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Electronics
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Advances and Applications of Networking and Multimedia Technologies
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Advances and Applications of Networking and Multimedia Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23282

Special Issue Editors

Dr. Yilong Hui

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China
Interests: vehicular networks; autonomous driving and intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zhisheng Yin

E-Mail Website
Guest Editor
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: space-air-ground integrated networks; wireless communications; AI; physical-layer security
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Shao

E-Mail Website
Guest Editor
1. Data61 of Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney 2015, Australia
2. School of Electrical and Computer Engineering, UC Davis, Davis, CA 95616, USA
Interests: spatio-temporal data; Internet of Things; reinforcement learning and ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue will include a selection of papers covering topics related with networking and multimedia technologies. There will be specific emphasis on networking and wireless communications, multimedia techniques and applications, intelligent and autonomous systems,  Internet of Things (IoT) and its applications, computer vision and pattern recognition techniques, cloud and edge computing, image and information processing, cyber-physical systems, and  artificial intelligence algorithms and applications. 

Networking and multimedia represent the main techniques to facilitate the advanced systems and applications, such as smart cities, intelligent transportation systems, and other industrial applications. These concepts fundamentally change the paradigms used in traditional networking, information systems, and information technology. This special issue seeks for novel and prominent research works on advances and applications of networking and multimedia technologies. Contents of the special issue will mainly focus on architecture, methods, and solutions towards advances and applications of networking and multimedia technologies. Topics of interest include, but are not limited to:

  • Wireless and mobile communications
  • Cloud and edge computing
  • Multimedia technologies and applications
  • Intelligent and autonomous systems
  • Computer vision and pattern recognition
  • Cloud and edge computing
  • Image and information processing
  • Artificial intelligence
  • Resource allocation and management
  • Content caching and delivery
  • Sensing and big data analysis
  • Advanced algorithms design and optimization
  • Security and privacy
  • Localization and positioning
  • Smart objects, devices, environments, platforms, and tools

Dr. Yilong Hui
Dr. Zhisheng Yin
Dr. Wei Shao
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. Electronics 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

  • networking and wireless communications
  • multimedia
  • intelligent and autonomous systems
  • IoT
  • computer vision
  • cloud computing
  • image processing
  • artificial intelligence
  • security and privacy

