EAI Endorsed Transactions on Industrial Networks and Intelligent Systems最新文献_第3页

Enhancing Single-Image Super-Resolution using Patch-Mosaic Data Augmentation on Lightweight Bimodal Network 基于轻量级双峰网络的补丁拼接数据增强单幅图像超分辨率

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2023-05-25 DOI: 10.4108/eetinis.v10i2.2774

Quoc Toan Nguyen, Tang Quang Hieu

{"title":"Enhancing Single-Image Super-Resolution using Patch-Mosaic Data Augmentation on Lightweight Bimodal Network","authors":"Quoc Toan Nguyen, Tang Quang Hieu","doi":"10.4108/eetinis.v10i2.2774","DOIUrl":"https://doi.org/10.4108/eetinis.v10i2.2774","url":null,"abstract":"With the advancement of deep learning, single-image super-resolution (SISR) has made significant strides. However, most current SISR methods are challenging to employ in real-world applications because they are doubtlessly employed by substantial computational and memory costs caused by complex operations. Furthermore, an efficient dataset is a key factor for bettering model training. The hybrid models of CNN and Vision Transformer can be more efficient in the SISR task. Nevertheless, they require substantial or extremely high-quality datasets for training that could be unavailable from time to time. To tackle these issues, a solution combined by applying a Lightweight Bimodal Network (LBNet) and Patch-Mosaic data augmentation method which is the enhancement of CutMix and YOCO is proposed in this research. With patch-oriented Mosaic data augmentation, an efficient Symmetric CNN is utilized for local feature extraction and coarse image restoration. Plus, a Recursive Transformer aids in fully grasping the long-term dependence of images, enabling the global information to be fully used to refine texture details. Extensive experiments have shown that LBNet with the proposed data augmentation with zero-free additional parameters method outperforms the original LBNet and other state-of-the-art techniques in which image-level data augmentation is applied.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45584408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Fully Convolutional Network with Waterfall Atrous Spatial Pooling and Localized Active Contour Loss for Fish Segmentation 基于瀑布形空间池化和局部活动轮廓损失的全卷积网络在鱼类分割中的应用

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2023-04-20 DOI: 10.4108/eetinis.v10i1.2942

Thanh Viet Le, Van Yem Vu, Van-Truong Pham, Thi-Thao Tran

{"title":"A Fully Convolutional Network with Waterfall Atrous Spatial Pooling and Localized Active Contour Loss for Fish Segmentation","authors":"Thanh Viet Le, Van Yem Vu, Van-Truong Pham, Thi-Thao Tran","doi":"10.4108/eetinis.v10i1.2942","DOIUrl":"https://doi.org/10.4108/eetinis.v10i1.2942","url":null,"abstract":"Accurate measurements and statistics of fish data are important for sustainable development of aqua-enviroment and marine fisheries. For data measurements and statistics, automatic segmentation of fish is one of key tasks. The fish segmentation however is a challenging task due to arterfacts in underwater images. In this study, we introduce a deep-learning approach, namely FCN-WRN-WASP for automatic fish segmentation from the underwater images. In particular, we introduce a computational-efficient variation called Waterfall Atrous Spatial Pooling (WASP) module into a Fully convolutional network with Wide ResNet baseline. We also proposed a loss function inspired from active contour approach that can exploit the local intensity information from the input image. The approach has been validated on the DeepFish data and the SIUM data set. The results are promissing for fish segmentation, with higher Intersection over Union (IoU) scores compared to state of the arts. The evaluation results showed that the incorporation of the image based active contour loss helps increase the segmentation performance. In addition, the use of the WASP in the architecture is effective especially for forground fish segmentation.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"106 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80961213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA MIMO球面检测器在FPGA上的次优深度流水线实现

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2023-03-29 DOI: 10.4108/eetinis.v10i1.2630

Minh Le Nguyen, X. Tran, Vu-Duc Ngo, Quang-Kien Trinh, Duc-Thang Nguyen, Tien Anh Vu

引用次数: 0

AgFAB - A Farmer-centered Agricultural Bower AgFAB -以农民为中心的农业凉亭

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2023-02-08 DOI: 10.4108/eetinis.v10i1.2714

M. Iqbal, Brianna B. Posadas, Fudi Qin, Bohan Liu, Ali Siddique

引用次数: 0

Functional Security and Trust in Ultra-Connected 6G Ecosystem 超连接6G生态系统的功能安全与信任

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-12-20 DOI: 10.4108/eetinis.v9i4.2846

Vishal Sharma

引用次数: 1

Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm 基于加速度计数据和随机森林算法的中等性能单片机人体活动识别系统

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-11-09 DOI: 10.4108/eetinis.v9i4.2571

