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Special Issue "Electronic Materials and Sensors Innovation and Application"

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

Deadline for manuscript submissions: 30 November 2022 | Viewed by 6769

Special Issue Editors

Prof. Dr. Sheng-Joue Young
E-Mail Website
Guest Editor
Department of Electronic Engineering, National United University, Miaoli City 36063, Taiwan
Interests: semiconductor physics; optoelectronic devices; 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
Dr. Yi-Hsing Liu
E-Mail Website
Guest Editor
Institute of Microelectronics, Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
Interests: semiconductor physics; optoelectronic devices; 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
Dr. Zi-Hao Wang
E-Mail Website
Guest Editor
Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
Interests: microsystem design; nanotechnology

Special Issue Information

Dear Colleagues,

The 7th IEEE International Conference on Applied System Innovation 2021 (IEEE ICASI 2021, https://2021.icasi-conf.net/) will be held in Alishan, Chiayi, Taiwan on 24–25 September 2021 and will provide a unified communication platform for a wide range of topics. In recent years, application of advanced materials in microelectronic and optical sensors has been a highly developing field. Due to the flexibility and light weight for daily use, it has the potential to be deployable. The scopes of iTIKI IEEE ICASI 2021 not only encompass material sizes at the nanoscale, but also various dimensions where the onset of size-dependent phenomena usually enables novel applications.

This Special Issue selects excellent papers from iTIKI IEEE ICASI 2021 and covers fundamental materials of electrical and optical engineering, 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 investigators to contribute original research articles, as well as review articles, that will stimulate continuing efforts to understand microelectronic and optical sensors. Potential topics include but are not limited to:

  • Advanced materials with new electronic and optical properties;
  • Advanced materials for preparation and applications;
  • 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. Liang-Wen Ji
Dr. Yi-Hsing Liu
Dr. Zi-Hao Wang
Dr. Stephen D. Prior
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 (11 papers)

