Background Noise Interference Research Articles

Periodic group-sparse method via generalized minimax-concave penalty for machinery fault diagnosis

Abstract Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.

LightCF-Net: A Lightweight Long-Range Context Fusion Network for Real-Time Polyp Segmentation.

Automatically segmenting polyps from colonoscopy videos is crucial for developing computer-assisted diagnostic systems for colorectal cancer. Existing automatic polyp segmentation methods often struggle to fulfill the real-time demands of clinical applications due to their substantial parameter count and computational load, especially those based on Transformer architectures. To tackle these challenges, a novel lightweight long-range context fusion network, named LightCF-Net, is proposed in this paper. This network attempts to model long-range spatial dependencies while maintaining real-time performance, to better distinguish polyps from background noise and thus improve segmentation accuracy. A novel Fusion Attention Encoder (FAEncoder) is designed in the proposed network, which integrates Large Kernel Attention (LKA) and channel attention mechanisms to extract deep representational features of polyps and unearth long-range dependencies. Furthermore, a newly designed Visual Attention Mamba module (VAM) is added to the skip connections, modeling long-range context dependencies in the encoder-extracted features and reducing background noise interference through the attention mechanism. Finally, a Pyramid Split Attention module (PSA) is used in the bottleneck layer to extract richer multi-scale contextual features. The proposed method was thoroughly evaluated on four renowned polyp segmentation datasets: Kvasir-SEG, CVC-ClinicDB, BKAI-IGH, and ETIS. Experimental findings demonstrate that the proposed method delivers higher segmentation accuracy in less time, consistently outperforming the most advanced lightweight polyp segmentation networks.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs.

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method. Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination. Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Improving Polyp Segmentation with Boundary-Assisted Guidance and Cross-Scale Interaction Fusion Transformer Network

Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, they have two main shortcomings: (1) multi-level semantic information at the output of the encoder may result in information loss during the fusion process and (2) failure to adequately suppress background noise during segmentation. To address these challenges, we propose a cross-scale interaction fusion transformer for polyp segmentation (CIFFormer). Firstly, a novel feature supplement module (FSM) supplements the missing details and explores potential features to enhance the feature representations. Additionally, to mitigate the interference of background noise, we designed a cross-scale interactive fusion module (CIFM) that combines feature information between different layers to obtain more multi-scale and discriminative representative features. Furthermore, a boundary-assisted guidance module (BGM) is proposed to help the segmentation network obtain boundary-enhanced details. Extensive experiments on five typical datasets have demonstrated that CIFFormer has an obvious advantage in segmenting polyps. Specifically, CIFFormer achieved an mDice of 0.925 and an mIoU of 0.875 on the Kvasir-SEG dataset, achieving superior segmentation accuracy to competing methods.

CMNet: Cascaded context fusion and multi-attention network for multiple lesion segmentation of diabetic retinopathy images

Diabetic retinopathy (DR) is a leading cause of blindness in humans, and regular screening is helpful to early detection and containment of DR. Therefore, automated and accurate lesion segmentation is crucial for DR grading and diagnosis. However, it is full of challenges due to complex structures, inconsistent scales and blurry edges of different kinds of lesions. To address the above issues, this paper proposes a cascaded context fusion and multi-attention network (CMNet) for multiple lesion segmentation of DR images. The CMNet includes triple attention module (TAM), cascaded context fusion module (CFM) and balanced attention module (BAM). Firstly, TAM is proposed to extract channel attention, spatial attention and pixel-point attention features for addition fusion to ensure selectivity and consistency of information representation. Moreover, CFM applies adaptive average pooling and non-local operation to capture local and global contextual features for concatenation fusion to expand the receptive field of lesions. Finally, BAM computes foreground, background and boundary attention maps of lesions, as well as uses Squeeze-and-Excitation block to weigh the feature channels and rebalance attention of three regions to make the network focus on the edge details for fine segmentation. We performed comprehensive comparison experiments and ablation studies on three public datasets of DDR, IDRiD and E-Ophtha, and mAUC of four lesions segmentation reached 0.6765, 0.7466 and 0.6710, respectively. The experimental results show that the proposed model outperforms current state-of-the-arts, which overcomes the adverse interference of background noise, and preserves contour details for realizing precise segmentation of multi-scale lesion.

