Wirel. Commun. Mob. Comput.最新文献

{"title":"A Study on the Diffraction Correction Prediction of Electromagnetic Field Intensity Based on the Method of Estimating Aerial Access Network Signal","authors":"Jialuan He, Zirui Xing, Qiang Wang, Feihong Wu, Fuyong Lu","doi":"10.1155/2021/8136833","DOIUrl":"https://doi.org/10.1155/2021/8136833","url":null,"abstract":"Field strength is a typical indicator of air access network signals, and the prediction of field strength has important reference significance for the estimation of aerial access network signals. However, many factors affecting the field strength, such as path, terrain, sunshine, and climate, increase the computational complexity, which greatly increases the difficulty of establishing an accurate prediction system. After persistent research by researchers in recent years, the ITU-R P.1546 model has gradually become a point-to-surface forecasting method for ground services recommended by ITU for ground operations in the frequency range of 30 MHz~3000 MHz. In view of the characteristics of electromagnetic signal propagation in mountainous environment, the influence of diffraction is also considered in this paper. Based on more accurate scene information such as actual terrain, the prediction calculation of electromagnetic signal propagation in a mountainous environment is proposed by using the corrected ITU-R P.1546 model. In addition, the influence of the actual terrain is taken into account to correct the relevant parameters, and the predicted results are compared with the measured data. The results indicate that field strength prediction results of the ITU-R P.1546 model based on the diffraction effect correction proposed in this paper in specific physical areas have better performance than those of the traditional ITU-R P.1546 model. Among them, the determination coefficient between the measured data and the predicted results is 0.87, the average error is 5.097 dBμV/m, and the root mean square error is 6.6228 dBμV/m, which proves that the ITU-R P.1546 model based on the corrected model is effective in the prediction of electromagnetic field intensity in the actual mountainous environment.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"1 1","pages":"8136833:1-8136833:13"},"PeriodicalIF":0.0,"publicationDate":"2021-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76341824","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}

{"title":"Analyzing the Effectiveness of Touch Keystroke Dynamic Authentication for the Arabic Language","authors":"Suliman A. Alsuhibany, Afnan S. Almuqbil","doi":"10.1155/2021/9963129","DOIUrl":"https://doi.org/10.1155/2021/9963129","url":null,"abstract":"The keystroke dynamic authentication (KDA) technique was proposed in the literature to develop a more effective authentication technique than traditional methods. KDA analyzes the rhythmic typing of the owner on a keypad or keyboard as a source of verification. In this study, we extend the findings of the system by analyzing the existing literature and validating its effectiveness in Arabic. In particular, we examined the effectiveness of the KDA system in Arabic for touchscreen-based digital devices using two KDA classes: fixed and free text. To this end, a KDA system was developed and applied to a selected device operating on the Android platform, and various classification methods were used to assess the similarity between log-in and enrolment sessions. The developed system was experimentally evaluated. The results showed that using Arabic KDA on touchscreen devices is possible and can enhance security. It attains a higher accuracy with average equal error rates of 0.0% and 0.08% by using the free text and fixed text classes, respectively, implying that free text is more secure than fixed text.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"7 1","pages":"9963129:1-9963129:15"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84402959","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}

