Matching Network Research Articles

SEMScene: Semantic-Consistency Enhanced Multi-Level Scene Graph Matching for Image-Text Retrieval

Image-text retrieval, a fundamental cross-modal task, performs similarity reasoning for images and texts. The primary challenge for image-text retrieval is cross-modal semantic heterogeneity, where the semantic features of visual and textual modalities are rich but distinct. Scene graph is an effective representation for images and texts as it explicitly models objects and their relations. Existing scene graph based methods have not fully taken the features regarding various granularities implicit in scene graph into consideration (e.g. triplets), the inadequate feature matching incurs the absence of non-trivial semantic information (e.g. inner relations among triplets). Therefore, we propose a S emantic-Consistency E nhanced M ulti-Level Scene Graph Matching (SEMScene) network, which exploits the semantic relevance between visual and textual scene graphs from fine-grained to coarse-grained. Firstly, under the scene graph representation, we perform feature matching including low-level node matching, mid-level semantic triplet matching, and high-level holistic scene graph matching. Secondly, to enhance the semantic-consistency for object-fused triplets carrying key correlation information, we propose a dual-step constraint mechanism in mid-level matching. Thirdly, to guide the model to learn the semantic-consistency of matched image-text pairs, we devise effective loss functions for each stage of the dual-step constraint. Comprehensive experiments on Flickr30K and MS-COCO datasets demonstrate that SEMScene achieves state-of-the-art performances with significant improvements.

A Compact GaN Power Amplifier Module for New Generation Cellular Basestations

This paper presents a compact class-AB Power Amplifier Module (PAM) designed for new generation massive Multiple Input Multiple Output (MiMo) cellular base stations. The module is designed at the center frequency of 3.5GHz targeting Long Term Evolution (LTE) and 5G New Radio (NR) bands. The module is a hybrid design that incorporates a Gallium Nitride (GaN) High Mobility-Electron Transistor (HEMT) die, discrete components-based input, and output matching networks. The entire design is realized on a 8.5 x 5.2 mm, 2-layer Rogers4003C substrate. The module is assembled on a PCB as an open-top for post-characterization tuning. The small signal and large signal measurements are in quite good agreement. The measurement results show that the amplifier is unconditionally stable, the input return loss is 12.2 dB, the output return loss is 7.7 dB, and the small signal gain is 13.4 dB. The saturated output power is 33.3 dBm with a Power Added Efficiency of 20.1%. The small signal gain drops to 11.2 dB at around 22 dBm of input power due to GaN technology’s intrinsic soft compression characteristics.

Segmentation and Tracking Based on Equalized Memory Matching Network and Its Application in Electric Substation Inspection

With the wide application of deep learning, power inspection technology has made great progress. However, substation inspection videos often present challenges such as complex backgrounds, uneven lighting distribution, variations in the appearance of power equipment targets, and occlusions, which increase the difficulty of object segmentation and tracking, thereby adversely affecting the accuracy and reliability of power equipment condition monitoring. In this paper, a pixel-level equalized memory matching network (PEMMN) for power intelligent inspection segmentation and tracking is proposed. Firstly, an equalized memory matching network is designed to collect historical information about the target using a memory bank, in which a pixel-level equalized matching method is used to ensure that the reference frame information can be transferred to the current frame reliably, guiding the segmentation tracker to focus on the most informative region in the current frame. Then, to prevent memory explosion and the accumulation of segmentation template errors, a mask quality evaluation module is introduced to obtain the confidence level of the current segmentation result so as to selectively store the frames with high segmentation quality to ensure the reliability of the memory update. Finally, the synthetic feature map generated by the PEMMN and the mask quality assessment strategy are unified into the segmentation tracking framework to achieve accurate segmentation and robust tracking. Experimental results show that the method performs excellently on real substation inspection scenarios and three generalized datasets and has high practical value.

Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits

BackgroundThe rumen microbiome enables ruminants to digest otherwise indigestible feedstuffs, thereby facilitating the production of high-quality protein, albeit with suboptimal efficiency and producing methane. Despite extensive research delineating associations between the rumen microbiome and ruminant production traits, the functional roles of the pervasive and diverse rumen virome remain to be determined.ResultsLeveraging a recent comprehensive rumen virome database, this study analyzes virus-microbe linkages, at both species and strain levels, across 551 rumen metagenomes, elucidating patterns of microbial and viral diversity, co-occurrence, and virus-microbe interactions. Additionally, this study assesses the potential role of rumen viruses in microbial diversification by analyzing prophages found in rumen metagenome-assembled genomes. Employing CRISPR–Cas spacer-based matching and virus-microbe co-occurrence network analysis, this study suggests that the viruses in the rumen may regulate microbes at strain and community levels through both antagonistic and mutualistic interactions. Moreover, this study establishes that the rumen virome demonstrates responsiveness to dietary shifts and associations with key animal production traits, including feed efficiency, lactation performance, weight gain, and methane emissions.ConclusionsThese findings provide a substantive framework for further investigations to unravel the functional roles of the virome in the rumen in shaping the microbiome and influencing overall animal production performance.3QAbSJqYZPn2njEBJ23VpAVideo

Design and operation of a load-tolerant ICRH system in Experimental Advanced Superconducting Tokamak

Ion Cyclotron Resonance Heating (ICRH) has been a dependable tool for sturdy plasma heating with high RF power of several megawatts. However, a sudden increase in the reflected power during ICRH heating experiments is a problem that should be solved for future fusion experimental devices. To solve this issue, a load tolerant matching network has been designed for the ICRH system in EAST. The matching network includes a 3-stub tuner impedance matching system with conjugate-T structure, 30 Ω to 50 Ω transmission line and center grounded antenna strap. By maintaining a low reflection ratio in the network for a wide range of resistance, this matching network can allow sturdy high-power operations without fast impedance matching in EAST. In our matching network, the two arms of a conjugate-T are designed to have a λ/2 length difference which could mitigate current imbalance and antenna poloidal phasing outside of the control problem. The T-point corresponds to the maximum point of the standing wave voltage, which could greatly improve the input impedance of the antenna.

Absolute measurement of vacuum ultraviolet fluxes from inductively coupled plasmas via metal surface photoemission currents

Abstract Quantifying vacuum-ultraviolet (VUV) fluxes typically requires vacuum compatible spectrometers and is often associated with significant cost and effort. A simple technique for the absolute measurement of local VUV fluxes from plasmas using the photoemission from a set of coated metal plates, is described. The radiant power from a 13.56 MHz hydrogen plasma operating at 40-100 mTorr and with an RF input power from 100 to 120 W was investigated by irradiating a set of 2cm diameter Au, Ag and Cu plates. The variation in photoemission currents was compared with the photoelectric yield curves to estimate the absolute flux incident on the surfaces in the 113-190 nm range. The measured fluxes were found to have an uncertainty of 5 – 30% when compared with the VUV spectrometer measurements. The VUV output power was found to have a maximum at a pressure of 70-80 mTorr and to increase with RF power. In all cases, the VUV output power was measured to be approximately 12 - 16% of the RF input power to the matching network, in good agreement with spectroscopy results.

Miniaturized design of multi-octave PA using spoof surface plasmon polaritons

Surface Plasmon Polariton (SSPP), which exhibits subwavelength-scaled field confinement, can be realized at microwave frequencies by ultrathin corrugated metallic strips, providing significant potential for circuit and system miniaturization. A number of SSPP units were employed in this study to design input and output matching networks of a multi-octave RF power amplifier (PA) for the purpose of reducing the overall circuit size. The designed SSPP-based PAs were fabricated and demonstrated to have an output power between 39.1 dBm and 41.8 dBm, and a power added efficiency (PAE) ranging between 60.2 % and 69.8 % in the frequency range of 0.8 GHz to 3.1 GHz, with a size of 35.5 mm * 46.7 mm, which achieves a 47 % size reduction compared to the traditional transmission-line based PA. A highly efficient global optimization method, the Bayesian Optimization (BO), was utilized to further enhance performance. SSPP PA's average output power and PAE have been improved by approximately 0.3 dBm and 2.6 %, respectively. Its overall size remains at the same level. With the use of a 20 MHz 5GNR signal, digital pre-distortion (DPD) has been implemented, and the linearity of the PA has been improved.

