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Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW)

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 28164

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Special Issue Editors

Prof. Dr. Ming-Lin Chuang

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Guest Editor

Department of Communication Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
Interests: Internet of Things (IoT); wireless power transfer; RF circuit and antenna

Prof. Dr. Wen-Huang Cheng

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Guest Editor

Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Interests: multimedia signal processing; artificial intelligence and deep learning for consumer technology

Dr. Ching-Chun Huang

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Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Interests: computer vision and multimedia system; intelligent control system; machine learning for signal processing; human–machine interface
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Special Issue Information

Dear Colleagues,

The IEEE International Conference on Consumer Electronics—Taiwan (IEEE 2021 ICCE-TW) will be held on June 16–18 in Penghu, Taiwan. Over the years, the ICCE-TW conference has continuously provided a platform for experts, scholars, and researchers from all over the world to convene and share novel ideas on Consumer Electronics. Authors of selected papers are invited to submit the extended versions (at least 50% extension for the submissions) of their original papers and contributions regarding the following topics:

Biomedical sensors and actuators;
Consumer electronics devices for sensors and actuators;
Healthcare sensors and actuators;
Internet of things (IoT) and artificial intelligence (AI) techniques;
Mobile ad-hoc and wireless sensor networks;
Sensor applications on autonomous cars;
Sensor applications on computer communication techniques;
Sensor applications on multimedia techniques.

Prof. Dr. Ming-Lin Chuang
Prof. Dr. Wen-Huang Cheng
Dr. Ching-Chun Huang
Guest Editors

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Keywords

Artificial intelligence (AI)
Autonomous car
Biomedical sensors
Computer communication
Healthcare
Internet of things (IoT)
Multimedia techniques
Sensor networks

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Published Papers (7 papers)

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Research

14 pages, 3879 KiB

Open AccessArticle

SRAM-Based CIM Architecture Design for Event Detection

by Muhammad Bintang Gemintang Sulaiman, Jin-Yu Lin, Jian-Bai Li, Cheng-Ming Shih, Kai-Cheung Juang and Chih-Cheng Lu

Sensors 2022, 22(20), 7854; https://doi.org/10.3390/s22207854 - 16 Oct 2022

Viewed by 2845

Abstract

Convolutional neural networks (CNNs) play a key role in deep learning applications. However, the high computational complexity and high-energy consumption of CNNs trammel their application in hardware accelerators. Computing-in-memory (CIM) is the technique of running calculations entirely in memory (in our design, we use SRAM). CIM architecture has demonstrated great potential to effectively compute large-scale matrix-vector multiplication. CIM-based architecture for event detection is designed to trigger the next stage of precision inference. To implement an SRAM-based CIM accelerator, a software and hardware co-design approach must consider the CIM macro’s hardware limitations to map the weight onto the AI edge devices. In this paper, we designed a hierarchical AI architecture to optimize the end-to-end system power in the AIoT application. In the experiment, the CIM-aware algorithm with 4-bit activation and 8-bit weight is examined on hand gesture and CIFAR-10 datasets, and determined to have 99.70% and 70.58% accuracy, respectively. A profiling tool to analyze the proposed design is also developed to measure how efficient our architecture design is. The proposed design system utilizes the operating frequency of 100 MHz, hand gesture and CIFAR-10 as the datasets, and nine CNNs and one FC layer as its network, resulting in a frame rate of 662 FPS, 37.6% processing unit utilization, and a power consumption of 0.853 mW. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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14 pages, 6395 KiB

Open AccessArticle

An Efficient IAKF Approach for Indoor Positioning Drift Correction

by Shang-Hsien Lin, Hung-Hsien Chang Chien, Wei-Wen Wang, Kuang-Hao Lin and Guan-Jin Li

Sensors 2022, 22(15), 5697; https://doi.org/10.3390/s22155697 - 29 Jul 2022

Cited by 3 | Viewed by 1637

Abstract

In this study, an indoor positioning shift correction architecture was developed with an improved adaptive Kalman filter (IAKF) algorithm for the people interference condition. Indoor positioning systems (IPSs) use ultra-wideband (UWB) communication technology. Triangulation positioning algorithms are generally employed for determining the position of a target. However, environmental communication factors and different network topologies produce localization drift errors in IPSs. Therefore, the drift error of real-time positioning points under various environmental factors and the correction of the localization drift error are discussed. For localization drift error, four algorithms were simulated and analyzed: movement average (MA), least square (LS), Kalman filter (KF), and IAKF. Finally, the IAKF algorithm was implemented and verified on the UWB indoor positioning system. The measurement results showed that the drift errors improved by 60% and 74.15% in environments with and without surrounding crowds, respectively. Thus, the coordinates of real-time positioning points are closer to those of actual targets. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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16 pages, 5280 KiB

