Submit to Electronics Review for Electronics Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Applications of Deep Neural Network for Smart City

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 12466

Share This Special Issue

Special Issue Editors

Dr. Zhonghua Hong

E-Mail Website
Guest Editor

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: satellite/aerial photogrammetry; high-speed videogrammetric; planetary mapping; 3D emergency mapping; GNSS-R; deep learning and processing of geospatial big data

Dr. Shenlu Jiang

E-Mail Website
Guest Editor

Faculty of Innovation Technology, Macau University of Science and Technology, Taipa, Macau, China
Interests: robot vision; remote sensing; deep learning

Dr. Haiyan Pan

E-Mail Website
Guest Editor

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: image classification; change detection; deep learning

Special Issue Information

Dear Colleagues,

With rapid urbanization, cities require transformation to smart cities to benefit the living standards in many aspects, i.e., government, citizens, transportation, environmental sustainability, emergency rescue, etc. Modern technologies are key to modifying classical cities into smart cities based on Artificial Intelligence (AI). Currently, deep neural networks have proved to be a breakthrough technology to succeed in various research communities, autonomous driving, traffic management, urban big data analytics, and land-use classifications. However, a limited number of applications of DNNs have been truly used in the real world to enhance the development of smart cities.

The Special Issue aims to generate a state-of-the-art multidisciplinary reference for both theoretical and real-world challenges and innovative solutions by inviting high-quality research papers spanning machine learning and deep learning related to the development of smart cities.

Topics of interest include, but are not limited to, the following:

Urban Big data analysis
Applications for urban traffic management
UAV-assisted system for urban traffic surveillance and rescue response
Semantic knowledge for Urban Big Data Analysis
Technology and Theory for Autonomous Driving in Smart city
Robotics in Smart City
IoT applications in smart cities (e.g., Smart Home, Smart Grid, Industrial IoT, Connected Car, etc.)
Remote sensing application in smart city

Dr. Zhonghua Hong
Dr. Shenlu Jiang
Dr. Haiyan Pan
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. Electronics 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 2400 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

smart city
machine learning
deep learning

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

14 pages, 1610 KiB

Open AccessArticle

An Orientation-Aware Attention Network for Person Re-Identification

by Dongshu Xu, Jun Chen and Xiaoyu Chai

Electronics 2024, 13(5), 910; https://doi.org/10.3390/electronics13050910 - 27 Feb 2024

Viewed by 472

Abstract

Humans always identify persons through their characteristics, salient attributes, and these attributes’ locations on the body. Most person re-identification methods focus on global and local features corresponding to the former two discriminations, cropping person images into horizontal strips to obtain coarse locations of body parts. However, discriminative clues corresponding to location differences cannot be discovered, so persons with similar appearances are often confused because of their alike components. To address the above problem, we introduce pixel-wise relative positions for the invariance of their orientations in viewpoint changes. To cope with the scale change of relative position, we combine relative positions with self-attention modules that perform on multi-level features. Moreover, in the data augmentation stage, mirrored images are given new labels due to the conversion of the relative position along a horizontal orientation and change in visual chirality. Extensive experiments on four challenging benchmarks demonstrate that the proposed approach shows its superiority and effectiveness in discovering discriminating features. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

12 pages, 2018 KiB

Open AccessArticle

Image-Fused-Guided Underwater Object Detection Model Based on Improved YOLOv7

by Zhenhua Wang, Guangshi Zhang, Kuifeng Luan, Congqin Yi and Mingjie Li

Electronics 2023, 12(19), 4064; https://doi.org/10.3390/electronics12194064 - 27 Sep 2023

