Noise Immunity Research Articles

Modal frequency identification using the predictor subspace approach for a space solar power station

The system’s modal frequency parameters are determined by using a technique known as predictor-based subspace identification (PBSID), which is based on the dynamic properties of space solar power stations’ huge size and low frequency. First, the dynamic equation for the space solar power station with an Abacus configuration’s attitude-vibration coupling is developed. Additionally, the PBSID approach is used to build the relevant parameter matrix, and singular value decomposition (SVD) is chosen to evaluate the system’s structural frequency parameters. The right input signals are created via numerical simulation, and the best sensor placement technique yields the associated vibration response signals. The computing results then demonstrate that the modal frequency characteristics of the space solar power station may be successfully identified by the PBSID method, which is based on SVD. Additionally, the findings demonstrate the superior noise immunity capabilities of the PBSID algorithm when compared to the standard modal parameter identification approaches.

An Ensemble Classification Model for Depression Based on Wearable Device Sleep Data.

Depression is one of the most common mental disorders, with sleep disturbances as typical symptoms. With the popularity of wearable devices increasing in recent years, more and more people wear portable devices to track sleep quality. Based on this, we believe that depression detection through wearable sleep data is more intelligent and economical. However, the majority of wearable devices face the problem of missing data during the data collection process. Otherwise, most existing studies of depression identification focus on the utilization of complex data, making it difficult to generalize and susceptible to noise interference. To address these issues, we propose a systematic ensemble classification model for depression (ECD). For the missing data problem of wearable devices, we design an improved GAIN method to further control the generation range of interpolated values, which can achieve a more reasonable treatment of missing values. Compared with the original GAIN approach, the improved method shows a 28.56% improvement when using MAE as the metric. For depression recognition, we use ensemble learning to construct a depression classification model which combines five classification models, including SVM, KNN, LR, CBR, and DT. Ensemble learning can improve the model's robustness and generalization. The voting mechanism is used in several places to improve noise immunity. The final classification model performed great on the dataset, with a precision of 92.55% and a recall of 91.89%. These results illustrate how efficient this method is in automatically detecting depression.

Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.

Study on Ill-Conditioned Total Least Squares Load Identification Method Based on the IGG Weight Function

To address the ill-conditioned problem and unknown noise interference in ship load identification, this study proposes the ill-conditioned Total Least Squares (TLS) load identification method using the anti-error principle of the IGG weight function (IGG-TLS). First, the weight factor is iteratively updated through the IGG weight function. The weight matrix is constructed adaptively by multiplying the weight factor with the initial unit weight matrix. Then, the regularization criterion for the ill-conditioned IGG-TLS model is established based on the Tikhonov regularization principle. And the Lagrange multiplier is employed to solve the IGG-TLS method. Finally, the load identification accuracies of the IGG-TLS method are investigated through the simulation analysis and experimental verification of the rectangular plate under different noise and SNR conditions. The results demonstrate that the IGG-TLS method can achieve high accuracy in load identification without the need to pre-construct the weight matrix, even when the SNR is unknown. Additionally, the IGG-TLS method can effectively correct the identification errors in the TLS method, demonstrating high accuracy and noise immunity under conditions of equal-weight and unequal-weight noise.

Fast and Highly Accurate Zonal Wavefront Reconstruction from Multi-Directional Slope and Curvature Information Using Subregion Cancelation

The wavefront reconstruction is a crucial step in determining the performance of wavefront detection instruments. The wavefront reconstruction algorithm is primarily evaluated in three dimensions: accuracy, speed, and noise immunity. In this paper, we propose a hybrid zonal reconstruction algorithm that introduces slope and curvature information in the diagonal, anti-diagonal, horizontal, and vertical directions by dividing the neighbor sampling points into subregions in groups of four. By canceling the same parameters in integration equations, an algorithm using multi-directional slope–curvature information is achieved with only two sets of integration equations in each subregion, reducing the processing time. Simulation experiments show that the relative root-mean-square reconstruction error of this algorithm is improved by about 4 orders of magnitude compared with existing algorithms that use multi-directional slope information or slope–curvature information alone. Compared with the hybrid multi-directional slope–curvature algorithm, the proposed algorithm can reduce computation time by about 50% as well as provide better noise immunity and reconstruction accuracy. Finally, the validity of the proposed algorithm is verified by the null test experiment.

