A New Gridding Technique for High Density Microarray Images Using Intensity Projection Profile of Best Sub Image

As the technologies for the fabrication of high quality microarray advances rapidly, quantification of microarray data becomes a major task. Gridding is the first step in the analysis of microarray images for locating the subarrays and individual spots within each subarray. For accurate gridding of high-density microarray images, in the presence of contamination and background noise, precise calculation of parameters is essential. This paper presents an accurate fully automatic gridding method for locating suarrays and individual spots using the intensity projection profile of the most suitable subimage. The method is capable of processing the image without any user intervention and does not demand any input parameters as many other commercial and academic packages. According to results obtained, the accuracy of our algorithm is between 95-100% for microarray images with coefficient of variation less than two. Experimental results show that the method is capable of gridding microarray images with irregular spots, varying surface intensity distribution and with more than 50% contamination

Automatic text block separation in document images

Separation of printed text blocks from the non-text areas, containing signatures, handwritten text, logos and other such symbols, is a necessary first step for an OCR involving printed text recognition. In the present work, we compare the efficacy of some feature-classifier combinations to carry out this separation task. We have selected length-nomalized horizontal projection profile (HPP) as the starting point of such a separation task. This is with the assumption that the printed text blocks contain lines of text which generate HPP's with some regularity. Such an assumption is demonstrated to be valid. Our features are the HPP and its two transformed versions, namely, eigen and Fisher profiles. Four well known classifiers, namely, Nearest neighbor, Linear discriminant function, SVM's and artificial neural networks have been considered and efficiency of the combination of these classifiers with the above features is compared. A sequential floating feature selection technique has been adopted to enhance the efficiency of this separation task. The results give an average accuracy of about 96.

Improved recognition of aged Kannada documents by effective segmentation of merged characters

In optical character recognition of very old books, the recognition accuracy drops mainly due to the merging or breaking of characters. In this paper, we propose the first algorithm to segment merged Kannada characters by using a hypothesis to select the positions to be cut. This method searches for the best possible positions to segment, by taking into account the support vector machine classifier's recognition score and the validity of the aspect ratio (width to height ratio) of the segments between every pair of cut positions. The hypothesis to select the cut position is based on the fact that a concave surface exists above and below the touching portion. These concave surfaces are noted down by tracing the valleys in the top contour of the image and similarly doing it for the image rotated upside-down. The cut positions are then derived as closely matching valleys of the original and the rotated images. Our proposed segmentation algorithm works well for different font styles, shapes and sizes better than the existing vertical projection profile based segmentation. The proposed algorithm has been tested on 1125 different word images, each containing multiple merged characters, from an old Kannada book and 89.6% correct segmentation is achieved and the character recognition accuracy of merged words is 91.2%. A few points of merge are still missed due to the absence of a matched valley due to the specific shapes of the particular characters meeting at the merges.

Robust/optimal temperature profile control using neural networks

An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order, finite-dimensional lumped parameter model for this problem is obtained by using Galerkin projection and basis functions designed through the 'Proper Orthogonal Decomposition' technique (POD) and the 'snap-shot' solutions. A suboptimal neurocontroller is obtained with a single-network-adaptive-critic (SNAC). Further contribution of this paper is to develop an online robust controller to account for unmodeled dynamics and parametric uncertainties. A weight update rule is presented that guarantees boundedness of the weights and eliminates the need for persistence of excitation (PE) condition to be satisfied. Since, the ADP and neural network based controllers are of fairly general structure, they appear to have the potential to be controller synthesis tools for nonlinear distributed parameter systems especially where it is difficult to obtain an accurate model.

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Projection Error Propagation-based Regularization (PEPR) method for resistivity reconstruction in Electrical Impedance Tomography (EIT)

A novel Projection Error Propagation-based Regularization (PEPR) method is proposed to improve the image quality in Electrical Impedance Tomography (EIT). PEPR method defines the regularization parameter as a function of the projection error developed by difference between experimental measurements and calculated data. The regularization parameter in the reconstruction algorithm gets modified automatically according to the noise level in measured data and ill-posedness of the Hessian matrix. Resistivity imaging of practical phantoms in a Model Based Iterative Image Reconstruction (MoBIIR) algorithm as well as with Electrical Impedance Diffuse Optical Reconstruction Software (EIDORS) with PEPR. The effect of PEPR method is also studied with phantoms with different configurations and with different current injection methods. All the resistivity images reconstructed with PEPR method are compared with the single step regularization (STR) and Modified Levenberg Regularization (LMR) techniques. The results show that, the PEPR technique reduces the projection error and solution error in each iterations both for simulated and experimental data in both the algorithms and improves the reconstructed images with better contrast to noise ratio (CNR), percentage of contrast recovery (PCR), coefficient of contrast (COC) and diametric resistivity profile (DRP). (C) 2013 Elsevier Ltd. All rights reserved.