Designing a High Gain Rectangular Microstrip Patch Antenna Working at 3 GHz for RADAR

This study presents the design and analysis of a microstrip patch antenna optimized for radar applications operating at a frequency of 3 GHz. The antenna is designed to meet the requirements of radar systems, offering characteristics such as high gain, low profile, and broad bandwidth. The design process involves simulation using electromagnetic simulation software to achieve the desired antenna performance parameters. Various antenna parameters, including substrate material selection, patch dimensions, feeding techniques, and impedance matching networks, are carefully optimized to enhance antenna performance. The proposed antenna design is expected to exhibit excellent radiation characteristics, making it suitable for radar applications requiring reliable and efficient communication at 3 GHz frequency. This antenna designed for operating at 3 GHz, type Rogers Ultralam1217 (tm) with dielectric constant, ∈r= 2.2 tanδ (0.0009) and thickness of h = 1.6mm. The antennas are simulated using Ansys HFSS software. The performance is evaluated in terms of return loss, gain, and directivity and is deemed to be compatible with simulations.

GaN low noise amplifier MMIC with LPF and HPF noise matching

In this paper, we introduce two innovative two-stage low noise amplifiers (LNAs), each with distinct noise-matching networks. The first LNA features a low pass filter (LPF) for noise-matching in both stages, while the second uses a high pass filter (HPF) in a similar capacity. Our research focuses on evaluating the performance differences that arise from using varied matching networks within specific frequency ranges. Highlighting the critical role of appropriate network selection for optimizing gain and noise performance, our approach includes the development of two Monolithic Microwave Integrated Circuits (MMICs) using cutting-edge 0.25μm Gallium Nitride (GaN) technology. The C-band LNA, targeting a frequency range of 4–6 GHz, achieves an impressive average noise fig. (NF) of 1.5 dB and a gain of 17 dB. For the X-band range of 8–10 GHz, the LNA records a commendable average NF of 1.7 dB and a gain of 16 dB, demonstrating the effectiveness of our novel design strategies.

Stereo matching from monocular images using feature consistency

AbstractSynthetic images facilitate stereo matching. However, synthetic images may suffer from image distortion, domain bias, and stereo mismatch, which would significantly restrict the widespread use of stereo matching models in the real world. The first goal in this paper is to synthesize real‐looking images for minimizing the domain bias between the synthesized and real images. For this purpose, sharpened disparity maps are produced from a mono real image. Then, stereo image pairs are synthesized using these imperfect disparity maps and the single real image in the proposed pipeline. Although the synthesized images are as realistic as possible, the domain styles of the synthesized images are always very different from the real images. Thus, the second goal is to enhance the domain generalization ability of the stereo matching network. For that, the feature extraction layer is replaced with a teacher–student model. Then, a constraint of binocular contrast features is imposed on the output of the model. When tested on the KITTI, ETH3D, and Middlebury datasets, the accuracy of the method outperforms traditional methods by at least 30%. Experiments demonstrate that the approaches are general and can be conveniently embedded into existing stereo networks.

