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Special Issue "Sensors and Embedded Systems in the Internet of Things"

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

Deadline for manuscript submissions: closed (15 December 2020).

Special Issue Editors

Prof. Dr. Olivier Romain
E-Mail Website
Guest Editor
Cergy Paris University, France
Interests: smart embedded systems
Dr. Julien Le Kernec
E-Mail Website
Guest Editor
Glasgow University, UK
Prof. Dr. Qian He
E-Mail Website
Guest Editor
EE Department, University of Electronic Science and Technology of China, Sichuan 611731, China
Prof. Dr. Cheng Hu
E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing, China
Interests: wireless communication systems; IoT; wearable applications; blockchain; resource management; Device-to-Device (D2D) communication
Dr. Hong Hong
E-Mail Website
Guest Editor
Nanjing University of Science and Technology, Nanjing, China

Special Issue Information

Dear Colleagues,

Embedded Systems have been instrumental for the Internet of Things (IoT) and in our lives in the 21st century. Today, the world has been colonized by more than fifty billion IoT devices: sensors, smart meters, ultra-low power communication neyworks, and connectivity to cloud, fog, and edge computing. While researchers are currently investigating these challenging systems to increase our quality of life, some questions remain on the design of adaptable and low-footprint functions for the future generation of IoT devices. This Special Issue deals with the general topic of smart methods, circuits, and systems dedicated to adapting the embedded architecture of the device in a constrained (harsh) environment with limited onboard resources.      

MDPI Sensors solicits paper submissions and aims to bring together researchers and application developers working on the intersection of IoT with next-generation sensor development, low-footprint circuits and systems, real-time, edge computing, secure and privacy-preserving computing, and adaptive hardware. We also aim to explore the application of novel IoT computing results and describe and assess their impact. The peer-reviewed articles will showcase potentially high-impact research topics, directions, and/or position papers. The objective of this issue is to identify topics that are likely to result in a noteworthy impact on society in the next 25 to 50 years.

Topics of interest include but are not limited to:

Research topics include (but are not limited to):

  • Design and development of low-footprint smart embedded systems;
  • Design of non-conventional smart embedded systems;
  • New paradigm and systems for ULP communication;
  • Ultra-low power circuits and systems for IoT;
  • Reconfigurable smart embedded systems;
  • Hardware embedded security and privacy function for IoT;
  • Smart embedded systems for edge, fog and cloud computing;
  • Wearable or mobile smart embedded systems;
  • Gait performance monitoring.

Applications include (but are not limited to):

  • Smart embedded IoT system for health applications;
  • Smart embedded IoT system for forensic applications;
  • Smart embedded IoT system for environmental applications;
  • Smart embedded IoT system for autonomous vehicles—IoV;
  • Smart embedded IoT system for robotics.

Prof. Dr. Olivier Romain
Dr. Julien Le Kernec
Prof. Dr. Qian He
Prof. Dr. Muhammad Ali Imran
Prof. Dr. Cheng Hu
Dr. Hong Hong
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 semimonthly 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 2200 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

Communication
End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification
Sensors 2021, 21(3), 891; https://doi.org/10.3390/s21030891 - 28 Jan 2021
Cited by 3 | Viewed by 709
Abstract
This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers [...] Read more.
This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach. Full article
(This article belongs to the Special Issue Sensors and Embedded Systems in the Internet of Things)
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