Matching Network Research Articles

Advancements in Diffusion Models for Image Generation: A Comparative Analysis of DDPM, LDM, and DDIM

Abstract. This research provides a thorough exploration of diffusion models in image generation, comparing various methodologies to assess their efficacy and efficiency. The study begins with an introduction to foundational technologies and key concepts, progressing through an analysis of basic and advanced models, including Latent Diffusion Models (LDMs), Denoising Diffusion Implicit Models (DDIMs), and control models. The research evaluates these models based on their performance, computational efficiency, and future development potential. The review details the evolution of diffusion models from early stochastic processes to their current status as advanced generative models. Key principles, such as iterative noise addition and removal, are examined to understand the transformation from simple distributions to complex data representations. Innovations enhancing model efficiency, including advancements in score matching and neural network integration, are discussed. A thorough comparative analysis highlights the strengths and limitations of each model. The study identifies ongoing challenges such as interpretability and computational cost and proposes future research directions to address these issues. The findings aim to guide researchers and practitioners in advancing diffusion model technologies, offering insights into their impact on image generation and potential future developments.

Internally Harmonic Matched Compact GaN Power Amplifier with 78.5% PAE for 2.45 GHz Wireless Power Transfer Systems

In this paper, a high-efficiency compact power amplifier is designed and fabricated with a 0.25 μm GaN high electron mobility transistor (HEMT) to meet the demands of a high integration level and high efficiency for microwave wireless power transfer (WPT) systems. The proposed power amplifier (PA) is implemented using an internally matched method to achieve a compact circuit size. The output second and third harmonic impedances can be optimized through output matching circuits, eliminating the need for additional harmonic matching networks. This approach simplifies the design of matching circuits and reduces the circuit size. Furthermore, the input third harmonic has been controled for improving the efficiency of DC-to-RF conversion. The total size of the proposed PA is 13.4 × 13.5 mm2. The test results obtained from the continuous wave (CW) testing indicate that the output power of the power amplifier at 2.45 GHz reaches 43.75 dBm. Additionally, the large-signal gain is measured at 15.75 dB, and the power-added efficiency (PAE) achieves a value of 78.5%.

Multifunctional and tunable bandpass filters with RF codesigned isolator and impedance matching capabilities

Abstract This paper presents the radio frequency (RF) design and experimental validation of a multifunctional bandpass filtering (BPF) concept with center frequency tunability and RF codesigned isolator and impedance matching functionality. The multifunctional bandpass filter/isolator (BPFI) concept combines frequency-tunable reciprocal resonators and nonreciprocal frequency-selective stages (NFSs) to realize center frequency tunability and fully directional transfer characteristics. The NFS, as the core component of the BPFI concept, exhibits frequency-selective transmission response in the forward direction and signal cancellation in the reverse direction. Its tunability is achieved by combining a transistor-based network with a tunable capacitively loaded coupled-line section. Furthermore, the NFS facilitates matching of different source loads allowing for the BPFIs to be used as reconfigurable matching networks. For experimental validation, an NFS and two BPFIs were designed, manufactured, and measured at L band. Their features include (i) NFS: center frequency tuning from 1.55 to 1.9 GHz with maximum directivity from 20 to 52 dB and gain from 0.3 to 1.3 dB. (ii) BPFI (topology A): center frequency tuning from 1.52 to 1.9 GHz with maximum directivity from 20 to 44 dB and gain from −1.5 to −0.5 dB. (iii) BPFI (topology C): ability to match complex loads with 26 + j18 Ω and 26 − j14 Ω.

Design of a stagger‐tuned high‐power low‐stress capacitive wireless power transfer system

AbstractCapacitive power transfer (CPT) has emerged as a promising alternative to traditional inductive methods. CPT offers advantages like cost‐effectiveness, reduced weight and volume, and greater tolerance to alignment errors. However, the high‐Q resonant circuits used as matching networks can be susceptible to high voltage stress, especially when transmitting substantial power. Consequently, designing matching networks for CPT systems necessitates consideration of multiple parameters, including practical constraints such as component losses and breakdown thresholds. In this work an innovative algorithm is presented for designing practical matching networks in CPT systems. The algorithm conducts a methodical search of potential solutions, and converges on component values that maximize power transfer efficiency whilst also minimizing component voltage stress. The proposed algorithm is demonstrated theoretically and experimentally.

