Search Results (55)

Topological photonics has provided revolutionary ideas for the design of next-generation photonic devices due to its unique light transmission properties. This paper proposes a honeycomb photonic crystal structure based on a mirror-symmetric interface and numerically simulates the precise manipulation of topological edge states and the robust excitation of high-order topological corner states in this structure. Specifically, two honeycomb photonic crystals with non-trivial topological properties form an interface through mirror-symmetric stitching. Continuous adjustment of the spacing between their coupling pillars can induce the closure and reopening of topological edge state energy bands, accompanied by significant band inversion, revealing the dynamic process of topological phase transitions. Furthermore, zero-dimensional high-order topological corner states are observed at the junction of boundaries with different topological properties. Their localized field strengths are strictly confined and exhibit strong robustness against structural defects. This study not only provides a new mechanism for the local symmetry manipulation of topological edge states but also lays a foundation for the design of high-order topological photonic crystals and the practical application of topological photonic devices. Full article

The topological phase of matter brings extra inspiration for efficient light manipulation. Here, we propose two-parameter tunable topological transitions based on distorted Kagome photonic crystals. By selecting specific splicing boundaries, we successfully visualize several diverse types of robust edge states and corner states. Through introducing optical vortices with tunable orbital angular momentum, we demonstrate the directional excitation of multi-dimensional topological states as needed. Furthermore, we have studied the coupling effects of multi-dimensional photonic states and the modulation of source in three typical areas. This work provides an instructive avenue for manipulating light in integrated topological photonic devices. Full article

Three-dimensional topological insulators possess surface-conducting states in the bulk energy gap, which are topologically protected and can be well described as helical 2 + 1 Dirac fermions. The electromagnetic response is given by axion electrodynamics in the bulk, leading to a Maxwell–Chern–Simons theory at the boundary, which is the source of the Hall conductivity. In this paper, we extend the formulation of axion electrodynamics such that it captures higher-derivative corrections to the Hall conductivity. Using the underlying 2 + 1 quantum field theory at the boundary, we employ thermal field theory techniques to compute the vacuum polarization tensor at finite chemical potential in the zero-temperature limit. Applying the derivative expansion method, we obtain higher-order derivative corrections to the Chern–Simons term in 2 + 1 dimensions. To first order the corrections, we find that the Hall conductivity receives contributions proportional to ${\omega }^2$ and ${\mathbf{k}}^2$ from the higher-derivative Chern–Simons term. Finally, we discuss the electrodynamic consequences of these terms on the topological Faraday and Kerr rotations of light, as well as on the image monopole effect. Full article

With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. Self-supervised methods (SSL) for remote sensing image change detection (CD) can effectively address the issue of limited labeled data. However, most SSL algorithms for CD in remote sensing image rely on convolutional neural networks with fixed receptive fields as their feature extraction backbones, which limits their ability to capture objects of varying scales and model global contextual information in complex scenes. Additionally, these methods fail to capture essential topological and structural information from remote sensing images, resulting in a high false positive rate. To address these issues, we introduce a graph diffusion model into the field of CD and propose a novel network architecture called CGD-CD Net, which is driven by a structure-sensitive SSL strategy based on contrastive learning. Specifically, a superpixel segmentation algorithm is applied to bi-temporal images to construct graph nodes, while the k-nearest neighbors algorithm is used to define edge connections. Subsequently, a diffusion model is employed to balance the states of nodes within the graph, enabling the co-evolution of adjacency relationships and feature information, thereby aggregating higher-order feature information to obtain superior feature embeddings. The network is trained with a carefully crafted contrastive loss function to effectively capture high-level structural information. Ultimately, high-quality difference images are generated from the extracted bi-temporal features, then use thresholding analysis to obtain a final change map. The effectiveness and feasibility of the suggested method are confirmed by experimental results on three different datasets, which show that it performs better than several of the top SSL-CD methods. Full article

