Interference Signal Research Articles

Combined scheme for mitigating coupling interference in dual‐band shared‐aperture base station antenna

AbstractA dual‐band shared‐aperture antenna array is presented, involving a combined scheme to suppress mutual coupling interference. The proposed array is comprised of a low‐band (LB) antenna, a frequency selective surface (FSS), sixteen high‐band (HB) antennas, and a reflector. The LB antenna utilises electromagnetic transparent (ET) characteristics to minimise scattering interference, while the FSS isolates resonance interference and port signal interference. The combined scheme integrates the benefits of both the ET antenna and FSS to suppress mutual coupling interference effectively. A prototype of the proposed antenna was fabricated and measured, operating at 1.7–2.7 GHz and 3.3–3.9 GHz. The simulated and measured results demonstrate stable patterns for both the LB antenna and the HB antennas. The cross‐band isolation is higher than 28 dB in the low‐band. With these advantages, the antenna array is suitable for base station applications.

Canonical and noncanonical roles of Hop1 are crucial for meiotic prophase in the fungus Sordaria macrospora.

We show here that in the fungus Sordaria macrospora, the meiosis-specific HORMA-domain protein Hop1 is not essential for the basic early events of chromosome axis development, recombination initiation, or recombination-mediated homolog coalignment/pairing. In striking contrast, Hop1 plays a critical role at the leptotene/zygotene transition which is defined by transition from pairing to synaptonemal complex (SC) formation. During this transition, Hop1 is required for maintenance of normal axis structure, formation of SC from telomere to telomere, and development of recombination foci. These hop1Δ mutant defects are DSB dependent and require Sme4/Zip1-mediated progression of the interhomolog interaction program, potentially via a pre-SC role. The same phenotype occurs not only in hop1Δ but also in absence of the cohesin Rec8 and in spo76-1, a non-null mutant of cohesin-associated Spo76/Pds5. Thus, Hop1 and cohesins collaborate at this crucial step of meiotic prophase. In addition, analysis of 4 non-null mutants that lack this transition defect reveals that Hop1 also plays important roles in modulation of axis length, homolog-axis juxtaposition, interlock resolution, and spreading of the crossover interference signal. Finally, unexpected variations in crossover density point to the existence of effects that both enhance and limit crossover formation. Links to previously described roles of the protein in other organisms are discussed.

Research progress of deep learning applications in mass spectrometry imaging data analysis

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

Adaptive Multi-Head Self-Attention Based Supervised VAE for Industrial Soft Sensing With Missing Data

Variational auto-encoders (VAEs) have been widely used in soft sensing due to their ability to provide a probabilistic description of the hidden space. However, VAEs are static models that do not consider process dynamics, which can limit the ability of VAEs to accurately model complex industrial processes. To tackle this problem, this paper proposes a model called adaptive multi-head self-attention based supervised VAE (AMSA-SVAE). In AMSA-SVAE, an adaptive multi-head self-attention mechanism (AMSA) is proposed based on the multi-head self-attention mechanism (MSA). AMSA can dynamically extract different attention information depending on specific tasks. By adjusting the attention weights based on the input sequence, AMSA allows for more accurate and efficient modeling of complex industrial processes. Then, AMSA is used as the encoder and decoder of SVAE for soft sensing. Furthermore, with the data generation capabilities of VAE, an adaptive multi-head self-attention based VAE (AMSA-VAE) framework is proposed to address the issue of missing data. The AMSA-VAE is used to dynamically fill in missing data, thereby extending the capabilities of AMSA-SVAE. Finally, the performance of AMSA-SVAE is verified by a set of real industrial data, and the ability of AMSA-VAE framework is demonstrated by simulating different degrees of data missing rates. By combining the dynamic modeling capabilities of AMSA-SVAE with the data generation capabilities of AMSA-VAE, the proposed approach provides a robust solution to the challenges of incomplete data in soft sensing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — Soft sensors are widely used to measure key parameters in industrial processes, but missing values in the data are common due to sensor failures or transmission signal interference. This poses a significant challenge for traditional soft sensors, which require complete data to accurately model. Meanwhile, the dynamic nature of industrial process data further complicates the modeling process. To solve these challenges, this paper proposes an AMSA-SVAE model for soft sensing and an AMSA-VAE framework for filling in the missing values in the data, thereby extending the capabilities of AMSA-SVAE to handle missing data. When facing a dataset with missing values, AMSA-VAE framework is first used to fill in the missing values before the filled complete data is fed into AMSA-SVAE for modeling. Finally, the proposed approaches are evaluated through two sets of experiments using a real industrial dataset, showing the excellent performance of AMSA-SVAE and AMSA-VAE framework in modeling dynamic industrial process data and addressing the missing data problem.

Noise coupling reduction using temperature enhanced device for future integrated circuit integration applications

&lt;p&gt;Information technology-to-internet of things may have succeeded because of fast silicon chip capability expansion. Moore's law, which reduces device size, boosted integrated circuit (IC) performance. Delay rises with highdensity connection parasitic capacitance. Interconnect delays have surpassed transistor delays and slowed progress. An alternative is required now to reduce connection latency. The third dimension is used in popular 3D IC technology IC technology requires through silicon via (TSV) for signal integrity and heat mitigation. Noise coupling hinders electrical communication between signal-carrying TSVs (aggressive TSVs) and ground TSVs (victim TSVs), a 3D IC bottleneck. TSVs must be dielectrically insulated from Si substrates to avoid electrical signal interference. Additionally, first-order modelling will confirm the suggestions. This article proposes using the nanosheet field effect transistor (NSFET) to overcome 3D IC noise coupling and complementary metal oxide semiconductor (CMOS) technology nodes. After discussing the electronic industry and sub nm, several basic metrics and criteria for developing electronic components are presented. The first technique uses Perylene-N's exceptional noise-cancelling characteristics. Second technique uses electrical TSV (ETSV), thermal TSV (TTSV), and heat source models to measure noise coupling on numerous ICs. The third proposes many noise-reducing materials. The suggested structures outperform traditional approaches.&lt;/p&gt;

Use of Neurophysiological Monitoring during MR Imaging–Guided Ablation Procedures at 1.5 T: Workflow and Safety Considerations

PurposeTo evaluate the feasibility of intraoperative neurophysiological monitoring (IONM) during magnetic resonance (MR) imaging–guided ablations and identify strategies to reduce IONM electrode radiofrequency (RF) heating during MR imaging. Materials and MethodsEx vivo experiments with a porcine tissue phantom simulating a typical high RF heating risk IONM setup during an MR imaging–guided ablation procedure on the shoulder were performed using a 1.5-T scanner. Mutual interference between MR imaging and IONM was evaluated. To assess RF heating risks, 4 pairs of IONM electrodes were inserted into the phantom at regions corresponding to the shoulders, midarm, and wrist. MR imaging of the “shoulder” was performed at 3 different specific absorption rates (SARs) with electrode wires positioned in various geometric configurations. Different combinations of electrode connections to the IONM system were investigated. Temperatures of each electrode were recorded using fiber-optic sensors. ResultsSimultaneous IONM readout and MR imaging resulted in distortion of the IONM signal, but interleaving MR imaging and IONM without moving electrodes was feasible. During MR imaging, temperature elevations greater than 60°C at the electrode insertion sites were observed. Temperature reductions were achieved by routing electrode wires along the scanner central axis, reducing the wire length within the scanner bore, or lowering the SAR of the imaging sequence. Altering the electrode connection with the IONM system did not result in consistent changes in RF heating. ConclusionsWith electrodes in the scanner bore, interleaving IONM and MR imaging is desired to avoid signal interference, and several strategies identified herein can reduce risk of electrode RF heating during MR imaging–guided ablation.