Research and Development in the Field of Creating Materials, Technologies and Safety Equipment

Technologies for the manufacture of microwave electromagnetic shields based on powdered carbon, foil and composite multilayer materials are presented. Technological methods for their manufacture and shielding properties are described, and the main mechanisms leading to increased shielding properties are considered depending on the manufacturing technology, composition and alternation of composite layers. It is shown that the values of the reflection coefficient of electromagnetic radiation in the frequency range 2–17 GHz for the shields considered reach a value of (–20) dB. Microwave absorbers with a reflection characteristic lower than (–10.0) dB (down to (–52.0) dB) in the frequency band 8.0–12.0 GHz are developed, and the electrophysical parameters, composition and concentration of components of the composite materials for such microwave absorbers are determined. The systems for active protection of speech information that use combined masking signals consisting of “white” noise and speech-like signals as masking signals, along with “white” noise are considered. Criteria have been developed for the approach to selecting speakers and audiences when assessing speech intelligibility to solve problems of protecting speech information and methods for experimental studies of speech intelligibility against a background of masking acoustic noise. Technologies for the formation of nanocomposites based on carbon nanotubes and nanoparticles of ferromagnetic materials are presented to increase the degree of coherence of the spin texture at macroscopic distances, increase the degree of security of electronic products to strong magnetic fields and noise immunity. It has been shown that at certain concentrations of ferromagnetic nanoparticles, micromagnetic parameters increase, ensuring high performance of active elements in magnetic fields. The role of carbon in this case turns out to be decisive. The presented results seem promising for use in the creation of anechoic chambers, partitions for separating indoor zones intended for the location of electronic devices, products for protecting humans from the effects of electromagnetic radiation in the microwave range, and the formation of combined masking signals in the form of “white” noise for devices protection of speech information, composite coatings to level out the effects of a constant magnetic field on electronic products.

ОПТИМАЛЬНОЕ РАЗМЕЩЕНИЕ АНТЕННЫХ ЭЛЕМЕНТОВ ШИРОКОДИАПАЗОННОГО АДАПТИВНОГО КОМПЕНСАТОРА ПОМЕХ В ДЕКАМЕТРОВЫХ ЛИНИЯХ РАДИОСВЯЗИ МЧС РОССИИ

The analysis of adaptive spatial interference compensation methods converging to the optimal Wiener solution with accuracy up to a constant multiplier is carried out. 
 A mathematical apparatus is presented for calculating the gain in noise immunity of decameter radio communication lines due to the use of an adaptive interference compensator from two spaced antenna emitters. The results of modeling the calculation of the dependence of the normalized gain in noise immunity on the ratio of antenna element spacing and wavelength are presented. Proposals are formulated for the optimal spatial placement of emitters in terms of gain in noise immunity at a given direction to a useful signal in the interests of increasing the efficiency of signal reception in decameter communication lines of the EMERCOM of Russia.

Noise-resistant technique for recording pulse waves and measuring cardiac intervals to improve the accuracy of operational assessment of the patient’s functional state during a magnetic therapy session

Modern non-invasive means of quickly assessing the functional state of a patient, widely used in clinical practice and especially in intensive care units, are based on the method of variational pulsometry, where the sources of information are the pulse wave signal (photoplethysmogram signal) and the duration of cardio intervals. However, due to a number of reasons caused by the action of various interferences, artifacts and the nature of the change in the photoplethysmogram signal over time, the measured values of the durations of cardiac intervals may be distorted and unreliable. These factors reduce the diagnostic accuracy of the method. To develop a methodology and hardware and software that work effectively in conditions of all kinds of interference, increasing the accuracy of recording the photoplethysmogram signal and measuring the duration of cardiac intervals. A method and technical means are proposed that ensure reliable collection of a pulse wave signal from a patient during a magnetic therapy session, its conversion into code, processing, noise-resistant registration of signal implementations on each cardiac cycle and measurement of their durations, which are used for prompt assessment of the patient’s current functional state using a number of calculated diagnostic indicators. Physical modeling of the pulse wave signal and simulation of possible random interference and artifacts made it possible to determine the potential noise immunity of the proposed technique and estimate the errors in measuring the durations of cardiac intervals. The introduction of the developed methodology and hardware and software into the “Multimag” chronomagnetic therapy complex will make it possible to significantly increase the diagnostic accuracy of the operational determination of the patient’s functional state during a magnetic therapy session, and accordingly increase the effectiveness of treatment.

Design and simulation optimization of phase-locked loop structure for phase-shifting power supply

A phase-locked loop (PLL) of a phase-shifting power supply for the power system is designed in this paper. Taking the industrial frequency current as the reference signal, the input and output characteristics of the phase-frequency detector with charge pump are analyzed. In addition, the transfer function of the PLL system is analyzed by combining the passive second-order low-pass filter and the voltage-controlled oscillator. As a result, the influence of parameters such as charge pump current, phase margin, and loop bandwidth on the performance of the PLL is investigated, and the optimal parameters of each section are obtained. The results show that the optimized PLL has a locking time of 0.75 s, a pull-out range of 63.16 Hz, and a locking range of 43.82 Hz, with high stability and noise immunity.

