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Signal Processing and Sensing for Multimedia Communication System
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Signal Processing and Sensing for Multimedia Communication System

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

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 5598

Special Issue Editor

Dr. Chin-Feng Lin

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202, Taiwan
Interests: mobile telemedicine; biomedical signal processing; underwater acoustics multimedia communication; underwater signal processing

Special Issue Information

Dear Colleagues,

The development of the advanced signal processing and sensing schemes is related to the growth of the multimedia communication system. Cellular mobile, satellite, and underwater multimedia communication systems, as well as healthcare and telemedicine systems, are integrated toward seamless applications. The state-of-the-art research methods employed on these emerging technologies are of particular interest.

This Special Issue is addressed to all types of signal processing and sensing schemes for multimedia communication systems.

Dr. Chin-Feng Lin
Guest Editor

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 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.

Keywords

  • signal processing
  • sensing
  • underwater
  • satellite
  • cellular mobile
  • healthcare
  • telemedicine
  • multimedia communication system

Published Papers (4 papers)

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Research

19 pages, 7212 KiB  
Article
New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods
by Chin-Feng Lin, Bing-Run Wu, Shun-Hsyung Chang, Ivan A. Parinov and Sergey Shevtsov
Sensors 2023, 23(16), 7228; https://doi.org/10.3390/s23167228 - 17 Aug 2023
Viewed by 813
Abstract
Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals [...] Read more.
Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980–3725, 3725–4470, 4470–5215, 10,430–11,175, and 11,175–11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745–1490 and 1490–2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980–3725, 3725–4470, and 11,175–11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals. Full article
(This article belongs to the Special Issue Signal Processing and Sensing for Multimedia Communication System)
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21 pages, 3668 KiB  
Article
EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations
by Chai-Sheng Wen, Chin-Feng Lin and Shun-Hsyung Chang
Sensors 2023, 23(12), 5416; https://doi.org/10.3390/s23125416 - 7 Jun 2023
Cited by 2 | Viewed by 1174
Abstract
To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information [...] Read more.
To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals. Full article
(This article belongs to the Special Issue Signal Processing and Sensing for Multimedia Communication System)
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20 pages, 832 KiB  
Article
Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays
by Anh Nguyen and Zhisheng Yan
Sensors 2023, 23(8), 4016; https://doi.org/10.3390/s23084016 - 15 Apr 2023
Cited by 1 | Viewed by 1748
Abstract
Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is [...] Read more.
Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users’ fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users’ head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users’ Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users’ head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches. Full article
(This article belongs to the Special Issue Signal Processing and Sensing for Multimedia Communication System)
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20 pages, 4103 KiB  
Article
IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT
by Chin-Feng Lin and Hong-Chang Lin
Sensors 2023, 23(3), 1078; https://doi.org/10.3390/s23031078 - 17 Jan 2023
Cited by 3 | Viewed by 1349
Abstract
This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert–Huang transformation (HHT) [...] Read more.
This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert–Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5–4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500–600, 800–900, 800–900, and 500–600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons. Full article
(This article belongs to the Special Issue Signal Processing and Sensing for Multimedia Communication System)
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