A Novel Flying Robot Swarm Formation Technique Based on Adaptive Wireless Communication using MUSIC Algorithm

This paper presents a novel technique to address the challenge of coordinating swarm flying robots in a leader-follower configuration. A combination of the Multi Signal Classification (MUSIC) estimation algorithm, based on a wireless MIMO array antenna, along with onboard robot control are used for precise route tracking of an individual robot. Employing an array antenna reduces energy consumption for followers in passive mode and reduces computational complexity when measuring the angles of leader angle interferences, which depends on the phase difference of the impinging signal on the antenna elements of the array. Additionally, the angles estimation and beamforming processes, utilizing MUSIC algorithm, form an inner loop that furnishes orientation angles in 3D (Azimuth and elevation angles) for both the leader and potential interference sources. The outer loop, contingent on the onboard controller and the robot's GPS system, enabling fine adjustments in angle and position relative to the leader's location. The simulation results illustrated the efficiency of the proposed technique in estimating the orientation angles of the leader and the interference sources. The technique robustness is confirmed through testing the performance on different trajectories. Where the follower perfectly generates a main radiation beam directed towards the leader, effectively mitigates interference signals from neighboring group leaders, and successfully tracks the leader path.

Analysisof Beam Walk in Inter-Satellite Laser Link: Implications for Differential Wavefront Sensing in Gravitational Wave Detection

Achieving space-based gravitational wave detection requires the establishment of an interferometer constellation. It is necessary to establish and maintain stable laser interferometric links using the differential wavefront sensing (DWS) technnique. When the distant measurement beam experiences pointing jitter, it causes beam walk on the surface of the local detector. The reduced overlap between the local reference spot and the distant spot increases the nonlinear errors in the DWS technique, which need to be suppressed. Numerical analysis was conducted on the spatial beam interference signals of the DWS technique when the distant measurement beam experienced pointing jitter. An experimental measurement system was designed, and the beam walk was suppressed using a conjugate imaging system. The results show that within a range of 300 μrad, the optical path with the imaging system can reduce measurement errors by at least 83%. This way also helps to reduce pointing jitter noise in inter-satellite links, thereby improving laser pointing control accuracy.This method would provide a valuable reference for future DWS measurement systems.

Novel dual‐resonance damped alternating current testing system for offline partial discharge measurement of power cables

AbstractTo accurately detect the early deterioration of electrical insulation in power cables, an energizing system is required for offline partial discharge (PD) measurements. The damped alternating current (DAC) testing system has emerged as an effective tool for inducing PD events. However, the existing systems often suffer from limitations such as signal interference, low sensitivity, and high costs. To address these issues, this study proposes a novel dual‐resonance DAC testing system that is simple in structure and cost‐effective. The prototype was built for testing 10‐kV distribution cables, and its effectiveness was validated through experimental tests on a cable sample. The results show that this approach significantly improves PD detection sensitivity and successfully completes PD localization. This research contributes to the development of more efficient and affordable DAC generation technology for power cable testing applications.

Multi-mode vehicle pose estimation under different GNSS conditions

The integrated navigation system combining the global navigation satellite system (GNSS) and inertial navigation system (INS) is a crucial method for pose estimation in the field of autonomous driving technologies. Nevertheless, the accuracy of pose estimation is severely compromised when GNSS signals are obstructed or disrupted. To address this issue, this study introduces a multi-mode pose estimation framework designed to ensure accurate pose estimation even under unstable GNSS conditions. By integrating vehicle kinematics model that considers steering characteristics (VKMSC) and the convolutional neural network-long short-term memory (CNN-LSTM) neural network (NN) model into various estimation modes, the framework enhances the robustness of the integrated navigation system against signal interference. The system dynamically selects the optimal estimation strategy based on the degree of GNSS signal disruption. The proposed method has been validated through real-vehicle experiments, which demonstrate its efficacy in providing precise pose estimation across a spectrum of interference scenarios. Under the multipath and non-line-of-sight (MP/NLOS) mode, compared to the integrated navigation system and the fusion of traditional vehicle kinematic models, the proposed method improved positional estimation accuracy by 61.8 % and 19.7 %, respectively. In GNSS outage mode, the proposed method increased the estimation accuracy by 36.5 % and 12.0 %, respectively, compared to the INS navigation system assisted by the VKMSC and CNN-LSTM network model. The proposed method effectively reduces pose estimation errors in the integrated navigation system during interference and suppresses data fluctuations, thereby enhancing the system's precision and robustness.