An active two-stage class-J power amplifier design for smart grid’s 5G wireless networks

<span>The wireless communication networks in the smart grid’s advanced metering infrastructure (AMI) applications need 5G technology to support large data transmission efficiently. As the 5G wireless communication network’s overall bandwidth (BW) and efficiency depend on its power amplifier (PA), in this work, a two-stage class-J power amplifier’s design methodology that operates at 3.5 GHz centre frequency by utilizing the CGH40010F model gallium nitride (GaN) transistor is presented. The proposed design methodology involves proper designing of input, output, and interstage matching networks to achieve class-J operation with improved power gain over desired BW using the advanced design system (ADS) electronic design automation (EDA) tool and estimating its integration feasibility through active element-based design approach using the Mentor Graphics EDA tool. The proposed PA provides 54% drain efficiency (D.E), 53% power added efficiency (PAE) with a small signal gain of 27 dB at 3.5 GHz and 41 dBm power output with 21 dB of improved power gain across a BW of around 400 MHz using 28 V power supply into 50 Ω load. By replacing the two-stage PA's passive elements with active elements, its layout size is estimated to be (15.5×29.2) μm2 . The results of the proposed PA exhibit its integration feasibility and suitability for the smart grid’s 5G wireless networks.</span>

LostNet: A smart way for lost and find.

The rapid population growth in urban areas has led to an increased frequency of lost and unclaimed items in public spaces such as public transportation, restaurants, and other venues. Services like Find My iPhone efficiently track lost electronic devices, but many valuable items remain unmonitored, resulting in delays in reclaiming lost and found items. This research presents a method to streamline the search process by comparing images of lost and recovered items provided by owners with photos taken when items are registered as lost and found. A photo matching network is proposed, integrating the transfer learning capabilities of MobileNetV2 with the Convolutional Block Attention Module (CBAM) and utilizing perceptual hashing algorithms for their simplicity and speed. An Internet framework based on the Spring Boot system supports the development of an online lost and found image identification system. The implementation achieves a testing accuracy of 96.8%, utilizing only 0.67 GFLOPs and 3.5M training parameters, thus enabling the recognition of images in real-world scenarios and operable on standard laptops.

Research on efficient matching method of coal gangue recognition image and sorting image

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

A High Efficiency Broadband Gallium Nitride Power Amplifier with Harmonic Control technology

ABSTRACT In order to meet the requirements of high efficiency and broadband for the wireless communication system, a high efficiency and broadband gallium nitride power amplifier (PA) is designed and verified. Using the step-matching structure, its bandwidth has been improved. Furthermore, its input matching network is constructed by connecting several microband lines serially to reduce the matching Q value. Meanwhile, the harmonic control network is designed as the output matching network, which is built by a quarter-wave (λ/4) line open circuit and short circuit microwave lines in parallel. Thus, its drain efficiency (DE)can be improved. This design is simulated and tested at 2.2–3 GHz with the gallium nitride (GaN) process. After measuring, it attains an output power (Pout) of 39.5–41.5 dBm, Gain of 10.5–12.5 dB, DE of 65.1%–78.3% with power added efficiency (PAE) of 64.8%–76.2%.

Ultra-wideband GaN RF power amplifier based on low-pass ladder matching network

Ultra-wideband GaN RF power amplifier based on low-pass ladder matching network

Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting.

The proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs.

Smith Chart-Based Design of High-Frequency Broadband Power Amplifiers

This paper presents a comprehensive study on the design and performance of a high-power amplifier (PA) covering a broad frequency band from 0.1 to 4.8 GHz. Leveraging a 10 W GaN device, the amplifier achieves output power levels surpassing 10 W across the entire frequency band. Furthermore, the power-added efficiency (PAE) of the amplifier ranges from 47% to 59%, indicating its energy-efficient operation. With consistent gain characteristics varying between 7 and 15 dB, the design ensures reliable signal amplification for diverse applications. Notably, the approach introduces a simplified output matching network based on an LC network, prioritizing practicality without sacrificing performance. Additionally, comprehensive guidance is provided on utilizing the Smith chart for streamlined amplifier design, enabling engineers with an accessible methodology. Through a meticulous analysis, this work contributes to advancing the field of high-power amplification, offering enhanced performance and usability for next-generation wireless communication systems.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Lumped-element SNAIL parametric amplifier with two-pole matching network

