Interference Environment Research Articles

Application of the AVMD-AIMCKD method to the enhancement of rolling bearing fault features

Abstract Rolling bearings are prone to failure due to harsh working environments, which can affect production efficiency. Given the background environmental noise interference, it is difficult to extract the fault characteristics of rolling bearings in the early stages of weak fault signals. Based on adaptive VMD combined with adaptive IMCKD (AVMD-AIMCKD), a rolling bearing fault diagnosis method is proposed in this paper. The vibration signal from the rolling element bearing is processed by adaptive variational modal decomposition (AVMD) to extract and select the time-frequency domain signal characteristics. The Adaptive Improved maximum correlated kurtosis deconvolution (AIMCKD) algorithm is used for noise reduction optimization to obtain the best extraction results. Because the traditional method requires input parameter values for VMD and IMCKD, it is not adaptive. The particle swarm optimization algorithm (PSO) is utilized in this study to optimize two variables: the penalty factor and decomposition mode number of variable mode decomposition (VMD). The filter length and shift order of the improved maximum correlated kurtosis deconvolution (IMCKD) are optimized to make it adaptive. The viability of the method is substantiated through simulations, and the efficacy and precision are validated by comparative studies. The experimental results show that the AVMD-AIMCKD method has high accuracy and robustness in rolling bearing fault diagnosis and provides an accurate reference for rolling bearing health monitoring in engineering practice. The AVMD-AIMCKD method effectively extracts the early fault features through adaptive optimization, overcomes the restrictions of traditional methods, and provides a more accurate and reliable tool for bearing fault signal diagnosis.

Research on the Sensing Properties and Vibration Reduction Performance of Self-Sensing Self-Tuning Magnetic Fluid Damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper's natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit's output induced electromotive force model is created, followed by an expression of the damper's natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage.
Finally, an experimental platform was developed to assess the damper's self-sensing and self-tuning vibration reduction performance. The experimental results demonstrate that the suggested self-tuning magnetic fluid damper exhibits excellent self-sensing capabilities and effectively reduces vibrations. This offers an innovative research concept for using magnetic fluid in self-sensing technology and vibration reduction domains.

Multiple Defect-Induced High-Resolution Near-Infrared Mechanoluminescent Materials for Non-Destructive Detection of Blood Glucose and Lipids.

Mechanoluminescence (ML) materials, known for their ability to convert mechanical energy into light, are increasingly recognized for their potential applications, such as in intelligent stress sensing, in vivo bioimaging, and stress non-destructive monitoring. However, the low signal-to-noise ratio (SNR) and narrow-band emission of single-defect-induced ML materials usually limit their biological-related practical applications. Here, these limitations will be addressed by modulating the microstructure evolution in Y3Ga3MgSiO12:Cr3+ through the [Si4++Mg2+] → [Ga3++Ga3+] chemical substitution strategy. Density functional theory (DFT) calculation reveals the defect types and dynamic charge migration processes. In addition, Y3Ga3MgSiO12: Cr3+ with continuously distributed "shallow-deep" defects (0.68-1.61eV) can avoid persistent luminescence (PersL) and bright-field environment interference. Herein, such high SNR near-infrared broadband ML emission may provide a reliable way for high-quality biological non-destructive sensing and detection. Finally, benefiting from the different absorption of ML signals in glucose/lipid, one may find a novel non-invasive blood glucose/lipid testing technology in patients.

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

UUV cluster navigation data extraction fusion positioning algorithm with information geometry

With the rapid development of the ocean economy, underwater unmanned vehicle (UUV) clusters have become the main tool for ocean development, but the complex interference and occlusion in the underwater environment have posed great challenges to the positioning of UUV clusters, becoming a key problem in the application of UUV clusters. To address this issue, this paper proposes a distributed heterogeneous navigation information geometric fusion positioning method for underwater unmanned vehicle clusters based on data extraction(DEFP-IG). This method converts the distributed navigation source information of UUVs into an information geometric probability model, and constructs a factor graph fusion architecture based on data extraction to improve the suppression of interference, thereby achieving high-precision positioning of UUV clusters. Comparing the method proposed in this paper with existing methods in different scenarios, the results show that the proposed method has higher positioning accuracy and good suppression capability for abrupt errors.

Retraction Note: Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment

Retraction Note: Blind signal-to-interference-plus-noise ratio estimation of OFDM/OQAM system in radio frequency interference environment

Preprocessing and Denoising Techniques for Electrocardiography and Magnetocardiography: A Review

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

Experimental study on high-precision measurement of surface temperature in friction stir welding aluminium alloy 2219 based on infrared thermometry

In friction stir welding (FSW) of aluminium alloy 2219, the welding temperature critically influences the weld quality. Accurate measurement of the welding temperature is essential for effective welding process control. However, the conventional approach of non-contact infrared thermometry faces significant challenges in FSW due to the inherently low and variable emissivity of aluminium alloys, making it susceptible to environmental radiation interference. This paper proposes a novel, high-precision method for measuring the surface temperature of FSW aluminium alloy 2219 based on infrared thermometry principles. Initially, the study delves into these principles, analysing factors that affect the accuracy of infrared thermometry in the context of FSW in industrial settings. To enhance the surface emissivity of the weldments, a high-emissivity coating is applied to the aluminium alloy surface. Additionally, strategic adjustments in the positioning of the thermal imager and careful selection of the measuring area effectively mitigate interference from the FSW tool’s radiation. Conventional methods report a temperature measurement error greater than 2%, while the proposed method reduces this error to 1.2%. The effectiveness and practical utility of the method are validated through FSW experiments on flat plates and rocket tank rings, corroborated by comparative analyses with surface thermocouple temperature measurements. This confirms the viability of the proposed method for high-precision temperature measurement in aluminium alloy FSW applications.

Continuous solid-phase extraction spectroscopy and its quantification method for trace analysis

This study designed and developed an innovative online detection device based on Continuous Solid-Phase Extraction Spectroscopy (CSPES) for rapid quantitative analysis of environmental water pollutants. The device is highly automated, eliminating environmental interference. Leveraging CSPES technology and adsorption kinetics theory, an online quantitative analysis model between the spectrum and component concentrations was established, along with a concentration calculation method based on the least squares method. The quantitative analysis method was validated using single-component and binary-component sample systems containing Fluoranthene, Benzo[k]Fluoranthene, and Rhodamine 6G. The model exhibited excellent predictive performance, with overall prediction concentration relative errors (RE) ranging from 0.45 % to 8.75 % and relative standard deviations (RSD) of less than 3 %. In real sample applications, recovery rates ranged from 86.8 % to 124.4 %, with RSDs between 0.33 % and 2.22 %. This method provides a robust tool for water quality monitoring and environmental analysis, holding significant potential for application across various fields.

Automated Fingerprint Identification System

Abstract: AFIS is regarded as one of the greatest advancements in biometric systems as it enables fast and efficient identification of an individual by their distinct fingerprint designs. This system employs the use of digital imaging, finger-power techniques and other technologies in the storage and comparison of fingerprints. With AFIS, fingerprint images are all digitized making it easier to carry out crime investigations, border control and general identification of persons. The strength of AFIS systems is evidenced by the increased accuracy levels along with the capacity to accommodate numerous records and still allow for fast searches by fingerprinting matching to existing images for law enforcement on file. Some problems nevertheless persist which include the quality of fingerprints and environmental interferences, however machine learning and artificial intelligence improvements are still being incorporated into the systems to make them work even better. This paper investigates the system architecture and components, operational aspects, and prospects for development of the AFIS technology, which is essential in contemporary informational security

Wi-Fi sensing gesture control algorithm based on semi-supervised generative adversarial network

A Wi-Fi-sensing gesture control system for smart homes has been developed based on a theoretical investigation of the Fresnel region sensing model, addressing the need for non-contact gesture control in household environments. The system collects channel state information (CSI) related to gestures from Wi-Fi signals transmitted and received by network cards within a specific area. The collected data undergoes preprocessing to eliminate environmental interference, allowing for the extraction of complete gesture sets. Dynamic feature extraction is then performed, followed by the identification of unknown gestures using pattern recognition techniques. An improved dynamic double threshold gesture interception algorithm is introduced, achieving a gesture interception accuracy of 98.20%. Furthermore, dynamic feature extraction is enhanced using the Gramian Angular Summation Field (GASF) transform, which converts CSI data into GASF graphs for more effective gesture recognition. An enhanced generative adversarial network (GAN) algorithm with an embedded classifier is employed to classify unknown gestures, enabling the simultaneous recognition of multiple gestures. A semi-supervised learning algorithm designed to perform well even with limited labeled data demonstrates high performance in cross-scene gesture recognition. Compared to traditional fully-supervised algorithms like linear discriminant analysis (LDA), Light Gradient Boosting Machine (LightGBM), and support vector machine (SVM), the semi-supervised GAN algorithm achieves an average accuracy of 95.67%, significantly outperforming LDA (58.20%), LightGBM (78.20%), and SVM (75.67%). In conclusion, this novel algorithm maintains an accuracy of over 94% across various scenarios, offering both faster training times and superior accuracy, even with minimal labeled data.

Assessing the Impact of Environmental Conditions on Reflectance Values in Inland Waters Using Multispectral UAS Imagery

This study investigates the impact of environmental conditions on reflectance values obtained from multispectral Unmanned Aerial System (UAS) imagery in inland waters, focusing on sun glint, cloud glint, wind-generated waves, and cloud shading projections. Conducted in two reservoirs with differing water qualities, UAS platforms equipped with MicaSense Altum and DJI Phantom 4 Multispectral sensors were used to collect multispectral images. The results show that sun glint significantly increases reflectance variability as solar elevation rises, particularly beyond 54°, compromising data quality. Optimal flight operations should occur within a solar elevation angle range of 25° to 47° to minimize these effects. Cloud shading introduces complex variability, reducing median reflectance. Wind-generated waves enhance sun glint, increasing variability across all spectral bands, while cloud glints amplify reflectance non-uniformly, leading to inconsistent data variability. These findings underscore the need for precise correction techniques and strategic UAS deployment to mitigate environmental interferences. This study offers valuable insights for improving UAS-based monitoring and guiding future research in diverse aquatic environments.

Persistent chemiluminescence-based nanosensor for portable point-of-care testing of H2O2.

Anovel glow-type chemiluminescence (CL)-based nanosensing system was developed for sensitive and rapid point-of-care testing (POCT) of H2O2 in food. CuSe nanoparticles (CuSeNPs) have excellent peroxidase-like activity. After being modified with thiols of 4-mercaptophenylboronic acid (MBA) (CuSeNPs@MBA), luminol can be catalyzed to produce long-lasting CL in the presence of H2O2. The possible reason for the long-lasting glow-type CL behavior was explored. Under the optimized condition, H2O2 can be sensitively detected with improved repeatability. The limit of detection isas low as 0.30 μM. To meet the requirement of in situ and outside of laboratory detection, a 3D-printed portable device was designed which can eliminate the environmental interference to improve detection accuracy. The developed multifunctional platform also has the advantages of simple operation and low cost, suggesting its great potential for applications in food and agricultural fields.

High-precision dynamic axial clearance measurement method based on an all-fiber heterodyne microwave-AMCW with an all-phase tracking algorithm

Rotor-stator axial clearance is critical to the safety and efficiency of major rotating machinery. However, factors such as high-speed rotation, narrow space, high temperature, and vibration present significant challenges for high-precision dynamic measurement of axial clearance. This paper proposes an axial clearance measurement method based on an all-fiber heterodyne microwave amplitude-modulated continuous wave (microwave-AMCW) system combined with an all-phase tracking algorithm, characterized by high precision, wide bandwidth, and a large measurement range. To mitigate environmental influences, a heterodyne all-fiber microwave-AMCW optical path structure is developed, and a compact dual-core fiber sensor probe is designed. The all-phase tracking algorithm is introduced to enhance dynamic precision and expand bandwidth. Additionally, what we believe to be a novel bandwidth test method based on time division multiplexing is proposed to evaluate the system's wide-bandwidth performance. The proposed system's performance is validated through simulations and experiments. The results demonstrate that the system exhibits excellent resistance to environmental interference, with a measurement range up to 24.5 mm and a static precision better than 4.5µm. Dynamic experiments further confirm the algorithm's effectiveness, achieving a precision better than 5.3µm at 100kHz bandwidth. Compared to other clearance measurement algorithms including the Hilbert transform and FFT, the proposed method reduces dynamic error by over 74%.

Successional changes in aphyllophorales macromycetes at different stages of coniferous xylolysis

The state of modern protective plantations in coniferous-deciduous forests of the Non-Chernozem zone is characterized by great heterogeneity. Currently, both protective forest plantations and protective tree plantations are managed by an extensive method, often without taking into account biological relationships in the agrophytocenosis. Successional changes in Aphyllophorales macromycetes (AFMM) at different stages of xylolysis of large tree debris represent a complex biochemical process. The limiting conditions for the active growth of fungal mycelium inside wood include light, access to moisture and air, violations in water transport. As mycogenic xylolysis develops, there is an increase in the biodiversity of AFMM species, which reaches a maximum at stage III–IV. On a large data set of 332 model trees and 3,543 basidiomes of xylotrophic basidiomycetes, the presence of correlations between the settlement on the substrate of various ecological groups of AFMM involved in xylolysis was established. It has also been shown that the fruit bodies of fungi are actively formed under conditions of abiotic stress and their number increases in subsequent seasons. When using AFMM as indicators of the xylolysis stage of coniferous species, it is necessary to take into account that the development of mycelium depends on the structural features of the wood. The course of mycelium development in the trunk cross-section is associated with both the action of external environmental factors and interference processes between species. At the same time, the frequency of basidioma formation and their age are of great importance in field identification.

Room-Temperature Quantum Coherence of Reversible Photo-Generated Radical and its Film.

Electron spin qubits are becoming an important research direction in the field of quantum computing and information storage. However, the quantum decoherence has seriously hindered the development of this field. So far, few qubits exhibit long phase memory time (Tm), and even fewer qubits that can reach room temperature. Some reports show that the coherence times of radicals are generally long, so radicals may be the preferred spin carriers for qubits. Here, we demonstrate the qubit properties of a photogenerated radical (1 a) based on 2,4,6-Tri(4-pyridyl)-1,3,5-triazine (tpt, 1). More importantly, the photogenerated radical is a spin self-diluting complex, which the dilution is generally used in the investigation of qubits to reduce the interference of environment on qubits in order to overcome the decoherence of qubits. It is surprised that radical tpt has a stable Tm=1.1 μs above 20 K, even keep it to room temperature. In addition, the tpt-film prepared by the vacuum evaporation is significantly increase the T1 and Tm at low temperature.

Brainstem auditory evoked responses: Objective hearing threshold assessment in Holstein cows.

Animal audiology utilizes brainstem auditory evoked responses (BAER) as a non-invasive tool to assess hearing in animals, including Holstein dairy cows. Understanding cows' auditory capabilities is critical for their welfare, especially given their exposure to farm noise. This study provides preliminary normative BAER data for Holstein cows by focusing on absolute and interpeak wave latencies. The objective is to assess the impact of farm noise and expand audiologists' practice scope. Ten Holstein cows were tested using monoaural broadband click stimuli with contralateral masking. Earphones with foam ear tips were used to minimize environmental noise interference. The BAER responses were recorded via subdermal needle electrodes placed at standardized locations on the cows' heads. The data were analysed using descriptive statistics to determine auditory thresholds and wave latencies. The cows exhibited auditory thresholds at 90 dB SPL (55 dB nHL). Detailed wave and interpeak latencies were recorded at intensities from 85 to 105 dB SPL. At 90 dB SPL, the average latency for wave V was 5.17 ms, marking the auditory threshold for Holstein cows. These findings provide key insights into the auditory sensitivity of Holstein cows, highlighting BAER's potential for monitoring auditory health and evaluating the effects of noise pollution on animal welfare. This research underscores the value of integrating animal audiology into the audiologist's scope, ultimately enhancing both animal welfare and farming sustainability.Contribution:This study adds to the limited literature on farm animal auditory health and suggests strategies to improve welfare through better auditory management.

Semantic Segmentation Model-Based Boundary Line Recognition Method for Wheat Harvesting

The wheat harvesting boundary line is vital reference information for the path tracking of an autonomously driving combine harvester. However, unfavorable factors, such as a complex light environment, tree shade, weeds, and wheat stubble color interference in the field, make it challenging to identify the wheat harvest boundary line accurately and quickly. Therefore, this paper proposes a harvest boundary line recognition model for wheat harvesting based on the MV3_DeepLabV3+ network framework, which can quickly and accurately complete the identification in complex environments. The model uses the lightweight MobileNetV3_Large as the backbone network and the LeakyReLU activation function to avoid the neural death problem. Depth-separable convolution is introduced into Atrous Spatial Pyramid Pooling (ASPP) to reduce the complexity of network parameters. The cubic B-spline curve-fitting method extracts the wheat harvesting boundary line. A prototype harvester for wheat harvesting boundary recognition was built, and field tests were conducted. The test results show that the wheat harvest boundary line recognition model proposed in this paper achieves a segmentation accuracy of 98.04% for unharvested wheat regions in complex environments, with an IoU of 95.02%. When the combine harvester travels at 0~1.5 m/s, the normal speed for operation, the average processing time and pixel error for a single image are 0.15 s and 7.3 pixels, respectively. This method could achieve high recognition accuracy and fast recognition speed. This paper provides a practical reference for the autonomous harvesting operation of a combine harvester.

CRTF-MoeICP: A robust coarse-to-fine reflector-based LiDAR indoor positioning algorithm

The reflector-based Light Detection and Ranging (LiDAR) positioning method is susceptible to environmental interferences, resulting in instability. This instability not only reduces movement accuracy but also poses safety hazards. To solve the above problems in the application of LiDAR sensors in the field of indoor positioning, we propose a Coarse Registration algorithm based on the Triangular Feature (CRTF) and a fine registration algorithm based on Multi-level outlier elimination and Iterative Closest Point (MoeICP) for the reflector-based LiDAR positioning. The proposed coarse-to-fine positioning algorithm CRTF-MoeICP addresses the issue of reflector-based LiDAR positioning failure arising from the improper selection of the initial transformation matrix and outlier interference in indoor structured industrial environments. The experiment results show that the CRTF-MoeICP algorithm can ensure the stable registration of the LiDAR point cloud and the reflector map by completely removing all outliers, greatly improving the indoor positioning stability of LiDAR sensors. Besides, the proposed algorithm can be realized by LiDARs with different performance, and improve the static positioning repeatability to ±3 mm. The high precision and stable positioning results improve the motion accuracy, ensuring that the Automatic Guided Vehicle (AGV) can accurately and stably complete the handling task.