Channel measurement plays an important role in the emerging 5G-enabled Internet of Things (IoT) networks, which reflects the channel quality and link reliability. In this paper, we address the channel measurement for link reliability evaluation in filter-bank multicarrier with offset quadrature amplitude modulation- (FBMC/OQAM-) based IoT network, which is considered as a promising technique for future wireless communications. However, resulting from the imaginary interference and the noise correlation among subcarriers in FBMC/OQAM, the existing frequency correlation method cannot be directly applied in the FBMC/OQAM-based IoT network. In this study, the concept of the block repetition is applied in FBMC/OQAM. It is demonstrated that the noises among subcarriers are independent by the block repetition and linear combination, instead of correlated. On this basis, the classical frequency correlation method can be applied to achieve the channel measurement. Then, we also propose an advanced frequency correlation method to improve the accuracy of the channel measurement, by assuming channel frequency responses to be quasi-invariant for several successive subcarriers. Simulations are conducted to validate the proposed schemes.
{"title":"Channel Measurement and Noise Estimation in FBMC/OQAM-Based IoT Networks","authors":"Jun Sun, X. Mu, Dejin Kong","doi":"10.1155/2022/6518066","DOIUrl":"https://doi.org/10.1155/2022/6518066","url":null,"abstract":"Channel measurement plays an important role in the emerging 5G-enabled Internet of Things (IoT) networks, which reflects the channel quality and link reliability. In this paper, we address the channel measurement for link reliability evaluation in filter-bank multicarrier with offset quadrature amplitude modulation- (FBMC/OQAM-) based IoT network, which is considered as a promising technique for future wireless communications. However, resulting from the imaginary interference and the noise correlation among subcarriers in FBMC/OQAM, the existing frequency correlation method cannot be directly applied in the FBMC/OQAM-based IoT network. In this study, the concept of the block repetition is applied in FBMC/OQAM. It is demonstrated that the noises among subcarriers are independent by the block repetition and linear combination, instead of correlated. On this basis, the classical frequency correlation method can be applied to achieve the channel measurement. Then, we also propose an advanced frequency correlation method to improve the accuracy of the channel measurement, by assuming channel frequency responses to be quasi-invariant for several successive subcarriers. Simulations are conducted to validate the proposed schemes.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"43 1","pages":"6518066:1-6518066:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90896105","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}
The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.
{"title":"A Real-Time Complex Road AI Perception Based on 5G-V2X for Smart City Security","authors":"Cheng Xu, Hongjun Wu, Yinong Zhang, Songyin Dai, Hongzhe Liu, Jinzhao Tian","doi":"10.1155/2022/4405242","DOIUrl":"https://doi.org/10.1155/2022/4405242","url":null,"abstract":"The Internet of Vehicles and information security are key components of a smart city. Real-time road perception is one of the most difficult tasks. Traditional detection methods require manual adjustment of parameters, which is difficult, and is susceptible to interference from object occlusion, light changes, and road wear. Designing a robust road perception algorithm is still challenging. On this basis, we combine artificial intelligence algorithms and the 5G-V2X framework to propose a real-time road perception method. First, an improved model based on Mask R-CNN is implemented to improve the accuracy of detecting lane line features. Then, the linear and polynomial fitting methods of feature points in different fields of view are combined. Finally, the optimal parameter equation of the lane line can be obtained. We tested our method in complex road scenes. Experimental results show that, combined with 5G-V2X, this method ultimately has a faster processing speed and can sense road conditions robustly under various complex actual conditions.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"38 1","pages":"4405242:1-4405242:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81248901","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}
Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.
{"title":"A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix","authors":"Xiaodong Zhang, Con-Gdong Lv, Zhoubao Sun","doi":"10.1155/2022/3795183","DOIUrl":"https://doi.org/10.1155/2022/3795183","url":null,"abstract":"Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"230 1","pages":"3795183:1-3795183:7"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87115749","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}
Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the K -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.
{"title":"Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology","authors":"Haifeng Zhang, Lian Zhou","doi":"10.1155/2022/7261207","DOIUrl":"https://doi.org/10.1155/2022/7261207","url":null,"abstract":"Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the \u0000 \u0000 K\u0000 \u0000 -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"35 1","pages":"7261207:1-7261207:8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88902608","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}
Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the k -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale k -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch k -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.