An enhanced real-time human pose estimation method based on modified YOLOv8 framework

The objective of human pose estimation (HPE) derived from deep learning aims to accurately estimate and predict the human body posture in images or videos via the utilization of deep neural networks. However, the accuracy of real-time HPE tasks is still to be improved due to factors such as partial occlusion of body parts and limited receptive field of the model. To alleviate the accuracy loss caused by these issues, this paper proposes a real-time HPE model called CCAM-Person\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extbf {CCAM-Person}}$$\\end{document} based on the YOLOv8 framework. Specifically, we have improved the backbone and neck of the YOLOv8x-pose real-time HPE model to alleviate the feature loss and receptive field constraints. Secondly, we introduce the context coordinate attention module (CCAM) to augment the model’s focus on salient features, reduce background noise interference, alleviate key point regression failure caused by limb occlusion, and improve the accuracy of pose estimation. Our approach attains competitive results on multiple metrics of two open-source datasets, MS COCO 2017 and CrowdPose. Compared with the baseline model YOLOv8x-pose, CCAM-Person improves the average precision by 2.8% and 3.5% on the two datasets, respectively.

SwinPA-Net: Swin Transformer-Based Multiscale Feature Pyramid Aggregation Network for Medical Image Segmentation.

The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

Enhancing angular resolution in single-photon LIDAR for daytime long-range 3D imaging

Single photon LIDAR exhibits exceptional detection sensitivity and finds extensive applications in long-distance measurement and three-dimensional imaging domains. However, it is susceptible to background noise interference, leading to a trade-off between scanning imaging efficiency and angular resolution, thereby limiting its overall performance. In this paper, we propose a novel ‘compression-resampling’ high-resolution imaging method for single-photon scanning LIDAR based on the spatial correlation of the target scene. This approach significantly enhances the system's angular resolution without compromising imaging efficiency. By employing this method, we successfully conducted 3D imaging over distances exceeding 26 km even under challenging conditions with high levels of background noise during daylight hours. Within a target field of view measuring 1° × 1°, we reconstructed a high-resolution 3D point cloud image comprising 1000 × 1410 pixels. Notably, this achievement yielded an impressive angular resolution of 17.4 urad while maintaining an acceptable imaging time of approximately 20 minutes. The results unequivocally demonstrate the effectiveness of our proposed method in improving the quality of three-dimensional imaging and enhancing angular resolution while simultaneously reducing imaging time requirements, thus expanding potential application scenarios for single-photon LIDAR technology.

A recursive multi-head self-attention learning for acoustic-based gear fault diagnosis in real-industrial noise condition

Acoustic-based diagnosis (ABD) is a promising method for rotating machinery fault detection in real-industrial fields due to its advantage of non-contact measurement by air-couple. However, most ABD methods, which rely on single function module of denoising or anti-noise diagnosis, lacks sufficient ability against the strong and highly non-stationary background noise interference in practical industrial application. To deal with the obstacle, a novel two-stage ABD system, which performs denoising and anti-noise diagnosis in a step-by-step fashion, is proposed in this paper. In our method, the denoising sub-system can adaptively track and suppress real-industrial background noise from collected multi-channel signal by our designed recursive multi-head self-attention (RMHSA) mechanism, which take into account the global and long-range information of signal to recursively exclude noise component at each position in signal sequence for overcoming the limitation of traditional method in balancing denoising performance and computational complexity and discontinuity in diagnosis procedure. Subsequently, a novel anti-noise method is explored to automatically track the residual noise components after initial suppression and estimating the noise interference probability within time-frequency (T-F) unit of each signal sample by our designed spatial multi-head self-attention (SMHSA) mechanism in recursive manner. Simultaneously, the anti-noise protocol interacts with diagnosis model based on estimated probability in anti-noise diagnosis sub-system to further improve the diagnosis performance of ABD system under the interference of background noise. Experiment result in both real-industrial noise condition and simulated noise conditions with extremely worse SNRs indicate that the proposed two-stage ABD system is effective in dealing with gear fault diagnosis task under noise condition.

