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Applied Sciences
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New Technology Trends in Smart Sensing
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New Technology Trends in Smart Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 2891

Special Issue Editors

Dr. Xinlei Chen

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Guest Editor
Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing 518055, China
Interests: AIoT; artificial intelligence; pervasive computing; cyber physical system; robotics; urban sensing; brain computer interface; human computer interface
Special Issues, Collections and Topics in MDPI journals
Dr. Rui Na

E-Mail
Guest Editor
Advanced Research Institute of Multidisciplinary Sciences, Beijing Institute of Technology, Beijing, China
Interests: smart sensing; human computer interface
Dr. Lei Zhang

E-Mail Website
Guest Editor
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Interests: multimedia systems and applications; edge computing; cloud computing; social networking; mobile computing

Special Issue Information

Dear Colleagues,

Sensing systems have become increasingly crucial in our daily lives, contributing significantly to the functionality of smart environments. These systems are applied to execute intricate tasks such as indoor localization and navigation, anonymous environment monitoring, human-machine interactive sensing, and fine-grained activity and gesture recognition. By leveraging these technologies, intelligent and advanced services are provided to enhance the quality of life.

Currently, smart sensing systems increasingly utilize advanced techniques such as multi-sensor, multi-source, and multi-process information fusion to enhance accuracy. Information fusion stands out as a promising technology in smart sensing, driving progress in related fields like the Internet of Things, intelligent unmanned systems, and mobile computing. Researchers have historically focused on improving information acquisition and fusion through signal processing, estimation theory, and decision theory. However, attention has shifted toward leveraging artificial intelligence (AI) technologies such as fuzzy logic and neural networks for more effective information fusion, leading to the development of smart and collaborative sensing systems. This advancement expands the applications of intelligent information fusion technologies in smart sensing and environments, including human-machine interactive sensing, intrusion detection, and autonomous environment monitoring, ultimately enhancing the quality of human life.

This Special Issue invites original research articles from researchers in academia and industry to discuss their contributions to new technologies and applications for smart sensing. We are seeking studies that report innovative ideas and solutions of new smart sensing methods, with a particular emphasis on exploring new and compelling mobile scenarios and applications. By showcasing recent advances in smart sensing, this special issue will enable readers to stay informed about the latest developments in the field. Additionally, review articles that discuss the current state of the art are also welcome.

Potential topics include but are not limited to the following:

  • Advanced principles for intelligent sensing in multi-sensor environments
  • Computer vision strategies tailored for resource-constrained and mobile platforms
  • Data fusion techniques leveraging artificial intelligence
  • Development of protocols and standards specific to smart sensing environments
  • Utilizing artificial intelligence in multi-sensor information fusion
  • Resource-efficient machine learning and AI algorithms for mobile devices
  • Security and privacy considerations in sensing networks
  • New applications or systems inspired by smart sensing and information fusion
  • Machine learning and deep learning techniques applied to sensor data
  • Modeling approaches for big data generated by multi-sensor systems
  • Mobile computing support for pervasive computing
  • Integration of fuzzy logic and neural network interfaces in distributed sensor systems
  • Addressing fairness, equity, and transparency issues in IoT and CPS
  • Systems enabling location and context sensing with awareness capabilities
  • Novel information fusion methods for smart sensing
  • Design and testing of multi-functional sensors
  • Strategies for data acquisition and storage in collaborative sensor networks

Dr. Xinlei Chen
Dr. Rui Na
Dr. Lei Zhang
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. Applied Sciences 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

  • sensing networks
  • intelligent sensing
  • multi-sensor systems
  • distributed sensor systems

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Published Papers (2 papers)

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13 pages, 4201 KiB  
Article
Convolutional Neural Network for Interface Defect Detection in Adhesively Bonded Dissimilar Structures
by Damira Smagulova, Vykintas Samaitis and Elena Jasiuniene
Appl. Sci. 2024, 14(22), 10351; https://doi.org/10.3390/app142210351 - 11 Nov 2024
Cited by 2 | Viewed by 1147
Abstract
This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to [...] Read more.
This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects. Full article
(This article belongs to the Special Issue New Technology Trends in Smart Sensing)
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20 pages, 4340 KiB  
Article
Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks
by Majid H. Alsulami
Appl. Sci. 2024, 14(17), 7763; https://doi.org/10.3390/app14177763 - 3 Sep 2024
Viewed by 1219
Abstract
Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method [...] Read more.
Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively. Full article
(This article belongs to the Special Issue New Technology Trends in Smart Sensing)
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