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Research

Article
Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain–Computer Interfaces
Sensors 2022, 22(21), 8303; https://doi.org/10.3390/s22218303 - 29 Oct 2022
Viewed by 344
Abstract
Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate [...] Read more.
Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain–computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
Sensors 2022, 22(21), 8117; https://doi.org/10.3390/s22218117 - 23 Oct 2022
Viewed by 252
Abstract
This paper focuses on the hardware components of smartphones, namely, the use of displays and cameras in mobile devices as transmitters and receivers to establish a near-field multiple-input–multiple-output (MIMO) visible light communication (VLC) system. Based on the relationship between the grayscale values of [...] Read more.
This paper focuses on the hardware components of smartphones, namely, the use of displays and cameras in mobile devices as transmitters and receivers to establish a near-field multiple-input–multiple-output (MIMO) visible light communication (VLC) system. Based on the relationship between the grayscale values of transmitted and received signals, the physical channel responses are detected and approximated with a high-order regression to obtain the channel gain. With the constraint of bit numbers in the MIMO VLC system, an integer-type water-filling scheme was designed for bit allocation to improve transmission efficiency. The physical examinations show that bit error rate (BER) reduction can be 26.4% with Gaussian noise of 30 dB and detected channel gain compared with the equal bit allocation. The optimization of the simulation was confirmed with the bit assignments in real cases. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light
Sensors 2022, 22(20), 7774; https://doi.org/10.3390/s22207774 - 13 Oct 2022
Viewed by 300
Abstract
This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking [...] Read more.
This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Design and Testing of Real-Time Sensing System Used in Predicting the Leakage of Subsea Pipeline
Sensors 2022, 22(18), 6846; https://doi.org/10.3390/s22186846 - 09 Sep 2022
Viewed by 437
Abstract
This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of [...] Read more.
This study integrates the array sensing module and the flow leakage algorithm. In this study, a real-time monitoring deep-sea pipeline damage sensing system is designed to provide decision-making parameters such as damage coordinates and damage area. The array sensor module is composed of multiple YF-S201 hall sensors and controllers. YF-S201 hall sensors are arranged inside the pipeline in an array. The flow signal in the deep-sea pipeline can be transmitted to the electronic control interface to analyze the leakage position and leakage flowrate of the pipeline. The theory of this system is based on the conservation of mass. Through the flow of each sensor, it is judged whether the pipeline is damaged. When the pipeline is not damaged, the flowrate of each sensor is almost the same. When the pipeline is damaged, the flowrate will drop significantly. When the actual size of leakage in the pipeline is 5.28 cm2, the size calculated by the flowrate of hall sensors is 2.58 cm2 in average, indicating the error between experimental data and theoretical data is 46%. When the actual size of leakage in the pipeline is 1.98 cm2, the size calculated by the flowrate of hall sensors is 1.31 cm2 in average, indicating the error between experimental data and theoretical data is 21%. This can accurately confirm the location of the broken pipeline, which is between sensor A and sensor B, so that the AUV/ROV can accurately locate and perform pipeline maintenance in real time. It is expected to be able to monitor the flowrate through the array magnetic sensing module designed in this study. It can grasp the status of deep-sea pipelines, improve the quality of deep-sea extraction and pipeline maintenance speed. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
A Self-Powered Strain Sensor Applied to Real-Time Monitoring for Movable Structures
Sensors 2022, 22(16), 6084; https://doi.org/10.3390/s22166084 - 15 Aug 2022
Cited by 1 | Viewed by 396
Abstract
This study uses near-field electrospinning (NFES) technology to make a novel self-powered strain sensor and applies it to the real-time monitoring of a bending structure, so that the measurement equipment can be reduced in volume. A self-powered strain sensor consists of polyvinylidene difluoride [...] Read more.
This study uses near-field electrospinning (NFES) technology to make a novel self-powered strain sensor and applies it to the real-time monitoring of a bending structure, so that the measurement equipment can be reduced in volume. A self-powered strain sensor consists of polyvinylidene difluoride (PVDF) fibers, a PDMS fixed substrate, and an aluminum electrode. PVDF fibers are spun with DMSO and acetone using NFES technology, with a diameter of about 8 μm, Young’s modulus of 1.1 GPa, and piezoelectric effect of up to 230 mV. The fixed substrate is a film made of PDMS by thermal curing, then adhered to the PDMS film surface of the sheet Al metal as an Al electrode, and then combined with PVDF fiber film, to become a self-powered strain sensor. As a result, the XRD β value of the self-powered strain sensor reaches 2112 and the sensitivity is increased by 20% over a traditional strain sensor. The cumulative angle algorithm can be applied to measure the angular change of the object over a unit of time or the cumulative displacement of the object over the entire period of motion. The experimental results demonstrate that the self-powered strain sensor combined with the angle accumulation algorithm may be applied to monitor the bending structure, thereby achieving continuous measurements of bending structure changes, and improving on traditional piezoelectric sensors, which can only be sensed once. In the future, self-powered strain sensors will have the ability to continuously measure in real-time, enabling the use of piezoelectric sensors for long-term monitoring of structural techniques. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Applying Image Recognition and Tracking Methods for Fish Physiology Detection Based on a Visual Sensor
Sensors 2022, 22(15), 5545; https://doi.org/10.3390/s22155545 - 25 Jul 2022
Viewed by 432
Abstract
The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet [...] Read more.
The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish’s physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Sonar Dome Geometry Design Using CFD to Reduce Ship Resistance at Cruise Speed
Sensors 2022, 22(14), 5342; https://doi.org/10.3390/s22145342 - 18 Jul 2022
Viewed by 407
Abstract
The objective of this study is to design the hull-mounted sonar dome of a ship. The goal is to reduce the ship total resistance and improve the flow field around the sonar dome for the ship design speed. OpenFOAM 6 was applied to [...] Read more.