A Cascaded GRU-Based Stereoscopic Matching Network for Precise Plank Measurement

Abstract Wooden plank images in industrial measurements often contain numerous textureless areas. Furthermore, due to the thin plate structure, the three-dimensional disparity of these planks is predominantly confined to a narrow range. Consequently, achieving accurate three-dimensional matching of wooden plank images has consistently presented a challenging task within the industry. In recent years, deep learning has progressively supplanted traditional stereo matching methods due to its inherent advantages, including rapid inference and end-to-end processing. Nonetheless, the acquisition of datasets for stereo matching networks poses an additional challenge, primarily attributable to the difficulty in obtaining accurate disparity data. Thus, this paper presents a novel stereo matching method incorporating three key innovations. Firstly, an enhanced Gated Recurrent Unit(GRU) network is introduced, accompanied by a redesigned structure to achieve higher matching accuracy. Secondly, an efficient preprocessing module is proposed, aimed at improving the algorithm's efficiency. Lastly, in response to the challenges posed by datasets acquisition, we innovatively employed image simulation software to obtain a high-quality simulated dataset of wooden planks. To assess the feasibility of our approach, we conducted both simulated and real experiments. The experiments results clearly exhibit the superiority of our method when compared to existing approaches in terms of both stability and accuracy. In the simulation experiment, our method attained a bad1.0 score of 2.1% (compared to the baseline method's 9.76%); In the real experiment, our method achieved an average error of 0.104mm (compared to the baseline method's 0.268mm). It is worth noting that our study aims to address the challenge of acquiring datasets for deep learning and bridging the gap between simulated and real data, resulting in increased applicability of deep learning in more industrial measurement domains.

Stereo-DICNet: An efficient and unified speckle matching network for stereo digital image correlation measurement

Digital image correlation (DIC) is an image-based, non-contact optical measurement technique for measuring the deformation of objects. Recently, deep learning has been preliminarily used in monocular-DIC measurement and has brought significant advantages in efficiency and robustness. However, the deep learning-based stereo-DIC methods is still underexplored, although stereo-DIC can measure three-dimensional deformation and shape compared to monocular-DIC. Different from monocular-DIC, stereo-DIC not only requires temporal matching (inter-frame) but also stereo matching (inter-camera), and the difference in image deformation scale between the two matching tasks make it difficult to perform them using a unified network. To solve this problem, we propose a unified speckle matching network that can be used for deformation measurement at different scales, referred to as Stereo-DICNet. Stereo-DICNet consists of three modules: feature extraction, cost volume generation, and displacement map prediction. Firstly, the feature extraction module composed of the residual layer and the spatial pyramid pooling layer is designed to extract the features of different receptive fields. Then, an attention connect volume was proposed to represent the similarity information between pixels at different spatial scales. Finally, the displacement map is predicted by a stacked hourglass-shaped sub-net. Simulation and physical experiments verify that Stereo-DICNet can perform both temporal matching and stereo matching. Moreover, material tensile experiments show that the MAE of Stereo-DICNet in the U, V, and W directions are no more than 0.005 mm, 0.015 mm, 0.01 mm respectively.

Self-Support Matching Networks with Multiscale Attention for Few-shot Semantic Segmentation

Recent advancements in Few-shot segmentation (FSS) have displayed remarkable capabilities in predicting segmentation masks for unseen class images, using only a limited number of annotated images. However, existing methods have overlooked the influence of contextual information on segmentation and primarily rely on supporting prototypes, with limited research on query prototyping. Effectively utilizing multi-scale features and query information poses a challenging problem in this domain. To address these challenges, this paper proposes a novel approach called the multi-scale and attention-based self-support prototype few-shot semantic segmentation network (MASNet). First, a multi-scale feature enhancement module is designed to obtain features at different scales to enrich global context information. Then, simple and efficient channel attention is utilized to guide the query features related to the target class. Finally, the query prototype is matched with the query features using a self-supporting matching module. This strategy efficiently captures class-based features and addresses the issue of intra-class variance in few-shot segmentation. The experimental results on Pascal-5i, COCO-20i and Abdominal MRI datasets demonstrate that the proposed method achieves remarkable robustness and improved accuracy performance.