Ship SINS/CNS Integrated Navigation Aided by LSTM Attitude Forecast

Under the strong interference of sky background noise, the reliability of celestial navigation system (CNS) measurement will drop sharply, which leads to performance deterioration for ships’ strapdown inertial navigation system (SINS)/CNS integrated navigation. To solve this problem, a long short-term memory (LSTM) model is trained to forecast a ship’s attitude to detect the attitude provided by the CNS, and the LSTM forecasted attitude can also be used as a backup in case of CNS failure. First, the SINS/CNS integrated model is derived based on an attitude solution of the CNS, which provides more favorable feature data for LSTM learning. Then, the key techniques of LSTM modeling such as dataset construction, LSTM coding method, hyperparameter optimization and training strategy are described in detail. Finally, an experiment is conducted to evaluate the actual performance of the investigated methods. The results show that the LSTM model can accurately forecast a ship’s attitude: the horizon reference error is less than 0.5′ and the yaw error is less than 0.6′, which can provide reliable reference attitude for the SINS when the CNS is invalid.

A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.

Monitoring SF6 Gas Leakage Based on a Customized Binocular System

Sulfur hexafluoride (SF6) gas is extensively utilized as an insulating and arc-quenching medium in the circuit breakers and isolating switches of electrical equipment. It effectively isolates the circuits from the atmosphere and promptly extinguishes arcs. Therefore, the issue of SF6 gas leakage poses a significant threat to the related application fields, and the detection of SF6 gas leakage becomes extremely important. Infrared imaging detection offers advantages including non-contact, high precision, and visualization. However, most existing infrared detection systems are equipped with only one filter to detect SF6 gas. The images captured contain background noise and system noise, making these systems vulnerable to interference from such noises. To address these issues, we propose a method for monitoring SF6 gas leakage based on a customized binocular imaging (CBI) system. The CBI system has two filters, greatly reducing the interference of system noise and background noise. The first filter features the absorption resonant peak of SF6 gas. The second filter is used to record background noise and system noise. One aspect to note is that, in order to avoid the interference of other gases, the central wavelength of this second filter should keep away from the absorption resonant peaks of those gases. Accordingly, the central wavelengths of our customized filters were determined as 10,630 nm and 8370 nm, respectively. Then, two cameras of the same type were separately assembled with a customized filter, and the CBI prototype was accomplished. Finally, we utilized the difference method using two infrared images captured by the CBI system, to monitor the SF6 gas leakage. The results demonstrate that our developed system achieves a high accuracy of over 99.8% in detecting SF6 gas. Furthermore, the CBI system supports a plug-and-play customization to detect various gases for different scenarios.

RaSRNet: An End-to-End Relation-Aware Semantic Reasoning Network for Change Detection in Optical Remote Sensing Images

Optical Remote sensing images (RSIs) are used in surface observation, and one of the most interesting research topics is change detection (CD). The internal problem of RSIs, including multi-scales changed objects and cluttered background, still deserve attention. Existing methods make great efforts to solve this problem but inevitably miss detection, which affects the model performance. To address this dilemma, this article proposes a Relation-aware Semantic Reasoning Network (RaSRNet) in an end-to-end manner to pop-out change objects in RSIs, where the key point is to perceive contextual semantic information. The relation-aware module in RaSRNet combats the lack of contextual information caused by the limited receptive field of the general convolutional layer, which facilitates all-around changed object detection. The multi-level semantic reasoning encoder-decoder backbone in RaSRNet extracts and reconstructs pixel semantic information, alleviates the interference of background noise and improves the integrity recognition of changed objects. In addition, the decoder backend undertakes two semantic segmentation branches, and introduces a semantic reasoning loss between the two branches to infer pixel semantic categories, which provides more accurate semantic features for the CD. Extensive experiments are conducted on the three public RSIs CD datasets, and the results demonstrate that the proposed RaSR-Net can accurately locate changed objects, which consistently outperforms the state-of-the-art CD competitors.