Cited by 4 | Viewed by 1457

Abstract

Underwater object detection, as the principal means of underwater environmental sensing, plays a significant part in the marine economic, military, and ecological fields. Due to the degradation problems of underwater images caused by color cast, blurring, and low contrast, we proposed a model for underwater object detection based on YOLO v7. In the presented detection model, an enhanced image branch was constructed to expand the feature extraction branch of YOLOv7, which could mitigate the feature degradation issues existing in the original underwater images. The contextual transfer block was introduced to the enhanced image branch, following the underwater image enhancement module, which could extract the domain features of the enhanced image, and the features of the original images and the enhanced images were fused before being fed into the detector. Focal EIOU was adopted as a new model bounding box regression loss, aiming to alleviate the performance degradation caused by mutual occlusion and overlapping of underwater objects. Taking URPC2020 and UTDAC2020 (Underwater Target Detection Algorithm Competition 2020) datasets as experimental datasets, the performance of our proposed model was compared against with other models, including YOLOF, YOLOv6 v3.0, DETR, Swin Transformer, and InternImage. The results show that our proposed model presents a competitive performance, achieving 80.71% and 86.32% in mAP@0.5 on URPC2020 and UTDAC2020, respectively. Comprehensively, the proposed model is capable of effectively mitigating the problems encountered in the task of object detection in underwater images with degraded features and exhibits great advancement. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

16 pages, 4615 KiB

Open AccessArticle

WLP-VBL: A Robust Lightweight Model for Water Level Prediction

by Congqin Yi, Wenshu Huang, Haiyan Pan and Jinghan Dong

Electronics 2023, 12(19), 4048; https://doi.org/10.3390/electronics12194048 - 27 Sep 2023

Cited by 1 | Viewed by 752

Abstract

Accurate and reliable water level prediction plays a crucial role in the optimal management of water resources and reservoir scheduling. Water level data have the characteristics of volatility and temporality; a single water level prediction model can only be applied to specific hydrological conditions and reservoirs. Therefore, in this paper, we present a robust lightweight model for water level prediction, namely WLP-VBL, by using a combination of VMD, BA, and LSTM. The proposed WLP-VBL model consists of three steps: first, the water level dataset is decomposed by EMD to obtain a number of decomposition layers K, and then VMD is used to decompose the original water level dataset into K intrinsic modal functions (IMFs) to produce a clearer signal. Next, the IMF data are sent to an LSTM neural network optimized by BA for prediction, and finally each component is superimposed to obtain the predicted value. In order to evaluate the effectiveness of the model, experiments were carried out on water level data for the Gan River. The results indicate that: (1) Compared with state-of-the art methods, e.g., LSTM, VMD-LSTM, and EMD-LSTM, WLP-VBL exhibited the best performance. The MSE and MAE of WLP-VBL decreased by 69.6~74.7% and 45~98.5%, respectively. (2) The proposed model showed stronger robustness for water level prediction, and was able to handle highly volatile and noisy data. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

20 pages, 13580 KiB

Open AccessArticle

Hyperspectral Image Classification Based on Dual-Scale Dense Network with Efficient Channel Attentional Feature Fusion

by Zhongyang Shi, Ming Chen and Zhigao Wu

Electronics 2023, 12(13), 2991; https://doi.org/10.3390/electronics12132991 - 07 Jul 2023

Cited by 1 | Viewed by 983

Abstract

Hyperspectral images (HSIs) have abundant spectral and spatial information, which shows bright prospects in the application industry of urban–rural. Thus, HSI classification has drawn much attention from researchers. However, the spectral and spatial information-extracting method is one of the research difficulties in HSI classification tasks. To meet this tough challenge, we propose an efficient channel attentional feature fusion dense network (CA-FFDN). Our network has two structures. In the feature extraction structure, we utilized a novel bottleneck based on separable convolution (SC-bottleneck) and efficient channel attention (ECA) to simultaneously fuse spatial–spectral features from different depths, which can make full use of the dual-scale shallow and deep spatial–spectral features of the HSI and also significantly reduce the parameters. In the feature enhancement structure, we used 3D convolution and average pooling to further integrate spatial–spectral features. Many experiments on Indian Pines (IP), University of Pavia (UP), and Kennedy Space Center (KSC) datasets demonstrated that our CA-FFDN outperformed the other five state-of-the-art networks, even with small training samples. Meanwhile, our CA-FFDN achieved classification accuracies of 99.51%, 99.91%, and 99.89%, respectively, in the case where the ratio of the IP, UP, and KSC datasets was 2:1:7, 1:1:8, and 2:1:7. It provided the best classification performance with the highest accuracy, fastest convergence, and slightest training and validation loss fluctuations. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