Mobile Ad Hoc Networks (MANETs) are decentralized wireless networks characterized by dynamic topologies and the absence of fixed infrastructure. These unique features make MANETs critical for applications such as disaster recovery, military operations, and IoT systems. However, they also pose significant challenges for efficient and effective routing. This study evaluates the performance of eight MANET routing protocols: Optimized Link State Routing (OLSR), Destination-Sequenced Distance Vector (DSDV), Ad Hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV), Temporally Ordered Routing Algorithm (TORA), Zone Routing Protocol (ZRP), and Geographic Routing Protocol (GRP). Using a custom simulation environment in OMNeT++ 6.0.1 with INET-4.5.0, the protocols were tested under four scenarios with varying node densities (20, 80, 200, and 500 nodes). The simulations utilized the Random Waypoint Mobility model to mimic dynamic node movement and evaluated key performance metrics, including network load, throughput, delay, energy consumption, jitter, packet loss rate, and packet delivery ratio. The results reveal that proactive protocols like OLSR are ideal for stable, low-density environments, while reactive protocols such as AOMDV and TORA excel in dynamic, high-mobility scenarios. Hybrid protocols, particularly GRP, demonstrate a balanced approach; achieving superior overall performance with up to 30% lower energy consumption and higher packet delivery ratios compared to reactive protocols. These findings provide practical insights into the optimal selection and deployment of MANET routing protocols for diverse applications, emphasizing the potential of hybrid protocols for modern networks like IoT and emergency response systems. Full article

With increasing power system complexity and distributed energy penetration, traditional voltage control methods struggle with dynamic changes and complex conditions. While existing deep reinforcement learning (DRL) methods have advanced grid control, challenges persist in leveraging topological features and ensuring computational efficiency. To address these issues, this paper proposes a DRL method combining Graph Convolutional Networks (GCNs) and soft actor-critic (SAC) for voltage control through load shedding. The method uses GCNs to extract higher-order topological features of the power grid, enhancing the state representation capability, while the SAC optimizes the load shedding strategy in continuous action space, dynamically adjusting the control scheme to balance load shedding costs and voltage stability. Results from the simulation of the IEEE 39-bus system indicate that the proposed method significantly reduces the amount of load shedding, improves voltage recovery levels, and demonstrates strong control performance and robustness when dealing with complex disturbances and topological changes. This study provides an innovative solution to voltage control problems in smart grids. Full article

Orientation transitions in a cholesteric liquid crystal (CLC) layer with negative dielectric anisotropy, under the influence of a non-uniform spatially periodic electric field created using a planar system of interdigitated electrodes, were studied experimentally and numerically. In the interelectrode space, transitions are observed from a planar Grandjean texture, with the helix axis perpendicular to the layer plane, to states with a lying helix, when the helix axis is parallel to the layer plane and perpendicular to the electrode stripes. It was found that the relaxation time of the induced state in the Grandjean zones, corresponding to two or more half-turns of the helix, significantly exceeded the relaxation time for the first Grandjean zone with one half-turn. An analysis of experimentally observed and numerically simulated textures shows that slow relaxation to the initial state in the second Grandjean zone, as well as in higher-order zones, is associated with the formation of local topologically equivalent states. In these states, the helix has a reduced integer number of helix half-turns throughout the layer thickness or unwound into the planar alignment state. Full article

This paper proposes a decentralized adaptive event-triggered fault-tolerant cooperative control (ET-FTCC) scheme for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and external disturbances under denial-of-service (DoS) attacks. The multiple UAVs and UGVs have a larger search radius, which is important in both the civilian and military domains. The different dynamics between UAVs and UGVs result in unbalanced interactions in the communication topologies, which increases the complexity of cooperative control. DoS attacks are conducted in both sensor and control channels. The dynamic models of UAVs and UGVs are introduced firstly, and the unified heterogeneous multiagent system model with actuator faults is established. The composite observer is designed to obtain the information of state and lumped disturbance, which is used to design the controller. In order to save the limited communication network resources, the event-triggered mechanism is introduced. The transformed error is presented by using the prescribed performance function (PPF). Then, the sliding-mode manifold is presented by combining the event-triggered control scheme to achieve the tracking purpose with actuator faults, external disturbances, and DoS attacks. Based on the Lyapunov function approach, the tracking errors are bounded within the prescribed boundary. Finally, the effectiveness of the proposed method is verified by qualitative analysis and quantitative analysis of the simulation results. This study can enhance the security and reliability of heterogeneous multiagent systems, providing technical support for the safe operation of unmanned systems. This paper mainly solves the FTCC problem of second-order nonlinear heterogeneous multiagent systems, and further research is needed for the FTCC problem of higher-order nonlinear heterogeneous multi-agent systems. In addition, the system may encounter multiple cyber attacks. As one of the future research works, we can extend the results of this paper to high-order nonlinear systems under multiple cyber attacks, which contain DoS attacks and deception attacks, and achieve fault-tolerant cooperative control of heterogeneous multiagent systems. Full article

