Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Advanced Signal and Image Processing Techniques for Sensor Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Signal and Image Processing Techniques for Sensor Applications

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

Deadline for manuscript submissions: 20 May 2024 | Viewed by 2527

Special Issue Editors

Dr. Jesmin Farzana Khan

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, 307 Luther H. Foster Hall, Tuskegee University, Tuskegee, AL 36088, USA
Interests: signal processing; image processing; pattern recognition; communications; power electronics; computer vision; machine learning; biomedical engineering; smart grids; RF; radar; remote sensing; hyper-spectral imaging; education
Dr. Mandoye Ndoye

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, 301 Luther H. Foster Hall, Tuskegee University, Tuskegee, AL 36088, USA
Interests: sensor signal processing; image processing pattern recognition; machine learning; radar signal processing; intelligent infrastructure systems

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 are 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 sensor signal data, further enhancement of the acquired sensor signals, such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images, and their analysis via computer-based medical systems, is necessary. The processing of the data 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 signal and image-processing algorithms and techniques for all types of sensors and sensing methodologies. Experimental and theoretical results, as well as review papers, will also be considered.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • 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.

We look forward to receiving your contributions.

Dr. Jesmin Farzana Khan
Dr. Mandoye Ndoye
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

  • signal processing
  • image processing
  • machine learning
  • wireless sensor networks
  • Internet of Things
  • deep neural networks
  • dictionary learning
  • compressive sensing
  • big data
  • brain–computer interface
  • artificial intelligence

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 6987 KiB  
Article
Comprehensive Separation Algorithm for Single-Channel Signals Based on Symplectic Geometry Mode Decomposition
by Xinyu Wang, Jin Zhao and Xianliang Wu
Sensors 2024, 24(2), 462; https://doi.org/10.3390/s24020462 - 11 Jan 2024
Viewed by 493
Abstract
This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, [...] Read more.
This paper aims to explore the difficulty of obtaining source signals from complex mixed signals and the issue that the FastICA algorithm cannot directly decompose the received single-channel mixed signals and distort the signal separation in low signal-to-noise environments. Thus, in this work, a comprehensive single-channel mixed signal separation algorithm was proposed based on the combination of Symplectic Geometry Mode Decomposition (SGMD) and the FastICA algorithm. First, SGMD-FastICA uses SGMD to decompose single-channel mixed signals, and then it uses the Pearson correlation coefficient to select the Symplectic Geometry Components that exhibit higher correlation coefficients with the mixed signals. Then, these components are expanded with the single-channel mixed signals into virtual multi-channel signals and input into the FastICA algorithm. The simulation results show that the SGMD algorithm could eliminate noise interference while keeping the raw time series unchanged, which is achievable through symplectic geometry similarity transformation during the decomposition of mixed signals. Comparative experiment results also show that compared with the EMD-FastICA and VMD-FastICA, the SGMD-FastICA algorithm has the best separation effect for single-channel mixed signals. The SGMD-FastICA algorithm represents an improved solution that addresses the limitations of the FastICA algorithm, enabling the direct separation of single-channel mixed signals, while also addressing the challenge of proper signal separation in noisy environments. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
►▼ Show Figures

Figure 1

19 pages, 4684 KiB  
Article
Optimization of Deep Learning Parameters for Magneto-Impedance Sensor in Metal Detection and Classification
by Hoijun Kim, Hobyung Chae, Soonchul Kwon and Seunghyun Lee
Sensors 2023, 23(22), 9259; https://doi.org/10.3390/s23229259 - 18 Nov 2023
Cited by 1 | Viewed by 728
Abstract
Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, [...] Read more.
Deep learning technology is generally applied to analyze periodic data, such as the data of electromyography (EMG) and acoustic signals. Conversely, its accuracy is compromised when applied to the anomalous and irregular nature of the data obtained using a magneto-impedance (MI) sensor. Thus, we propose and analyze a deep learning model based on recurrent neural networks (RNNs) optimized for the MI sensor, such that it can detect and classify data that are relatively irregular and diverse compared to the EMG and acoustic signals. Our proposed method combines the long short-term memory (LSTM) and gated recurrent unit (GRU) models to detect and classify metal objects from signals acquired by an MI sensor. First, we configured various layers used in RNN with a basic model structure and tested the performance of each layer type. In addition, we succeeded in increasing the accuracy by processing the sequence length of the input data and performing additional work in the prediction process. An MI sensor acquires data in a non-contact mode; therefore, the proposed deep learning approach can be applied to drone control, electronic maps, geomagnetic measurement, autonomous driving, and foreign object detection. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
►▼ Show Figures

Figure 1

22 pages, 9660 KiB  
Article
Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation
by Mingqing Li, Yuqing Wang and Haijiang Sun
Sensors 2023, 23(20), 8424; https://doi.org/10.3390/s23208424 - 12 Oct 2023
Viewed by 989
Abstract
In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to [...] Read more.
In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to such challenges, we propose a method for the correction of non-uniformity in single-frame infrared images based on noise separation in the wavelet domain. More specifically, we commence by decomposing the noisy image into distinct frequency components through wavelet transformation. Subsequently, we employ a clustering algorithm to extract high-frequency noise from the vertical components within the wavelet domain, concurrently employing a method of surface fitting to capture low-frequency noise from the approximate components within the wavelet domain. Ultimately, the restored image is obtained by subtracting the combined noise components. The experimental results demonstrate that the proposed method, when applied to simulated noisy images, achieves the optimal levels among seven compared methods in terms of MSE, PSNR, and SSIM metrics. After correction on three sets of real-world test image sequences, the average non-uniformity index is reduced by 75.54%. Moreover, our method does not impose significant computational overhead in the elimination of superimposed noise, which is particularly suitable for applications necessitating stringent requirements in both image quality and processing speed. Full article
(This article belongs to the Special Issue Advanced Signal and Image Processing Techniques for Sensor Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop