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Topical Collection "Machine Learning and Signal Processing in Sensing and Sensor Applications"

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Sensing and Imaging".

Editors

Dr. Gianni D’Angelo
E-Mail Website
Collection Editor
Department of Computer Science, University of Salerno, 84084 Fisciano, 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, Collections and Topics in MDPI journals
Dr. Arcangelo Castiglione
E-Mail Website
Collection Editor
Department of Computer Science, University of Salerno, 84084 Fisciano, Italy
Interests: cryptography; information/data security; computer security; digital watermarking; cloud computing
Special Issues, Collections and Topics in MDPI journals

Topical Collection 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
Collection 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 collection 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 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.

Published Papers (8 papers)

2022

Jump to: 2021

Communication
Steganography in IoT: Information Hiding with APDS-9960 Proximity and Gestures Sensor
Sensors 2022, 22(7), 2612; https://doi.org/10.3390/s22072612 - 29 Mar 2022
Viewed by 278
Abstract
This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data [...] Read more.
This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data come from an external source or are pre-existing. In data input mode, the data to embed come directly from the sensor that may detect gestures or RGB color. The secrets are embedded in time-lapse photographs, which are later converted to videos. Selected hardware and steganographic methods allowed for smooth operation in the IoT environment. The system may cooperate with a digital camera and other sensors. Full article
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Article
Parallel Genetic Algorithms’ Implementation Using a Scalable Concurrent Operation in Python
Sensors 2022, 22(6), 2389; https://doi.org/10.3390/s22062389 - 20 Mar 2022
Viewed by 399
Abstract
This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic [...] Read more.
This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option, so the models were implemented using the Python programming language, RabbitMQ, and SCOOP. Based on the implementation results and testing performed, a comparison of the hardware utilization of each deployed model is provided. The results’ implementation using SCOOP was investigated from three aspects. The first aspect was the parallelization and integration of the SCOOP module into the resulting Python module. The second was the communication within the genetic algorithm topology. The third aspect was the performance of the parallel genetic algorithm model depending on the hardware. Full article
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Article
An Intelligent Approach for Cloud-Fog-Edge Computing SDN-VANETs Based on Fuzzy Logic: Effect of Different Parameters on Coordination and Management of Resources
Sensors 2022, 22(3), 878; https://doi.org/10.3390/s22030878 - 24 Jan 2022
Viewed by 673
Abstract
The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In [...] Read more.
The integration of cloud-fog-edge computing in Software-Defined Vehicular Ad hoc Networks (SDN-VANETs) brings a new paradigm that provides the needed resources for supporting a myriad of emerging applications. While an abundance of resources may offer many benefits, it also causes management problems. In this work, we propose an intelligent approach to flexibly and efficiently manage resources in these networks. The proposed approach makes use of an integrated fuzzy logic system that determines the most appropriate resources that vehicles should use when set under various circumstances. These circumstances cover the quality of the network created between the vehicles, its size and longevity, the number of available resources, and the requirements of applications. We evaluated the proposed approach by computer simulations. The results demonstrate the feasibility of the proposed approach in coordinating and managing the available SDN-VANETs resources. Full article
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2021

Jump to: 2022

Article
Two-Exposure Image Fusion Based on Optimized Adaptive Gamma Correction
Sensors 2022, 22(1), 24; https://doi.org/10.3390/s22010024 - 22 Dec 2021
Viewed by 784
Abstract
In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same [...] Read more.
In contrast to conventional digital images, high-dynamic-range (HDR) images have a broader range of intensity between the darkest and brightest regions to capture more details in a scene. Such images are produced by fusing images with different exposure values (EVs) for the same scene. Most existing multi-scale exposure fusion (MEF) algorithms assume that the input images are multi-exposed with small EV intervals. However, thanks to emerging spatially multiplexed exposure technology that can capture an image pair of short and long exposure simultaneously, it is essential to deal with two-exposure image fusion. To bring out more well-exposed contents, we generate a more helpful intermediate virtual image for fusion using the proposed Optimized Adaptive Gamma Correction (OAGC) to have better contrast, saturation, and well-exposedness. Fusing the input images with the enhanced virtual image works well even though both inputs are underexposed or overexposed, which other state-of-the-art fusion methods could not handle. The experimental results show that our method performs favorably against other state-of-the-art image fusion methods in generating high-quality fusion results. Full article
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Article
Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches
Sensors 2021, 21(22), 7527; https://doi.org/10.3390/s21227527 - 12 Nov 2021
Cited by 1 | Viewed by 573
Abstract
Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible [...] Read more.
Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches. Full article
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Communication
Luminance-Degradation Compensation Based on Multistream Self-Attention to Address Thin-Film Transistor-Organic Light Emitting Diode Burn-In
Sensors 2021, 21(9), 3182; https://doi.org/10.3390/s21093182 - 03 May 2021
Viewed by 982
Abstract
We propose a deep-learning algorithm that directly compensates for luminance degradation because of the deterioration of organic light-emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by high cost of the development and manufacturing processes [...] Read more.
We propose a deep-learning algorithm that directly compensates for luminance degradation because of the deterioration of organic light-emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuits are encumbered by high cost of the development and manufacturing processes because of their complexity. However, given that deep-learning algorithms are typically mounted onto systems on chip (SoC), the complexity of the circuit design is reduced, and the circuit can be reused by only relearning the changed characteristics of the new pixel device. The proposed approach comprises deep-feature generation and multistream self-attention, which decipher the importance of the variables, and the correlation between burn-in-related variables. It also utilizes a deep neural network that identifies the nonlinear relationship between extracted features and luminance degradation. Thereafter, luminance degradation is estimated from burn-in-related variables, and the burn-in phenomenon can be addressed by compensating for luminance degradation. Experiment results revealed that compensation was successfully achieved within an error range of 4.56%, and demonstrated the potential of a new approach that could mitigate the burn-in phenomenon by directly compensating for pixel-level luminance deviation. Full article
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Article
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
Cited by 8 | Viewed by 1333
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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Article
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
Cited by 2 | Viewed by 1357
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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