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Special Issue "Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020"

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 6762

Special Issue Editors

Prof. Dr. Sheng-Joue Young
E-Mail Website
Guest Editor
Department of Electronic Engineering, National United University (NUU), Miaoli, Taiwan
Interests: semiconductor physics; optoelectronic devices; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shoou-Jinn Chang
E-Mail Website
Guest Editor
Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Interests: optical and electronic devices; semi-conductive materials; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Dr. Stephen D. Prior
E-Mail Website
Guest Editor
Aeronautics, Astronautics and Computational Engineering, University of Southampton, Southampton SO16 7QF, UK
Interests: microsystem design; nanotechnology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang-Wen Ji
E-Mail Website
Guest Editor
Department of Electro-Optical Engineering, National Formosa University, Yunlin, Taiwan
Interests: nano-optoelectronics; photo detector, nano-materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2019 International Conference on Innovation, Communication, and Engineering (TIKI ICICE 2019, http://2019.icice.net/) will be held in Zhengzhou, Henan Province, China, on 25–30 October 2019. The 6th IEEE International Conference on Applied Systems Innovation 2020 (IEEE ICASI 2020, http://2020.icasi-conf.net/) will be held in Tokyo, Japan, on 13–17 May 2020. These two conferences will provide a unified communication platform for a wide range of topics. In recent years, the application of advanced materials in microelectronic and optical sensors has been a rapidly developing field. Due to their flexibility and light weight for daily use, such materials have the potential to be very useful on a practical level. The scopes of TIKI IEEE ICICE 2019 and ICASI 2020 not only encompass material sizes at the nanoscale but also various dimensions where the potential for size-dependent phenomena usually enables novel applications.

This Special Issue will compile excellent papers from TIKI IEEE ICICE 2019 and ICASI 2020 and cover fundamental topics on the engineering of advanced materials in microelectronic and optical sensors, including their synthesis, engineering, integration with many elements, designing of electrical or optical devices, evaluation of various performances, and exploring their broad applications, such as in industry, environmental control, material analysis, etc. We invite authors to contribute original research articles as well as review articles, which will stimulate continuing efforts to understand microelectronic and optical sensors. Potential topics include, but are not limited to, the following:

  • Advanced materials with new electronic and optical properties;
  • Advanced materials for preparation and application;
  • Subjects related to electro-optical thin films and coatings;
  • Synthesis engineering in advanced materials;
  • Properties of microelectronic and optical sensors.

Prof. Dr. Sheng-Joue Young
Prof. Dr. Shoou-Jinn Chang
Dr. Stephen D. Prior
Prof. Dr. Liang-Wen Ji
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 submissions that pass pre-check are 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 2400 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.

Keywords

  • advanced materials
  • microelectronic devices
  • optical sensors

Published Papers (5 papers)

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Research

Communication
Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip
Sensors 2021, 21(11), 3917; https://doi.org/10.3390/s21113917 - 06 Jun 2021
Cited by 3 | Viewed by 1307
Abstract
Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and [...] Read more.
Most existing commercial real-time polymerase chain reaction (RT-PCR) instruments are bulky because they contain expensive fluorescent detection sensors or complex optical structures. In this paper, we propose an RT-PCR system using a camera module for smartphones that is an ultra small, high-performance and low-cost sensor for fluorescence detection. The proposed system provides stable DNA amplification. A quantitative analysis of fluorescence intensity changes shows the camera’s performance compared with that of commercial instruments. Changes in the performance between the experiments and the sets were also observed based on the threshold cycle values in a commercial RT-PCR system. The overall difference in the measured threshold cycles between the commercial system and the proposed camera was only 0.76 cycles, verifying the performance of the proposed system. The set calibration even reduced the difference to 0.41 cycles, which was less than the experimental variation in the commercial system, and there was no difference in performance. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020)
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Article
Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition
Sensors 2021, 21(7), 2520; https://doi.org/10.3390/s21072520 - 04 Apr 2021
Viewed by 1226
Abstract
Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. [...] Read more.
Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020)
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Article
Detection and Classification of Stroke Gaits by Deep Neural Networks Employing Inertial Measurement Units
Sensors 2021, 21(5), 1864; https://doi.org/10.3390/s21051864 - 07 Mar 2021
Cited by 7 | Viewed by 1288
Abstract
This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make [...] Read more.
This paper develops Deep Neural Network (DNN) models that can recognize stroke gaits. Stroke patients usually suffer from partial disability and develop abnormal gaits that can vary widely and need targeted treatments. Evaluation of gait patterns is crucial for clinical experts to make decisions about the medication and rehabilitation strategies for the stroke patients. However, the evaluation is often subjective, and different clinicians might have different diagnoses of stroke gait patterns. In addition, some patients may present with mixed neurological gaits. Therefore, we apply artificial intelligence techniques to detect stroke gaits and to classify abnormal gait patterns. First, we collect clinical gait data from eight stroke patients and seven healthy subjects. We then apply these data to develop DNN models that can detect stroke gaits. Finally, we classify four common gait abnormalities seen in stroke patients. The developed models achieve an average accuracy of 99.35% in detecting the stroke gaits and an average accuracy of 97.31% in classifying the gait abnormality. Based on the results, the developed DNN models could help therapists or physicians to diagnose different abnormal gaits and to apply suitable rehabilitation strategies for stroke patients. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020)
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Article
High-Availability Computing Platform with Sensor Fault Resilience
Sensors 2021, 21(2), 542; https://doi.org/10.3390/s21020542 - 13 Jan 2021
Cited by 3 | Viewed by 915
Abstract
Modern computing platforms usually use multiple sensors to report system information. In order to achieve high availability (HA) for the platform, the sensors can be used to efficiently detect system faults that make a cloud service not live. However, a sensor may fail [...] Read more.
Modern computing platforms usually use multiple sensors to report system information. In order to achieve high availability (HA) for the platform, the sensors can be used to efficiently detect system faults that make a cloud service not live. However, a sensor may fail and disable HA protection. In this case, human intervention is needed, either to change the original fault model or to fix the sensor fault. Therefore, this study proposes an HA mechanism that can continuously provide HA to a cloud system based on dynamic fault model reconstruction. We have implemented the proposed HA mechanism on a four-layer OpenStack cloud system and tested the performance of the proposed mechanism for all possible sets of sensor faults. For each fault model, we inject possible system faults and measure the average fault detection time. The experimental result shows that the proposed mechanism can accurately detect and recover an injected system fault with disabled sensors. In addition, the system fault detection time increases as the number of sensor faults increases, until the HA mechanism is degraded to a one-system-fault model, which is the worst case as the system layer heartbeating. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020)
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Article
Fabrication of Ultraviolet Photodetectors Based on Fe-Doped ZnO Nanorod Structures
Sensors 2020, 20(14), 3861; https://doi.org/10.3390/s20143861 - 10 Jul 2020
Cited by 20 | Viewed by 1201
Abstract
In this paper, 100 nm-thick zinc oxide (ZnO) films were deposited as a seed layer on Corning glass substrates via a radio frequency (RF) magnetron sputtering technique, and vertical well-aligned Fe-doped ZnO (FZO) nanorod (NR) arrays were then grown on the seed layer-coated [...] Read more.
In this paper, 100 nm-thick zinc oxide (ZnO) films were deposited as a seed layer on Corning glass substrates via a radio frequency (RF) magnetron sputtering technique, and vertical well-aligned Fe-doped ZnO (FZO) nanorod (NR) arrays were then grown on the seed layer-coated substrates via a low-temperature solution method. FZO NR arrays were annealed at 600 °C and characterized by using field emission scanning microscopy (FE-SEM) and X-ray diffraction spectrum (XRD) analysis. FZO NRs grew along the preferred (002) orientation with good crystal quality and hexagonal wurtzite structure. The main ultraviolet (UV) peak of 378 nm exhibited a red-shifted phenomenon with Fe-doping by photoluminescence (PL) emission. Furthermore, FZO photodetectors (PDs) based on metal–semiconductor–metal (MSM) structure were successfully manufactured through a photolithography procedure for UV detection. Results revealed that compared with pure ZnO NRs, FZO NRs exhibited a remarkable photosensitivity for UV PD applications and a fast rise/decay time. The sensitivities of prepared pure ZnO and FZO PDs were 43.1, and 471.1 for a 3 V applied bias and 380 nm UV illumination, respectively. Full article
(This article belongs to the Special Issue Selected Papers from TIKI IEEE ICICE 2019& ICASI 2020)
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