Multimodal sentiment analysis (MSA) often suffers from missing or degraded modalities. Although Transformer-based reconstruction methods can recover missing features, their high computational cost limits scalability. To address these challenges, we propose a lightweight Dual-Channel Guided Reconstruction Mamba Network (DCGRM-Net), which can be embedded into pre-trained language models (PLMs). Leveraging Mamba’s linear-time modeling and MLP-based conditional generation, DCGRM-Net reduces model complexity while maintaining high computational efficiency. It comprises a Bidirectional Interactive Reconstruction Channel (BIRC) for cross-modal missing feature generation and a Text-Guided Modality Refinement Channel (TGMR) for multi-scale and multi-attention refinement of non-verbal modalities. The reconstructed textual features are fused with both channel outputs and fed into the PLM for sentiment prediction. Evaluations on two benchmarks (CMU-MOSI, 2,199 segments; CMU-MOSEI, 23,454 segments) show that DCGRM-Net surpasses state-of-the-art methods by 2.1%/1.1% and 6.63%/5.46%, respectively, under text-missing and text-based bimodal absence, demonstrating strong robustness and transferability. Furthermore, we conducted a preliminary exploration of the integration of Prompt-Learning methods with DCGRM-Net, revealing their potential and efficient scalability in lightweight modal reconstruction. These results indicate that DCGRM-Net provides an effective and lightweight solution for robust MSA in incomplete data scenarios.

In this letter, a compact high-gain full-space beam scanning leaky-wave antenna based on dual-layer spoof surface plasmon polaritons (SSPPs) is proposed. By exploiting the asymmetric characteristics of the dual-layer SSPPs unit, the proposed antenna enables independent eigenmode dispersion tuning and effective open-stopband (OSB) suppression, while maintaining mode-1-dominant propagation. Moreover, lateral slots are introduced to redistribute the surface current and stabilize the leakage characteristics near broadside. Compared with equal-length-stub SSPP units, the proposed unit provides a higher phase constant, leading to an enhanced scanning rate. The antenna is fabricated and measured, and the measured results agree well with the simulations. With an overall length of 5.23λ0, the antenna achieves continuous full-space beam scanning over 7.53 − 11.31 GHz with a scanning rate of 4.67°/%. The realized gain ranges from 8.28 to 11.32 dBi, corresponding to a realized gain per unit length of 2.01 dBi/λ0. Owing to effective OSB suppression, the gain fluctuation around broadside is less than 0.5 dB, while the radiation-efficiency variation is below 5%. In addition, the radiation efficiency exceeds 70% across the entire operating band, and the sidelobe level remains below −8 dB.

A dispersive volume-average polarized effective permittivity (D-VP-EP) algorithm is introduced for the analysis of arbitrarily shaped three-dimensional dispersive materials, utilizing the complex-conjugate pole-residue (CCPR) model within the finite-difference time-domain (FDTD) method. The D-VP-EP employs the fitting algorithm as the preprocessor in the frequency domain, followed by an interpolation algorithm in the spatial domain, to avoid conformal-induced changes in the form of the constitutive equations during subsequent conformal mesh generation. The D-VP-EP algorithm maintains the iterative formulation of conventional CCPR-FDTD while enabling conformal meshing between 2 dispersive materials with an arbitrary number of poles. In scattering simulations of metal-dielectric dispersive bilayer nanosphere and transmission spectrum simulations of a dispersive micro-ring, the D-VP-EP effectively reduces mesh mismatch errors at curved interfaces when compared to volume-averaged or staircase, and only needs 1/16 of the computing resources of the comparison group to achieve the same accuracy.

This study addresses the critical challenge of incomplete downhole drilling tool attitude data in rotary steerable drilling systems, a significant issue impacting drilling trajectory control, efficiency, and safety. Attitude data loss often occurs due to sensor malfunctions, communication errors, and harsh drilling environments, posing risks to accurate geological modeling and decision-making. Traditional interpolation methods struggle with the nonlinear, time-dependent nature of drilling data, resulting in poor accuracy and reliability. To overcome these limitations, a Long Short-Term Memory (LSTM) model, a type of deep learning algorithm known for capturing complex time-series dependencies, is proposed for data completion. This model was trained and validated using a dataset collected from a rotary steerable system laboratory test platform, including acceleration, magnetic flux, and Hall sensor pulse signals under varying inclination and rotational speeds. Experimental results show that the LSTM model achieves superior performance, with a coefficient of determination (R2) exceeding 0.95 and significantly lower mean squared errors compared to Fully Connected Neural Networks (R2 = 0.88) and other regression-based methods. The completed attitude data closely replicates original signal trends, accurately reconstructing gravitational acceleration, magnetic flux, and TICK signals despite noise and data loss. This work goes beyond previous studies by applying deep learning specifically to downhole drilling tool attitude data completion, a novel approach not previously explored in petroleum exploration literature. The demonstrated accuracy and robustness of the LSTM model provide a reliable, data-driven solution for enhancing drilling path optimization, reducing non-productive time, and minimizing operational risks. This research contributes a scalable method to improve drilling efficiency and resource recovery rates, with potential applicability to broader data-driven drilling automation systems.

Existing multimodal sentiment analysis (MSA) methods usually adopt fixed convolution kernels or static windows to model features from limited or fixed scales, making it difficult to dynamically model emotional features under different scale combinations. Furthermore, the absence of mechanisms to suppress redundant information in non-linguistic (video and audio) modalities hinders further performance improvements. To address these limitations, we propose a text guided multimodal scale path fusion network (TMSPF-Net). TMSPF-Net contains three main modules: Multi-scale Adaptive Transformer (MAT), Text-guided Conflict Elimination Module (TGCEM), and Channel Fusion Module. MAT captures the interaction of intra-modal and inter-modal through the combination of patches of different sizes and the dual attention mechanism, fully extracting multi-level global and local emotional information. Meanwhile, the adaptive routing module in MAT dynamically optimizes the feature paths through a learnable mechanism, enabling MAT to adaptively select the optimal path and increasing the flexibility of the model when dealing with heterogeneous data. TGCEM leverages multi-scale text-guided dynamic memory in MAT to filter conflicting signals and selectively preserve emotionally salient patterns in non-linguistic modalities, thereby improving the consistency and semantic richness of multimodal representations. Channel Fusion Module fuses the output results of these two modules and inputs them into the pre-trained language model to complete the MSA task. Extensive experiments on the MOSI and MOSEI datasets demonstrate that TMSPF-Net outperforms in most metrics than state-of-the-art methods. The results show that TMSPF-Net effectively guides the learning of non-linguistic modalities, integrates multi-level sentiment features, showing great potential in sentiment analysis.

This paper proposes a machine learning technique based on “training while drilling” to improve the integrity and accuracy of resistivity logging data in the oil and gas industry. Resistivity logging is a critical tool for assessing hydrocarbon content in geological formations; however, it often suffers from data loss due to hardware failures caused by the harsh high-temperature conditions encountered downhole. To address these challenges, this study introduces Long Short-Term Memory (LSTM) neural networks, a deep learning technique well-suited for handling time-series data and predicting missing values. Unlike traditional surface-based data processing, this method integrates the trained LSTM model into embedded devices, enabling real-time downhole data completion and significantly enhancing the efficiency and immediacy of data processing. “training while drilling” refers to the continuous learning and model updating based on newly acquired data during drilling operations. This process utilizes downtime, such as during drill pipe or bit changes, to input fresh data as a training set, allowing for real-time model optimization. Experimental results demonstrate that this method notably improves the predictive accuracy of the LSTM model, with enhanced capability to capture detailed information and adapt to changes in formation characteristics when compared to the initially trained model. Moreover, the LSTM model outperforms other deep learning algorithms, including Fully Connected Neural Networks (FCNN), in terms of prediction accuracy and stability. The dataset used in this study is based on field data from the Shengli Oilfield in Dongying, with measurements spanning depths of 1140 to 1690 m. Through comparative experiments, the LSTM model achieved a mean squared error (MSE) of 0.0610, significantly lower than the MSE of 0.0961 observed for the FCNN and other traditional methods. These findings highlight the superior performance of the “training while drilling” approach in improving prediction accuracy of electromagnetic wave logging curves, providing a novel solution with considerable practical value and broad potential for future development in the oil and gas exploration sector.

Significance Electronic-photonic convergence (EPC) represents a crucial technology for addressing performance limitations in traditional electronic systems and improving information processing efficiency. The design of electronic-photonic integrated chips for next-generation computing interconnects and high-speed communications necessitates addressing challenges in cross-domain co-optimization between photonic and electronic components. Current EPC co-simulation methods encounter several technical constraints, including insufficient integration of electronic and photonic toolchains, inefficient cross-platform data exchange, and the lack of unified multiphysics domain solvers. Photonic device modeling can be implemented through hardware description language (HDL) by utilizing established complementary metal oxide semiconductor (CMOS) platforms and electronic design automation (EDA) tools. This methodology enables photonic simulation integration into microelectronic design environments, reduces dependence on specialized photonic simulation tools, and facilitates efficient electronic-photonic system development within a unified platform. Process This paper presents a comprehensive review of photonic device modeling in the context of emerging trends in electronic-photonic co-simulation technologies. Photonic devices can be modeled using various hardware description languages, such as Verilog-A or SPICE, which rely on the differential-algebraic equation (DAE) solving framework intrinsic to EDA platforms. These flexible mathematical expression and computational capabilities are particularly well-suited for describing the wave properties and optical field variations of photonic devices. Current photonic device modeling primarily employs hardware description language, including Verilog-A behavioral-level modeling, SPICE circuit-level modeling, and hybrid techniques that combine both methods. Verilog-A, a widely employed hardware description language in integrated circuit design, is intended to model the dynamic behavior of devices and analog circuits. Verilog-A behavioral modeling focuses on describing the relationship between device inputs and outputs from a higher level of abstraction. The modeling principle lies in establishing mathematical relationships, via analytical functions or scattering matrices, between input signals and device responses, thereby avoiding the direct modeling of complex physical mechanisms. With its inherent flexibility and numerical computation capabilities, Verilog-A has been effectively extended to optoelectronic device modeling, enabling accurate representation of the dynamic evolution of optical fields. In 2013, Kononov established a photonic Verilog-A library that included elements such as lasers, photodetectors, waveguides, and directional couplers, enabling optical signal transmission and computation within an EDA environment. Several studies on the Verilog-A modeling approach have aimed to enhance the expressive capabilities for photonic devices. For example, in 2024, Zhang proposed an improved model structure to capture nonlinear effects (Fig. 2). SPICE typically refers to circuit netlists composed of fundamental electrical components such as resistors, capacitors, inductors, and controlled sources. This modeling language characterizes circuit structures by describing the interconnections of circuit nodes and formulates equations based on Kirchhoff's laws to solve for voltage and current distributions. Since SPICE represents the most fundamental unit in electronic design, modeling photonic devices as equivalent electrical components facilitates the co-design and co-simulation with surrounding driving circuits. For active photonic components such as lasers and photodetectors, the presence of significant carrier transport phenomena makes SPICE equivalent circuit models particularly well-suited for capturing the electrical and optical behavior through rate equation formulations (Fig. 3 and Fig. 4). Meanwhile, as modulators are driven by electrodes, their electrical characteristics can be inherently represented using circuit-level models. In 2017, Shin proposed an equivalent circuit modeling method for modulators, in which the device was divided into three sections based on coupled-mode theory [Figs. 5(a) and 5(b)]. The equivalent circuit modeling of passive devices presents challenges, as their behavior primarily depends on electromagnetic field distributions rather than carrier transport. Consequently, extracting electrical equivalent models directly from their modal characteristics or geometric structures proves difficult. In 2022, Ye proposed an SPICE modeling method for passive devices, where S-parameters were modeled using the complex vector fitting (CVF) algorithm through pole-residue decomposition [Fig. 5(c)]. In 2024, Ming proposed an SPICE equivalent circuit model for photonic devices based on a numerical equivalence approach. By utilizing controlled sources, functional operations were implemented to map the analytical expressions of photonic devices into logic computation circuits [Fig. 5(d)]. In addition, hybrid modeling approaches that integrate SPICE with Verilog-A combine the circuit-level accuracy with the lightweight flexibility of behavioral-level (Fig. 6). This enables efficient mapping of photonic devices into EDA environments, enhancing the capabilities for system-level design and analysis in electronic-photonic integrated systems. A comparison of the functional capabilities of different modeling methods is summarized (Table 1). However, the fundamental differences in the nature and propagation mechanisms between optical and electrical signals pose significant challenges in directly incorporating photonic devices into conventional microelectronic simulation workflows. In 2015, Agaskar proposed the equivalent baseband shifting solution, in which the optical carrier frequency was shift-down to zero frequency by selecting a reference frequency. To address accuracy degradation caused by cumulative errors in cascaded photonic device models, Jiang proposed an infinite impulse response (IIR) modeling method in 2024. This method enabled frequency-range extrapolation during modeling by adjusting the sampling frequency, effectively suppressing numerical error accumulation. In the same year, Feng from the same research group introduced a polynomial extrapolation approach for frequency-domain S-parameters, further improving the practicality and scalability of the modeling framework (Fig. 7). In 2024, Fang proposed an efficient and high-accuracy simulation method for optoelectronic integrated links based on an EDA platform, significantly improving the efficiency of electronic-photonic co-simulation. Conclusions and Prospects Substantial advances have been achieved in hardware description language modeling of photonic devices, establishing a crucial foundation for EPC. This field enables the design and simulation of integrated optoelectronic systems on established microelectronic platforms, following IC design principles. The integration of photonic models into the electronic design workflow eliminates reliance on external simulation engines and removes technical barriers between these domains, providing a viable pathway toward electronic-photonic design automation (EPDA). This paper reviews the progress in photonic device modeling. While the proposed methodologies and implemented module characteristics vary, the fundamental aim remains consistent: accurately representing optical and physical characteristics of photonic devices within EDA environments. The paper addresses challenges in modeling accuracy and simulation efficiency, presenting corresponding solutions. Future developments should emphasize the hierarchical and collaborative advancement of modeling approaches, progressing from behavioral models to phenomenological models, and ultimately to full-physics models, based on the unified solution of electrical Kirchhoff's laws and optical Maxwell's equations.

Recent research in multimodal sentiment analysis (MSA) has primarily focused on the fusion of modal characteristics and the interactions between multiple modalities. Most existing modal feature interaction methods either directly concatenate features from individual modalities or employ transformer-based approaches to extract and facilitate interaction between them. However, direct fusion methods often fail to capture the interaction between modalities and overlook the central role of text information in sentiment analysis. While transformer models can effectively enable intermodal interaction, they typically result in an increase in model parameters, which can hinder practical deployment. Additionally, noise unrelated to sentiment in non-linguistic modalities can compromise model accuracy. To address these challenges, this paper proposes a multimodal sentiment analysis model based on the All-MLP architecture, called CU-SEMLP. First, we introduce a novel modal interaction module, Shift-MLP, designed to facilitate the sharing of text-based information. Shift-MLP enhances modal interaction through spatial shift operation, and can capture rich emotional information in text-based mixed modalities, improving the expressiveness and adaptability of the model. Second, we propose the EM-MLP module as a replacement for transformer-based approaches, targeting noise suppression in non-linguistic modalities. EM-MLP simulates attention mechanisms through shift operations and a series of linear layers and normalization. Since both modules are based on the All-MLP architecture, the overall model parameter count is significantly reduced. We evaluate CU-SEMLP on the CMU-MOSI and CMU-MOSEI datasets. Compared to baseline methods, CU-SEMLP achieves better performance with fewer parameters, and a series of ablation experiments demonstrate the effectiveness of each module. The experimental results show that CU-SEMLP can effectively complete the multimodal sentiment analysis task.

Electromagnetic wave logging while drilling (LWD) technology is an important tool for evaluation of formation oil and gas content. It generally adopts multi-transmitter–receiver coil system structure and the symmetrical coil system arrangement with equal transmitter–receiver spacing can obtain the measurement results with borehole compensation. Here, we develop a method to realize wellbore compensation by deep learning for logging data inversion. This paper focuses on reasonable inversion of logging data through deep learning technology, which is combined with Levenberg–Marquardt (LM) algorithm, modular and fast construction of deep neural network (DNN) model. Under the condition of reducing the outermost transmitting coil, the logging data are inversed, and the inversion effect is evaluated. Our research shows that the combination of neural network and logging data can realize the measurement results with borehole compensation under the condition of reducing one transmitting coil, thereby shortening the instrument length to reduce drilling tool sticking risk and effectively reducing the LWD instrument structure complexity, high power and other problems. At the same time, the accuracy of logging data inversion is tested. The test results show that the DNN method can achieve high-precision inversion, and the average error is reduced by about 50

Emotion recognition in conversation (ERC) represents a pivotal research domain within affective computing, concentrating on discerning the emotional nuances embedded within individual utterances during conversational exchanges. The majority of current research focuses on modeling situational cues, with relatively little attention paid on affective tendencies inherent in emotional expression. Moreover, ensuring the fair representation of diverse modalities in emotional expression presents a significant challenge in effectively extracting synergies and insights from multi-modal data sources. To tackle these challenges, this study proposes a novel approach termed the Uni-Modal and Cross-Modal Encoding Network based on Multi-Head Attention (UCEMA) for ERC. The framework leverages two distinct encoding techniques, namely Uni-Modal Encoding based on Multi-Head Attention (UEMA) and Cross-Modal Encoding based on Multi-Head Attention (CEMA), to extract distinct emotional features from individual modes and facilitate the fusion of emotional attributes within the multi-modal context. Particular emphasis is placed on textual input as the primary mode of interaction. Additionally, this study employs Context Modeling (CM) to analyze the outcomes of emotion recognition in conversational contexts. A comprehensive comparative analysis of the UCEMA was conducted on two publicly available datasets, IEMOCAP and MELD. The results demonstrated that the UCEMA exhibited superior efficacy. It is noteworthy that the proposed framework effectively balances intra-modal emotional orientation information, inter-modal emotional association information, and context-related cues, thereby demonstrating superior performance in recognition accuracy compared to current state-of-the-art (SOTA) models.

In the paper, a circularly polarized (CP) frequency beam-scanning antenna based on photonic crystal (PC) is proposed, which possesses the characteristics of eliminating open-stopband (OSB). In the microwave field, the PC is typically constructed by embedding the air or metal column into the material. However, it is difficult for the PC with perfect magnetic conductor walls to accomplish the degeneracy of eigenmodes at the Gamma point. Additionally, antennas based on the PC with perfect electric conductor walls exhibit a narrow scanning angle. The proposed unit based on 1D PC possesses perfect electric and magnetic conductor walls. The unit can not only eliminate OSB but also has a wide scanning range. In addition, the unit breaks into the original symmetrical field distributions of 1D PC. Based on the perturbation theory, leaky-wave slots can be designed to generate electromagnetic waves with the CP characteristics. And a CP antenna based on the unit is designed. The antenna can achieve a 70 degrees beam-scanning within the entire operating band (7.6-9.2 GHz), and the variation of the realized gains is below 1.0 dBi around the broadside frequency. Simultaneously, the antenna is fabricated and measured. The measured results are in accordance with the simulation, which validates the success of the design.

With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

A new type of low terahertz (THz) leaky-wave antennas (LWAs) with circular polarization (CP) characteristic is proposed in this paper. The CP LWA is based on the asymmetric structure and the accidental degeneracy of modes at the Γ-point in 1D photonic crystals. Because the composite right/left handed and CP properties can be achieved by unit cells of the proposed antenna, as well as the refractive index is zero at the broadside frequency, the beam scanning of the proposed CP LWA is from backward direction to forward direction through the broadside. There won’t be any deterioration of broadside performances, especially the gain whose fluctuation is only 0.25 dBi at and around broadside frequency. The gain varies from 14.85 to 16.75 dBi and the scan range is from − 23.5° to 40.4° over the entire operating frequency band. By optimizing the “mouth” slots, the axial ratio (AR) is less than 3 dB from 1.387 to 1.639 THz.

A dual-band metasurface antenna is designed consisting of three-layer metal patches and two-layer dielectric substrates. To facilitate the modal analysis of the metasurface, Characteristic Mode Analysis (CMA) is used to analyze the metasurface antenna with 4 x 4 rectangular patches, and the performance of the antenna is optimized based on the Modal Significance (MS) curves. In order to excite the current of different characteristic modes at certain frequencies, symmetric resonant arms and cross-shaped impedance matching converters are used in the feeding structure. The measured results are consistent with the simulated values, and the designed antenna can yield gains of 7.67 dBi at 3.5 GHz and 7.28 dBi at 4.9 GHz, which provides the potential applications in 5G and other wireless communications.

A terahertz plasmonic sensor based on parallel bulk Dirac semimetals (BDSs) is proposed and numerically investigated in this paper. The physical origin of the sensor is induced by the electromagnetically induced reflectance (EIR)-like effect between two identical resonators. The performance of the sensor is influenced by changing the coupling distance between the waveguide and resonator, the Fermi levels of the BDSs, and the analyte permittivity. Due to the strong field confinement of surface plasmon polaritons (SPPs) between the parallel BDSs, the saturated thickness of the analyte is low enough to ensure the reliable performance. In addition, the performance can be further improved by increasing the resonant order of the resonator, and the maximum sensitivity and FOM values can attain 120 GHz/RIU and 4.2, respectively. The proposed sensor owns potential prospects in terahertz sensing applications.

A dual-band dual-polarized frequency beam-scanning (FBS) antenna based on half-mode substrate integrated waveguide (HMSIW) and photonic crystal (PC) is proposed. HMSIW can decrease the width of the substrate, and each eigenmode of PC has own unique electric-field distribution, which can be good for generating different polarization types. Therefore, the proposed antenna not only has a narrow substrate, but also has the charac-teristic of circularly polarized (CP) and linearly polarized (LP) radiation, which can be used for overcoming effectively the multipath fading and enhancing the channel capacity. For increasing the bandwidth of 3 dB axial ratio (AR), the slots on top surface are not perfectly symmetrical in the improved unit cell. The asymmetrical antenna based on the improved unit is manufactured and measured. The antenna can accomplish 33 degrees scanning angle with the realized gains of 7.9-10.6 dBi in the CP working band (7.2 to 8.2 GHz), and can also achieve 24 degrees beam-scanning for the LP radiation (8.8 to 9.6 GHz), whose realized gains are from 9.5 to 11.5 dBi. In addition, the experimental results are highly consistent with the simulated results, verifying that the design is feasible.

This letter proposes a novel simple planar circularly polarized (CP) frequency beam-scanning (FBS) antenna using slotline (SL) and spoof surface plasmon polariton (SSPP) structure. A new SL-SSPP slow-wave transmission line (TL) is initiated by periodically introducing orthogonal tilted corrugated grooves on both sides of the SL, which can generate orthogonal electric-field components in the same cross-section along the TL, and its dispersion characteristic can be flexibly adjusted. In order to excite an effective radiating mode from the propagating slow-wave mode, a simple double-sided tilted modulation method is devised to periodically modulate the SL-SSPP TL. Furthermore, to obtain the CP characteristic, a π/2 phase displacement between the sinusoidal modulation structures on both sides of the TL is designed. The configuration of presented CP beam-scanning antenna owns the merits of simpler structure, low profile, and single dielectric layer. A prototype is manufactured and then tested to verify the proposed design. The experimental results confirm that, within the 3 dB axial ratio bandwidth (from 7.2 to 8.35 GHz), the designed antenna achieves a 35° beam-steering range with CP characteristic. This antenna also realizes appropriate gain and radiation efficiency in the working band.

In this study, a triple-band microstrip antenna with compact size and simple structure is proposed for WLAN and 5G applications. The radiating element consists of a circular patch, a Y-shaped patch, and a monopole impedance converter. In order to obtain the desired operating bands, the monopole impedance converter is inserted between the circular patch and Y-shaped patch. The proposed antenna can work in the frequency range of 2.38∼2.53 GHz, 3.29∼4.11 GHz, and 4.72∼5.01 GHz, with the corresponding peak gains of 4.09 dBi (2.4 GHz), 2.95 dBi (3.5 GHz), and 4.01 dBi (4.9 GHz), respectively. The measures’ results are in approximate agreement with the simulated values, which shows that the proposed compact antenna can offer omnidirectional radiation, appropriate gains, and sufficient bandwidths.

Y In this paper, we present a novel frequency beam-scanning leaky-wave antenna (LWA) based on a composite halfmode substrate integrated waveguide/spoof surface plasmon polariton (HMSIW/SSPP) waveguide. The new composite HMSIW/SSPP waveguide is accomplished by embedding periodical symmetric subwavelength grooves on both copper surfaces of the HMSIW, which owns flexible transmission characteristics attributed to its features from both HMSIW and SSPP. The conversion of propagating slow-wave mode to radiation mode is devised by a double-sided symmetric sinusoidal modulation method with a pi/2 displacement between the top- and bottomsurface. This double-sided modulation method not only offers a compact structure but also effectively eliminates the open stopband. A prototype of the proposed antenna is fabricated and measured to verify the predicted performance from simulation. Experimental results show that the proposed antenna achieves a continuous wide scanning range of 125 degrees from backward to forward for lateral unidirectional radiation with an average peak gain level of 9.1 dBi from 6.3 to 9.7 GHz. And the compact antenna volume of 0.40 x 8.0 x 0.013 lambda(3)(0) offers a well scanning rate (36.8 degrees/GHz) and a very high gain value per unit volume (219 dBi/lambda(3)(0)).