1,297 research outputs found

    Boosting Handwriting Text Recognition in Small Databases with Transfer Learning

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    In this paper we deal with the offline handwriting text recognition (HTR) problem with reduced training datasets. Recent HTR solutions based on artificial neural networks exhibit remarkable solutions in referenced databases. These deep learning neural networks are composed of both convolutional (CNN) and long short-term memory recurrent units (LSTM). In addition, connectionist temporal classification (CTC) is the key to avoid segmentation at character level, greatly facilitating the labeling task. One of the main drawbacks of the CNNLSTM-CTC (CLC) solutions is that they need a considerable part of the text to be transcribed for every type of calligraphy, typically in the order of a few thousands of lines. Furthermore, in some scenarios the text to transcribe is not that long, e.g. in the Washington database. The CLC typically overfits for this reduced number of training samples. Our proposal is based on the transfer learning (TL) from the parameters learned with a bigger database. We first investigate, for a reduced and fixed number of training samples, 350 lines, how the learning from a large database, the IAM, can be transferred to the learning of the CLC of a reduced database, Washington. We focus on which layers of the network could be not re-trained. We conclude that the best solution is to re-train the whole CLC parameters initialized to the values obtained after the training of the CLC from the larger database. We also investigate results when the training size is further reduced. The differences in the CER are more remarkable when training with just 350 lines, a CER of 3.3% is achieved with TL while we have a CER of 18.2% when training from scratch. As a byproduct, the learning times are quite reduced. Similar good results are obtained from the Parzival database when trained with this reduced number of lines and this new approach.Comment: ICFHR 2018 Conferenc

    Tree-structure Expectation Propagation for Decoding LDPC codes over Binary Erasure Channels

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    Expectation Propagation is a generalization to Belief Propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal distribution constraints in some check nodes of the LDPC Tanner graph. These additional constraints allow decoding the received codeword when the BP decoder gets stuck. In this paper, we first present the new decoding algorithm, whose complexity is identical to the BP decoder, and we then prove that it is able to decode codewords with a larger fraction of erasures, as the block size tends to infinity. The proposed algorithm can be also understood as a simplification of the Maxwell decoder, but without its computational complexity. We also illustrate that the new algorithm outperforms the BP decoder for finite block-siz

    Turbo EP-based Equalization: a Filter-Type Implementation

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    This manuscript has been submitted to Transactions on Communications on September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted on April 25, 2018 We propose a novel filter-type equalizer to improve the solution of the linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational complexity constrained to be quadratic in the filter length. When high-order modulations and/or large memory channels are used the optimal BCJR equalizer is unavailable, due to its computational complexity. In this scenario, the filter-type LMMSE turbo equalization exhibits a good performance compared to other approximations. In this paper, we show that this solution can be significantly improved by using expectation propagation (EP) in the estimation of the a posteriori probabilities. First, it yields a more accurate estimation of the extrinsic distribution to be sent to the channel decoder. Second, compared to other solutions based on EP the computational complexity of the proposed solution is constrained to be quadratic in the length of the finite impulse response (FIR). In addition, we review previous EP-based turbo equalization implementations. Instead of considering default uniform priors we exploit the outputs of the decoder. Some simulation results are included to show that this new EP-based filter remarkably outperforms the turbo approach of previous versions of the EP algorithm and also improves the LMMSE solution, with and without turbo equalization

    Towards a Subject-Oriented Model-Driven Framework

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    AbstractModel-Driven Architecture is an approach which tackles such problems as: the high availability that a software product requires to be ready for use, the high degree of evolution that a software system has nowadays, etc. However, in the development of large complex systems, the benefits of that approach have been diminished due to the size and complexity of models that describe these kinds of systems. At this point Aspect-Oriented Software Development (AOSD) appears to improve the understanding, reusability and adaptation of the software artefacts. Its mechanism is based on modularization of crosscutting concerns in well-identified isolated entities called aspects. For this reason we propose to use together AOSD and MDA in the hope of reducing the shortcomings of the latter. Thus, aspects like security, replication, real-time constraints, etc., will be modelled by specialist modellers independently throughout the MDA framework. Our proposal exploits a tool for checking the consistency between different models (aspects) at the same level of abstraction; supporting the traceability of UML elements, requirements, and concerns; and controlling the impact of changes throughout the MDA framework

    Coordinating heterogeneous IoT devices by means of the centralized vision of the SDN controller

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    The IoT (Internet of Things) has become a reality during recent years. The desire of having everything connected to the Internet results in clearly identified benefits that will impact on socio economic development. However, the exponential growth in the number of IoT devices and their heterogeneity open new challenges that must be carefully studied. Coordination among devices to adapt them to their users' context usually requires high volumes of data to be exchanged with the cloud. In order to reduce unnecessary communications and network overhead, this paper proposes a novel network architecture based on the Software-Defined Networking paradigm that allows IoT devices coordinate and adapt them within the scope of a particular context.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Entorno extensible para la monitorización y detección de síntomas de depresión

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    La depresión es una enfermedad silenciosa que está aumentando de forma alarmante debido al ritmo de vida de la sociedad. Los propios síntomas de la depresión hacen que los pacientes se enfrenten a barreras psicológicas que dificultan la búsqueda de tratamiento. Actualmente, los dispositivos móviles están siendo usados para monitorizar el comportamiento de las personas y, así, identificar si presentan distintas enfermedades. En este artículo se presenta un conjunto de aplicaciones que detectan síntomas de depresión de forma pasiva para el usuario, reduciendo los posibles obstáculos para la identificación de esta enfermedad. Estas aplicaciones han sido desarrolladas para que los datos monitorizados puedan ser reutilizados por otros sistemas, sin que ello conlleve un incremento en el consumo de recursos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Tree-Structure Expectation Propagation for LDPC Decoding over the BEC

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    We present the tree-structure expectation propagation (Tree-EP) algorithm to decode low-density parity-check (LDPC) codes over discrete memoryless channels (DMCs). EP generalizes belief propagation (BP) in two ways. First, it can be used with any exponential family distribution over the cliques in the graph. Second, it can impose additional constraints on the marginal distributions. We use this second property to impose pair-wise marginal constraints over pairs of variables connected to a check node of the LDPC code's Tanner graph. Thanks to these additional constraints, the Tree-EP marginal estimates for each variable in the graph are more accurate than those provided by BP. We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of differential equations that represents the expected residual graph evolution as a function of the code parameters. The solution of these equations is used to predict the TEP decoder performance in both the asymptotic regime and the finite-length regime over the BEC. While the asymptotic threshold of the TEP decoder is the same as the BP decoder for regular and optimized codes, we propose a scaling law (SL) for finite-length LDPC codes, which accurately approximates the TEP improved performance and facilitates its optimization

    Investigating the transformations of polyoxoanions using mass spectrometry and molecular dynamics

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    The reactions of [γ-SiW10O36]8– represent one of the most important synthetic gateways into a vast array of polyoxotungstate chemistry. Herein, we set about exploring the transformation of the lacunary polyoxoanion [β2-SiW11O39]8– into [γ-SiW10O36]8– using high-resolution electrospray mass spectrometry, density functional theory, and molecular dynamics. We show that the reaction proceeds through an unexpected {SiW9} precursor capable of undertaking a direct β → γ isomerization via a rotational transformation. The remarkably low-energy transition state of this transformation could be identified through theoretical calculations. Moreover, we explore the significant role of the countercations for the first time in such studies. This combination of experimental and the theoretical studies can now be used to understand the complex chemical transformations of oxoanions, leading to the design of reactivity by structural control
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