sensors-logo

Journal Browser

Journal Browser

Special Issue "Data, Signal and Image Processing and Applications in Sensors"

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

Deadline for manuscript submissions: 31 May 2020.

Special Issue Editor

Dr. Manuel J.C.S. Reis
E-Mail Website
Guest Editor
University of Trás-os-Montes e Alto Douro, IEETA & Engineering Department, Room E2.10, Quinta de Prados, 5001-801 Vila Real, PORTUGAL
Tel. (+351)964-145-002
Interests: signal & image processing and applications; study and development of devices & systems for friendly smart environments; development of multimedia-based teaching/learning methods and tools, with particular emphasis on the use of the Internet

Special Issue Information

Dear Colleagues,

With the rapid advance of sensor technology, a vast and ever-growing amount of data in various domains and modalities is readily available. However, presenting raw signal data collected directly from sensors is sometimes inappropriate, due to the presence of, for example, noise or distortion, among others. In order to obtain relevant and insightful metrics from sensors signals’ data, further enhancement of the sensor signals acquired, such as the noise reduction in the one-dimensional electroencephalographic (EEG) signals or color correction in the endoscopic images, and their analysis by computer-based medical systems, is needed. The processing of the data in itself and the consequent extraction of useful information are also vital and included in the topics of this Special Issue.

This Special Issue of Sensors aims to highlight advances in the development, testing, and application of data, signal, and image processing algorithms and techniques to all types of sensors and sensing methodologies. Experimental and theoretical results, in as much detail as possible, are very welcome. Review papers are also very welcome. There is no restriction on the length of the papers.

Topics include but are not limited to:

  • Advanced sensor characterization techniques
  • Ambient assisted living
  • Biomedical signal and image analysis
  • Signal and image processing (e.g., deblurring, denoising, super-resolution)
  • Signal and image understanding (e.g., object detection and recognition, action recognition, semantic segmentation, novel feature extraction)
  • Internet of things (IoT)
  • Machine learning (e.g., deep learning) in signal and image processing
  • Radar signal processing
  • Real-time signal and image processing algorithms and architectures (e.g., FPGA, DSP, GPU)
  • Remote sensing processing
  • Sensor data fusion and integration
  • Sensor error modelling and online calibration
  • Smart environments and smart cities
  • Wearable sensor signal processing and its applications

Dr. Manuel J.C.S. Reis
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 2000 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

  • Sensors data, signal and image processing
  • Sensors data, signal and image applications
  • Sensors applications

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions
Sensors 2020, 20(2), 459; https://doi.org/10.3390/s20020459 - 14 Jan 2020
Abstract
Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The [...] Read more.
Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Separation of Partial Discharge Sources Measured in the High-Frequency Range with HFCT Sensors Using PRPD-teff Patterns
Sensors 2020, 20(2), 382; https://doi.org/10.3390/s20020382 - 09 Jan 2020
Abstract
During the last two decades, on-line partial discharge (PD) measurements have been proven as a very efficient test to evaluate the insulation condition of high-voltage (HV) installations in service. Among the different PD-measuring techniques, the non-conventional electromagnetic methods are the most used due [...] Read more.
During the last two decades, on-line partial discharge (PD) measurements have been proven as a very efficient test to evaluate the insulation condition of high-voltage (HV) installations in service. Among the different PD-measuring techniques, the non-conventional electromagnetic methods are the most used due to their effectiveness and versatility. However, there are two main difficulties to overcome in on-line PD measurements when these methods are applied: the ambient electric noise and the simultaneous presence of various types of PD or pulse-shaped signals in the HV facility to be evaluated. A practical and effective method is presented to separate and identify PD sources acting simultaneously in HV systems under test. This method enables testers to carry out a first accurate diagnosis of the installation while performing the measurements in situ with non-invasive high-frequency current transformers (HFCT) used as sensors. The data acquisition in real-time reduces the time of postprocessing by an expert. This method was implemented in a Matlab application named PRPD-time tool, which consists of the analysis of the Phase-Resolved Partial Discharge (PRPD) pattern in combination with two types of interactive graphic representations. These graphical depictions are obtained including a feature parameter, effective time (teff), related to the duration of single measured pulses as a third axis incorporated in a classical PRPD representation, named the PRPD-teff pattern. The resulting interactive diagrams are complementary and allow the pulse source separation of pulses and clustering. The effectiveness of the proposed method and the developed Matlab application for separating PD sources is demonstrated with a practical laboratory experiment where various PD sources and pulse-type noise interferences were simultaneously measured. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Graphical abstract

Open AccessArticle
A Multidimensional Hyperjerk Oscillator: Dynamics Analysis, Analogue and Embedded Systems Implementation, and Its Application as a Cryptosystem
Sensors 2020, 20(1), 83; https://doi.org/10.3390/s20010083 - 21 Dec 2019
Abstract
A lightweight image encryption algorithm is presented based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) network. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend [...] Read more.
A lightweight image encryption algorithm is presented based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) network. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend exclusively on the system’s initial values. Further, a simple implementation of the circuit was used to validate its ability to exhibit chaotic dynamical properties. Second, an Arduino UNO platform is used to confirm the usability of our oscillator in embedded system implementation. Finally, an efficient image encryption application is executed using the proposed chaotic networks based on the use of permutation-substitution sequences. The superior qualities of the proposed strategy are traced to the dynamic set of keys used in the substitution process which heralds the generation of the final ciphered image. Based on the average results obtained from the entropy analysis (7.9976), NPCR values (99.62), UACI tests (33.69) and encryption execution time for 512 × 512 images (0.1141 s), the proposed algorithm is adjudged to be fast and robust to differential and statistical attacks relative to similar approaches. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Figure 1

Open AccessArticle
Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error
Sensors 2019, 19(23), 5202; https://doi.org/10.3390/s19235202 - 27 Nov 2019
Abstract
Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error [...] Read more.
Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details. Full article
(This article belongs to the Special Issue Data, Signal and Image Processing and Applications in Sensors)
Show Figures

Figure 1

Back to TopTop