Broadband impedance-matched Josephson parametric amplifiers are key components for high-fidelity single-shot multi-qubit readout. Nowadays, several types of impedance matched parametric amplifiers have been proposed: the first is an impedance-matched parametric amplifier based on a Klopfenstein taper, and the second is the impedance-matched parametric amplifier based on auxiliary resonators. Here, we present the quantum-limited 3-wave-mixing lumped-element SNAIL parametric amplifier with two-pole impedance matching transformer. A two-pole Chebyshev matching network with shunted resonators is based on parallel-plate capacitors and superconducting planar coil. Operating in a flux-pumped mode, we experimentally demonstrate an average gain of 15 dB across a 600 MHz bandwidth, along with an average saturation power of −107 dBm and quantum-limited noise temperature.

Retraction: A multi-objective optimization based doherty power amplifier and its matching network optimization method.

Retraction: A multi-objective optimization based doherty power amplifier and its matching network optimization method.

HAGMN-UQ: Hyper association graph matching network with uncertainty quantification for coronary artery semantic labeling

Coronary artery disease (CAD) is one of the leading causes of death worldwide. Accurate extraction of individual arterial branches from invasive coronary angiograms (ICA) is critical for CAD diagnosis and detection of stenosis. Generating semantic segmentation for coronary arteries through deep learning-based models presents challenges due to the morphological similarity among different types of coronary arteries, making it difficult to maintain high accuracy while keeping low computational complexity. To address this challenge, we propose an innovative approach using the hyper association graph-matching neural network with uncertainty quantification (HAGMN-UQ) for coronary artery semantic labeling on ICAs. The graph-matching procedure maps the arterial branches between two individual graphs, so that the unlabeled arterial segments are classified by the labeled segments, and the coronary artery semantic labeling is achieved. Leveraging hypergraphs not only extends representation capabilities beyond pairwise relationships, but also improves the robustness and accuracy of the graph matching by enabling the modeling of higher-order associations. In addition, employing the uncertainty quantification to determine the trustworthiness of graph matching reduces the required number of comparisons, so as to accelerate the inference speed. Consequently, our model achieved an accuracy of 0.9211 for coronary artery semantic labeling with a fast inference speed, leading to an effective and efficient prediction in real-time clinical decision-making scenarios.

A 2–36 GHz CMOS LNA with π-Network-Based wideband interstage matching technique for software-defined radio systems

With the advance in wireless communication, next-generation software-defined radio (SDR) systems require transceivers to operate in both sub-6GHz and millimeter-wave (mmWave) band and support multiple standards. However, bridging sub-6GHz frequencies with millimeter-wave bands exceeding 30 GHz remains a challenge for conventional wideband LNAs. To surmount this, a 2–36 GHz CMOS low-noise amplifier (LNA) designed for SDR systems is introduced in this paper. An innovative wideband input matching network capable of spanning both frequency domains is proposed. The methodology effectively mitigates the impact of vast parasitic capacitances, achieving wideband input matching against Electrostatic Discharge (ESD) and on-chip decoupling capacitor parasitic. In addition, a π-network-based wideband interstage matching technique is adopted to extend bandwidth of gain. A tri-stage prototype of the proposed LNA, designed using a 40-nm CMOS process, is designed to validate our design strategies. The post-simulation outcomes reveal a peak gain of 14 dB with a -3dB bandwidth ranging from 2 to 36 GHz, equating to a fractional bandwidth of 178 %. The Noise Figure (NF) is commendably uniform across the frequency spectrum, stabilizing at 5 dB. Furthermore, the third-order input intercept point (IIP3) is −4.2dBm to -3dBm across the bandwidth. The performance is achieved with a power of 19.4 mW and within a core area of 0.1 mm2.

