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Selected Papers from the 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2021)

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 21905

Special Issue Editors

Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Interests: intelligent system engineering and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Friends and Colleagues,

We are pleased to inform you that the 2021 International Symposium on Intelligent Signal Processing and Communication Systems will be held on 16–19 November 2021 in Hualien, on the east coast of Taiwan.

Recently, technology trends and popular discourse have been dominated by 5th-generation mobile networks. In daily life, related devices such as smartphones, tablets, and smart wearable devices have become an indispensable part of people's lives. In view of this, ISPACS 2021 has determined this year’s main theme as “5G: Dream to Reality” to enable comprehensive discussion on the impact of 5G’s signal processing and communication. We also expect that ISPACS 2021 could promote the development of related fields in Taiwan and provide a platform for global students, researchers, and industries to discuss and exchange the latest information. The authors of conference papers related to the scope of Sensors are invited to submit extended papers to this Special Issue. Topics of interest include:

  • Wireless systems for 5G;
  • Signal processing for 5G;
  • Wireless sensor networks;
  • Analog and digital ICs for communications;
  • VLSI architecture for signal processing;
  • Internet of Things;
  • Circuits and systems for IoT and AI systems;
  • Multimedia processing for e-learning, IoT, and AI applications;
  • Antenna, radio propagation, and channel modeling;
  • Cloud computing, deep learning, and big data.

Prof. Dr. Shun Hsyung Chang
Guest Editor

Prof. Dr. Te-Jen Su
Co-Guest Editor

Manuscript Submission Information

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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 2600 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 (3 papers)

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Research

12 pages, 6812 KiB  
Article
IoT-Based Fish Farm Water Quality Monitoring System
by Chiung-Hsing Chen, Yi-Chen Wu, Jia-Xiang Zhang and Ying-Hsiu Chen
Sensors 2022, 22(17), 6700; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176700 - 05 Sep 2022
Cited by 13 | Viewed by 15083
Abstract
Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, [...] Read more.
Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, and life expectancy of the sensor in the fish farm to the server. The integrated data are transmitted to mobile devices through the Internet of Things, enabling administrators to monitor the water quality in a fish farm through mobile devices. Because the current pH sensors cannot be submerged in the liquid for a long time for measurements, human resources and time are required to take the instrument to each fish farm for testing at a fixed time. Therefore, a robotic arm was developed to complete automatic measurement and maintenance actions. We designed this arm with a programmable logic controller, a single chip combined with a wireless transmission module, and an embedded system. This system is divided into control, measurement, server, and mobility. The intelligent measurement equipment designed in this study can work 24 h per day, which effectively reduces the losses caused by personnel, material resources, and data errors. Full article
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15 pages, 5000 KiB  
Article
A Low-Cost AI Buoy System for Monitoring Water Quality at Offshore Aquaculture Cages
by Hoang-Yang Lu, Chih-Yung Cheng, Shyi-Chyi Cheng, Yu-Hao Cheng, Wen-Chen Lo, Wei-Lin Jiang, Fan-Hua Nan, Shun-Hsyung Chang and Naomi A. Ubina
Sensors 2022, 22(11), 4078; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114078 - 27 May 2022
Cited by 22 | Viewed by 3842
Abstract
The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time [...] Read more.
The ocean resources have been rapidly depleted in the recent decade, and the complementary role of aquaculture to food security has become more critical than ever before. Water quality is one of the key factors in determining the success of aquaculture and real-time water quality monitoring is an important process for aquaculture. This paper proposes a low-cost and easy-to-build artificial intelligence (AI) buoy system that autonomously measures the related water quality data and instantly forwards them via wireless channels to the shore server. Furthermore, the data provide aquaculture staff with real-time water quality information and also assists server-side AI programs in implementing machine learning techniques to further provide short-term water quality predictions. In particular, we aim to provide a low-cost design by combining simple electronic devices and server-side AI programs for the proposed buoy system to measure water velocity. As a result, the cost for the practical implementation is approximately USD 2015 only to facilitate the proposed AI buoy system to measure the real-time data of dissolved oxygen, salinity, water temperature, and velocity. In addition, the AI buoy system also offers short-term estimations of water temperature and velocity, with mean square errors of 0.021 °C and 0.92 cm/s, respectively. Furthermore, we replaced the use of expensive current meters with a flow sensor tube of only USD 100 to measure water velocity. Full article
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17 pages, 4325 KiB  
Article
Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition
by Chai-Sheng Wen, Chin-Feng Lin and Shun-Hsyung Chang
Sensors 2022, 22(7), 2737; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072737 - 02 Apr 2022
Cited by 4 | Viewed by 1665
Abstract
This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD [...] Read more.
This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41–43 Hz in the MF of IMF1 for Class I BWBCV and 11–13 Hz in the MF of IMF2 for Class II BWBCV, respectively. Full article
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