{"id":5443,"date":"2023-11-23T11:17:39","date_gmt":"2023-11-23T09:17:39","guid":{"rendered":"https:\/\/howlab-dev.i3a.es\/?p=5443"},"modified":"2023-11-23T11:17:40","modified_gmt":"2023-11-23T09:17:40","slug":"jorge-vizarraga-turmo","status":"publish","type":"post","link":"https:\/\/howlab.i3a.es\/es\/jorge-vizarraga-turmo\/","title":{"rendered":"Jorge Viz\u00e1rraga Turmo"},"content":{"rendered":"
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    Investigadores<\/a><\/h3>\n

    Jorge Viz\u00e1rraga Turmo<\/strong><\/h1>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<\/li>\n\t\t<\/ul>\t\t\t\t
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      Research Staff<\/em><\/p>\n

      Tel\u00e9fono:<\/strong> +34 876 555 457<\/p>\n

      Email:<\/strong> jvizarra@unizar.es<\/p>\n

      Address:<\/strong> Lab. 6.2.04, Edificio I+D+i, Campus R\u00edo Ebro, C\/ Mariano Esquillor s\/n 50018 (Zaragoza, Spain)<\/p>\n<\/blockquote>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n\n

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      Jorge Viz\u00e1rraga obtained his degree in Electronic and Automatic Engineering from the School of Engineering and Architecture (EINA) of the University of Zaragoza (UZ) in 2018 and obtained a Master’s Degree in Electronic Engineering from that same school in 2019.<\/p>\n

      Motivated by the development of electronic devices for IoT, industry 4.0, efficient harvesting, he develops in his final degree project a solar harvesting device for interiors and tunnels. This work takes place in Howlab, where he began his research career in September 2018, in the field of hardware development for intelligent systems.<\/p>\n

      A year later, he decided to develop algorithms for data compression for this type of intelligent sensors, defending his final master’s work developed in the same group. Currently with a published paper of this work.<\/p>\n

      At present in Howlab working with smart sensors applying different communication strategies, data processing but always seeking to maximize autonomy and performance.\u00a0Expanding his knowledge in PCB development, microcontroller programming, 3D enclosures and data analytics.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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      2020<\/h3>

      Viz\u00e1rraga, Jorge; Casas, Roberto; Marco, \u00c1lvaro; Buldain, David J<\/p>

      Dimensionality Reduction for Smart IoT Sensors<\/a> Art\u00edculo de revista<\/span> <\/p>

      En: <\/span>Electronics, <\/span>vol. 9, <\/span>no 12, <\/span>2020<\/span>, ISSN: 2079-9292<\/span>.<\/p>

      Resumen<\/a><\/span> | Enlaces<\/a><\/span> | BibTeX<\/a><\/span><\/p>

      @article{electronics9122035,
      \r\ntitle = {Dimensionality Reduction for Smart IoT Sensors},
      \r\nauthor = {Jorge Viz\u00e1rraga and Roberto Casas and \u00c1lvaro Marco and David J Buldain},
      \r\nurl = {https:\/\/www.mdpi.com\/2079-9292\/9\/12\/2035},
      \r\ndoi = {10.3390\/electronics9122035},
      \r\nissn = {2079-9292},
      \r\nyear  = {2020},
      \r\ndate = {2020-01-01},
      \r\njournal = {Electronics},
      \r\nvolume = {9},
      \r\nnumber = {12},
      \r\nabstract = {Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook’s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.},
      \r\nkeywords = {},
      \r\npubstate = {published},
      \r\ntppubtype = {article}
      \r\n}
      \r\n<\/pre><\/div>
      Cerrar<\/a><\/p><\/div>
      Smart IoT sensors are characterized by their ability to sense and process signals, producing high-level information that is usually sent wirelessly while minimising energy consumption and maximising communication efficiency. Systems are getting smarter, meaning that they are providing ever richer information from the same raw data. This increasing intelligence can occur at various levels, including in the sensor itself, at the edge, and in the cloud. As sending one byte of data is several orders of magnitude more energy-expensive than processing it, data must be handled as near as possible to its generation. Thus, the intelligence should be located in the sensor; nevertheless, it is not always possible to do so because real data is not always available for designing the algorithms or the hardware capacity is limited. Smart devices detecting data coming from inertial sensors are a good example of this. They generate hundreds of bytes per second (100 Hz, 12-bit sampling of a triaxial accelerometer) but useful information comes out in just a few bytes per minute (number of steps, type of activity, and so forth). We propose a lossy compression method to reduce the dimensionality of raw data from accelerometers, gyroscopes, and magnetometers, while maintaining a high quality of information in the reconstructed signal coming from an embedded device. The implemented method uses an adaptive vector-quantisation algorithm that represents the input data with a limited set of codewords. The adaptive process generates a codebook that evolves to become highly specific for the input data, while providing high compression rates. The codebook&rsquo;s reconstruction quality is measured with a peak signal-to-noise ratio (PSNR) above 40 dB for a 12-bit representation.<\/div>
      Cerrar<\/a><\/p><\/div>