{"title":"An Overview and Mechanism for the Coexistence of 5G NR-U (New Radio Unlicensed) in the Millimeter-Wave Spectrum for Indoor Small Cells","authors":"Rony Kumer Saha","doi":"10.1155/2021/8661797","DOIUrl":"https://doi.org/10.1155/2021/8661797","url":null,"abstract":"In this paper, we first give an overview of the coexistence of cellular with IEEE 802.11 technologies in the unlicensed bands. We then present a coexistence mechanism for Fifth-Generation (5G) New Radio on Unlicensed (NR-U) small cells located within buildings to coexist with the IEEE 802.11ad/ay, also termed as Wireless Gigabit (WiGig). Small cells are dual-band enabled operating in the 60 GHz unlicensed and 28 GHz licensed millimeter-wave (mmW) bands. We develop an interference avoidance scheme in the time domain to avoid cochannel interference (CCI) between in-building NR-U small cells and WiGig access points (APs). We then derive average capacity, spectral efficiency (SE), and energy efficiency (EE) performance metrics of in-building small cells. Extensive system-level numerical and simulation results and analyses are carried out for a number of variants of NR-U, including NR standalone, NR-U standalone, and NR-U anchored. We also analyze the impact of the spatial reuse of both mmW spectra of multiple NR-U anchored operators with a WiGig operator. It is shown that NR-U anchored provides the best average capacity and EE performances, whereas NR-U standalone provides the best SE performance. Moreover, both vertical spatial reuse intrabuilding level and horizontal spatial reuse interbuilding level of mmW spectra in small cells of an NR-U anchored can improve its SE and EE performances. Finally, we show that by choosing appropriate values of vertical and horizontal spatial reuse factors, the proposed coexistence mechanism can achieve the expected SE and EE requirements for the future Sixth-Generation (6G) mobile networks.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"2 1","pages":"8661797:1-8661797:18"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82850065","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}

{"title":"Optimization Method of Integrated Light-Screen Array with External Parameters Based on Genetic Algorithm","authors":"Rui Chen, Bowen Ji, Ding Chen, C. Duan","doi":"10.1155/2021/2953827","DOIUrl":"https://doi.org/10.1155/2021/2953827","url":null,"abstract":"Due to the high sensitivity and fast response, the light-screen array measurement principle is suitable for the dynamic parameter measurement of small and fast targets including projectile. Since the spatial structures of the light-screen array determine the measurement accuracy, internal parameters such as the angles between the light-screens are usually calibrated and then directly used in the field. However, the effect of the measuring state is ignored in the test field. This paper takes the integrated light-screen array sky vertical target as the research object, and two rotation angles are introduced as external parameters to describe the deviation between the calibration state and measuring state of the target, so as to optimize the measurement model. Aiming at the problem that the external parameters cannot be measured directly, an external parameter inversion method of machine learning based on a genetic algorithm is designed under a complex engineering model. The deviation between the projectile hole and the light-screen array measurement coordinates is used to build an inversion database for the genetic algorithm during the machine learning process. The simulation and the live firing test show that the optimization method and parameter identification algorithm in this paper can optimize the measurement model and improve the measurement accuracy of the light-screen array principle directly and can also provide a reference for the optimization and parameter identification in other engineering problems.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"29 1","pages":"2953827:1-2953827:8"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74236135","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}

{"title":"Image Source Identification Using Convolutional Neural Networks in IoT Environment","authors":"Yan Wang, Qindong Sun, Dongzhu Rong, Shancang Li, Lida Xu","doi":"10.1155/2021/5804665","DOIUrl":"https://doi.org/10.1155/2021/5804665","url":null,"abstract":"Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"26 1","pages":"5804665:1-5804665:12"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85138998","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}

{"title":"Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks","authors":"Jian Huang, Shan Liu, Yutian Tang, Xiushan Zhang","doi":"10.1155/2021/1230279","DOIUrl":"https://doi.org/10.1155/2021/1230279","url":null,"abstract":"With the continuous development of deep learning in computer vision, semantic segmentation technology is constantly employed for processing remote sensing images. For instance, it is a key technology to automatically mark important objects such as ships or port land from port area remote sensing images. However, the existing supervised semantic segmentation model based on deep learning requires a large number of training samples. Otherwise, it will not be able to correctly learn the characteristics of the target objects, which results in the poor performance or even failure of semantic segmentation task. Since the target objects such as ships may move from time to time, it is nontrivial to collect enough samples to achieve satisfactory segmentation performance. And this severely hinders the performance improvement of most of existing augmentation methods. To tackle this problem, in this paper, we propose an object-level remote sensing image augmentation approach based on leveraging the U-Net-based generative adversarial networks. Specifically, our proposed approach consists two components including the semantic tag image generator and the U-Net GAN-based translator. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. State-of-the-art generative models, DCGAN, WGAN, and CycleGAN, are selected as baselines. According to the experimental results, our proposed approach significantly outperforms the baselines in terms of not only drawing the outlines of target objects but also capturing their meaningful details.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"21 1","pages":"1230279:1-1230279:12"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86616253","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}

