the precision and recall (F1), recall, average precision score (APS), receiver operating characteristic (ROC)/area under the curve (AUC), accuracy, and precision