sensors-logo

Journal Browser

Journal Browser

Special Issue "Advances in Nanotechnology and Nano-Inspired Computing for Sensors"

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

Deadline for manuscript submissions: closed (29 March 2019).

Special Issue Editors

Dr. Hiram Ponce
E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Panamericana, 03920 Mexico City, Mexico
Tel. +52 55 54821600 Ext 5254
Interests: machine learning; nature-inspired computing; robotics; ambient assisted living; sensors.
Special Issues and Collections in MDPI journals
Dr. Pengda Hong
E-Mail
Guest Editor
Electrical and Computer Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA
Interests: imaging techniques; nano-measurement; remote sensing; image processing; image restoration; nonlinear optics; lasers; optical parametric oscillation; terahertz’s generation and applications
Special Issues and Collections in MDPI journals
Prof. Dr. Aime' Lay-Ekuakille
E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: Environmental, industrial and Biomedical instrumentation & measurements even using nanotechnology devices, Renewable energy
Special Issues and Collections in MDPI journals
Prof. Dr. Ramiro Velázquez
E-Mail Website
Guest Editor
Mechatronics and Control Systems Lab., Universidad Panamericana , MEXICO
Interests: assistive robotics; haptics and tactile displays; human–computer interaction; human perception
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to submit your scientific contributions to this Special Issue, titled “Advances in Nanotechnology and Nano-Inspired Computing for Sensors”.

This Special Issue aims to highlight advances in nanotechnology and nano-inspired computing for sensors.

The impact of technology around instrumentation and measurements is hugely increasing the impact on human daily life, as well as in nanotechnology issues. Further research and development is of critical importance to tackle not only processes in nanotechnology but also to expand their applications. Nanotechnology is not only a geometric problem, it is also a process for reaching difficult objectives with efficient possibilities compared to other ways. Manuscripts in nanotechnology area are considered for publication.

This Special Issue is also cooperating with "4th International Conference on Nanotechnology for Instrumentation and Measurement 2018 (NANOfIM 2018)". Authors of papers presented at this conference and within the scope of Sensors may submit a technically extended version to this Special Issue.

Dr. Hiram Ponce
Dr. Pengda Hong
Dr. Aimè Lay-Ekuakille
Dr. Ramiro Velázquez
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 (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm
Sensors 2019, 19(14), 3110; https://doi.org/10.3390/s19143110 - 14 Jul 2019
Abstract
Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, [...] Read more.
Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, gradient optimization method is implemented for the training process, with perhaps the large number of iterations leading to slow convergence, and not always achieving the optimal solution. Since metaheuristic optimization methods deal with searching for weight values in a broad optimization space, the training computational effort is reduced and ensures an optimal solution. This work shows an efficient training process that is a suitable solution for detection of mobile phone usage while driving. The main advantage of training ANN using the Earthquake Algorithm (EA) lies in its versatility to search in a fine or aggressive way, which extends its field of application. Additionally, a basic example of a linear classification is illustrated using the proposal-training method, so the number of applications could be expanded to nano-sensors, such as reversible logic circuit synthesis in which a genetic algorithm had been implemented. The fine search is important for the studied logic gate emulation due to the small searching areas for the linear separation, also demonstrating the convergence capabilities of the algorithm. Experimental results validate the proposed method for smart mobile phone applications that also can be applied for optimization applications. Full article
(This article belongs to the Special Issue Advances in Nanotechnology and Nano-Inspired Computing for Sensors)
Show Figures

Figure 1

Open AccessArticle
Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface
Sensors 2019, 19(8), 1923; https://doi.org/10.3390/s19081923 - 24 Apr 2019
Abstract
Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or [...] Read more.
Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or physical features of a user. In this sense, it has been sought out the development of small interfaces to avoid any type of user annoyance. In this paper, bioelectric signals have been analyzed and characterized in order to propose a more natural human-machine interaction system. The proposed scheme is controlled by electromyographic signals that a person can create through arm movements. Such arm signals have been analyzed and characterized by a back-propagation neural network, and by a wavelet analysis, in this way control commands were obtained from such arm electromyographic signals. The developed interface, uses Extensible Messaging and Presence Protocol (XMPP) to send control commands remotely. In the experiment, it manipulated a vehicle that was approximately 52 km away from the user, with which it can be showed that a characterized electromyographic signal can be sufficient for controlling embedded devices such as a Raspberri Pi, and in this way we can use the neural network and the wavelet analysis to generate control words which can be used inside the Internet of Things too. A Tiva-C board has been used to acquire data instead of more popular development boards, with an adequate response. One of the most important aspects related to the proposed interface is that it can be used by almost anyone, including people with different abilities and even illiterate people. Due to the existence of individual efforts to characterize different types of bioelectric signals, we propose the generation of free access Bioelectric Control Dictionary, to define and consult each characterized biosignal. Full article
(This article belongs to the Special Issue Advances in Nanotechnology and Nano-Inspired Computing for Sensors)
Show Figures

Figure 1

Back to TopTop