Emilio Granell

Smart collaborative system using the sensors of mobile devices for monitoring disabled and elderly people

According to statistics data, the population of many countries is aging. Moreover, there is also a small sector of the population that is affected by disabilities. Researchers should develop systems to improve their quality of life. In recent years, the development of smartphones and reduced size devices, but with high processing capacity, has increased dramatically. We can take profit of the sensors and applications embedded in the smartphones in order to monitor disabled and elderly people. In this paper, we describe a smart collaborative system based on the sensors placed in the mobile devices, which allow us to monitor the status of a person based on what is happening in the group people. The network algorithm and the smart system protocol is described and simulated in order to show the performance of our proposal.

Combining handwriting and speech recognition for transcribing historical handwritten documents

Transcription of historical documents is an interesting task for libraries in order to make available their funds. In the lasts years, the use of Handwritten Text Recognition allowed paleographs to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is obtaining the draft transcription by dictating the contents to an Automatic Speech Recognition system. When both sources (image and speech) are available, a multimodal combination is possible, and an iterative process can be used in order to refine the final hypothesis. In this work, a multimodal combination based on confusion networks is presented. Results on two different sets of data, with different difficulty level, show that the proposed technique provides similar or better draft transcriptions than a previously proposed approach, allowing for a faster transcription process.

Energy Consumption of Wireless Network Access Points

The development of low cost technology based on IEEE 802.11 standard permits to build telecommunication networks at low cost, allowing providing Internet access in rural areas in developing countries. The lack of access to the electrical grid is a problem when the network is being developed in rural areas, so that wireless access points should operate using solar panels and batteries. Many cases can be found where the energy consumption becomes a key point in wireless network design. In this paper we present a comparative study of the energy consumption of several wireless network access points. We will compare the energy consumption of different brands and models, for several operation scenarios and operating modes. Obtained results allow us to achieve the objective of this article, that is, promote the development of wireless communication networks energetically efficient.

A Multimodal Crowdsourcing Framework for Transcribing Historical Handwritten Documents

Transcription of handwritten historical documents is one of the main topics in document analysis systems, due to cultural reasons. State-of-the-art handwritten text recognition systems allow to speed up the transcription task. Currently, this automatic transcription is far from perfect, and human expert revision is required in order to obtain the actual transcription. In this context, crowdsourcing emerged as a powerful tool for massive transcription at a relatively low cost, since the supervision effort of professional transcribers may be dramatically reduced. However, current transcription crowdsourcing platforms are mainly limited to the use of non-mobile devices, since the use of keyboards in mobile devices is not friendly enough for most users. This work presents the alternative of using speech dictation of handwritten text lines as transcription source in a crowdsourcing platform. The experiments explore how an initial handwritten text recognition hypothesis can be improved by using the contribution of speech recognition from several speakers, providing as a final result a better hypothesis to be amended by a professional transcriber with less effort.

Less Is More: Efficient Back-of-Device Tap Input Detection Using Built-in Smartphone Sensors

Back-of-device (BoD) interaction using current smartphone sensors (e.g. accelerometer, microphone, or gyroscope) has recently emerged as a promising novel input modality. Researchers have used a different number of features derived from these commodity sensors, however it is unclear what sensors and which features would allow for practical use, since not all sensor measurements have an equal value for detecting BoD interactions reliably and efficiently. In this paper, we primarily focus on constructing and selecting a subset of features that is a good predictor of BoD tap-based input while ensuring low energy consumption. As a result, we build several classifiers for a variety of use cases (e.g. single or double taps with the dominant or non-dominant hand). We show that a subset of just 5 features provides high discrimination power and results in high recognition accuracy. We also make our software publicly available, so that others can build upon our work.