18 pages, 11412 KiB

Open AccessArticle

An Adaptive Remote Sensing Image-Matching Network Based on Cross Attention and Deformable Convolution

by Peiyan Chen, Ying Fu, Jinrong Hu, Bing He, Xi Wu and Jiliu Zhou

Electronics 2023, 12(13), 2889; https://doi.org/10.3390/electronics12132889 - 30 Jun 2023

Viewed by 935

Abstract

There are significant background changes and complex spatial correspondences between multi-modal remote sensing images, and it is difficult for existing methods to extract common features between images effectively, leading to poor matching results. In order to improve the matching effect, features with high robustness are extracted; this paper proposes a multi-temporal remote sensing matching algorithm CMRM (CNN multi-modal remote sensing matching) based on deformable convolution and cross-attention. First, based on the VGG16 backbone network, Deformable VGG16 (DeVgg) is constructed by introducing deformable convolutions to adapt to significant geometric distortions in remote sensing images of different shapes and scales; second, the features extracted from DeVgg are input to the cross-attention module to better capture the spatial correspondence of images with background changes; and finally, the key points and corresponding descriptors are extracted from the output feature map. In the feature matching stage, in order to solve the problem of poor matching quality of feature points, BFMatcher is used for rough registration, and then the RANSAC algorithm with adaptive threshold is used for constraint. The proposed algorithm in this paper performs well on the public dataset HPatches, with MMA values of 0.672, 0.710, and 0.785 when the threshold is selected as 3–5. The results show that compared to existing methods, our method improves the matching accuracy of multi-modal remote sensing images. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

13 pages, 897 KiB

Open AccessArticle

Study on Driver Cross-Subject Emotion Recognition Based on Raw Multi-Channels EEG Data

by Zhirong Wang, Ming Chen and Guofu Feng

Electronics 2023, 12(11), 2359; https://doi.org/10.3390/electronics12112359 - 23 May 2023

Cited by 4 | Viewed by 1126

Abstract

In our life, emotions often have a profound impact on human behavior, especially for drivers, as negative emotions can increase the risk of traffic accidents. As such, it is imperative to accurately discern the emotional states of drivers in order to preemptively address and mitigate any negative emotions that may otherwise manifest and compromise driving behavior. In contrast to many current studies that rely on complex and deep neural network models to achieve high accuracy, this research aims to explore the potential of achieving high recognition accuracy using shallow neural networks through restructuring the structure and dimensions of the data. In this study, we propose an end-to-end convolutional neural network (CNN) model called simply ameliorated CNN (SACNN) to address the issue of low accuracy in cross-subject emotion recognition. We extracted features and converted dimensions of EEG signals from the SEED dataset from the BCMI Laboratory to construct 62-dimensional data, and obtained the optimal model configuration through ablation experiments. To further improve recognition accuracy, we selected the top 10 channels with the highest accuracy by separately training the EEG data of each of the 62 channels. The results showed that the SACNN model achieved an accuracy of 88.16% based on raw cross-subject data, and an accuracy of 91.85% based on EEG channel data from the top 10 channels. In addition, we explored the impact of the position of the BN and dropout layers on the model through experiments, and found that a targeted shallow CNN model performed better than deeper and larger perceptual field CNN models. Furthermore, we discuss herein the future issues and challenges of driver emotion recognition in promising smart city applications. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

14 pages, 5425 KiB

Open AccessEditor’s ChoiceArticle

Swin-UperNet: A Semantic Segmentation Model for Mangroves and Spartina alterniflora Loisel Based on UperNet

by Zhenhua Wang, Jing Li, Zhilian Tan, Xiangfeng Liu and Mingjie Li

Electronics 2023, 12(5), 1111; https://doi.org/10.3390/electronics12051111 - 24 Feb 2023