The objective of this study is to design the hull-mounted sonar dome of a ship. The goal is to reduce the ship total resistance and improve the flow field around the sonar dome for the ship design speed. OpenFOAM 6 was applied to analyze the viscous flow around the ship bow and then optimize the sonar dome geometry. The length, width and depth of the original geometry were maintained. Only the local geometry was fine-tuned considering the back slope and front tip by using Rhinoceros 6. The verification and validation was performed for the original hull form against towing tank resistance data. The grid independence was checked for the optimal design in different design stages. To ensure less influence on the interior equipment installation and to be able to re-use the non-steel dome part, the best resistance reduction is almost 2%. With a larger allowance of shape deformation, the maximal reduction could reach slightly higher than 3%. The flow field is improved for smaller flow separation and vortex, and less fluid nose in sonar detection is expected. The main reason of the resistance reduction is the decrease of the pressure component. In conclusion, a sonar dome design procedure is proposed, and an optimal geometry is suggested. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring
Sensors 2022, 22(12), 4452; https://doi.org/10.3390/s22124452 - 12 Jun 2022
Viewed by 529
Abstract
This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted [...] Read more.
This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
PSPS: A Step toward Tamper Resistance against Physical Computer Intrusion
Sensors 2022, 22(5), 1882; https://doi.org/10.3390/s22051882 - 28 Feb 2022
Viewed by 891
Abstract
Cyberattacks are increasing in both number and severity for private, corporate, and governmental bodies. To prevent these attacks, many intrusion detection systems and intrusion prevention systems provide computer security by monitoring network packets and auditing system records. However, most of these systems only [...] Read more.
Cyberattacks are increasing in both number and severity for private, corporate, and governmental bodies. To prevent these attacks, many intrusion detection systems and intrusion prevention systems provide computer security by monitoring network packets and auditing system records. However, most of these systems only monitor network packets rather than the computer itself, so physical intrusion is also an important security issue. Furthermore, with the rapid progress of the Internet of Things (IoT) technology, security problems of IoT devices are also increasing. Many IoT devices can be disassembled for decompilation, resulting in the theft of sensitive data. To prevent this, physical intrusion detection systems of the IoT should be considered. We here propose a physical security system that can protect data from unauthorized access when the computer chassis is opened or tampered with. Sensor switches monitor the chassis status at all times and upload event logs to a cloud server for remote monitoring. If the system finds that the computer has an abnormal condition, it takes protective measures and notifies the administrator. This system can be extended to IoT devices to protect their data from theft. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition
Sensors 2022, 22(3), 803; https://doi.org/10.3390/s22030803 - 21 Jan 2022
Cited by 1 | Viewed by 784
Abstract
Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red–Green–Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has [...] Read more.
Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red–Green–Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has been further used to perform human gesture recognition on surveillance. Gesture recognition, including hand gesture intention recognition, is attracting great attention in the field of deep neural network (DNN) calculations. For further enhancing conventional CCD RGB-IR gesture recognition by DNN, this work proposes a deep learning framework for gesture recognition where a convolution neural network (CNN) incorporated with wavelet image fusion of CCD RGB-IR and additional depth-based depth-grayscale images (captured from depth sensors of the famous Microsoft Kinect device) is constructed for gesture intention recognition. In the proposed CNN with wavelet image fusion, a five-level discrete wavelet transformation (DWT) with three different wavelet decomposition merge strategies, namely, max-min, min-max and mean-mean, is employed; the visual geometry group (VGG)-16 CNN is used for deep learning and recognition of the wavelet fused gesture images. Experiments on the classifications of ten hand gesture intention actions (specified in a scenario of laboratory interactions) show that by additionally incorporating depth-grayscale data into CCD RGB-IR gesture recognition one will be able to further increase the averaged recognition accuracy to 83.88% for the VGG-16 CNN with min-max wavelet image fusion of the CCD RGB-IR and depth-grayscale data, which is obviously superior to the 75.33% of VGG-16 CNN with only CCD RGB-IR. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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Article
Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition
Sensors 2021, 21(24), 8294; https://doi.org/10.3390/s21248294 - 11 Dec 2021
Cited by 2 | Viewed by 930
Abstract
This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board [...] Read more.
This paper presents a wearable device, fitted on the waist of a participant that recognizes six activities of daily living (walking, walking upstairs, walking downstairs, sitting, standing, and laying) through a deep-learning algorithm, human activity recognition (HAR). The wearable device comprises a single-board computer (SBC) and six-axis sensors. The deep-learning algorithm employs three parallel convolutional neural networks for local feature extraction and for subsequent concatenation to establish feature fusion models of varying kernel size. By using kernels of different sizes, relevant local features of varying lengths were identified, thereby increasing the accuracy of human activity recognition. Regarding experimental data, the database of University of California, Irvine (UCI) and self-recorded data were used separately. The self-recorded data were obtained by having 21 participants wear the device on their waist and perform six common activities in the laboratory. These data were used to verify the proposed deep-learning algorithm on the performance of the wearable device. The accuracy of these six activities in the UCI dataset and in the self-recorded data were 97.49% and 96.27%, respectively. The accuracies in tenfold cross-validation were 99.56% and 97.46%, respectively. The experimental results have successfully verified the proposed convolutional neural network (CNN) architecture, which can be used in rehabilitation assessment for people unable to exercise vigorously. Full article
(This article belongs to the Special Issue Electronic Materials and Sensors Innovation and Application)
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