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Special Issue "Selected Papers from IEEE ICKII 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 2020).

Special Issue Editors

Prof. Dr. Teen­-Hang Meen
E-Mail Website
Guest Editor
Department of Electronic Engineering National Formosa University, Yunlin 632, Taiwan
Interests: IOT devices; photovoltaic devices; STEM education
Special Issues and Collections in MDPI journals
Prof. Dr. Wenbing Zhao
E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: human computer interaction; rehabilitation; computer vision; distributed systems
Special Issues and Collections in MDPI journals
Prof. Dr. Hsien-Wei Tseng
E-Mail Website
Guest Editor
School of Mathematics and Information Engineering, Longyan University, Longyan, Fujian, China
Interests: IoT Implementation; AI Application; Data Mining; Wireless Communication System

Special Issue Information

The 3rd IEEE International Conference on Knowledge Innovation and Invention 2020 (IEEE ICKII 2020, http://www.ickii.org) will be held in Busan, South Korea on 24–27 July 2020, and it will provide a unified communication platform for researchers on the topics of information technology, innovation design, communication science and engineering, industrial design, creative design, applied mathematics, computer science, electrical and electronic engineering, mechanical and automation engineering, green technology and architecture engineering, material science, and other related fields. The Special Issue on “Selected papers from IEEE ICKII 2020” is expected to select excellent papers presented in IEEE ICKII 2020 and other high-quality papers about the topics of sensors in science and technology. It publishes reviews (including comprehensive reviews on the complete sensors products) and regular research papers. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. The full experimental details must be provided so that the results can be reproduced. We invite investigators to contribute original research articles, as well as review articles, to this Special Issue. Potential topics include but are not limited to:

  • Electrochemical sensors/biosensors;
  • Electrical and thermal-based sensors;
  • Mass-sensitive and fiber-optic sensors;
  • Optoelectronic and photonic sensors;
  • Gas sensors;
  • Sensor applications for food industry, medicine, pharmacy, environmental monitoring, corrosion, etc.;
  • Sensor devices and sensor arrays/nanosensors;
  • Analytical methods, modeling, readout, and software for sensors;
  • Sensor technology and new sensor principles.

Prof. Dr. Teen­-Hang Meen
Prof. Dr. Wenbing Zhao
Prof. Dr. Hsien-Wei Tseng
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 2200 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

  • Electrochemical sensors/biosensors
  • Electrical and thermal-based sensors
  • Mass-sensitive and fiber-optic sensors
  • Optoelectronic and photonic sensors
  • Gas sensors

Published Papers (6 papers)

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Research

Article
An Improved Sensing Method of a Robotic Ultrasound System for Real-Time Force and Angle Calibration
Sensors 2021, 21(9), 2927; https://doi.org/10.3390/s21092927 - 22 Apr 2021
Viewed by 463
Abstract
An ultrasonic examination is a clinically universal and safe examination method, and with the development of telemedicine and precision medicine, the robotic ultrasound system (RUS) integrated with a robotic arm and ultrasound imaging system receives increasing attention. As the RUS requires precision and [...] Read more.
An ultrasonic examination is a clinically universal and safe examination method, and with the development of telemedicine and precision medicine, the robotic ultrasound system (RUS) integrated with a robotic arm and ultrasound imaging system receives increasing attention. As the RUS requires precision and reproducibility, it is important to monitor the real-time calibration of the RUS during examination, especially the angle of the probe for image detection and its force on the surface. Additionally, to speed up the integration of the RUS and the current medical ultrasound system (US), the current RUSs mostly use a self-designed fixture to connect the probe to the arm. If the fixture has inconsistencies, it may cause an operating error. In order to improve its resilience, this study proposed an improved sensing method for real-time force and angle calibration. Based on multichannel pressure sensors, an inertial measurement unit (IMU), and a novel sensing structure, the ultrasonic probe and robotic arm could be simply and rapidly combined, which rendered real-time force and angle calibration at a low cost. The experimental results show that the average success rate of the downforce position identification achieved was 88.2%. The phantom experiment indicated that the method could assist the RUS in the real-time calibration of both force and angle during an examination. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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Article
A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data
Sensors 2020, 20(23), 6990; https://doi.org/10.3390/s20236990 - 07 Dec 2020
Cited by 2 | Viewed by 942
Abstract
Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope [...] Read more.
Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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Article
Remaining Useful Life Prognosis for Turbofan Engine Using Explainable Deep Neural Networks with Dimensionality Reduction
Sensors 2020, 20(22), 6626; https://doi.org/10.3390/s20226626 - 19 Nov 2020
Viewed by 752
Abstract
This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. The proposed deep learning model affords a significantly improved accuracy by organizing networks with a one-dimensional convolutional [...] Read more.
This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. The proposed deep learning model affords a significantly improved accuracy by organizing networks with a one-dimensional convolutional neural network, long short-term memory, and bidirectional long short-term memory. In particular, this paper investigates two practical and crucial issues in applying the deep learning model for system prognosis. The first is the requirement of numerous sensors for different components, i.e., the curse of dimensionality. Second, the deep neural network cannot identify the problematic component of the turbofan engine due to its “black box” property. This study thus employs dimensionality reduction and Shapley additive explanation (SHAP) techniques. Dimensionality reduction in the model reduces the complexity and prevents overfitting, while maintaining high accuracy. SHAP analyzes and visualizes the black box to identify the sensors. The experimental results demonstrate the high accuracy and efficiency of the proposed model with dimensionality reduction and show that SHAP enhances the explainability in a conventional deep learning model for system prognosis. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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Article
Indoor Positioning System Based on Fuzzy Logic and WLAN Infrastructure
Sensors 2020, 20(16), 4490; https://doi.org/10.3390/s20164490 - 11 Aug 2020
Viewed by 602
Abstract
This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), [...] Read more.
This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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Article
A Sleep Apnea Detection System Based on a One-Dimensional Deep Convolution Neural Network Model Using Single-Lead Electrocardiogram
Sensors 2020, 20(15), 4157; https://doi.org/10.3390/s20154157 - 26 Jul 2020
Cited by 3 | Viewed by 981
Abstract
Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on [...] Read more.
Many works in recent years have been focused on developing a portable and less expensive system for diagnosing patients with obstructive sleep apnea (OSA), instead of using the inconvenient and expensive polysomnography (PSG). This study proposes a sleep apnea detection system based on a one-dimensional (1D) deep convolutional neural network (CNN) model using the single-lead 1D electrocardiogram (ECG) signals. The proposed CNN model consists of 10 identical CNN-based feature extraction layers, a flattened layer, 4 identical classification layers mainly composed of fully connected networks, and a softmax classification layer. Thirty-five released and thirty-five withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database were applied to train the proposed CNN model and validate its accuracy for the detection of the apnea events. The results show that the proposed model achieves 87.9% accuracy, 92.0% specificity, and 81.1% sensitivity for per-minute apnea detection, and 97.1% accuracy, 100% specificity, and 95.7% sensitivity for per-recording classification. The proposed model improves the accuracy of sleep apnea detection in comparison with several feature-engineering-based and feature-learning-based approaches. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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Article
A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach
Sensors 2020, 20(12), 3527; https://doi.org/10.3390/s20123527 - 22 Jun 2020
Cited by 1 | Viewed by 963
Abstract
In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and [...] Read more.
In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant. Full article
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
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