Nanoplasmonic Single-Tumoroid Microarray for Real-Time Secretion Analysis.

Organoid tumor models have emerged as a powerful tool in the fields of biology and medicine as such 3D structures grown from tumor cells recapitulate better tumor characteristics, making these tumoroids unique for personalized cancer research. Assessment of their functional behavior, particularly protein secretion, is of significant importance to provide comprehensive insights. Here, a label-free spectroscopic imaging platform is presented with advanced integrated optofluidic nanoplasmonic biosensor that enables real-time secretion analysis from single tumoroids. A novel two-layer microwell design isolates tumoroids, preventing signal interference, and the microarray configuration allows concurrent analysis of multiple tumoroids. The dual imaging capability combining time-lapse plasmonic spectroscopy and bright-field microscopy facilitates simultaneous observation of secretion dynamics, motility, and morphology. The integrated biosensor is demonstrated with colorectal tumoroids derived from both cell lines and patient samples to investigate their vascular endothelial growth factor A (VEGF-A) secretion, growth, and movement under various conditions, including normoxia, hypoxia, and drug treatment. This platform, by offering a label-free approach with nanophotonics to monitor tumoroids, can pave the way for new applications in fundamental biological studies, drug screening, and the development of therapies.

Characterisation of a microelectrochemical biosensor for real-time detection of brain extracellular d-serine

A modified development protocol and concomitant characterisation of a first generation biosensor for the detection of brain extracellular d-serine is reported. Functional parameters important for neurochemical monitoring, including sensor sensitivity, O2 interference, selectivity, shelf-life and biocompatibility were examined. Construction and development involved the enzyme d-amino acid oxidase (DAAO), utilising a dip-coating immobilisation method employing a new extended drying approach. The resultant Pt-based polymer enzyme composite sensor achieved high sensitivity to d-serine (0.76 ± 0.04 nA mm−2. μM−1) and a low μM limit of detection (0.33 ± 0.02 μM). The in-vitro response time was within the solution stirring time, suggesting potential sub-second in-vivo response characteristics. Oxygen interference studies demonstrated a 1 % reduction in current at 50 μM O2 when compared to atmospheric O2 levels (200 μM), indicating that the sensor can be used for reliable neurochemical monitoring of d-serine, free from changes in current associated with physiological O2 fluctuations. Potential interference signals generated by the principal electroactive analytes present in the brain were minimised by using a permselective layer of poly(o-phenylenediamine), and although several d-amino acids are possible substrates for DAAO, their physiologically relevant signals were small relative to that for d-serine. Additionally, changing both temperature and pH over possible in vivo ranges (34–40 °C and 7.2–7.6 respectively) resulted in no significant effect on performance. Finally, the biosensor was implanted in the striatum of freely moving rats and used to monitor physiological changes in d-serine over a two-week period.

Simulation Analysis of Mode Hopping Impacts on OFDR Sensing Performance

This article examines the impacts of mode hopping on the sensing performance of optical frequency domain reflectometry (OFDR) and explores the potential for developing economical OFDR interrogators employing low-cost distributed feedback (DFB) lasers. By conducting numerical simulations, this study reveals that mode hopping has minimal effects on distance sensing measurements in free space due to the limited duration of beat interference signal at the incorrect frequency within the coherence length. Additionally, the simulations indicate that mode hopping only slightly affects the distributed strain sensing of OFDR, resulting in an error range of less than ±1µε when 100µε is applied to the sensing fiber. These findings highlight the potential of using low-cost DFB lasers with a 1-nm wavelength sweep range and a 1-MHz linewidth as tunable laser sources in OFDR while maintaining reliable and accurate sensing performance.