Multimodal output combination for transcribing historical handwritten documents

Transcription of digitalised historical documents is an interesting task in the document analysis area. This transcription can be achieved by using Handwritten Text Recognition (HTR) on digitalised pages or by using Automatic Speech Recognition (ASR) on the dictation of contents. Moreover, another option is using both systems in a multimodal combination to obtain a draft transcription, given that combining the outputs of different recognition systems will generally improve the recognition accuracy. In this work, we present a new combination method based on Confusion Network. We check its effectiveness for transcribing a Spanish historical book. Results on both unimodal combination with different optical (for HTR) and acoustic (for ASR) models, and multimodal combination, show a relative reduction of Word and Character Error Rate of \(14.3\%\) and \(16.6\%\), respectively, over the HTR baseline.

Energy consumption study of network access switches to enhance energy distribution

The popularization of the Internet of Things will require a lot of access devices to enable the connection of these things to data networks. Switches, hubs and routers have different power consumption depending on the manufacturer and configuration, so the choice of the manufacturer and the configuration of these network devices are key to ensure sustainable green data networks and enhance the electrical energy distribution in the smart grid. In this paper, we present a comparative study of the energy consumption of some common network access switches and hubs. In this study we measure the power consumption of different manufacturers using different topologies and configurations. With this comparison we are seeking to know the impact of these devices on the energy consumption data networks. Our goal is to find the optimal parameters to achieve a reduction of the energy consumption in these devices thereby ensuring an energetically responsible design.

Smart devices fingerprint detection

The generalized use of mobile devices with Internet connectivity for both 3G and WiFi, allow users to choose the connection they want to use at all times. This behavior requires that Internet service providers must adapt their infrastructure to ensure good levels of Quality of Service in both types of connections. In this paper, we describe an intelligent system based on neural networks and finite state machines that lets the Internet service provider know to which type of device belongs the traffic going to its network. The system analyzes the transport and application layers from TCP packets to discriminate the percentage of Internet traffic generated by mobile devices and personal computers. Test results show the success of the developed system.

Multimodality, interactivity, and crowdsourcing for document transcription: Multimodality, Interactivity, and Crowdsourcing

Knowledge mining from documents usually use document engineering techniques that allow the user to access the information contained in documents of interest. In this framework, transcription may provide efficient access to the contents of handwritten documents. Manual transcription is a time‐consuming task that can be sped up by using different mechanisms. A first possibility is employing state‐of‐the‐art handwritten text recognition systems to obtain an initial draft transcription that can be manually amended. A second option is employing crowdsourcing to obtain a massive but not error‐free draft transcription. In this case, when collaborators employ mobile devices, speech dictation can be used as a transcription source, and speech and handwritten text recognition can be fused to provide a better draft transcription, which can be amended with even less effort. A final option is using interactive assistive frameworks, where the automatic system that provides the draft transcription and the transcriber cooperate to generate the final transcription. The novel contributions presented in this work include the study of the data fusion on a multimodal crowdsourcing framework and its integration with an interactive system. The use of the proposed solutions reduces the required transcription effort and optimizes the overall performance and usability, allowing for a better transcription process.

Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks

The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability. Another difficulty is the presence of a large amount of Out-Of-Vocabulary (OOV) words in ancient historical texts. A solution to this problem is to use external lexical resources, but such resources might be scarce or unavailable given the nature and the age of such documents. This work proposes a solution to avoid this limitation. It consists of associating a powerful optical recognition system that will cope with image noise and variability, with a language model based on sub-lexical units that will model OOV words. Such a language modeling approach reduces the size of the lexicon while increasing the lexicon coverage. Experiments are first conducted on the publicly available Rodrigo dataset, which contains the digitization of an ancient Spanish manuscript, with a recognizer based on Hidden Markov Models (HMMs). They show that sub-lexical units outperform word units in terms of Word Error Rate (WER), Character Error Rate (CER) and OOV word accuracy rate. This approach is then applied to deep net classifiers, namely Bi-directional Long-Short Term Memory (BLSTMs) and Convolutional Recurrent Neural Nets (CRNNs). Results show that CRNNs outperform HMMs and BLSTMs, reaching the lowest WER and CER for this image dataset and significantly improving OOV recognition.