A Compact Broadband Rectifier Based on Coupled Transmission Line for Wireless Power Transfer

Wireless Power Transfer (WPT) can effectively solve the problem of autonomous power supply for low-power devices. Rectifier is the key component in WPT technology. In this paper, a novel impedance matching network for the broadband rectifier is proposed. This impedance matching network compensates for the diode impedance and reduces its impedance change when the frequency or input power changes. The passive boosting mechanism utilizing coupled transmission lines (CTLs) improves the power conversion efficiency (PCE) of the diode in the low power region. The structure is especially optimized for low-power device applications. For validation, a broadband rectifier operating at 1.9–3 GHz is fabricated and measured. The structure fabricated on the Rogers 4003 substrate with a thickness of 1.508 mm and the diode is HSMS2860. The DC voltage Vout on the load (RL=1300 Ω) was measured. The results show that at 0 dBm, the PCE keeps more than 60% at 1.98–3 GHz. The peak PCE of 79.6% is obtained at 4 dBm. The compact size of the broadband rectifier is 19 mm × 21 mm. This broadband rectifier for low input power ranges can be applied to WPT technology.

AP-Net: Attention-fused volume and progressive aggregation for accurate stereo matching

Stereo matching plays an important role in advancing substantial applications such as 3D reconstruction, autonomous driving, depth sensing, and has recently made tremendous progress. It still poses a significant challenge, however, in the extraction of disparities from a pair of rectified images to achieve accurate stereo matching. In this paper, we propose an attention-fused cost volume construction and progressive cost aggregation network, namely AP-Net, to elevate the accuracy of stereo matching. Specifically, we introduce attention fusion to learn feature similarity between corresponding points on the left and right images while capturing long-dependent contextual information to construct attention-fused cost volumes. Moreover, we propose progressive cost aggregation, which consists of two stages to fully exploit information from different cost volumes. Furthermore, to make more accurate adjustments to the initial disparity, we implement an information entropy-guided disparity refinement module, which leverages information entropy as an explicit confidence measure of the initial disparity estimation to guide the refinement module in calculating the disparity residual more accurately and improving the overall accuracy of disparity estimation. Our implemented disparity refinement module can also be seamlessly embedded into a variety of stereo matching networks, significantly improving the model’s performance with only a small increase in computation and parameters. Experimental results across various stereo datasets confirm that our proposed AP-Net consistently delivers competitive performance. The code is available at https://github.com/zhuys-bupt/AP-Net.

Design and development of dual-band superconducting filter with a novel structure

In this paper, a novel design structure of a dual-band filter is proposed. The core of the structure is to form a dual-band filter by connecting two sub-filters through a matching network. The matching network can not only improve the flexibility of the design, but also minimize the influence between the two sub-filters. Two sub-filters are designed separately, one of which is composed of interdigital resonators and the other is composed of hairpin resonators. The center frequency and bandwidth of the two passbands are flexible and controllable. Finally, a dual-band high-temperature superconducting (HTS) filter is designed and successfully fabricated on a DyBa2Cu3O7 (DyBCO) thin film on thickness of 0.5 mm LaAlO3 wafer, and the measured results are in good agreement with the simulation results.

Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network

Bearing fault diagnosis presents challenges, such as insufficient fault samples and significant fault data distribution variation in different bearing operating conditions. These problems cause traditional deep learning models to show poor generality and accuracy during bearing fault diagnosis. To address these challenges, this paper proposed a few-shot bearing fault diagnosis method based on an Ensemble Empirical Mode Decomposition (EEMD) parallel neural network and a relation network (RN). First, the original bearing fault vibration signal was decomposed by EEMD, while the decomposed signal components were processed via Short Time Fourier Transfor (STFT) to obtain a two-dimensional time-frequency feature map. Then, a parallel neural network was used for initial fault feature extraction, after which the extracted features were fused to construct a more accurate multi-dimensional fault feature map. A more precise fault feature vector was generated via the feature embedding module of the RN, and the fault features of the support and query sets were stitched to create a fault feature vector set. Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. The experimental results showed that the proposed model obtained higher diagnostic accuracy than the matching network (MN) and RN meta-learning fault diagnosis (MLFD) methods. The accuracy of fault diagnosis of the model in 5Way-Nshot is >80%, and the accuracy of fault diagnosis in 5Way-10shot is the highest at 95.2%.