Digital cancellation of multi-band passive inter-modulation based on Wiener-Hammerstein model

Utilizing multi-band and multi-carrier techniques enhances throughput and capacity in Long-Term Evolution (LTE)-Advanced and 5G New Radio (NR) mobile networks. However, these techniques introduce Passive Inter-Modulation (PIM) interference in Frequency-Division Duplexing (FDD) systems. In this paper, a novel multi-band Wiener-Hammerstein model is presented to digitally reconstruct PIM interference signals, thereby achieving effective PIM Cancellation (PIMC) in multi-band scenarios. In the model, transmitted signals are independently processed to simulate Inter-Modulation Distortions (IMDs) and Cross-Modulation Distortions (CMDs). Furthermore, the Finite Impulse Response (FIR) filter, basis function generation, and B-spline function are applied for precise PIM product estimation and generation in multi-band scenarios. Simulations involving 4 carrier components from diverse NR frequency bands at varying transmitting powers validate the feasibility of the model for multi-band PIMC, achieving up to 19 dB in PIMC performance. Compared to other models, this approach offers superior PIMC performance, exceeding them by more than 5 dB in high transmitting power scenarios. Additionally, its lower sampling rate requirement reduces the hardware complexity associated with implementing multi-band PIMC.

Enhancing wireless sensor network connectivity and coverage using Hybrid GWO‐HSA algorithm

SummaryWireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains.

About the possibility of protecting UAVs from suppression of control signals

Unmanned aerial vehicles (UAVs) are widely used in a wide variety of sectors, both in the national economy and for military purposes. In the agricultural industry, they are used to monitor farmland, analyze soil samples, and even for livestock grazing. In search and rescue operations, UAVs are used to rescue people from life-threatening situations, and each time this technology proves its usefulness more than ever. UAVs are gradually finding their application in retail trade, transportation, entertainment, home security and even in construction using 3D printers. Simultaneously with the growth of the capabilities of UAVs, methods and means were developed to counteract them. The most affordable method of combating an UAV is to suppress the radio signals used to control it. As a result, the UAV loses contact with the operator, which leads to malfunctions in its operation. Therefore, the relevance of developing methods and means of combating suppression of control signals cannot be doubted and is becoming more and more necessary. The purpose of the work is to develop a scheme of the device (protection unit) that protects the UAV from interference radio signals in the operating mode. The peculiarity of the developed device is that in the absence of interference signals on the standard frequency of the UAV, the protection unit receives the operator's control signal and transmits it to the input of the UAV receiver, which moves in the operating mode and performs the tasks assigned to it. In the event of the appearance of interference radio signals on the standard frequency, it independently detects them and, using the UAV transmitter, issues a command to the operator to change the operating frequency to an additional frequency. At the same time, the output frequency of the protection unit remains constant and is equal to the standard frequency of the UAV. Thus, the protection unit is a simple and inexpensive device that detects interference signals and automatically switches the control of the UAV to an additional operating frequency. A functional diagram of the receiving device was developed, and a detailed description of its operation was provided.