Cited by 2 | Viewed by 2499

Abstract

As an ecosystem in transition from land to sea, mangroves play a vital role in wind and wave protection and biodiversity maintenance. However, the invasion of Spartina alterniflora Loisel seriously damages the mangrove wetland ecosystem. To protect mangroves scientifically and dynamically, a semantic segmentation model for mangroves and Spartina alterniflora Loise was proposed based on UperNet (Swin-UperNet). In the proposed Swin-UperNet model, a data concatenation module was proposed to make full use of the multispectral information of remote sensing images, the backbone network was replaced with a Swin transformer to improve the feature extraction capability, and a boundary optimization module was designed to optimize the rough segmentation results. Additionally, a linear combination of cross-entropy loss and Lovasz-Softmax loss was taken as the loss function of Swin-UperNet, which could address the problem of unbalanced sample distribution. Taking GF-1 and GF-6 images as the experiment data, the performance of the Swin-UperNet model was compared against that of other segmentation models in terms of pixel accuracy (PA), mean intersection over union (mIoU), and frames per second (FPS), including PSPNet, PSANet, DeepLabv3, DANet, FCN, OCRNet, and DeepLabv3+. The results showed that the Swin-UperNet model achieved the best PA of 98.87% and mIoU of 90.0%, and the efficiency of the Swin-UperNet model was higher than that of most models. In conclusion, Swin-UperNet is an efficient and accurate model for mangrove and Spartina alterniflora Loise segmentation synchronously, which will provide a scientific basis for Spartina alterniflora Loise monitoring and mangrove resource conservation and management. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

14 pages, 4192 KiB

Open AccessArticle

ESMD-WSST High-Frequency De-Noising Method for Bridge Dynamic Deflection Using GB-SAR

by Xianglei Liu, Songxue Zhao and Runjie Wang

Electronics 2023, 12(1), 54; https://doi.org/10.3390/electronics12010054 - 23 Dec 2022

Cited by 2 | Viewed by 1324

Abstract

Ground-based synthetic aperture radar (GB-SAR), as a new non-contact measurement technique, has been widely applied to obtain the dynamic deflection of various bridges without corner reflectors. However, it will cause some high-frequency noise in the obtained dynamic deflection with the low signal-to-noise ratio. To solve this problem, this paper proposes an innovative high-frequency de-noising method combining the wavelet synchro-squeezing transform (WSST) method with the extreme point symmetric mode decomposition (ESMD) method. First, the ESMD method is applied to decompose the observed dynamic deflection signal into a series of intrinsic mode functions (IMFs), and the frequency boundary of the original signal autocorrelation is filtered by the mutual information entropy (MIE) for each IMF pair. Second, the high-frequency IMF components are fused into a high-frequency sub-signal. WSST is performed to remove the influence of noise to reconstruct a new sub-signal. Finally, the de-noised bridge dynamic deflection is reconstructed by the new sub-signal, the remaining IMF components, and the residual curve R. For the simulated signal with 5 dB noise, the signal-to-noise ratio (SNR) after noise reduction is increased to 11.13 dB, and the root-mean-square error (RMSE) is reduced to 0.30 mm. For the on-site experiment for the Wanning Bridge, the noise rejection ratio (NRR) is 5.48 dB, and ratio of the variance root (RVR) is 0.05 mm. The results indicate that the proposed ESMD-WSST method can retain more valid information and has a better noise reduction ability than the ESMD, WSST, and EMD-WSST methods. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Figure 1

18 pages, 1015 KiB

Open AccessArticle

Research on Logistics Service Assessment for Smart City: A Users’ Review Sentiment Analysis Approach

by Shaozhong Zhang, Haidong Zhong, Chao Wei and Dingkai Zhang

Electronics 2022, 11(23), 4018; https://doi.org/10.3390/electronics11234018 - 04 Dec 2022

Cited by 1 | Viewed by 1315

Abstract

The innovative development of logistics has become a powerful starting point and strong support for the construction of smart cities. An accurate evaluation of logistics service quality can promote intelligent transformation, and upgrading logistics enterprises can improve the urban public service infrastructure. In this study, we propose a logistics service quality evaluation model based on a combination of sentiment analysis technology and a traditional index evaluation system. With the help of sentiment analysis technology, the model focuses on extracting the sentiment characteristics of logistics service quality from user reviews and analyzing user attitudes from different aspects. We designed a new logistics service quality evaluation index system by improving the SERVQUAL model. The system uses sentiment analysis technology to explore evaluation content through feature extraction and builds relations between the evaluation content and indices. Additionally, we use sentiment orientation analysis with different indices to comprehensively evaluate service quality. The experimental analysis shows that the proposed model and algorithm have high accuracy. Full article

(This article belongs to the Special Issue Applications of Deep Neural Network for Smart City)

► Show Figures

Journal Menu

Journal Browser

Applications of Deep Neural Network for Smart City

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI