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Sensor Techniques for Signal, Image and Video Processing
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Sensor Techniques for Signal, Image and Video Processing

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

Deadline for manuscript submissions: 20 May 2026 | Viewed by 4312

Special Issue Editors

Dr. Carlos Lima

E-Mail Website
Guest Editor
Centro ALGORITMI, Escola de Engenharia, Universidade do Minho, 4800-058 Guimarães, Portugal
Interests: speech recognition; statistical signal modeling; GNSS positioning systems; SAR-GMTI systems; biomedical signal and Image processing
Dr. Adriano José C. Tavares

E-Mail Website
Guest Editor
Centro ALGORITMI, Escola de Engenharia, Universidade do Minho, 4800-058 Guimarães, Portugal
Interests: embebdded systems; real-time systems; computer architectures and artificial intelligence

Special Issue Information

Dear Colleagues,

The development of sensor technologies is driven by the emergence of new challenges, such as autonomous driving, the Internet of Things, on-chip clinical analysis laboratories, and the need for localization and rescue in natural disasters, among many others.

Cars in the future will be equipped with a panoply of sensors dedicated to improving the comfort and security of passengers and other surrounding vehicles via point-to-point connections or cloud computing. Ubiquitous access to services relies heavily on advanced, increasingly sophisticated and self-controlled sensor technologies that make up a fundamental aspect of the Internet of Things and remote sensing. Currently, these technologies allow physicians in one continent to perform surgery in another continent. Miniaturized biosensors facilitate the existence of an entire clinical analysis laboratory on a chip, with less than 1 cm3 of volume. Unmanned aerial vehicles and satellites can photograph large areas in just a few minutes using radar or Lidar technologies, generating sequences of images, identifying moving targets, and summarizing videos so that rescue or assistance can be provided in a timely manner.   

This Special Issue welcomes submissions regarding, but not limited to,

  • Car sensors such as inertial, Lidar, ultra-sound and radar;
  • Image sensing for remote sensing and weather condition monitoring;
  • Sensors for biomedical signal applications such as lab-on-a-chip;
  • Optical image sensors;
  • VLSI architectures for high-speed signal, image and video processing;
  • Sensors for resources monitoring (e.g., water, electricity);
  • Sensor networks (including IoT for video processing and related areas);
  • Muti-sensor systems: signals, processing and interfaces;
  • Sensing technologies for real-time radar and sonar processing and image formation;
  • Sensors for image reconstruction, retrieval and modeling;
  • Applications using new imaging sensor technologies (e.g., biomedical image and video processing, wearable sensors or array sensors, etc.).

Dr. Carlos Lima
Dr. Adriano José C. Tavares
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 250 words) can be sent to the Editorial Office for assessment.

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

  • road noise sensor
  • road condition sensor
  • Lab on a chip
  • LiDAR
  • remote sensing
  • synthetic aperture radar ground moving target indicator (SAR-GMTI)
  • FPGA accelerating aeronautic applications
  • GNSS positioning systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

Jump to: Review, Other

49 pages, 4406 KB  
Article
Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR
by Sofia Fernandes, Carla Barros, Adriano Pinto, Vitor H. Pereira, Carlos Lima and Carlos A. Silva
Sensors 2026, 26(10), 2933; https://doi.org/10.3390/s26102933 - 7 May 2026
Viewed by 595
Abstract
Late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) permits non-invasive quantification of myocardial fibrosis; however, automated scar segmentation remains challenging due to limited expert annotations and reduced image quality caused by acquisition noise and artefacts. We investigate two related questions: (i) whether inversion of [...] Read more.
Late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) permits non-invasive quantification of myocardial fibrosis; however, automated scar segmentation remains challenging due to limited expert annotations and reduced image quality caused by acquisition noise and artefacts. We investigate two related questions: (i) whether inversion of a stochastic Gaussian diffusion process can reproduce the texture characteristics of low-signal-to-noise-ratio (SNR) LGE imaging, and (ii) whether the resulting synthetic data can improve automated fibrosis segmentation in annotation-limited settings. To this end, we introduce a mask-conditioned denoising diffusion probabilistic model (DDPM) that synthesises high-fidelity 2D short-axis LGE-CMR slices from three-class label maps (background, myocardium, scar), and we employ these synthetic images for training-set augmentation. The impact of augmentation was assessed using the nnU-Net v2 segmentation framework and benchmarked against exemplar-guided image synthesis with CoCosNet-v2 under identical data partitioning. On a held-out test set trained with 100 real cases, inclusion of 300 diffusion-generated cases increased the scar Dice coefficient from 0.173 to 0.271 (+56.7%), and the scar recall from 0.173 to 0.363, demonstrating enhanced sensitivity to fibrotic lesions. For comparable training budgets, diffusion-based augmentation consistently outperformed GAN-based augmentation, although performance improvements were non-monotonic with respect to the real-to-synthetic data ratio and attenuated as the size of the real dataset increased. A four-axis noise-fidelity analysis (spectral content, signal-dependent variance, short-range spatial correlation, distributional shape) further shows that the DDPM reproduces scanner-specific noise statistics substantially more faithfully than the GAN baseline, providing a mechanistic account for the augmentation gap. Full article
(This article belongs to the Special Issue Sensor Techniques for Signal, Image and Video Processing)
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18 pages, 4863 KB  
Article
Smartphone 3D Scanning Technology and 3D Semi-Synthetic Data for Processing Infant Head Deformities Using Artificial Intelligence
by Omar C. Quispe-Enriquez and José Luis Lerma
Sensors 2026, 26(5), 1444; https://doi.org/10.3390/s26051444 - 25 Feb 2026
Viewed by 539
Abstract
Background: Early assessment of cranial deformities in newborns, such as plagiocephaly, brachycephaly, dolichocephaly, turricephaly, and trigonocephaly, requires precise and non-invasive methods. Methodology: This study presents a methodology using a 3D scanning smartphone application to capture three-dimensional head point clouds. A total [...] Read more.
Background: Early assessment of cranial deformities in newborns, such as plagiocephaly, brachycephaly, dolichocephaly, turricephaly, and trigonocephaly, requires precise and non-invasive methods. Methodology: This study presents a methodology using a 3D scanning smartphone application to capture three-dimensional head point clouds. A total of 60 3D point cloud cases were classified according to six classes of head deformities. These 60 real 3D point clouds were expanded to 3600 semi-synthetic point clouds via controlled geometric transformations simulating realistic cranial variations. A total of 138 morphometric descriptors were extracted per class, representing spatial head features as distances from the centre of the point cloud to the head surface. These descriptors were used to train and compare three machine learning models: decision tree, random forest, and multilayer perceptron, enabling the automatic classification of six infant’s head deformities. Results: The machine learning models achieved high classification accuracy, with F1-scores up to 0.98, demonstrating the effectiveness of the approach. Conclusions: The results demonstrate the potential of combining mobile 3D sensors, image-based modelling, semi-synthetic data, and artificial intelligence to provide predictive support in clinical applications, highlighting the usefulness of low-cost portable optical sensors. Full article
(This article belongs to the Special Issue Sensor Techniques for Signal, Image and Video Processing)
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Review

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25 pages, 3946 KB  
Review
Advancements in Active-Pixel-Type CMOS Image Sensor Design Techniques and Architectures for Wide Dynamic Range
by Sangwoong Sim and Jaehoon Jun
Sensors 2026, 26(2), 489; https://doi.org/10.3390/s26020489 - 12 Jan 2026
Viewed by 1876
Abstract
Advances in CMOS image sensors (CISs) have led to utilization in various industrial fields, including machine vision, medical, surveillance, the automotive industry, and the Internet of Things (IoT). One critical metric for CISs is the dynamic range (DR), which indicates the range of [...] Read more.
Advances in CMOS image sensors (CISs) have led to utilization in various industrial fields, including machine vision, medical, surveillance, the automotive industry, and the Internet of Things (IoT). One critical metric for CISs is the dynamic range (DR), which indicates the range of light intensity that can clearly capture images. As the technology evolves, wide dynamic range (WDR) becomes increasingly required for more diverse applications. To further advance these industries, this paper presents the active-pixel-type CIS design techniques and architectures developed to achieve WDR. These include the following: the basic concepts of the active pixel sensor, readout mechanism, and DR of the CIS; multiple exposure and dual conversion gain (DCG) schemes that are conventionally used to address a trade-off in the CIS; lateral overflow integration capacitor (LOFIC) and dual photodiode (PD) architectures that can improve the DR by utilizing trade-offs in the DR and exposure mechanism; CISs with logarithmic and linear–logarithmic (Lin-Log) responses to enable non-linear characteristics; and techniques that can be employed for higher sensitivity in dark conditions. This comprehensive study of various techniques and architectures can also be utilized for cutting-edge tech advances and future research, including neuromorphic array architecture. Full article
(This article belongs to the Special Issue Sensor Techniques for Signal, Image and Video Processing)
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Other

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19 pages, 1153 KB  
Systematic Review
Technical Characteristics and Biomedical Applications of Flexible Pressure Sensor Matrices: A Scoping Review
by Stefano Cimignolo, Damiano Fruet, Giandomenico Nollo and Michela Masè
Sensors 2026, 26(6), 1971; https://doi.org/10.3390/s26061971 - 21 Mar 2026
Viewed by 749
Abstract
Flexible pressure sensors have been increasingly proposed for clinical monitoring applications. However, the available evidence on the technical characteristics and the biomedical applications of these technologies remains fragmented. To fill this gap, this scoping review aimed to map the available literature (i) to [...] Read more.
Flexible pressure sensors have been increasingly proposed for clinical monitoring applications. However, the available evidence on the technical characteristics and the biomedical applications of these technologies remains fragmented. To fill this gap, this scoping review aimed to map the available literature (i) to identify the existing flexible pressure sensor matrices proposed for biomedical applications, their technical characteristics, and usage contexts, and (ii) to determine the systems integrated into bed-based support surfaces for clinical monitoring functions. The scoping review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. PubMed, Scopus, and Web of Science databases were systematically searched to identify studies published between 2015 and 2025 that describe flexible pressure sensor matrices for biomedical monitoring and care applications. A total of 5021 records were screened, and 45 studies were included. Existing flexible pressure sensor matrices were mainly based on resistive and capacitive principles. Systems integrated into clinical support surfaces were primarily used for pressure distribution and posture monitoring, and spanned from experimental prototypes to commercially available technologies. A lack of technical specifications and relevant heterogeneity was observed among the studies. Flexible pressure sensors demonstrated potential for clinical monitoring, but standardized technological reporting and clinical validation protocols are needed to develop technically robust and clinically oriented pressure sensing solutions. Full article
(This article belongs to the Special Issue Sensor Techniques for Signal, Image and Video Processing)
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