sensors-logo

Journal Browser

Journal Browser

Nature-Inspired Algorithms for Sensor Networks and Image Processing

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 28196

Special Issue Editors

Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Interests: nature-inspired algorithms; metaheuristics; unsupervised learning; clustering
Special Issues, Collections and Topics in MDPI journals
Department of Telecommunications, AGH University of Science and Technology, 30-059 Krakow, Poland
Interests: quality of experience; video streaming; mobile users; field study; subjective experiments; ecological validity
Special Issues, Collections and Topics in MDPI journals
Institute of Telecommunications, AGH University of Science and Technology, Krakow, Poland
Interests: ad hoc networks; sensor networks; channel access; wireless networks

Special Issue Information

Dear Colleagues,

Nature-inspired algorithms mimicking mechanisms such as evolution, cognitive and neural processes, or swarming behaviors have been found to be excellent problem solvers in science and engineering. Their abilities to automatically extract useful information from unstructured data, adaptability, and robustness as well as the growing capabilities of computational hardware make them a top choice for many complex problems.


Our Special Issue will accept a broad range of new advances in the field of nature-inspired algorithms in both the fields of sensor networks and image processing. We invite contributions describing new techniques, novel data representations, and evaluation criteria, as well as papers dealing with specific case studies. A limited number of state-of-the-art reviews will also be considered for publication.

Prof. Dr. Szymon Łukasik
Prof. Dr. Mikołaj Leszczuk
Prof. Dr. Szymon Szott
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

  • Sensor networks
  • Image processing
  • Nature-inspired algorithms
  • Metaheuristics
  • Neural networks
  • Deep learning
  • Fuzzy logic
  • Quality of experience
  • Video streaming
  • Mobile users
  • Field study
  • Subjective experiments
  • Ecological validity

Published Papers (10 papers)

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

Research

18 pages, 2829 KiB  
Article
A Snapshot Multi-Spectral Demosaicing Method for Multi-Spectral Filter Array Images Based on Channel Attention Network
by Xuejun Zhang, Yidan Dai, Geng Zhang, Xuemin Zhang and Bingliang Hu
Sensors 2024, 24(3), 943; https://doi.org/10.3390/s24030943 - 01 Feb 2024
Viewed by 833
Abstract
Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications [...] Read more.
Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed. The raw images, which are captured by snapshot multi-spectral imaging systems, require a reconstruction procedure called demosaicing to estimate a fully defined multi-spectral image (MSI). With increasing spectral bands, the challenge of demosaicing becomes more difficult. Furthermore, the existing demosaicing methods will produce adverse artifacts and aliasing because of the adverse effects of spatial interpolation and the inadequacy of the number of layers in the network structure. In this paper, a novel multi-spectral demosaicing method based on a deep convolution neural network (CNN) is proposed for the reconstruction of full-resolution multi-spectral images from raw MSFA-based spectral mosaic images. The CNN is integrated with the channel attention mechanism to protect important channel features. We verify the merits of the proposed method using 5 × 5 raw mosaic images on synthetic as well as real-world data. The experimental results show that the proposed method outperforms the existing demosaicing methods in terms of spatial details and spectral fidelity. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

25 pages, 4458 KiB  
Article
A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
by Jaroslaw Sadowski and Jacek Stefanski
Sensors 2024, 24(2), 332; https://doi.org/10.3390/s24020332 - 05 Jan 2024
Viewed by 484
Abstract
This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position [...] Read more.
This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

18 pages, 4017 KiB  
Article
Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm
by Vikas Srivastava, Parulpreet Singh, Praveen Kumar Malik, Rajesh Singh, Sudeep Tanwar, Fayez Alqahtani, Amr Tolba, Verdes Marina and Maria Simona Raboaca
Sensors 2023, 23(4), 2011; https://doi.org/10.3390/s23042011 - 10 Feb 2023
Cited by 2 | Viewed by 1542
Abstract
A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that [...] Read more.
A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed “support vector machine-based red deer algorithm” (SVM-RDA) is resilient and has low complexity. The suggested algorithm’s experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

17 pages, 26663 KiB  
Article
“In the Wild” Video Content as a Special Case of User Generated Content and a System for Its Recognition
by Mikołaj Leszczuk, Marek Kobosko, Jakub Nawała, Filip Korus and Michał Grega
Sensors 2023, 23(4), 1769; https://doi.org/10.3390/s23041769 - 04 Feb 2023
Cited by 1 | Viewed by 1123
Abstract
In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality [...] Read more.
In the five years between 2017 and 2022, IP video traffic tripled, according to Cisco. User-Generated Content (UGC) is mainly responsible for user-generated IP video traffic. The development of widely accessible knowledge and affordable equipment makes it possible to produce UGCs of quality that is practically indistinguishable from professional content, although at the beginning of UGC creation, this content was frequently characterized by amateur acquisition conditions and unprofessional processing. In this research, we focus only on UGC content, whose quality is obviously different from that of professional content. For the purpose of this paper, we refer to “in the wild” as a closely related idea to the general idea of UGC, which is its particular case. Studies on UGC recognition are scarce. According to research in the literature, there are currently no real operational algorithms that distinguish UGC content from other content. In this study, we demonstrate that the XGBoost machine learning algorithm (Extreme Gradient Boosting) can be used to develop a novel objective “in the wild” video content recognition model. The final model is trained and tested using video sequence databases with professional content and “in the wild” content. We have achieved a 0.916 accuracy value for our model. Due to the comparatively high accuracy of the model operation, a free version of its implementation is made accessible to the research community. It is provided via an easy-to-use Python package installable with Pip Installs Packages (pip). Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

