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Special Issue "Artificial Intelligence and Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 February 2019

Special Issue Editors

Guest Editor
Dr. Hiram Ponce

Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico
Website | E-Mail
Phone: +52 55 54821600 Ext 5254
Interests: machine learning and soft computing, nature-inspired computing, control systems, sensors and mechatronics
Guest Editor
Dr. Ma. Lourdes Martínez-Villaseñor

Faculty of Engineering, Universidad Panamericana, 03920 Mexico City, Mexico
E-Mail
Fax: +52 55 54821600 Ext 5227
Interests: ubiquitous user modeling interoperability; wearable sensors; machine learning
Guest Editor
Dr. Miguel González-Mendoza

School of Engineering and Sciences, Tecnologico de Monterrey, Campus Estado de Mexico, 52926 Mexico City, Mexico
Website | E-Mail
Phone: +52 55 58645875
Interests: machine learning and soft computing; web ontologies and designing of mobile applications; business intelligence framework; ambient intelligence framework

Special Issue Information

Dear Colleagues,

MICAI was characterized by Springer as the premier conference in artificial intelligence. It is a high-level, peer-reviewed international conference covering all areas of artificial intelligence, traditionally held in Mexico. The conference is organized by the Mexican Society for Artificial Intelligence (SMIA). The scientific program includes keynote lectures, paper presentations, tutorials, panels, and workshops.

The 17th Mexican International Conference on Artificial Intelligence will be held in Guadalajara, Mexico from October 22 to 27, 2018.

Researchers presenting selected papers related to sensors are invited to submit the extended version of their original contributions on (but not limited to) the following topics:

  • Expert Systems and Knowledge-Based Systems
  • Knowledge Representation and Management
  • Knowledge Acquisition
  • Multi-agent Systems and Distributed AI
  • Intelligent Organizations
  • Natural Language Processing
  • Ontologies
  • Intelligent Interfaces: Multimedia, Virtual Reality
  • Computer Vision and Image Processing
  • Neural Networks
  • Genetic Algorithms
  • Fuzzy Logic
  • Machine Learning
  • Pattern Recognition
  • Belief Revision
  • Qualitative Reasoning
  • Uncertainty and Probabilistic Reasoning
  • Model-Based Reasoning
  • Non-monotonic Reasoning
  • Common Sense Reasoning
  • Case-Based Reasoning
  • Spatial and Temporal Reasoning
  • Constraint Programming
  • Logic Programming
  • Automated Theorem Proving
  • Robotics
  • Planning and Scheduling
  • Hybrid Intelligent Systems
  • Bioinformatics and Medical Applications
  • Philosophical and Methodological Issues of AI
  • Intelligent Tutoring Systems
  • Data Mining
  • Applications

Dr. Hiram Ponce
Dr. Ma. Lourdes Martínez-Villaseñor
Dr. Miguel González-Mendoza
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access bimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle Performance Analysis of a Deep Simple Recurrent Unit Recurrent Neural Network (SRU-RNN) in MEMS Gyroscope De-Noising
Sensors 2018, 18(12), 4471; https://doi.org/10.3390/s18124471
Received: 27 October 2018 / Revised: 28 November 2018 / Accepted: 14 December 2018 / Published: 17 December 2018
PDF Full-text (3754 KB) | HTML Full-text | XML Full-text
Abstract
Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling
[...] Read more.
Microelectromechanical System (MEMS) Inertial Measurement Unit (IMU) is popular in the community for constructing a navigation system, due to its small size and low power consumption. However, limited by the manufacturing technology, MEMS IMU experiences more complicated noises and errors. Thus, noise modeling and suppression is important for improving accuracy of the navigation system based on MEMS IMU. Motivated by this problem, in this paper, a deep learning method was introduced to MEMS gyroscope de-noising. Specifically, a recently popular Recurrent Neural Networks (RNN) variant Simple Recurrent Unit (SRU-RNN) was employed in MEMS gyroscope raw signals de-noising. A MEMS IMU MSI3200 from MT Microsystem Company was employed in the experiments for evaluating the proposed method. Following two problems were furtherly discussed and investigated: (1) the employed SRU with different training data length were compared to explore whether there was trade-off between the training data length and prediction performance; (2) Allan Variance was the most popular MEMS gyroscope analyzing method, and five basic parameters were employed to describe the performance of different grade MEMS gyroscope; among them, quantization noise, angle random walk, and bias instability were the major factors influencing the MEMS gyroscope accuracy, the compensation results of the three parameters for gyroscope were presented and compared. The results supported the following conclusions: (1) considering the computation brought from training dataset, the values of 500, 3000, and 3000 were individually sufficient for the three-axis gyroscopes to obtain a reliable and stable prediction performance; (2) among the parameters, the quantization noise, angle random walk, and bias instability performed 0.6%, 6.8%, and 12.5% improvement for X-axis gyroscope, 60.5%, 17.3%, and 34.1% improvement for Y-axis gyroscope, 11.3%, 22.7%, and 35.7% improvement for Z-axis gyroscope, and the corresponding attitude errors decreased by 19.2%, 82.1%, and 69.4%. The results surely demonstrated the effectiveness of the employed SRU in this application. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors)
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