{"title":"A Large-Scale k -Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation","authors":"Yunsheng Song, Xiaohan Kong, Chao Zhang","doi":"10.1155/2022/7409171","DOIUrl":"https://doi.org/10.1155/2022/7409171","url":null,"abstract":"Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the \u0000 \u0000 k\u0000 \u0000 -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale \u0000 \u0000 k\u0000 \u0000 -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch \u0000 \u0000 k\u0000 \u0000 -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"35 1","pages":"7409171:1-7409171:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77273797","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}
In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.
{"title":"Graph Convolutional Networks for Cross-Modal Information Retrieval","authors":"Xianben Yang, Wei Zhang","doi":"10.1155/2022/6133142","DOIUrl":"https://doi.org/10.1155/2022/6133142","url":null,"abstract":"In recent years, due to the wide application of deep learning and more modal research, the corresponding image retrieval system has gradually extended from traditional text retrieval to visual retrieval combined with images and has become the field of computer vision and natural language understanding and one of the important cross-research hotspots. This paper focuses on the research of graph convolutional networks for cross-modal information retrieval and has a general understanding of cross-modal information retrieval and the related theories of convolutional networks on the basis of literature data. Modal information retrieval is designed to combine high-level semantics with low-level visual capabilities in cross-modal information retrieval to improve the accuracy of information retrieval and then use experiments to verify the designed network model, and the result is that the model designed in this paper is more accurate than the traditional retrieval model, which is up to 90%.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"202 1","pages":"6133142:1-6133142:8"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73213684","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}
With the gradual development of the superior performance of the ultrasonic water meter, the use of the water meter gradually occupies most of the market due to its unique advantages. Through the analysis of the influencing factors of the ultrasonic water meter, the Kalman filter is used to analyze the influencing factors, and the differences are obtained. In this paper, combined with the application scope of the Kalman filter, it is introduced. Combined with the method of data fusion, the influencing factors of the ultrasonic water meter are analyzed. They are the flow rate, temperature, speed of sound, time difference, etc. The appropriate sensor is selected through the sensor selection method, and the corresponding data is obtained by the method of the corresponding sensor. We combine the data fusion method and use Kalman’s method to filter the data. By comparing the data before and after the processing, it is found that the data before and after the filtering of different influencing factors are small. Among them, the flow speed factor has the greatest impact on the accuracy of the ultrasonic water meter; temperature and sound velocity have little effect on the performance of the ultrasonic water meter. When designing an ultrasonic water meter, it is mainly necessary to consider the impact of flow rate and time difference on the performance of the ultrasonic water meter.
{"title":"Application of Multiparameter Kalman Filter in Ultrasonic Water Meter","authors":"Fuqiang Zuo, Yu Liu","doi":"10.1155/2022/3580376","DOIUrl":"https://doi.org/10.1155/2022/3580376","url":null,"abstract":"With the gradual development of the superior performance of the ultrasonic water meter, the use of the water meter gradually occupies most of the market due to its unique advantages. Through the analysis of the influencing factors of the ultrasonic water meter, the Kalman filter is used to analyze the influencing factors, and the differences are obtained. In this paper, combined with the application scope of the Kalman filter, it is introduced. Combined with the method of data fusion, the influencing factors of the ultrasonic water meter are analyzed. They are the flow rate, temperature, speed of sound, time difference, etc. The appropriate sensor is selected through the sensor selection method, and the corresponding data is obtained by the method of the corresponding sensor. We combine the data fusion method and use Kalman’s method to filter the data. By comparing the data before and after the processing, it is found that the data before and after the filtering of different influencing factors are small. Among them, the flow speed factor has the greatest impact on the accuracy of the ultrasonic water meter; temperature and sound velocity have little effect on the performance of the ultrasonic water meter. When designing an ultrasonic water meter, it is mainly necessary to consider the impact of flow rate and time difference on the performance of the ultrasonic water meter.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"14 1","pages":"3580376:1-3580376:12"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81631882","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}
Alaa M. Mukhtar, R. Saeed, R. Mokhtar, E. Ali, H. Alhumyani
Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.
