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Special Issue "Pervasive Intelligence – Intelligence Everywhere"

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

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

Special Issue Editors

Prof. Dr. Ahmet Kondoz
E-Mail Website
Guest Editor
Institute for Digital Technologies Loughborough University London, London, UK
Interests: Multimodal signal processing and communication; intelligent mobility, artificial intelligence; cyber security
Dr. Yogachandran Rahulamathavan
E-Mail Website
Guest Editor
Institute for Digital Technologies Loughborough University London, London, UK
Interests: Privacy-preserving techniques, applied cryptography, homomorphic machine learning, cybersecurity
Dr. Gholamreza Anbarjafari
E-Mail Website
Guest Editor
Institute for Digital Technologies Loughborough University London, London, UK
Interests: human behaviour analysis; affective computing; computer vision; machine learning; emotion recognition; human-robot interaction; biometric
Dr. Varuna De Silva
E-Mail Website
Guest Editor
Institute for Digital Technologies Loughborough University London, London, UK
Interests: Data driven policy learning, Multi-agent Systems, Multimodal machine learning, Intelligent mobility systems
Dr. Erhan Ekmekcioglu
E-Mail Website
Guest Editor
Institute for Digital Technologies, Loughborough University London, London E15 2GZ, UK
Interests: video processing; streaming; Quality of Experience; affective computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the deployment of the most advanced digital technologies with sensing, processing, actuating and communicating functions, many applications are moving towards being fully automated. Advancements in fast computing to provide support in the timely processing of sensed information, which was rather difficult only a decade ago,  have become an important factor for the optimisation and learning processes of sensed information. Finally, decision-making under various conditions and circumstances which can be rather complex due to uncertainty, takes place. The major advancements in the creation of intelligent systems consist of three main pillars: advanced sensing and gathering of the necessary, usually very high volume of data, followed by its fast and timely processing by way of advanced machine learning techniques, and decision-making with timely predictions of future events based on trained and optimised highly-accurate systems models. Sensing is not always performed by visible sensors such as cameras, microphones, etc., but also through software embeded within larger systems by way of monitoring data flow, enabling intelligence virtually everywhere where there is build up, collection or even flow of data.

The availability of large data sets and the evolution of deep neural network architectures powered by highly-parallelised computing architectures have enabled researchers and engineers to model real-world systems with unprecedented accuracy. Such data-driven system models or decision-making algorithms have surpassed state-of-the-art performance in many signal-processing domains, thus enabling increases in novel infrastructures where intelligence is embedded, such as intelligent mobility, assistive robotics, secure cyber–physical systems, and healthcare. In the coming decades, we envision a future in which seamless pervasive machine intelligence enriches and empowers human civilizations. However, for machine intelligence to be pervasive, many challenges need to be addressed, such as the transparency of deep learning models, embeded security, the confidentiality of the model parameters, the privacy of the intelligent infrastructure and the data used during inference, affective human–machine interactions, and many more application-level implementation challenges. Therefore, this Special Issue covers topics aligned with intelligence at all levels—Intelligence Everywhere.

Topics include but are not limited to:

  • Sensing technologies and methods
  • Ubiquitous data mining techniques
  • Data modelling techniques
  • Artificial Intelligence systems and applications
  • Deep learning techniques in pervasive intelligence
  • Human-understandable and explainable Artificial Intelligence
  • Security and privacy protection in pervasive intelligence
  • Confidentiality of the intelligent infrastructure parameters
  • Privacy preserving inference
  • Security of the AI systems
  • Multi-agent AI systems
  • Cognitive computing
  • Human–AI interaction
  • Human behaviour and activity analysis 
  • Use of 5G technology in pervasive intelligence applications
  • Intelligent mobility applications
  • Applications of IoTs and AI to: climate control, environment monitoring and preservation, and boarder control.

Prof. Dr. Ahmet Kondoz
Dr. Gholamreza Anbarjafari
Dr. Varuna De Silva
Dr. Erhan Ekmekcioglu
Dr. Yogachandran Rahulamathavan
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 2000 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
Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses
Sensors 2020, 20(4), 1031; https://doi.org/10.3390/s20041031 - 14 Feb 2020
Abstract
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, [...] Read more.
Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in real-world environment. The motion artifact suppression algorithms presented in this article, prevent the activation of the prosthesis drive during artifacts, thereby achieving a substantial performance boost. These algorithms classify the signal into muscle contractions and artifacts. Therefore, new time domain features, such as Mean Crossing Rate are introduced and well-established time domain features (e.g., Zero-Crossing Rate, Slope Sign Change) are modified and implemented. Various artificial intelligence models, which require low calculation resources for an application in a wearable device, were investigated. These models are neural networks, recurrent neural networks, decision trees and logistic regressions. Although these models are designed for a low-power real-time embedded system, high accuracies in discriminating artifacts to contractions of up to 99.9% are achieved. The models were implemented and trained for fast response leading to a high performance in real-world environment. For highest accuracies, recurrent neural networks are suggested and for minimum runtime ( 0.99–1.15 μ s), decision trees are preferred. Full article
(This article belongs to the Special Issue Pervasive Intelligence – Intelligence Everywhere)
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