The transition towards the fifth generation(5G)of communication systems has been fueled by the need for compact,high-speed and wide-bandwidth systems.These advancements necessitate the development of novel and highly ...The transition towards the fifth generation(5G)of communication systems has been fueled by the need for compact,high-speed and wide-bandwidth systems.These advancements necessitate the development of novel and highly efficient antenna designs characterized by the compact size.In this paper,a novel antenna design with a hexagonal-shaped resonating element and two U-shaped open-ended stubs is presented.Millimeter-wave(mmWave)frequency range suffers from attenuation due to atmosphere and path loss because of higher frequencies.To address these issues,the deployment of a high-gain antenna is imperative.This design is created through an evolutionary process to work best in the mmWave frequency range with a high gain.A thin Rogers RT5880 substrate with a thickness of 0.254 mm,a dielectric constant of 2.3 and a loss tangent of 0.0009 supports the copper-based radiating element.A partial ground plane with a square slot and trimmed corners at the bottom enhances the antenna’s bandwidth.The single-element antenna exhibits a wide bandwidth of nearly 6 GHz and a gain of 4.58 dBi.By employing the proposed antenna array,the antenna gain is significantly enhanced to 14.90 dBi while maintaining an ultra-compact size of 24 mm×46 mm at the resonant frequency of 31 GHz.The antenna demonstrates a wider impedance bandwidth of 15.73%(28-34 GHz)and an efficiency of 94%.The proposed design works well for 5G communication and satellite communication,because it has a simple planar structure and focused dual-beam radiation patterns from a simple feeding network.展开更多
The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same sampl...The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.展开更多
基金National Natural Science Foundation of China(No.12272092)。
文摘The transition towards the fifth generation(5G)of communication systems has been fueled by the need for compact,high-speed and wide-bandwidth systems.These advancements necessitate the development of novel and highly efficient antenna designs characterized by the compact size.In this paper,a novel antenna design with a hexagonal-shaped resonating element and two U-shaped open-ended stubs is presented.Millimeter-wave(mmWave)frequency range suffers from attenuation due to atmosphere and path loss because of higher frequencies.To address these issues,the deployment of a high-gain antenna is imperative.This design is created through an evolutionary process to work best in the mmWave frequency range with a high gain.A thin Rogers RT5880 substrate with a thickness of 0.254 mm,a dielectric constant of 2.3 and a loss tangent of 0.0009 supports the copper-based radiating element.A partial ground plane with a square slot and trimmed corners at the bottom enhances the antenna’s bandwidth.The single-element antenna exhibits a wide bandwidth of nearly 6 GHz and a gain of 4.58 dBi.By employing the proposed antenna array,the antenna gain is significantly enhanced to 14.90 dBi while maintaining an ultra-compact size of 24 mm×46 mm at the resonant frequency of 31 GHz.The antenna demonstrates a wider impedance bandwidth of 15.73%(28-34 GHz)and an efficiency of 94%.The proposed design works well for 5G communication and satellite communication,because it has a simple planar structure and focused dual-beam radiation patterns from a simple feeding network.
基金supported by National Natural Science Foundation of China(No.61806006)Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
