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Analyzation of Sensor Data with the Aid of Deep Learning
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Analyzation of Sensor Data with the Aid of Deep Learning

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 2550

Special Issue Editors

Dr. József Sütő

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Guest Editor
1. Department of IT Systems and Networks, University of Debrecen, 4028 Debrecen, Hungary
2. Department of IT, Eszterházy Károly Catholic University, 3300 Eger, Hungary
Interests: AI in embedded systems; AI for computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefan Oniga

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Guest Editor
Department of Electric, Electronic and Computer Engineering, Technical University of Cluj-Napoca, Baia Mare 430122, Romania
Interests: field-programmable gate arrays; digital design; ambient assisted living
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The appearance of deep learning caused a great breakthrough in several research fields. The idea of using deep networks with new types of layers was very interesting to researchers because these techniques can automatically build high-level representations of the raw information.

The recent developments in hardware technology resulted in the lightweight deep models being hardware implementable on various embedded systems frameworks. Therefore, the data that come from sensors can be analyzed not just on the “server” side but also in the edge (or sensing) device.

The aim of this Special Issue is to encourage researchers to present original research results on the analyzation of sensor data with the aid of deep learning.

Dr. József Sütő
Prof. Dr. Stefan Oniga
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. 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 2600 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

  • remote sensing data
  • deep learning
  • object detection
  • embedded systems
  • sensor networks

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Published Papers (3 papers)

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Research

20 pages, 1816 KiB  
Article
Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar
by Shengze Wang, Mondher Bouazizi, Siyuan Yang and Tomoaki Ohtsuki
Sensors 2025, 25(3), 619; https://doi.org/10.3390/s25030619 - 21 Jan 2025
Viewed by 543
Abstract
This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are [...] Read more.
This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is 3.98±5.81 mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is 6.52±7.51 mmHg. Full article
(This article belongs to the Special Issue Analyzation of Sensor Data with the Aid of Deep Learning)
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27 pages, 11186 KiB  
Article
Using Infrared Raman Spectroscopy with Machine Learning and Deep Learning as an Automatic Textile-Sorting Technology for Waste Textiles
by Pei-Fen Tsai and Shyan-Ming Yuan
Sensors 2025, 25(1), 57; https://doi.org/10.3390/s25010057 - 25 Dec 2024
Viewed by 563
Abstract
With the fast-fashion trend, an increasing number of discarded clothing items are being eliminated at the stages of both pre-consumer and post-consumer each year. The linear economy produces large volumes of waste, which harm environmental sustainability. This study addresses the pressing need for [...] Read more.
With the fast-fashion trend, an increasing number of discarded clothing items are being eliminated at the stages of both pre-consumer and post-consumer each year. The linear economy produces large volumes of waste, which harm environmental sustainability. This study addresses the pressing need for efficient textile recycling in the circular economy (CE). We developed a highly accurate Raman-spectroscopy-based textile-sorting technology, which overcomes the challenge of diverse fiber combinations in waste textiles. By categorizing textiles into six groups based on their fiber compositions, the sorter improves the quality of recycled fibers. Our study demonstrates the potential of Raman spectroscopy in providing detailed molecular compositional information, which is crucial for effective textile sorting. Furthermore, AI technologies, including PCA, KNN, SVM, RF, ANN, and CNN, are integrated into the sorting process, further enhancing the efficiency to 1 piece per second with a precision of over 95% in grouping textiles based on the fiber compositional analysis. This interdisciplinary approach offers a promising solution for sustainable textile recycling, contributing to the objectives of the CE. Full article
(This article belongs to the Special Issue Analyzation of Sensor Data with the Aid of Deep Learning)
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12 pages, 3459 KiB  
Article
Using Data Augmentation to Improve the Generalization Capability of an Object Detector on Remote-Sensed Insect Trap Images
by Jozsef Suto
Sensors 2024, 24(14), 4502; https://doi.org/10.3390/s24144502 - 11 Jul 2024
Cited by 1 | Viewed by 936
Abstract
Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable [...] Read more.
Traditionally, monitoring insect populations involved the use of externally placed sticky paper traps, which were periodically inspected by a human operator. To automate this process, a specialized sensing device and an accurate model for detecting and counting insect pests are essential. Despite considerable progress in insect pest detector models, their practical application is hindered by the shortage of insect trap images. To attenuate the “lack of data” issue, the literature proposes data augmentation. However, our knowledge about data augmentation is still quite limited, especially in the field of insect pest detection. The aim of this experimental study was to investigate the effect of several widely used augmentation techniques and their combinations on remote-sensed trap images with the YOLOv5 (small) object detector model. This study was carried out systematically on two different datasets starting from the single geometric and photometric transformation toward their combinations. Our results show that the model’s mean average precision value (mAP50) could be increased from 0.844 to 0.992 and from 0.421 to 0.727 on the two datasets using the appropriate augmentation methods combination. In addition, this study also points out that the integration of photometric image transformations into the mosaic augmentation can be more efficient than the native combination of augmentation techniques because this approach further improved the model’s mAP50 values to 0.999 and 0.756 on the two test sets, respectively. Full article
(This article belongs to the Special Issue Analyzation of Sensor Data with the Aid of Deep Learning)
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