T. Dao, Hai-Yen Hoang, Van-Nhat Hoang, Duc-Tan Tran, D. Tran

{"title":"Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm","authors":"T. Dao, Hai-Yen Hoang, Van-Nhat Hoang, Duc-Tan Tran, D. Tran","doi":"10.4108/eetinis.v9i4.2571","DOIUrl":"https://doi.org/10.4108/eetinis.v9i4.2571","url":null,"abstract":"There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure, and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"5 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83648298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning 基于深度学习的360度视频流准确视口估计方法

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-09-21 DOI: 10.4108/eetinis.v9i4.2218

Hung-Cuong Nguyen, Thu Ngan Dao, Ngoc Son Pham, Tran Long Dang, Trung Dung Nguyen, T. Truong

引用次数: 3

Internet Traffic Prediction Using Recurrent Neural Networks 利用递归神经网络预测互联网流量

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-09-02 DOI: 10.4108/eetinis.v9i4.1415

Mircea Eugen Dodan, Quoc-Tuan Vien, Tuan T. Nguyen

{"title":"Internet Traffic Prediction Using Recurrent Neural Networks","authors":"Mircea Eugen Dodan, Quoc-Tuan Vien, Tuan T. Nguyen","doi":"10.4108/eetinis.v9i4.1415","DOIUrl":"https://doi.org/10.4108/eetinis.v9i4.1415","url":null,"abstract":"Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"40 1","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79203487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization 智能反射面辅助射频能量收集移动边缘计算NOMA网络:性能分析和优化

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-08-11 DOI: 10.4108/eetinis.v9i32.1376

Dac-Binh Ha, Van-Truong Truong, Yoonill Lee

{"title":"Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization","authors":"Dac-Binh Ha, Van-Truong Truong, Yoonill Lee","doi":"10.4108/eetinis.v9i32.1376","DOIUrl":"https://doi.org/10.4108/eetinis.v9i32.1376","url":null,"abstract":"In this paper, we focus on the performance analysis and optimization of an RF energy harvesting (EH) mobile edge computing (MEC) network by the assistance of the intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) schemes. Specifically, a pair of users harvest RF energy from a hybrid access point (HAP) and offloads their tasks to the MEC server at HAP through wireless links by employing an IRS-aided and uplink NOMA scheme. To evaluate the performance of this proposed system, the closed-form expressions of successful computation and energy transfer efficiency probabilities are derived. We further formulate a multi-objective optimization problem and propose an algorithm to find the optimal energy harvesting time switching ratio value to achieve the best performance, namely SENSGA-II. Moreover, the impacts of the network parameters are provided to draw helpful insight into the system performance. Finally, the Monte-Carlo simulation results are shown to confirm the correctness of our analysis. The results have shown that the deployment of IRS can improve the performance of this considered RF EH NOMA system by increasing the number of reflecting elements.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"68 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87195717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks 基于边缘智能的工业物联网智能双向中继通信

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Pub Date : 2022-08-10 DOI: 10.4108/eetinis.v9i32.1909

J. Liu, Yuwei Zhang, Jing Wang, Tao Cui, Lin Zhang, C. Li, Kai Chen, Huan-guang Huang, Xuan-Yue Zhou, Wei Zhou, Zhao Wang, Sun Li, Suili Feng, D. Xie, Dahua Fan, Jianghong Ou, Jiangtao Ou, Yun Li, Haige Xiang, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Yajuan Tang

{"title":"Intelligent Bi-directional Relaying Communication for Edge Intelligence based Industrial IoT Networks","authors":"J. Liu, Yuwei Zhang, Jing Wang, Tao Cui, Lin Zhang, C. Li, Kai Chen, Huan-guang Huang, Xuan-Yue Zhou, Wei Zhou, Zhao Wang, Sun Li, Suili Feng, D. Xie, Dahua Fan, Jianghong Ou, Jiangtao Ou, Yun Li, Haige Xiang, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Yajuan Tang","doi":"10.4108/eetinis.v9i32.1909","DOIUrl":"https://doi.org/10.4108/eetinis.v9i32.1909","url":null,"abstract":"Within this specific record, our group study the two-way interact body (TWRN) that has a number of amplify-and-forward (AF) relays. In that, the best one is actually really got to help the info communication among sources. A interact option is actually really according to the obsolete channel problem information (CSI) in addition to our group analyze its own very personal effect on the system effectiveness in the Rayleigh fading atmospheres. Especially, we extremely preliminary acquire a restricted decreased connected for the outage opportunity and afterward current an asymptotic assessment for greater signal-to-noise ratio (SNR). Our group extra acquire a restricted decreased connected along with an asymptotic result on the authorize error cost (SER). Originating got via these results, our group easily quickly obtain that body system range order remain at unity offered that the CSI is actually really obsolete. Relative results reveal the rigidness on the effectiveness bounds along with the effects of obsolete interact option on the body system effectiveness. Simulation outcomes are likewise offered to corroborate the scholastic evaluation.","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"33 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88427201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7