{"title":"Intelligent Detection System Enabled Attack Probability Using Markov Chain in Aerial Networks","authors":"I. Khan, Asrin Abdollahi, Ryan Alturki, M. Alshehri, M. Ikram, Hasan J. Alyamani, Shahzad Khan","doi":"10.1155/2021/1542657","DOIUrl":"https://doi.org/10.1155/2021/1542657","url":null,"abstract":"The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT-powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by using queuing theory concept to evaluate the network safety. Also, the network scenario will change due to the dynamic nature of flying vehicles. Simulation describes the queue length when the ground station is under attack. The proposed IDS utilizes the optimal threshold to make a tradeoff between false positive and false negative states with Markov binomial and Markov chain distribution stochastic models. However, at each time slot, the results demonstrate maintaining queue length in normal mode with less packet loss and high attack detection.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"1 1","pages":"1542657:1-1542657:9"},"PeriodicalIF":0.0,"publicationDate":"2021-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88568369","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}

{"title":"A Centralized Win-Win Cooperative Framework for Wi-Fi and 5G Radio Access Networks","authors":"A. Raschellà, O. Aldhaibani, S. Pizzi, M. Mackay, F. Bouhafs, G. Araniti, Qi Shi, M. C. Lucas-Estañ","doi":"10.1155/2021/5515271","DOIUrl":"https://doi.org/10.1155/2021/5515271","url":null,"abstract":"Cooperation to access wireless networks is a key approach towards optimizing the use of finite radio spectrum resources in overcrowded unlicensed bands and to help satisfy the expectations of wireless users in terms of high data rates and low latency. Although solutions that advocate this approach have been widely proposed in the literature, they still do not consider a number of aspects that can improve the performance of the users’ connections, such as the inclusion of (1) cooperation among network operators and (2) users’ quality requirements based on their applications. To fill this gap, in this paper we propose a centralized framework that is aimed at providing a “win-win” cooperation among Wi-Fi and cellular networks, which takes into account 5G technologies and users’ requirements in terms of Quality of Service (QoS). Moreover, the framework is supported by smart Radio Access Technology (RAT) selection mechanisms that orchestrate the connection of the clients to the networks. In particular, we discuss details on the design of the proposed framework, the motivation behind its implementation, the main novelties, its feasibility, and the main components. In order to demonstrate the benefits of our solution, we illustrate efficiency results achieved through the simulation of a smart RAT selection algorithm in a realistic scenario, which mimics the proposed “win-win” cooperation between Wi-Fi and cellular 5G networks, and we also discuss potential benefits for wireless and mobile network operators.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"10 3 1","pages":"5515271:1-5515271:11"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83420155","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}

{"title":"Research on Flipped Classroom of Big Data Course Based on Graphic Design MOOC","authors":"Yanqi Wang","doi":"10.1155/2021/4042459","DOIUrl":"https://doi.org/10.1155/2021/4042459","url":null,"abstract":"With the rapid development of the Internet, traditional teaching models can no longer meet the needs of talent training in colleges and universities, and reform is imperative. With the advent of the era of big data, the emergence of a large number of rich and diverse teaching resources, MOOC (Massive Online Open Course), microclasses, flipped classrooms, and other teaching models on the Internet has provided reform thinking and directions for teaching reform. This model divides the entire teaching design into two major modules: SPOC (Small Private Online Course) platform teaching activity design and flipped classroom teaching activity design, and applies this model to the actual teaching of open education, designing detailed teaching activity plans, in a real teaching situation. This study uses questionnaire surveys and interview surveys to investigate the basic personal situation of course learners, learning expectations, course participation, learning experience, and learning effects. It is planned to use the questionnaire star platform to issue and return questionnaires and use EXCEL and SPSS software to analyze the data and perform analysis and processing, combined with in-depth interviews with learners and professors for comprehensive analysis, so as to obtain the most true views of students and teachers on this model. In this process, we collect a variety of data from the SPOC platform and the flipped classroom platform, including feedback from students studying on the SPOC platform before class, observation of students’ learning attitudes in flipped classrooms to display of students’ results after class, and academic performance, summarize experience based on the analysis results, and optimize the teaching design plan. In classification algorithms, support vector machines (SVM) are widely used due to their advantages such as less overfitting and inconspicuous dimensionality of feature vectors. The traditional SVM algorithm is not suitable for processing large-scale data sets due to factors such as high time complexity and long training time. In order to solve these shortcomings, parallelizing the SVM algorithm to process large-scale data sets is an effective solution. On the basis of comparison, a SPOC-based flipped classroom teaching design model was constructed, and empirical application was carried out in the Open University, in order to promote the sustainable development of open education.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"2 1","pages":"4042459:1-4042459:11"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78509256","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}

