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This paper describes the use of the LaTeX qjrms4.cls class file for setting papers for the Quarterly Journal of the Royal Meteorological Society

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6 page conference paper submission format, for the Cognitive Science Society Conference (COGSCI) as downloadable here.

Gene regulatory networks have an important role to study the behaviour of genes. By analysing these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its qualitative dynamical behaviour. René Thomas discrete modelling of gene regulatory network (GRN) is a well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN. We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs. As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra features i.e. CTL editor, parameters filtering and input/output files management. GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification, K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours and days of manual computation

Created using the Deedy CV/Resume XeLaTeX Template Version 1.0 (5/5/2014)

Created using the Deedy - One Page Two Column Resume from LaTeX Template, Version 1.1 (30/4/2014). Original author: Debarghya Das (http://debarghyadas.com) Original repository: https://github.com/deedydas/Deedy-Resume

Created using the Deedy CV/Resume XeLaTeX Template Version 1.0 (5/5/2014) This template has been downloaded from: http://www.LaTeXTemplates.com Original author: Debarghya Das (http://www.debarghyadas.com) With extensive modifications by: Vel (vel@latextemplates.com)

En este documento se encuentra el desarrollo del primer laboratorio de la asignatura Vídeo Digital y Procesamiento de Imágenes el cual trata sobre el análisis de los sensores fotográficos y los metadatos de las imágenes tomadas, Ademas de la creación de algunos scripts en el programa Matlab para lo que se tiene que tener en cuenta la información de geolocalización o GPS de las imágenes. Keywords — GPS, Matlab, Metadatos

Created using the Deedy CV/Resume XeLaTeX Template Version 1.0 (5/5/2014) This template has been downloaded from: http://www.LaTeXTemplates.com Original author: Debarghya Das (http://www.debarghyadas.com) With extensive modifications by: Vel (vel@latextemplates.com)