Multi-mode fiber Bragg grating for simultaneous detection of strain, torsion and temperature

In this study, a fiber Bragg grating sensor utilizing multi-mode optical fiber (MM-FBG) is proposed. This sensor can simultaneously detect both temperature and torsion, or strain and torsion. Unlike single-mode fiber, the multiple modes of multi-mode fiber can generate several reflection peaks within the FBG area, increasing data capacity and enabling multi-parameter detection. By monitoring the center wavelength and reflection intensity of the MM-FBG reflection peaks, the sensor can detect temperature, torsion, and strain simultaneously. This approach simplifies parameter characterization compared to traditional methods, reducing signal interference and enhancing the clarity of experimental results. Experimental findings demonstrate that the MM-FBG sensor structure exhibits maximum sensitivities of 0.571 dBm/°/cm for torsion, 12 pm/°C for temperature, and 0.7411 pm/με for strain. Therefore, the fiber sensor proposed in this study shows great potential for monitoring bridge and tunnel structures and for civil engineering applications.

Speckle noise detection and correction for frequency-scanning interferometry in vibration measurement

Speckle noise significantly impairs the accuracy of vibration measurements for non-cooperative targets in frequency-scanning interferometry (FSI). To restore the real vibration, this paper proposes a comprehensive vibration demodulation algorithm based on the FSI. We established an FSI vibration model accounting for speckle noise. The extraction of instantaneous frequency from the interference signal is performed using a complex-shifted Morlet wavelet. An autoregressive differential model, incorporating a smooth weight function, is applied for bidirectional prediction to correct speckle noise. Finally, the true vibration of the target is restored through Kalman filtering. We validated the algorithm’s accuracy using a Monte Carlo strategy. The experimental results show that the surface vibration of black aluminum oxide alloy and the axial runout of the DC motor during operation was successfully captured, and the time–frequency analysis results showed that this method has strong robustness against dense speckle noise.

A novel red-to-near-infrared AIE fluorescent probe for detection of Hg2+ with large Stokes shift in plant and living cells

Due to the highly toxic nature of mercury ions to living organisms, accurately detecting Hg2+ in water samples and biological systems is of great significance. In this study, we designed and synthesized a novel red-to-near-infrared Aggregation-Induced Emission (AIE) fluorescent probe (named as DS) based Fluorene derivatives on specifically for Hg2+ detection. Probe DS can visually identify Hg2+ through an red-to-near-infrared fluorescence enhancement change, characterized by a large Stokes shift (130 nm) and AIE feature. This probe offers a fast response, high selectivity and sensitivity. The Hg2+-induced deprotection reaction of the thioketal mechanism was thoroughly investigated using nuclear magnetic resonance spectroscopy (NMR), mass spectrometry (MS) and density functional theory (DFT) calculation. Additionly, dynamic light scattering (DLS) results indicated that the aggregation states changes of the molecular play a crucial role in the AIE fluorescence response of probe DS toward Hg2+. The red-to-near-infrared response with AIE feature not only avoids the interference of auto-fluorescence signals in complex environments, but also reduces the fluorescence quenching caused by probe molecular aggregation. This makes probe DS highly suitable for high-quality imaging detection of Hg2+ in aqueous environments. Furthermore, probe DS demonstrates the capability for visual fluorescence detection of Hg2+ concentrations in water sample, plant roots and living cells.

Research on Filtering Algorithm of Vehicle Dynamic Weighing Signal

This study analyzes the advantages and disadvantages of filtering algorithms for dynamic weighing signals. Highway road surface has road surface unevenness and other influencing factors. The body vibration of the vehicle driving process produces a certain amount of interference signals collected by the load cell to form noise signals. In addition, piezoelectric sensors and amplification circuits introduce a large amount of electrical noise. These noise signals are non-smooth, nonlinear, and have other characteristics. We study the filtering effects of moving average (MA), wavelet transform (WT), and variational mode decomposition (VMD) filtering algorithms on axle weight signals and evaluate the performance of the filtering algorithms through the Root Mean Square Error (RMSE), signal-to-noise ratio (SNR), and Normalized Correlation Coefficient (NCC). The comprehensive analysis shows that the variational modal decomposition filtering algorithm is more advantageous for axial weight signal processing. The design of the axle weight signal noise filtering algorithm is of great significance for improving the accuracy of the overall dynamic weighing system of the vehicle.