17 pages, 4303 KiB  
Article
A Self-Learning Mechanism-Based Approach to Helicopter Entry and Departure Recognition
by Zonglei Lyu, Xuepeng Chang, Wei An and Tong Yu
Sensors 2022, 22(20), 7852; https://doi.org/10.3390/s22207852 - 16 Oct 2022
Viewed by 1339
Abstract
In order to accurately record the entry and departure times of helicopters and reduce the incidence of general aviation accidents, this paper proposes a helicopter entry and departure recognition method based on a self-learning mechanism, which is supplemented by a lightweight object detection [...] Read more.
In order to accurately record the entry and departure times of helicopters and reduce the incidence of general aviation accidents, this paper proposes a helicopter entry and departure recognition method based on a self-learning mechanism, which is supplemented by a lightweight object detection module and an image classification module. The original image data obtained from the lightweight object detection module are used to construct an Automatic Selector of Data (Auto-SD) and an Adjustment Evaluator of Data Bias (Ad-EDB), whereby Auto-SD automatically generates a pseudo-clustering of the original image data. Ad-EDB then performs the adjustment evaluation and selects the best matching module for image classification. The self-learning mechanism constructed in this paper is applied to the helicopter entry and departure recognition scenario, and the ResNet18 residual network is selected for state classification. As regards the self-built helicopter entry and departure data set, the accuracy reaches 97.83%, which is 6.51% better than the bounding box detection method. To a certain extent, the strong reliance on manual annotation for helicopter entry and departure status classification scenarios is lifted, and the data auto-selector is continuously optimized using the preorder classification results to establish a circular learning loop in the algorithm. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

15 pages, 2604 KiB  
Article
Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation
by Mohammadamin Salimi, José J. M. Machado and João Manuel R. S. Tavares
Sensors 2022, 22(12), 4544; https://doi.org/10.3390/s22124544 - 16 Jun 2022
Cited by 10 | Viewed by 2971
Abstract
Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques [...] Read more.
Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

33 pages, 7655 KiB  
Article
Research on Coverage Optimization in a WSN Based on an Improved COOT Bird Algorithm
by Yihui Huang, Jing Zhang, Wei Wei, Tao Qin, Yuancheng Fan, Xuemei Luo and Jing Yang
Sensors 2022, 22(9), 3383; https://doi.org/10.3390/s22093383 - 28 Apr 2022
Cited by 15 | Viewed by 2112
Abstract
To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the [...] Read more.
To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

43 pages, 7748 KiB  
Article
Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems
by Mohammad Dehghani and Pavel Trojovský
Sensors 2022, 22(5), 1795; https://doi.org/10.3390/s22051795 - 24 Feb 2022
Cited by 5 | Viewed by 1649
Abstract
With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. [...] Read more.
With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

34 pages, 4382 KiB  
Article
Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications
by Pavel Trojovský and Mohammad Dehghani
Sensors 2022, 22(3), 855; https://doi.org/10.3390/s22030855 - 23 Jan 2022
Cited by 250 | Viewed by 11781
Abstract
Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the [...] Read more.
Optimization is an important and fundamental challenge to solve optimization problems in different scientific disciplines. In this paper, a new stochastic nature-inspired optimization algorithm called Pelican Optimization Algorithm (POA) is introduced. The main idea in designing the proposed POA is simulation of the natural behavior of pelicans during hunting. In POA, search agents are pelicans that search for food sources. The mathematical model of the POA is presented for use in solving optimization issues. The performance of POA is evaluated on twenty-three objective functions of different unimodal and multimodal types. The optimization results of unimodal functions show the high exploitation ability of POA to approach the optimal solution while the optimization results of multimodal functions indicate the high ability of POA exploration to find the main optimal area of the search space. Moreover, four engineering design issues are employed for estimating the efficacy of the POA in optimizing real-world applications. The findings of POA are compared with eight well-known metaheuristic algorithms to assess its competence in optimization. The simulation results and their analysis show that POA has a better and more competitive performance via striking a proportional balance between exploration and exploitation compared to eight competitor algorithms in providing optimal solutions for optimization problems. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

12 pages, 2547 KiB  
Article
Weakly Supervised Video Anomaly Detection Based on 3D Convolution and LSTM
by Zhen Ma, José J. M. Machado and João Manuel R. S. Tavares
Sensors 2021, 21(22), 7508; https://doi.org/10.3390/s21227508 - 12 Nov 2021
Cited by 6 | Viewed by 2457
Abstract
Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects [...] Read more.
Weakly supervised video anomaly detection is a recent focus of computer vision research thanks to the availability of large-scale weakly supervised video datasets. However, most existing research works are limited to the frame-level classification with emphasis on finding the presence of specific objects or activities. In this article, a new neural network architecture is proposed to efficiently extract the prominent features for detecting whether a video contains anomalies. A video is treated as an integral input and the detection follows the procedure of video-label assignment. The extraction of spatial and temporal features is carried out by three-dimensional convolutions, and then their relationship is further modeled using an LSTM network. The concise structure of the proposed method enables high computational efficiency, and extensive experiments demonstrate its effectiveness. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms for Sensor Networks and Image Processing)
Show Figures

Figure 1

Back to TopTop