In this paper, we examine the role played by topology, and some specific boundary conditions as well, on the physics of a higher-dimensional black hole. We analyze the line element of a five-dimensional non-extremal Reissner–Nordström black hole to obtain a new family of subspaces that are types of strong retractions and deformations, and then we extend these results to higher dimensions in order to deduce the relationship between various types of transformations. We also study the scalar field perturbations in the background under consideration and obtain an analytical expression for the quasibound state frequencies by using the Vieira–Bezerra–Kokkotas approach, which uses the polynomial conditions of the general Heun functions, and then we discuss the stability of the system and present the radial eigenfunctions. Our main goal is to discuss the physical meaning of these mathematical applications in such higher-dimensional effective metric. Full article

The combination of higher-order topological insulators and valley photonic crystals has recently aroused extensive attentions due to the great potential in flexible and efficient optical field manipulations. Here, we computationally propose a photonic device for the 1550 nm communication band, in which the topologically protected electromagnetic modes with high quality can be selectively triggered and modulated on demand. Through introducing two valley photonic crystal units without any structural alteration, we successfully achieve multi-dimensional coupled topological states thanks to the diverse electromagnetic characteristics of two valley edge states. According to the simulations, the constructed topological photonic devices can realize Fano lines on the spectrum and show high-quality localized modes by tuning the coupling strength between the zero-dimensional valley corner states and the one-dimensional valley edge states. Furthermore, we extend the valley-locked properties of edge states to higher-order valley topological insulators, where the selected corner states can be directionally excited by chiral source. More interestingly, we find that the modulation of multi-dimensional coupled photonic topological states with pseudospin dependence become more efficient compared with those uncoupled modes. This work presents a valuable approach for multi-dimensional optical field manipulation, which may support potential applications in on-chip integrated nanophotonic devices. Full article

This work proposes and examines the feasibility of next-generation 0.3 THz phase shifters realized with liquid crystals (LCs) as tunable dielectrics coaxially filled in the transmission line. The classic coaxial transmission line topology is robust to electromagnetic interference and environmental noise, but is susceptible to higher-order modes from microwave to millimeter-wave towards terahertz (THz) wavelength ranges, which impedes the low-insertion-loss phase-shifting functionality. This work thus focuses primarily on the suppression of the risky higher-order modes, particularly the first emerging ${\mathrm{T}\mathrm{E}}_{11}$ mode impacting the dielectric loss and metal losses in diverse manners. Based on impedance matching baselines at diverse tuning states of LCs, this work analytically derives and models two design geometries; i.e., design 1 for the coaxial geometry matched at the isotopically referenced state of LC for 50 Ω, and design 2 for geometry matched at the saturated bias of LC with the maximally achievable permittivity. The Figure-of-Merit for design 1 and design 2 reports as 35.15°/dB and 34.73°/dB per unit length, respectively. We also propose a constitutive power analysis method for understanding the loss consumed by constitutive materials. Notably, for the 0.3 THz design, the isotropic LC state results in an LC dielectric loss of 63.5% of the total input power (assuming 100%), which becomes the primary constraint on achieving low-loss THz operations. The substantial difference in the LC dielectric loss between the isotropic LC state and saturated bias state for the 0.3 THz design (35.76% variation) as compared to that of our past 60 GHz design (13.5% variation) indicates that the LC dielectric loss’s escalating role is further enhanced with the rise in frequency, which is more pronounced than the conductor losses. Overall, the results from analytical and finite-element optimization in this work shape the direction and feasibility of the unconventional THz coaxial phase shifting technology with LCs, actioned as continuously tunable dielectrics. Full article

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods. Full article

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Most previous network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node’s involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix, which is composed of the motif distribution vector of every node and the Jensen–Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models, as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings. Full article

With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols. Full article

The exploration of topologically nontrivial states in condensed matter systems, along with their novel transport properties, has garnered significant research interest. This review aims to provide a comprehensive overview of representative topological phases, starting from the initial proposal of the quantum Hall insulator. We begin with a concise introduction, followed by a detailed examination of first-order topological quantum phases, including gapped and gapless systems, encompassing relevant materials and associated phenomena in experiment. Subsequently, we delve into the realm of exotic higher-order topological quantum phases, examining both theoretical propositions and experimental findings. Moreover, we discuss the mechanisms underlying the emergence of higher-order topology, as well as the challenges involved in experimentally verifying materials exhibiting such properties. Finally, we outline future research directions. This review not only systematically surveys various types of topological quantum states, spanning from first-order to higher-order, but also proposes potential approaches for realizing higher-order topological phases, thereby offering guidance for the detection of related quantum phenomena in experiments. Full article