Journal of Sensors
 Journal metrics
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Acceptance rate40%
Submission to final decision48 days
Acceptance to publication21 days
CiteScore2.600
Journal Citation Indicator0.440
Impact Factor1.9

AHP and Machine Learning-Based Military Strategic Site Selection: A Case Study of Adea District East Shewa Zone, Ethiopia

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 Journal profile

Journal of Sensors publishes research focused on all aspects of sensors, from their theory and design, to the applications of complete sensing devices.

 Editor spotlight

Chief Editor, Professor Harith Ahmad, is currently the director of the Photonics Research Center, University of Malaya, Malaysia. His current research is in the exploration of various 2D and 3D nanomaterials for optoelectronics applications.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Isolated Word Sign Language Recognition Based on Improved SKResNet-TCN Network

This paper proposes an improved selective kernel network-temporal convolutional (SKResNet-TCN) network-based video recognition model for isolated word sign language with too large parameters, large computation, and difficult to extract effective features. SKResNet uses grouped convolution to save computational cost while dynamically selecting feature information of different perceptual fields to improve the feature extraction ability of the model for video frame images, and TCN introduces causal and inflation convolution to take full advantage of computer parallel computing and reduce memory overhead during computation. The introduction of causal convolution and dilation convolution allows the network to take full advantage of computer parallel computing and reduce memory overhead during computation, and it can capture the feature information between consecutive frames. In this paper, we design a hybrid SKResNet-TCN network model based on these two networks, and propose a solution of hybrid inflated convolution for the problem of losing information between data features in inflated convolution, using adaptive maximum pooling to preserve significant features of sign language instead of adaptive average pooling, and using Mish activation function to improve the generalization ability and accuracy of the model. The accuracy is 100% on the Argentine LSA64 dataset, and the experimental results show that the model in this paper has the advantages of fewer model parameters, smaller operations, and higher accuracy in sign language recognition compared with traditional 3D convolutional networks and long–short term memory, which effectively saves computational cost and time cost.

Research Article

Mapping Prosopis L. (Mesquites) Using Sentinel-2 MSI Satellite Data, NDVI and SVI Spectral Indices with Maximum-Likelihood and Random Forest Classifiers

Mapping of invasive alien plants (IAPs) is important for developing informed initiatives to assist environmentalists in managing the spread and impacts of IAPs. The Prosopis plant species is an aggressive IAP that has been considered a scourge in different regions of the globe. The aim of this study is to map the spatial distribution of the invasive alien Prosopis plant in southwestern Botswana using the higher spatial and spectral resolution Sentinel-2A (S2A) MultiSpectral Instrument (MSI) satellite sensor data. Supervised parametric maximum likelihood classification (MLC) was compared with the nonparametric Random Forest (RF) classifier for the detection and mapping of the Prosopis using 10 m S2A sensor bands, integrated with normalized difference vegetation index (NDVI) and Sentinel Improved Vegetation Index (SVI). Using S2A, S2A and NDVI, and S2A and SVI, MLC mapped the land use/land cover (LULC) in the study area with respective accuracies of 71.5%, 66.5%, and 79.9%, while RF mapped the LULC with accuracies of 93.2%, 77.3%, and 95.6%. Using RF, S2A multispectral data and the red edge wavelength-based SVI were found to be more suitable for mapping the distribution of Prosopis with classification accuracy of 18.3% higher than for NDVI. The study findings can be used by environmentalists, policy, and decision makers in the context of mapping, monitoring, and management of the invasive Prosopis.

Research Article

Deep Learning-Based Leaf Region Segmentation Using High-Resolution Super HAD CCD and ISOCELL GW1 Sensors

Super HAD CCD and ISOCELL GW1 imaging sensors are used for capturing images in high-resolution cameras nowadays. These high-resolution camera sensors were used in this work to acquire black gram plant leaf diseased images in natural cultivation fields. Segmenting plant leaf regions from the black gram cultivation field images is a preliminary step for disease identification and classification. It is also helpful for the farmers to assess the plants’ health and identify the diseases in their early stages. Even though plant leaf region segmentation has been effectively handled in many contributions, no universally applicable solution exists to solve all issues. Therefore, an approach for extracting leaf region from black gram plant leaf images is presented in this article. The novelty of the proposed method is that MobileNetV2 has been utilized as a backbone network for DeepLabv3+ layers to segment plant leaf regions. The DeepLabv3+ with MobileNetV2 segmentation model exhibited superior performance compared to the other models (SegNet, U-Net, DeepLabv3+ with ResNet18, ResNet50, Xception, and InceptionResNetV2) in terms of accuracy of 99.71%, Dice of 98.72%, and Jaccard/IoU of 97.47% when data augmentation was applied. The algorithms were developed and trained using MATLAB software. Each of the experimental trials reported in this article surpasses the prior findings.

Research Article

Wireless Sensor Deployment Based on Multiobjective Adaptive Fish Migration Optimization

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive-based repository and crowding degree-selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO.

Research Article

Automatic Cataract Severity Detection and Grading Using Deep Learning

Cataracts are an eye condition that causes the eye’s lens to become cloudy and is a significant cause of vision loss worldwide. Accurate and timely detection and diagnosis of cataracts can prevent vision loss. However, poor medical care and expensive treatments prevented cataract patients from receiving appropriate treatment on time. Therefore, an inexpensive system that diagnoses cataracts at an early stage needs to be developed. This study proposes an automatic method for detecting and classifying cataracts in their earliest stages by combining a deep learning (DL) model with the 2D-discrete Fourier transform (DFT) spectrum of fundus images. The proposed method calculates the spectrogram of fundus images using a 2D-DFT and uses this calculated spectrogram as an input to the DL model for feature extraction. After feature extraction, the classification task is performed by a softmax classifier. This study collected fundus images from various open-source databases that are freely available on the Internet and classified them into four classes based on an ophthalmologist’s assessment. All the collected fundus images from various datasets with open access are unsuitable for cataract diagnosis. Consequently, a module for identifying the fundus images of good and poor quality is also incorporated into this method. The experimental results show that the proposed system can outperform previous state-of-the-art works by a significant margin compared to a benchmark of four-class accuracy and achieves the four-class accuracy of 93.10%.

Research Article

Detection of Surface Defects of Magnetic Tiles Based on Improved YOLOv5

The typical defect detection algorithm is ineffective due to the contrast between the magnetic tile defect and the various defect features. An improved YOLOv5-based algorithm, for detecting magnetic tile defects with varying defect features, is suggested. The procedure begins by incorporating the CBAM into feature extraction network of YOLOv5. It improves the feature of network learning capabilities for the target region by filtering and weighting the feature vectors in such a way that the processing of network is dominated by the essential target characteristics. A new loss function of detection model is then proposed according to the properties of the magnetic tile picture, and the confidence of prediction box is increased. Data augmentation technologies are introduced to increase the number of data samples. Based on magnetic tile defect datasets, the evaluation results have shown that the precision of the proposed approach is 98.56%, 3.21%, and 7.22% greater than the original YOLOv5 and Faster R-CNN, respectively, all of which demonstrate the effectiveness and accuracy of the proposed method.

Journal of Sensors
 Journal metrics
See full report
Acceptance rate40%
Submission to final decision48 days
Acceptance to publication21 days
CiteScore2.600
Journal Citation Indicator0.440
Impact Factor1.9
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.