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Special Issue "Sensing, Imaging and Computing in Multimedia and Network"

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

Deadline for manuscript submissions: closed (15 August 2021).

Special Issue Editors

Dr. Moayad Aloqaily
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Guest Editor
Moayad Aloqaily, xAnalytics Inc., Ottawa, Canada
Interests: Blockchain; Cybersecurity; AI; Vehicular Networks
Dr. Mohammad Alsmirat
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Guest Editor
Jordan University of Science and Technology, Jordan
Interests: Multimedia Systems; Data Science; Digital Twin; Virtual reality
Prof. Dr. Jaime Lloret
E-Mail Website
Guest Editor
Department of Communications, Polytechnic University of Valencia, Valencia, Spain
Interests: network protocols; network algorithms; wireless sensor networks; ad hoc networks; multimedia streaming
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Dealing with a huge amount of data in general, and huge data from different modalities, is very common in today’s systems. With the continuous development in network technologies, acquisition systems, and computational power, creating and sharing data is becoming easier for all people around the world. On Facebook, for example, around 500,000 comments are posted and around 135,000 images are uploaded every second. Storing, searching, analyzing, and utilizing these data are highly challenging problems. That is why the International Conference on Multimedia Computing, Networking and Applications (MCNA) and The International Conference on Intelligent Data Science Technologies and Applications (IDSTA) have come together to give the opportunity for researchers and practitioners to present their efforts in addressing the challenges of dealing with data. Furthermore, we expect that the MCNA and IDSTA and their publications will trigger future related intelligent methods and technologies that will improve the data science field in general. The authors of papers accepted at the MCNA and IDSTA that are related to the topics of the Sensors journal will be invited to submit extended versions of their papers to this Special Issue. Moreover, new papers strictly related to the conferences themes will also be welcomed.

Topics include, but are not limited to, the following:

Energy efficience for IoT

AI-assisted sensing

Intelligent sensing systems

Intelligent multimedia data sensing systems

Sensors in video surveillance systems

Media sensing summarization

Media sensing forensics

Pattern recognition

Neural networks

Deep learning

Process mining

Granular computing

Mobile sensing

Smart city data sensing and management

Management of sensor data

Augmented reality sensing and processing

Sensors for games

Intelligent image/video analytics

Digital image and video processing

Image rendering and quality

Imaging sensors and acquisition systems

Virtual reality and simulations

Augmented reality image processing

Dr. Moayad Aloqaily
Dr. Mohammad Alsmirat
Prof. Dr. Jaime Lloret Mauri
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.

Published Papers (3 papers)

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Research

Article
Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures
Sensors 2021, 21(16), 5381; https://doi.org/10.3390/s21165381 - 09 Aug 2021
Viewed by 399
Abstract
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford [...] Read more.
This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures (GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes—normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen’s kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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Article
Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?
Sensors 2021, 21(15), 5204; https://doi.org/10.3390/s21155204 - 31 Jul 2021
Cited by 1 | Viewed by 452
Abstract
Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are [...] Read more.
Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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Article
Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation
Sensors 2021, 21(12), 3965; https://doi.org/10.3390/s21123965 - 08 Jun 2021
Cited by 1 | Viewed by 687
Abstract
The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, [...] Read more.
The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the first wave of the COVID-19 pandemic in Russia. The data were collected between 1 March 2020 and 1 June 2020. The database size includes 85,493,717 characters. To analyze the content of social media, a multimodal approach was used involving neural network technologies, text analysis, sentiment-analysis and analysis of lexical associations. The transformation of old digital services and applications, as well as the emergence of new ones were analyzed in terms of the perception of digital communications by actors. Full article
(This article belongs to the Special Issue Sensing, Imaging and Computing in Multimedia and Network)
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