Towards robust image matching in low-luminance environments: Self-supervised keypoint detection and descriptor-free cross-fusion matching

Image keypoint detection and feature matching are fundamental steps in computer vision tasks. However, variations in environment, time, and viewpoint pose a challenge to the stability of image keypoint detection and matching. Most traditional and deep learning-based methods cannot accurately and efficiently extract highly repeatable keypoints and robust match pairs in low-luminance environments. Therefore, we propose a two-step’detection + matching’ framework, which consists of deep neural networks in each step. Firstly, we design a self-supervised robust keypoint detection network, which utilizes multi-scale, multi-angle, and multi-luminance transformation techniques to create pseudo-labeled datasets to improve the model’s keypoint detection repeatability and luminance invariance. Secondly, we propose a descriptor-free cross-fusion matching network, which uses the cross-fusion attention mechanism to establish connections between keypoint-centered image patches and converts the feature-matching task into an image patch assignment task to improve the accuracy and efficiency of matching. Thirdly, the proposed framework is used to replace traditional SIFT in SfM. Experimental results on testing datasets show that the self-supervised robust keypoint detection network achieves higher keypoint repeatability in low luminance environments compared to SIFT, ORB, LIFT, and Superpoint. The descriptor-free cross-fusion matching network’s mean matching accuracy and efficiency are higher than the mainstream Superglue algorithm. Also, SfM achieves better performance regarding the number of sparse point clouds and accuracy.

Design of a Compact 2–6 GHz High-Efficiency and High-Gain GaN Power Amplifier

In this paper, a novel wideband power amplifier (PA) operating in the 2–6 GHz frequency range is presented. The proposed PA design utilizes a combination technique consisting of a distributed equalization technique, multiplexing the power supply network and matching network technique, an LR dissipative structure, and an RC stability network technique to achieve significant bandwidth while maintaining superior gain flatness, high efficiency, high gain, and compact size. For verification, a three-stage PA using the combination technique is designed and implemented in a 0.25 μm GaN high-electron-mobility transistor (HEMT) process. The fabricated prototype demonstrates a saturated output power of 4 W, a power gain of 21 dB, a gain flatness of ±0.6 dB, a power-added efficiency of 39–46%, and a fractional bandwidth of 100% under the operating conditions of drain voltage 28 V (continuous wave) and gate voltage −2.6 V. Moreover, the chip occupies a compact size of only 2.51 mm × 1.97 mm.

Design Techniques for Wideband CMOS Power Amplifiers for Wireless Communications

In this study, we designed a wideband CMOS power amplifier to support multi-band and multi-standard wireless communications. First, an input matching technique through LC network and a wideband design technique using a low Q-factor transformer were proposed. In addition, a design technique was proposed to improve output matching using RC feedback. To verify the feasibility of the proposed design methodology for wideband CMOS power amplifiers, the designed power amplifier was fabricated using a 180 nm RFCMOS process. The size including all of the matching network and test pads was 1.38 × 0.90 mm2. In addition, the effectiveness of the proposed power amplifier was verified through the measured results using modulated signals of WCDMA, LTE, and 802.11n WLAN.

Accuracy and efficiency stereo matching network with adaptive feature modulation.

Feature enhancement plays a crucial role in improving the quality and discriminative power of features used in matching tasks. By enhancing the informative and invariant aspects of features, the matching process becomes more robust and reliable, enabling accurate predictions even in challenging scenarios, such as occlusion and reflection in stereo matching. In this paper, we propose an end-to-end dual-dimension feature modulation network called DFMNet to address the issue of mismatches in interference areas. DFMNet utilizes dual-dimension feature modulation (DFM) to capture spatial and channel information separately. This approach enables the adaptive combination of local features with more extensive contextual information, resulting in an enhanced feature representation that is more effective in dealing with challenging scenarios. Additionally, we introduce the concept of cost filter volume (CFV) by utilizing guide weights derived from group-wise correlation. CFV aids in filtering the concatenated volume adaptively, effectively discarding redundant information, and further improving matching accuracy. To enable real-time performance, we designed a fast version named Fast-GFM. Fast-GFM employs the global feature modulation (GFM) block to enhance the feature expression ability, improving the accuracy and stereo matching robustness. The accurate DFMNet and the real-time Fast-GFM achieve state-of-the-art performance across multiple benchmarks, including Scene Flow, KITTI, ETH3D, and Middlebury. These results demonstrate the effectiveness of our proposed methods in enhancing feature representation and significantly improving matching accuracy in various stereo matching scenarios.