Open AccessArticle

Low-Voltage and Low-Power True-Single-Phase 16-Transistor Flip-Flop Design

by Jin-Fa Lin, Zheng-Jie Hong, Jun-Ting Wu, Xin-You Tung, Cheng-Hsueh Yang and Yu-Cheng Yen

Sensors 2022, 22(15), 5696; https://doi.org/10.3390/s22155696 - 29 Jul 2022

Cited by 4 | Viewed by 3305

Abstract

A low-voltage and low-power true single-phase flip-flop that minimum the total transistor count by using the pass transistor logic circuit scheme is proposed in this paper. Optimization measures lead to a new flip-flop design with better various performances such as speed, power, energy, and layout area. Based on post-layout simulation results using the TSMC CMOS 180 nm and 90 nm technologies, the proposed design achieves the conventional transmission-gate-based flip-flop design with a 53.6% reduction in power consumption and a 63.2% reduction in energy, with 12.5% input data switching activity. In order to further the performance parameters of the proposed design, a shift-register design has been realized. Experimental measurements at 0.5 V/0.5 MHz show that this proposed design reduces power consumption by 47.3% while achieving a layout area reduction of 30.5% compared to the conventional design. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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21 pages, 6918 KiB

Open AccessArticle

Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal

by Robert Chen-Hao Chang, Chia-Yu Wang, Wei-Ting Chen and Cheng-Di Chiu

Sensors 2022, 22(14), 5380; https://doi.org/10.3390/s22145380 - 19 Jul 2022

Cited by 19 | Viewed by 4060

Abstract

Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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12 pages, 3397 KiB

Open AccessArticle

A Business-to-Business Collaboration System That Promotes Data Utilization While Encrypting Information on the Blockchain

by Hiroaki Nasu, Yuta Kodera and Yasuyuki Nogami

Sensors 2022, 22(13), 4909; https://doi.org/10.3390/s22134909 - 29 Jun 2022

Cited by 4 | Viewed by 2478

Abstract

Ensuring the reliability of data gathering from every connected device is an essential issue for promoting the advancement of the next paradigm shift, i.e., Industry 4.0. Blockchain technology is becoming recognized as an advanced tool. However, data collaboration using blockchain has not progressed sufficiently among companies in the industrial supply chain (SC) that handle sensitive data, such as those related to product quality, etc. There are two reasons why data utilization is not sufficiently advanced in the industrial SC. The first is that manufacturing information is top secret. Blockchain mechanisms, such as Bitcoin, which uses PKI, require plaintext to be shared between companies to verify the identity of the company that sent the data. Another is that the merits of data collaboration between companies have not been materialized. To solve these problems, this paper proposes a business-to-business collaboration system using homomorphic encryption and blockchain techniques. Using the proposed system, each company can exchange encrypted confidential information and utilize the data for its own business. In a trial, an equipment manufacturer was able to identify the quality change caused by a decrease in equipment performance as a cryptographic value from blockchain and to identify the change one month earlier without knowing the quality value. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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25 pages, 13249 KiB

Open AccessArticle

Development of an Electronic Stethoscope and a Classification Algorithm for Cardiopulmonary Sounds

by Yu-Chi Wu, Chin-Chuan Han, Chao-Shu Chang, Fu-Lin Chang, Shi-Feng Chen, Tsu-Yi Shieh, Hsian-Min Chen and Jin-Yuan Lin

Sensors 2022, 22(11), 4263; https://doi.org/10.3390/s22114263 - 3 Jun 2022

Cited by 21 | Viewed by 7505

Abstract

With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5–45% and the best voting for lung sounds falls at 5–65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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17 pages, 12041 KiB

Open AccessArticle

Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement Learning

by Keigo Sakurai, Ren Togo, Takahiro Ogawa and Miki Haseyama

Sensors 2022, 22(10), 3722; https://doi.org/10.3390/s22103722 - 13 May 2022

Cited by 9 | Viewed by 3442

Abstract

In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users’ listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users’ long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users’ long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user’s unique preference. Full article

(This article belongs to the Special Issue Selected Papers from the IEEE International Conference on Consumer Electronics – Taiwan (IEEE 2021 ICCE-TW))

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