{"title":"Feasibility of Using Improved Convolutional Neural Network to Classify BI-RADS 4 Breast Lesions: Compare Deep Learning Features of the Lesion Itself and the Minimum Bounding Cube of Lesion","authors":"Meihong Sheng, Wei-qing Tang, Jiahuan Tang, Ming Zhang, S. Gong, Wei Xing","doi":"10.1155/2021/4430886","DOIUrl":"https://doi.org/10.1155/2021/4430886","url":null,"abstract":"To determine the feasibility of using a deep learning (DL) approach to identify benign and malignant BI-RADS 4 lesions with preoperative breast DCE-MRI images and compare two 3D segmentation methods. The patients admitted from January 2014 to October 2020 were retrospectively analyzed. Breast MRI examination was performed before surgical resection or biopsy, and the masses were classified as BI-RADS 4. The first postcontrast images of DCE-MRI T1WI sequence were selected. There were two 3D segmentation methods for the lesions, one was manual segmentation along the edge of the lesion slice by slice, and the other was the minimum bounding cube of the lesion. Then, DL feature extraction was carried out; the pixel values of the image data are normalized to 0-1 range. The model was established based on the blueprint of the classic residual network ResNet50, retaining its residual module and improved 2D convolution module to 3D. At the same time, an attention mechanism was added to transform the attention mechanism module, which only fit the 2D image convolution module, into a 3D-Convolutional Block Attention Module (CBAM) to adapt to 3D-MRI. After the last CBAM, the algorithm stretches the output high-dimensional features into a one-dimensional vector and connects 2 fully connected slices, before finally setting two output results (P1, P2), which, respectively, represent the probability of benign and malignant lesions. Accuracy, sensitivity, specificity, negative predictive value, positive predictive value, the recall rate and area under the ROC curve (AUC) were used as evaluation indicators. A total of 203 patients were enrolled, with 207 mass lesions including 101 benign lesions and 106 malignant lesions. The data set was divided into the training set (\u0000 \u0000 n\u0000 =\u0000 145\u0000 \u0000 ), the validation set (\u0000 \u0000 n\u0000 =\u0000 22\u0000 \u0000 ), and the test set (\u0000 \u0000 n\u0000 =\u0000 40\u0000 \u0000 ) at the ratio of 7 : 1 : 2; fivefold cross-validation was performed. The mean AUC based on the minimum bounding cube of lesion and the 3D-ROI of lesion itself were 0.827 and 0.799, the accuracy was 78.54% and 74.63%, the sensitivity was 78.85% and 83.65%, the specificity was 78.22% and 65.35%, the NPV was 78.85% and 71.31%, the PPV was 78.22% and 79.52%, the recall rate was 78.85% and 83.65%, respectively. There was no statistical difference in AUC based on the lesion itself model and the minimum bounding cube model (\u0000 \u0000 Z\u0000 =\u0000 0.771\u0000 \u0000 , \u0000 \u0000 p\u0000 =\u0000 0.4408\u0000 \u0000 ). The minimum bounding cube based on the edge of the lesion showed higher accuracy, specificity, and lower recall rate in identifying benign and malignant lesions. Based on the lesion 3D-ROI segmentation using a minimum bounding cube can more effectively reflect the information of the lesion itself and the surrounding tissues. Its DL model performs better than the lesion itself. Using the DL approach with a 3D attention mechanism based on ResNet50 to identify benign and malignant BI-RADS 4 lesions was feasible.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"410 1","pages":"4430886:1-4430886:9"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84877585","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}