Overview of coded light projection techniques for automatic 3D profiling

Obtaining automatic 3D profile of objects is one of the most important issues in computer vision. With this information, a large number of applications become feasible: from visual inspection of industrial parts to 3D reconstruction of the environment for mobile robots. In order to achieve 3D data, range finders can be used. Coded structured light approach is one of the most widely used techniques to retrieve 3D information of an unknown surface. An overview of the existing techniques as well as a new classification of patterns for structured light sensors is presented. This kind of systems belong to the group of active triangulation method, which are based on projecting a light pattern and imaging the illuminated scene from one or more points of view. Since the patterns are coded, correspondences between points of the image(s) and points of the projected pattern can be easily found. Once correspondences are found, a classical triangulation strategy between camera(s) and projector device leads to the reconstruction of the surface. Advantages and constraints of the different patterns are discussed

The noradrenergic projection from the locus coeruleus to the cochlear root neurons in rats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Facial Profile Evaluation of Isolated Pierre Robin Sequence

Objective: To evaluate numerically the facial profile of children with isolated Pierre Robin sequence (PRS) and to compare them with a control group that has no pathologies and exhibits regular and balanced facial growth, with no skeletal alterations. Patients: Eighty-three children aged 5 to 10 years (PRS group, n = 60; control group, n = 23) were selected. Setting: Hospital for Rehabilitation of Craniofacial Anomalies, University of Sao Paulo (HRAC-USP). Children from the control group were taken from the program of Interceptive Orthodontics at HRAC-USP. Design: Angular and ratio analyses of the facial profiles in both groups were realized through digital photographs. The PRS group was subdivided into two groups-complete and incomplete-according to the sagittal extension of the cleft palate, to investigate the possible influence of cleft extension on the face. Results: The facial convexity angle and the facial inferior third angle were considerably higher in the PRS groups than in the control group and were not significantly different between PRS groups. Nasolabial angle did not differ between groups. Conclusion: The facial profile was more convex in individuals with PRS than in those with regular facial growth and with no pathology. The mandible was responsible for the convexity of the profile in PRS because of its lack off anterior projection. An important relationship between the extension of the cleft palate and alterations in facial profile in PRS was not observed.

Study of the sensitivity of the XENON1T experiment with the profile likelihood method

Oggi sappiamo che la materia ordinaria rappresenta solo una piccola parte dell'intero contenuto in massa dell'Universo. L'ipotesi dell'esistenza della Materia Oscura, un nuovo tipo di materia che interagisce solo gravitazionalmente e, forse, tramite la forza debole, è stata avvalorata da numerose evidenze su scala sia galattica che cosmologica. Gli sforzi rivolti alla ricerca delle cosiddette WIMPs (Weakly Interacting Massive Particles), il generico nome dato alle particelle di Materia Oscura, si sono moltiplicati nel corso degli ultimi anni. L'esperimento XENON1T, attualmente in costruzione presso i Laboratori Nazionali del Gran Sasso (LNGS) e che sarà in presa dati entro la fine del 2015, segnerà un significativo passo in avanti nella ricerca diretta di Materia Oscura, che si basa sulla rivelazione di collisioni elastiche su nuclei bersaglio. XENON1T rappresenta la fase attuale del progetto XENON, che ha già realizzato gli esperimenti XENON10 (2005) e XENON100 (2008 e tuttora in funzione) e che prevede anche un ulteriore sviluppo, chiamato XENONnT. Il rivelatore XENON1T sfrutta circa 3 tonnellate di xeno liquido (LXe) e si basa su una Time Projection Chamber (TPC) a doppia fase. Dettagliate simulazioni Monte Carlo della geometria del rivelatore, assieme a specifiche misure della radioattività dei materiali e stime della purezza dello xeno utilizzato, hanno permesso di predire con accuratezza il fondo atteso. In questo lavoro di tesi, presentiamo lo studio della sensibilità attesa per XENON1T effettuato tramite il metodo statistico chiamato Profile Likelihood (PL) Ratio, il quale nell'ambito di un approccio frequentista permette un'appropriata trattazione delle incertezze sistematiche. In un primo momento è stata stimata la sensibilità usando il metodo semplificato Likelihood Ratio che non tiene conto di alcuna sistematica. In questo modo si è potuto valutare l'impatto della principale incertezza sistematica per XENON1T, ovvero quella sulla emissione di luce di scintillazione dello xeno per rinculi nucleari di bassa energia. I risultati conclusivi ottenuti con il metodo PL indicano che XENON1T sarà in grado di migliorare significativamente gli attuali limiti di esclusione di WIMPs; la massima sensibilità raggiunge una sezione d'urto σ=1.2∙10-47 cm2 per una massa di WIMP di 50 GeV/c2 e per una esposizione nominale di 2 tonnellate∙anno. I risultati ottenuti sono in linea con l'ambizioso obiettivo di XENON1T di abbassare gli attuali limiti sulla sezione d'urto, σ, delle WIMPs di due ordini di grandezza. Con tali prestazioni, e considerando 1 tonnellata di LXe come massa fiduciale, XENON1T sarà in grado di superare gli attuali limiti (esperimento LUX, 2013) dopo soli 5 giorni di acquisizione dati.

Motion picture projection and sound pictures.

"12th edition."--Dust jacket.

Photographic illustrations for projection, plain or colored ... : photographic views from nature in all parts of the world : extensive collection : astronomy, light and optics, anatomy, crystallography, physics, botany, geology, geography.

Cover title.

Digital-simulation and projection of head changes in the Potomac-Raritan-Magothy aquifer system, coastal plain, New Jersey /

"May 1980."

Map projection applications /

"Speech notes only, does not reflect NOS policy." -- p. i.