Aslan Nouri Moqadam, Hadi Sharifi, Reza Masoumi, Hossein Khalili, Mohammad Bemani
This paper introduces a novel, dual-band, compact 1:4 feed network employing a parallel power divider architecture designed to operate at 0.915 and 2.44 GHz, covering both industrial, scientific, and medical and ultra high frequency bands. The design leverages the non-linear phase characteristics of composite right/left-handed transmission lines to achieve dual-band functionality with high precision. Simulation results confirm the efficacy of the proposed network, which delivers quadrature-phase outputs with a 90° phase shift and uniform power distribution across all output ports, facilitating wideband circular polarisation in array antenna applications. Compared to traditional series power dividers, the parallel power divider offers significant advantages in terms of fabrication simplicity, reduced size, and lower manufacturing costs. The design avoids the use of non-radiating composite right/left-handed transmission lines and addresses impedance-matching challenges through the implementation of only three resistors, effectively isolating the output ports. The proposed architecture is highly scalable and can be easily adapted to various output port configurations, frequencies, and power division ratios, offering broad flexibility for a wide range of microwave applications.
{"title":"Compact Dual-Band Microstrip Array Feed Network Using CRLH-TL Power Dividers","authors":"Aslan Nouri Moqadam, Hadi Sharifi, Reza Masoumi, Hossein Khalili, Mohammad Bemani","doi":"10.1049/cmu2.70013","DOIUrl":"https://doi.org/10.1049/cmu2.70013","url":null,"abstract":"<p>This paper introduces a novel, dual-band, compact 1:4 feed network employing a parallel power divider architecture designed to operate at 0.915 and 2.44 GHz, covering both industrial, scientific, and medical and ultra high frequency bands. The design leverages the non-linear phase characteristics of composite right/left-handed transmission lines to achieve dual-band functionality with high precision. Simulation results confirm the efficacy of the proposed network, which delivers quadrature-phase outputs with a 90° phase shift and uniform power distribution across all output ports, facilitating wideband circular polarisation in array antenna applications. Compared to traditional series power dividers, the parallel power divider offers significant advantages in terms of fabrication simplicity, reduced size, and lower manufacturing costs. The design avoids the use of non-radiating composite right/left-handed transmission lines and addresses impedance-matching challenges through the implementation of only three resistors, effectively isolating the output ports. The proposed architecture is highly scalable and can be easily adapted to various output port configurations, frequencies, and power division ratios, offering broad flexibility for a wide range of microwave applications.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Babiyola Arulanandam, Khalid Nazim Abdul Sattar, Rocío Pérez de Prado, Bidare Divakarachar Parameshchari
Wireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta-heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective-perturbed learning and mutation strategy based artificial rabbits optimisation namely M-PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M-PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M-PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M-PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M-PMARO is 100 for 1200 rounds, which is higher than the EBPSO.
{"title":"An Efficient Cluster Based Routing in Wireless Sensor Networks Using Multiobjective-Perturbed Learning and Mutation Strategy Based Artificial Rabbits Optimisation","authors":"Babiyola Arulanandam, Khalid Nazim Abdul Sattar, Rocío Pérez de Prado, Bidare Divakarachar Parameshchari","doi":"10.1049/cmu2.70020","DOIUrl":"https://doi.org/10.1049/cmu2.70020","url":null,"abstract":"<p>Wireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta-heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective-perturbed learning and mutation strategy based artificial rabbits optimisation namely M-PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M-PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M-PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M-PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M-PMARO is 100 for 1200 rounds, which is higher than the EBPSO.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study proposed a complex fraud detection framework called CRAFIC (complex relationship analysis for fraud identification and cost management), which combines deep learning and graph neural networks to address complex fraud behaviours such as multi account collaborative fraud. The study used the DataCo Global supply chain dataset and the IEEE-CIS fraud detection dataset to extract order features using convolutional neural networks and long short term memory networks, and analysed the relationships between orders using graph attention networks to reveal complex fraud patterns. The results show that the CRAFIC framework performs well in both single order and collaborative fraud detection tasks. In single order fraud detection, the accuracy of the CRAFIC framework increased from the initial 45.21% to 93.75%, and the loss value decreased from 1.19 to 0.14, significantly better than other models. In collaborative fraud detection, the accuracy of the CRAFIC framework reached 90.3%, once again surpassing other models. These results validate the advantages of the CRAFIC framework in multimodal data fusion and complex relationship modelling. The CRAFIC framework reveals complex fraud patterns, optimizes internal controls and audit processes, enhances data security measures, prevents system vulnerabilities from being exploited, and enhances market reputation and customer trust.
{"title":"CRAFIC Framework: Multi-Account Collaborative Fraud Detection, Efficient Feature Extraction and Relationship Modelling Combined with CNN-LSTM and Graph Attention Network","authors":"Li Yangyan, Chen Tingting","doi":"10.1049/cmu2.70014","DOIUrl":"https://doi.org/10.1049/cmu2.70014","url":null,"abstract":"<p>This study proposed a complex fraud detection framework called CRAFIC (complex relationship analysis for fraud identification and cost management), which combines deep learning and graph neural networks to address complex fraud behaviours such as multi account collaborative fraud. The study used the DataCo Global supply chain dataset and the IEEE-CIS fraud detection dataset to extract order features using convolutional neural networks and long short term memory networks, and analysed the relationships between orders using graph attention networks to reveal complex fraud patterns. The results show that the CRAFIC framework performs well in both single order and collaborative fraud detection tasks. In single order fraud detection, the accuracy of the CRAFIC framework increased from the initial 45.21% to 93.75%, and the loss value decreased from 1.19 to 0.14, significantly better than other models. In collaborative fraud detection, the accuracy of the CRAFIC framework reached 90.3%, once again surpassing other models. These results validate the advantages of the CRAFIC framework in multimodal data fusion and complex relationship modelling. The CRAFIC framework reveals complex fraud patterns, optimizes internal controls and audit processes, enhances data security measures, prevents system vulnerabilities from being exploited, and enhances market reputation and customer trust.</p>","PeriodicalId":55001,"journal":{"name":"IET Communications","volume":"19 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cmu2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143481298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keming Ma, Qinlong Li, Kaizhi Huang, Ming Yi, Liang Jin
The development of reconfigurable intelligent surface (RIS) makes direction-of-arrival (DOA) estimation possible for single-antenna receivers. However, non-ideal situations such as spectral aliasing occur when facing communication signals using orthogonal frequency division multiplexing modulation. This paper proposes a RIS-based single-channel DOA estimation method for communication signals. Specifically, by extending the time intervals to dynamically reduce the RIS state change rate, a real-time DOA estimation is achieved while mitigating the impact of non-ideal spectral shifts on communication. Then, based on the compressed sensing and mutual incoherence property, the method exploits the sparse property of the signal in space to reduce the estimation time while improving the estimation accuracy. Simulation results show an