An adaptive fully convolutional network for bearing fault diagnosis under noisy environments.

Intelligent diagnostic algorithms based on convolutional neural networks (CNNs) have shown great potential in diagnosing various conditions. However, accurately and robustly diagnosing faults in noisy situations remains challenging. This study presents an adaptive fully convolutional network (AFCN) for identifying bearing defects in noisy environments. First, we use a novel large kernel convolution method for high-frequency noise reduction and wide-area temporal feature extraction. By utilizing a sequence of stacked residual adaptive convolution blocks, the AFCN achieves a selective emphasis on significant features and adaptive adjustment of feature weights at various convolution scales. The experimental results have shown that the AFCN achieves a diagnostic accuracy of over 90% for the faults in the CWRU dataset under the -8 dB noise and over 77% for the PU dataset in the case of -6 dB noise. The comparison results with five advanced baseline models have demonstrated the superiority of the AFCN in feature extraction, noise immunity, and robustness to the noise environment. The AFCN provides a better adaption to noise interference than conventional CNNs and other advanced adaptive networks.

Predefined-Time Noise Immunity ZNN Model for Dynamic Quaternion Least Squares Problem and Application to Synchronization of Hyperchaotic Systems

Predefined-Time Noise Immunity ZNN Model for Dynamic Quaternion Least Squares Problem and Application to Synchronization of Hyperchaotic Systems

Coherent Chaotic Communication Using Generalized Runge–Kutta Method

Computer simulation of continuous chaotic systems is usually performed using numerical methods. The discretization may introduce new properties into finite-difference models compared to their continuous prototypes and can therefore lead to new types of dynamical behavior exhibited by discrete chaotic systems. It is known that one can control the dynamics of a discrete system using a special class of integration methods. One of the applications of such a phenomenon is chaos-based communication systems, which have recently attracted attention due to their high covertness and broadband transmission capability. Proper modulation of chaotic carrier signals is one of the key problems in chaos-based communication system design. It is challenging to modulate and demodulate a chaotic signal in the same way as a conventional signal due to its noise-like shape and broadband characteristics. Therefore, the development of new modulation–demodulation techniques is of great interest in the field. One possible approach here is to use adaptive numerical integration, which allows control of the properties of the finite-difference chaotic model. In this study, we describe a novel modulation technique for chaos-based communication systems based on generalized explicit second-order Runge–Kutta methods. We use a specially designed test bench to evaluate the efficiency of the proposed modulation method and compare it with state-of-the-art solutions. Experimental results show that the proposed modulation technique outperforms the conventional parametric modulation method in both coverage and noise immunity. The obtained results can be efficiently applied to the design of advanced chaos-based communication systems as well as being used to improve existing architectures.

Enhancing Cable Vibration Measurement at Long Distances Through Super-Resolution Reconstruction and Target Foreground Segmentation

This paper presents a comprehensive non-contact computer vision-based system for monitoring cable vibrations in cable-supported bridges, addressing challenges related to low image resolution and feature extraction difficulties at long distances. The proposed system utilizes deep learning techniques to enhance cable vibration recognition accuracy and offers a practical solution for cable monitoring without the need for target assistance. The core of the system is a novel two-stage model, which combines a super-resolution (SR) video reconstruction algorithm with state-of-the-art Resnet-34 and Swin-B models for precise target foreground segmentation. This approach significantly improves the recognition of target details and enhances the accuracy of cable vibration data in monitoring videos. Furthermore, a phase-based motion estimation (PME) algorithm is employed for precise cable vibration measurement. Field tests conducted on two cable-supported bridges validate the effectiveness of the system. The results demonstrate superior accuracy and noise immunity compared to traditional methods, achieving sub-pixel level precision with a maximum error rate below 2%. This system represents a significant advancement in non-contact structural health monitoring for long-distance cable vibration monitoring in cable-supported bridges.

Multiscale fluctuation-based symbolic dynamic entropy: a novel entropy method for fault diagnosis of rotating machinery

Health monitoring has garnered significant and increasing attention from the research community and industrial practices thanks to its critical role in ensuring the safe operation of machinery and maintenance schedule. With regard to this, this paper introduces a novel diagnostic approach called fluctuation-based symbolic dynamic entropy (FSDE), which can enhance noise immunity and computational efficiency by utilizing fluctuation-based entropy algorithm and symbolic dynamic filtering. Moreover, the noise immunity and computational efficiency of FSDE, permutation entropy, fuzzy entropy, and sample entropy are compared by simulation signals. The simulation results show that FSDE has a more robust anti-noise performance and higher computational efficiency than the other three entropy methods. In order to extract fault features more thoroughly, multiscale analysis is applied to the entropy method, and multiscale FSDE (MFSDE) is proposed. MFSDE divides the measurement data into several scale series by coarse-grained technology, and then, FSDE is used to process each scale series separately. A series of experiments verify the effectiveness of MFSDE in fault feature extraction of fault diagnosis. Furthermore, the experimental results substantiate that MFSDE outperforms the other three currently used entropy-based approaches in terms of accuracy in classifying different health conditions of transmission systems.

AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

Inertial Algorithm with Dry Fraction and Convolutional Sparse Coding for 3D Localization with Light Field Microscopy

Light field microscopy is a high-speed 3D imaging technique that records the light field from multiple angles by the microlens array(MLA), thus allowing us to obtain information about the light source from a single image only. For the fundamental problem of neuron localization, we improve the method of combining depth-dependent dictionary with sparse coding in this paper. In order to obtain higher localization accuracy and good noise immunity, we propose an inertial proximal gradient acceleration algorithm with dry friction, Fast-IPGDF. By preventing falling into a local minimum, our algorithm achieves better convergence and converges quite fast, which improves the speed and accuracy of obtaining the locolization of the light source based on the matching depth of epipolar plane images (EPI). We demonstrate the effectiveness of the algorithm for localizing non-scattered fluorescent beads in both noisy and non-noisy environments. The experimental results show that our method can achieve simultaneous localization of multiple point sources and effective localization in noisy environments. Compared to existing studies, our method shows significant improvements in both localization accuracy and speed.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent on a prior information, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems. Approach. This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence on a priori information, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable region and the results are used as the basis for its next movement. Main Results. The performance of the proposed framework is evaluated with a series of simulations and in vivo experiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility. Significance. The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.

DEFR-net: A decompose-enhance fourier residual network for fault diagnosis of rotating machine with high noise immunity

The actual operating environment of rotating mechanical device contains a large number of noisy interference sources, leading to complex components, strong coupling, and low signal to noise ratio for vibration. It becomes a big challenge for intelligent fault diagnosis from high-noise vibration signals. Thus, this paper proposes a new deep learning approach, namely decomposition-enhance Fourier residual network (DEFR-net), to achieve high noise immunity for vibration signal and learn effective features to discriminate between different types of rotational machine faults. In the proposed DEFR-net, a novel algorithm is proposed to explicitly model high-noise signals for noisy data filtering and effective feature enhancement based on a hard threshold decomposition function and muti-channel self-attention mechanism. Furthermore, it deeply integrates complementary analysis based on fast Fourier transform in the time-frequency domain and extends the breadth of network. The performance of the proposed model is verified by comparison with five state-of-the-art algorithms on two public datasets. Moreover, the noise experimental results show that the fault diagnosis accuracy is still 85.91% when the signal-to-noise-ratio reaches extreme noise of –8 dB. The results demonstrate that the proposed method is a valuable study for intelligent fault diagnosis of rotating machines in high-noise environments.

Two-dimensional small leak detection of pipeline based on time sequence coding

In this study, a two-dimensional pipeline small leak detection based on time series imaging is introduced. First, the sampling points and data of pipeline signals are converted into polar diameters and polar angles in polar coordinate system to form vectors, and then the inner product between vectors is mapped to 2-dimensional pixels through the use of Gram angular field (GAF). The resulting image preserves both the temporal and spatial features of the original signal. Wavelet threshold (WT) denoising and adaptive segmentation constant approximation (APCA) are used to denoise and fit the original signal in order to ensure the noise immunity and computational efficiency of the imaging. The image generated after WT-APCA-GAF processing is input into Swin Transformer for training, and the final identification accuracy of small leakage can reach 100%, effectively avoiding the problem of missing and false positive in detecting small leaks.

Optimization analysis of a Stokes polarimeter forbroadband liquid crystal variable retarders underthe optimal objective function.

A liquid crystal variable retarder (LCVR) is the core device to realize fast and high-precision broadband polarization imaging, and its ability to suppress the noise will have an impact on the polarization measurement results. In order to obtain better imaging quality and measurement accuracy, it is crucial to solve the optimization problem of the LCVR. In this paper, the optimal objective function for solving the optimization problem of the LCVR is analytically derived and verified based on the genetic algorithm in the band range of 350-700nm. Meanwhile, considering that the minimum number of four measurements at this time cannot achieve the optimal state, the relationship between the number of measurements and the overall performance relative to the error propagation (optimized conditions number) is discussed. The results show that a better optimal set of angles can be obtained by using the optimal objective function. In this paper, a set of the most favorable angles is obtained, and the optimized average of the CN is 2.0000, which is reduced by 0.32% compared with previous optimization results and is closer to the ideal value of the CN. In addition, in this paper, the noise immunity of the set of most favorable angles is simulated and analyzed, and the optimized system can effectively improve the measured performance of the wide-band liquid crystal variable retarder polarimeter.