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Special Issue "Machine Learning and Signal Processing in Sensing and Sensor Applications"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Gianni D’Angelo
Website
Guest Editor
Department of Computer Science, University of Salerno,Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
Interests: Soft Computing algorithms; Data Mining and Machine Learning; Deep Learning; Knowledge Discovery; Optimization Problems; Pervasive Computing; Trustworthiness modeling; High Performance Machines, Parallel Computing, Big data analytics
Special Issues and Collections in MDPI journals
Dr. Arcangelo Castiglione
Website
Guest Editor
Department of Computer Science, University of Salerno, Fisciano, Salerno, Italy
Interests: cryptography; information/data security; computer security; digital watermarking; cloud computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, machine learning (ML) technologies have made it possible to collect, analyze, and interpret a large amount of sensory information. As a result, a new era of intelligent sensors is emerging that changes the ways of perceiving and understanding the world. The integration of ML algorithms with artificial intelligence (AI) technology benefits other areas such as Industry 4.0, Internet of Things, etc. leveraging these two technologies, it is possible to design sensors tailored to specific applications. To this end, signal data, such as electrical signals, vibrations, sounds, accelerometer signals, as well as any other kind of sensory data like images, numerical data, etc. need to be analyzed and processed from real-time algorithms to mine useful insights and to embed these algorithms in sensors.

This Special Issue calls for innovative work that explores new frontiers and challenges in the field of applying ML/AI technologies and algorithms for high-sample-rate sensors. It includes new ML and AI models, hybrid systems, as well as case studies or reviews of the state-of-the-art.

The topics of interest include, but are not limited to the following:

  • ML algorithms in smart sensor systems
  • AI models in smart sensor systems
  • ML/AI‐enabled smart sensor systems
  • Practical smart-sensor applications
  • Practical smart-sensing systems
  • Health and disease data management
  • Medical image diagnosis and analysis
  • Biology data analysis
  • Smart visual imaging sensing systems
  • Object detection and recognition
  • Smart-sensors for environmental pollution management
  • Smart-sensors for precision agriculture and food science
  • Big data analytics for sensor data
  • Intelligent real-time algorithms for sensor data
  • Features for signal classification
  • Feature discovery
  • Applications of AI and ML in sensor domains: energy, IoT, Industry 4.0, etc.

Dr. Gianni D’Angelo
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 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 (2 papers)

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Research

Open AccessArticle
CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning
Sensors 2021, 21(2), 617; https://doi.org/10.3390/s21020617 - 17 Jan 2021
Abstract
Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a [...] Read more.
Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network. Full article
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Open AccessArticle
Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images
Sensors 2021, 21(2), 505; https://doi.org/10.3390/s21020505 - 12 Jan 2021
Abstract
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral [...] Read more.
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested multi-stage CNNs with 430 lateral and 430 MIP lateral cephalograms synthesized by cone-beam computed tomography (CBCT) to make a combination dataset. Fifteen landmarks were manually and respectively identified by experienced examiner, at the preprocessing phase. The intra-examiner reliability was high (ICC = 0.99) in manual identification. The results of prediction of the system for average mean radial error (MRE) and standard deviation (SD) were 1.03 mm and 1.29 mm, respectively. In conclusion, different types of image data might be the one of factors that affect the prediction accuracy of a fully-automated landmark identification system, based on multi-stage CNNs. Full article
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