Reliability Analysis and Optimization of a Reconfigurable Matching Network for Communication and Sensing Antennas in Dynamic Environments through Gaussian Process Regression.

During the implementation of the Internet of Things (IoT), the performance of communication and sensing antennas that are embedded in smart surfaces or smart devices can be affected by objects in their reactive near field due to detuning and antenna mismatch. Matching networks have been proposed to re-establish impedance matching when antennas become detuned due to environmental factors. In this work, the change in the reflection coefficient at the antenna, due to the presence of objects, is first characterized as a function of the frequency and object distance by applying Gaussian process regression on experimental data. Based on this characterization, for random object positions, it is shown through simulation that a dynamic environment can lower the reliability of a matching network by up to 90%, depending on the type of object, the probability distribution of the object distance, and the required bandwidth. As an alternative to complex and power-consuming real-time adaptive matching, a new, resilient network tuning strategy is proposed that takes into account these random variations. This new approach increases the reliability of the system by 10% to 40% in these dynamic environment scenarios.

A 3.2-3.8 GHz low-noise amplifier for 5G/6G satellite-cellular convergence applications

The rapid evolution of wireless communication systems towards 5G standards has imposed stringent requirements on the performance of radio frequency front-end components. Among these, the Low-Noise Amplifier (LNA) plays a pivotal role in determining the overall noise figure and sensitivity of the receiver chain. This paper presents a comprehensive design and analysis of a 3.2-3.8 GHz LNA tailored for 5G applications, employing a 0.3 μmm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology process. The proposed LNA design focuses on achieving a low noise figure (NF), high gain, and robust linearity to accommodate the dense signal environment and wide bandwidth of 5G networks. The design leverages advanced matching networks and feedback topologies to enhance stability and reduce the noise contribution from the active devices. Simulation results predict a noise figure of 1.3-1.4 dB, a gain of 20-21 dB across the band of interest, and an input-referred third-order intercept point (IIP3) of 18 dBm. The LNA demonstrates excellent performance in a 5G testbed, showing a significant improvement in the signal-to-noise ratio and the potential to enhance 5G receiver sensitivity. The research substantiates the LNA’s viability for integration into 5G base stations and user equipment, underscoring its potential to contribute to the efficient and reliable operation of next-generation wireless networks. This LNA can be used for 5G New Release (NR) band of n77 and n78 (3.5-3.7 GHz)

Development of non-invasive flexible directional microwave ablation for central lung cancer: a simulation study

Transbronchial microwave ablation (MWA) with flexible antennas has gradually become an attractive alternative to percutaneous MWA for lung cancer due to its characteristic of non-invasiveness. However, flexible antennas for the precision ablation of lung tumors that are adjacent to critical bronchial structures are still not available. In this study, a non-invasive flexible directional (FD) antenna for early stage central lung tumors surrounding the bronchia was proposed. A comprehensive numerical MWA model with the FD antenna was developed in a real human-sized left lung model. The structure of the antenna and the treatment protocol were optimized by a generic algorithm for the precision ablation of two cases of early stage central lung cancer (i.e. spherical-like and ellipsoidal tumors). The electromagnetic efficiency of the optimized antenna was also improved by implementing an optimized π -matching network for impedance matching. The results indicate that the electromagnetic energy of MWA can be restricted to a particular area for precision ablation of specific lung tumors using the FD antenna. This study contributes to the field of lung cancer management with MWA.