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

27 pages, 12499 KiB  
Article
Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique
by Saud Altaf, Muhammad Haroon, Shafiq Ahmad, Emad Abouel Nasr, Mazen Zaindin, Shamsul Huda and Zia ur Rehman
Electronics 2023, 12(2), 374; https://doi.org/10.3390/electronics12020374 - 11 Jan 2023
Cited by 4 | Viewed by 2916
Abstract
Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, [...] Read more.
Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, a system that can determine the human pose by analyzing the entire human body, from the head to the toes, is required. This paper presents a 3D human pose recognition framework based on ANN for learning error estimation. A workable laboratory-based multisensory testbed has been developed to verify the concept and validation of results. A case study was discussed to determine the conditions under which an acceptable estimation rate can be achieved in pose analysis. Using the Butterworth filtering technique, environmental factors are de-noised to reduce the system’s computational cost. The acquired signal is then segmented using an adaptive moving average technique to determine the beginning and ending points of an activity, and significant features are extracted to estimate the activity of each human pose. Experiments demonstrate that RFID transceiver-based solutions can be used effectively to estimate a person’s pose in real time using the proposed method. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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17 pages, 1416 KiB  
Article
Deep Cascade AdaBoost with Unsupervised Clustering in Autonomous Vehicles
by Jianghua Duan, Hongfei Ye, Hongyu Zhao and Zhiqiang Li 
Electronics 2023, 12(1), 44; https://doi.org/10.3390/electronics12010044 - 22 Dec 2022
Cited by 5 | Viewed by 1925
Abstract
In recent years, deep learning has achieved excellent performance in a growing number of application fields. With the help of high computation and large-scale datasets, deep learning models with huge parameters constantly enhance the performance of traditional algorithms. Additionally, the AdaBoost algorithm, as [...] Read more.
In recent years, deep learning has achieved excellent performance in a growing number of application fields. With the help of high computation and large-scale datasets, deep learning models with huge parameters constantly enhance the performance of traditional algorithms. Additionally, the AdaBoost algorithm, as one of the traditional machine learning algorithms, has a minimal model and performs well on small datasets. However, it is still challenging to select the optimal classification feature template from a large pool of features in any scene quickly and efficiently. Especially in the field of autonomous vehicles, images taken by onboard cameras contain all kinds of targets on the road, which means the images are full of multiple features. In this paper, we propose a novel Deep Cascade AdaBoost model, which effectively combines the unsupervised clustering algorithm based on deep learning and the traditional AdaBoost algorithm. First, we use the unsupervised clustering algorithm to classify the sample data automatically. We can obtain classification subsets with small intra-class and large inter-class errors by specifying positive and negative samples. Next, we design a training framework for Cascade-AdaBoost based on clustering and mathematically demonstrate that our framework has better detection performance than the traditional Cascade-AdaBoost framework. Finally, experiments on the KITTI dataset demonstrate that our model performs better than the traditional Cascade-AdaBoost algorithm in terms of accuracy and time. The detection time was shortened by 30%, and the false detection rate was reduced by 20%. Meanwhile, the training time of our model is significantly shorter than the traditional Cascade-AdaBoost algorithm. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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17 pages, 3189 KiB  
Article
Bounding the Upper Delays of the Tactile Internet Using Deterministic Network Calculus
by Qian Wang, Ziying Mo, Benle Yin, Lianming Zhang and Pingping Dong
Electronics 2023, 12(1), 21; https://doi.org/10.3390/electronics12010021 - 21 Dec 2022
Cited by 4 | Viewed by 1882
Abstract
With the increasing popularity of time-sensitive network applications and the gradual integration of the Tactile Internet into people’s lives, how to ensure ultra-low latency has become a demand and challenge for network performance. Therefore, it is extremely important to analyze the performance of [...] Read more.
With the increasing popularity of time-sensitive network applications and the gradual integration of the Tactile Internet into people’s lives, how to ensure ultra-low latency has become a demand and challenge for network performance. Therefore, it is extremely important to analyze the performance of the Tactile Internet. In this paper, we propose an analytical model based on deterministic network calculus (DNC) to quantitatively derive the end-to-end performance bounds of the Tactile Internet, develop a tandem model describing the communication of the Tactile Internet network, and analyze delay-related traffic parameters, such as arrival rate and burst size. We investigate the variation of the accuracy of the DNC analytical model and the measurement model under different parameters, and verify the accuracy of the proposed DNC analytical model by theoretical derivation and analysis and comparison with the measurement model under the NS3 platform. We discuss the impact of relevant parameters on the delay boundaries to determine which network configuration enables the end-to-end delay to meet the established requirements. This will provide valuable guidance for the design of Tactile Internet architectures. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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15 pages, 1369 KiB  
Article
Deep Learning-Based Modulation Recognition for Low Signal-to-Noise Ratio Environments
by Peng He, Yang Zhang, Xinyue Yang, Xiao Xiao, Haolin Wang and Rongsheng Zhang
Electronics 2022, 11(23), 4026; https://doi.org/10.3390/electronics11234026 - 5 Dec 2022
Cited by 6 | Viewed by 3171
Abstract
Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and [...] Read more.
Automatic modulation classification (AMC), which plays a significant role in wireless communication, can recognize the modulation type of the received signal without large amounts of transmitted data and parameter information. Supported by deep learning, which is a powerful tool for functional expression and feature extraction, the development of AMC can be greatly promoted. In this paper, we propose a deep learning-based modulation classification method with 2D time-frequency signal representation. In our proposed method, signals which have been received are first analyzed by time-frequency based on continuous wavelet transform (CWT). Then, CWT images of received signals are obtained and input to the deep learning model for classifying. We create a new CWT image dataset including 12 modulation types of signals under various signal-to-noise ratio (SNR) environment to verify the effectiveness of the proposed method. The experimental results demonstrate that our proposed method can reach to a high classification accuracy over the SNR of −11 dB. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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25 pages, 1399 KiB  
Article
Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection
by Shamshair Ali, Saif Ur Rehman, Azhar Imran, Ghazif Adeem, Zafar Iqbal and Ki-Il Kim
Electronics 2022, 11(23), 3934; https://doi.org/10.3390/electronics11233934 - 28 Nov 2022
Cited by 20 | Viewed by 10565
Abstract
Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have [...] Read more.
Many intrusion detection and prevention systems (IDPS) have been introduced to identify suspicious activities. However, since attackers are exploiting new vulnerabilities in systems and are employing more sophisticated advanced cyber-attacks, these zero-day attacks remain hidden from IDPS in most cases. These features have incentivized many researchers to propose different artificial intelligence-based techniques to prevent, detect, and respond to such advanced attacks. This has also created a new requirement for a comprehensive comparison of the existing schemes in several aspects ; after a thorough study we found that there currently exists no detailed comparative analysis of artificial intelligence-based techniques published in the last five years. Therefore, there is a need for this kind of work to be published, as there are many comparative analyses in other fields of cyber security that are available for readers to review.In this paper, we provide a comprehensive review of the latest and most recent literature, which introduces well-known machine learning and deep learning algorithms and the challenges they face in detecting zero-day attacks. Following these qualitative analyses, we present the comparative evaluation results regarding the highest accuracy, precision, recall, and F1 score compared to different datasets. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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14 pages, 7471 KiB  
Article
Piecewise Iterative Extrapolation Method for Bandlimited Signal
by Yusi Zhang, Xuejun Sha, Xiaojie Fang and Xu Lin
Electronics 2022, 11(8), 1175; https://doi.org/10.3390/electronics11081175 - 7 Apr 2022
Cited by 2 | Viewed by 1857
Abstract
In some high spectrally efficient communication structures, the Gerchberg-Papoulis extrapolation algorithm can be used to reconstruct nonorthogonal signals. However, the extrapolation efficiency and accuracy degrades when the energy of known signal is small or the iterative filter bandwidth is large. Focused on these [...] Read more.
In some high spectrally efficient communication structures, the Gerchberg-Papoulis extrapolation algorithm can be used to reconstruct nonorthogonal signals. However, the extrapolation efficiency and accuracy degrades when the energy of known signal is small or the iterative filter bandwidth is large. Focused on these drawbacks, a piecewise iterative extrapolation method is proposed to extrapolate bandlimited signals with higher extrapolation efficiency and accuracy. In this paper, the amplitude change of the unknown part over iterations is analyzed and the feasibility of the proposed extrapolation method is discussed. Numerical simulation shows the accuracy superiority of proposed method with the frequency offset compared to the GP algorithm with fixed computational complexity. Besides, the feasibility of the proposed method is verified in fractional Fourier domain with performance advantages. Full article
(This article belongs to the Special Issue Advances and Applications of Networking and Multimedia Technologies)
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