{"title":"Performance Evaluation of Downlink Coordinated Multipoint Joint Transmission under Heavy IoT Traffic Load","authors":"Alaa M. Mukhtar, R. Saeed, R. Mokhtar, E. Ali, H. Alhumyani","doi":"10.1155/2022/6837780","DOIUrl":"https://doi.org/10.1155/2022/6837780","url":null,"abstract":"Emerging 5G network cellular promotes key empowering techniques for pervasive IoT. Evolving 5G-IoT scenarios and basic services like reality augmented, high dense streaming of videos, unmanned vehicles, e-health, and intelligent environments services have a pervasive existence now. These services generate heavy loads and need high capacity, bandwidth, data rate, throughput, and low latency. Taking all these requirements into consideration, internet of things (IoT) networks have provided global transformation in the context of big data innovation and bring many problematic issues in terms of uplink and downlink (DL) connectivity and traffic load. These comprise coordinated multipoint processing (CoMP), carriers’ aggregation (CA), joint transmissions (JTs), massive multi-inputs multi-outputs (MIMO), machine-type communications, centralized radios access networks (CRAN), and many others. CoMP is one of the most significant technical enhancements added to release 11 that can be implemented in heterogonous networks implementation approaches and the homogenous networks’ topologies. However, in a massive 5G-IoT device scenario with heavy traffic load, most cell edge IoT users are severely suffering from intercell interference (ICI), where the users have poor signal, lower data rates, and limited QoS. This work is aimed at addressing this problematic issue by proposing two types of DL-JT-CoMP techniques in 5G-IoT that are compliant with release 18. Downlink JT-CoMP with two homogeneous network CoMP deployment scenarios is considered and evaluated. The scenarios used are IoT intrasite and intersite CoMP, which performance evaluated using downlink system-level simulator for long-term evolution-advanced (LTE-A) and 5G. Numerical simulation scenarios were results under high dense scenario—with IoT heavy traffic load which shows that intersite CoMP has better empirical cumulative distribution function (ECDF) of average UE throughput than intrasite CoMP approximately 4%, inter-site CoMP has better ECDF of average user entity (UE) spectral efficiency than intrasite CoMP almost 10%, and intersite CoMP has approximately same ECDF of average signal interference noise ratio (SINR) as intrasite CoMP and intersite CoMP has better fairness index than intrasite CoMP by 5%. The fairness index decreases when the users’ number increase since the competition among users is higher.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"72 1","pages":"6837780:1-6837780:16"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84015697","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}
Qiong Lou, Junfeng Li, Yaguan Qian, Anlin Sun, Fang Lu
RGB-infrared (RGB-IR) person reidentification is a challenge problem in computer vision due to the large crossmodality difference between RGB and IR images. Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE-ResNet-50, which has achieved ideal results. It consists of three modules, including alignGAN module ( A ), crossmodality paired-images generation module ( G ), and feature alignment module ( F ). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality-specific features of RGB and IR images to perform global set-level and fine-grained instance-level alignment. Finally, the SE-ResNet-50 network is used to replace the commonly used ResNet-50 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU-MM01 dataset demonstrate that the proposed method favorably outperforms state-of-the-art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB-IR re-ID method will show better performance on a stronger baseline.
{"title":"Crossmodality Person Reidentification Based on Global and Local Alignment","authors":"Qiong Lou, Junfeng Li, Yaguan Qian, Anlin Sun, Fang Lu","doi":"10.1155/2022/4330804","DOIUrl":"https://doi.org/10.1155/2022/4330804","url":null,"abstract":"RGB-infrared (RGB-IR) person reidentification is a challenge problem in computer vision due to the large crossmodality difference between RGB and IR images. Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE-ResNet-50, which has achieved ideal results. It consists of three modules, including alignGAN module (\u0000 \u0000 A\u0000 \u0000 ), crossmodality paired-images generation module (\u0000 \u0000 G\u0000 \u0000 ), and feature alignment module (\u0000 \u0000 F\u0000 \u0000 ). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality-specific features of RGB and IR images to perform global set-level and fine-grained instance-level alignment. Finally, the SE-ResNet-50 network is used to replace the commonly used ResNet-50 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU-MM01 dataset demonstrate that the proposed method favorably outperforms state-of-the-art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB-IR re-ID method will show better performance on a stronger baseline.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"106 11","pages":"4330804:1-4330804:13"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91408443","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}
With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.
{"title":"Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features","authors":"Wenjin Xu, Shaokang Dong","doi":"10.1155/2022/9540944","DOIUrl":"https://doi.org/10.1155/2022/9540944","url":null,"abstract":"With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.","PeriodicalId":23995,"journal":{"name":"Wirel. Commun. Mob. Comput.","volume":"84 1","pages":"9540944:1-9540944:11"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83829471","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：