Special Issue "Selected Papers from the 2019 International Conference on Smart Sensors (ICSS)"

A special issue of Micromachines (ISSN 2072-666X).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Dr. Kin-Fong Lei
E-Mail Website
Guest Editor
Graduate Institute of Biomedical Engineering Chang Gung University, Taiwan
Tel. +886-3-2118800 ext. 5345
Interests: bio-microfluidics; bio-sensing; rapid diagnostics; cancer biology; orthopaedics
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Special Issue Information

Dear Colleagues,

The 2019 International Conference on Smart Sensors (ICSS) will be held from 3–4 June 2019 at Sheraton, Hsinchu, Taiwan. As with last year, the conference is a joint event of the 24th Symposium of Association for Chemical Sensors and the 22nd Nano Engineering and Microsystem Technology Conference.

The ICSS is a premier conference in Taiwan focusing on the promotion of advanced research and industrial collaboration. We encourage contributions of significant and original works on sensors, microfluidics and MEMS/NEMS. Manuscripts submitted to the journal Micromachines should be extended by at least 40% compared with the conference paper. The conference will cover the following main topics:

A. Chemical Sensors

A1. Transducer-Based Chemical Sensors
A2. Emerging Sensing Technologies
A3. Environmental, Energy, Agricultural, etc. Chemical Sensors

B. Biosensors

B1. Nanomaterial-Based Sensors
B2. Food, Pharmaceutical, Clinical, etc. Biosensors
B3. Semiconductor and Electric Biosensing Technologies

C. Microfluidics for Medical Applications

C1. Lab-on-a-chip Microdevices
C2. Portable and Emerging Microfluidics Technologies
C3. Medical Application Techniques Classified by Clinical Subjects

D. MEMS and NEMS Fabrication

D1. Material and Device Characterization
D2. Mechanical/Physical Sensors and Microsystems
D3. Micro- and Nanoengineering

E. Microfluidics

E1. Micro and Nano Fluidic Devices and Systems
E2. Microfluidics for Separations, Reactions, and Synthesis
E3. Fundamentals in Microfluidics and Nanofluidics

F. Applied MEMS and Applied Microfluidics

F1. Energy Harvesting/Power/RF/Optical/Acoustic MEMS
F2. BioMEMS and Integrated Microfluidic Platforms
F3. Enabling Technologies for IoT Applications

The papers attracting the most interest at the conference, or that make novel contributions, will be selected for publication in Micromachines. These papers will be peer-reviewed for validation of the research results, developments and applications.

Prof. Kin Fong Lei
Guest Editor

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. Micromachines is an international peer-reviewed open access monthly 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 1400 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
Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface
Micromachines 2019, 10(10), 681; https://doi.org/10.3390/mi10100681 - 10 Oct 2019
Abstract
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate [...] Read more.
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min). Full article
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