ETSR-YOLO: An improved multi-scale traffic sign detection algorithm based on YOLOv5.

In the application of driverless technology, current traffic sign recognition methods are susceptible to the influence of ambient light interference, target size changes and complex backgrounds, resulting in reduced recognition accuracy. To address these challenges, this study introduces an optimisation algorithm called ETSR-YOLO, which is based on the YOLOv5s algorithm. First, this study improves the path aggregation network (PANet) of YOLOv5s to enhance multi-scale feature fusion by generating an additional high-resolution feature layer to improve the recognition of YOLOv5s for small-sized objects. Second, the study introduces two improved C3 modules that aim to suppress background noise interference and enhance the feature extraction capabilities of the network. Finally, the study uses the Wise-IoU (WIoU) function in the post-processing stage to improve the learning ability and robustness of the algorithm to different samples. The experimental results show that ETSR-YOLO improves mAP@0.5 by 6.6% on the Tsinghua-Tencent 100K (TT100K) dataset and by 1.9% on the CSUST Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB2021) dataset. In the experiments conducted on the embedded computing platform, ETSR-YOLO demonstrates a short average inference time, thereby affirming its capability to deliver dependable traffic sign detection for intelligent vehicles operating in real-world traffic scenes. The source code and test results of the models used in this study are available at https://github.com/cbrook16/ETSR-YOLO.

Uncertainty-Aware Dual-Evidential Learning for Weakly-Supervised Temporal Action Localization.

Weakly-supervised temporal action localization (WTAL) aims to localize the action instances and recognize their categories with only video-level labels. Despite great progress, existing methods suffer from severe action-background ambiguity, which mainly arises from background noise and neglect of non-salient action snippets. To address this issue, we propose a generalized evidential deep learning (EDL) framework for WTAL, called Uncertainty-aware Dual-Evidential Learning (UDEL), which extends the traditional paradigm of EDL to adapt to the weakly-supervised multi-label classification goal with the guidance of epistemic and aleatoric uncertainties of which the former comes from models lacking knowledge, while the latter comes from the inherent properties of samples themselves. Specifically, targeting excluding the undesirable background snippets, we fuse the video-level epistemic and aleatoric uncertainties to measure the interference of background noise to video-level prediction. Then, the snippet-level aleatoric uncertainty is further deduced for progressive mutual learning, which gradually focuses on the entire action instances in an "easy-to-hard" manner and encourages the snippet-level epistemic uncertainty to be complementary with the foreground attention scores. Extensive experiments show that UDEL achieves state-of-the-art performance on four public benchmarks. Our code is available in github/mengyuanchen2021/UDEL.

Handcrafted silver substrates boost surface plasmon resonance for ultra-sensitive lipid analysis

Lipid monitoring plays a crucial role in biomedical research, particularly in the areas of cardiovascular health, metabolic disorders and nutrition. However, direct and highly sensitive detection of lipids poses significant challenges due to the interference of high SERS background noise in lipid samples. In this study, we present a SERS platform for the quantitative analysis of lipids. By harnessing the Surface Plasmon Resonance (SPR) effect of nanostructured grooves and leveraging deuterium oxide, a remarkable enhancement of in-situ Raman signals originating from cholesterol is achieved. This approach yielded an impressive average enhancement factor of 7.3 × 105 and a detection limit of 1.9 × 10−4 mg/mL, highlighting the exceptional sensitivity and precision of our method. We have obtained high quality, in-situ SERS signals for six distinct lipid molecules. Rapid identification of lipid samples in mixed systems has been achieved through the combination of characteristic peak analysis and PCA-LDA, including the detection of SERS signals from lipids in milk. Notably, univariate monitoring of in-situ cholesterol in human serum was successfully achieved for the first time using deuterium water as an internal standard. In addition, silver substrate demonstrated outstanding reproducibility, maintaining consistent SERS activity even after more than 10 repetitions. Therefore, this platform offers the distinct advantages of high sensitivity, specificity and cost-effectiveness for lipid detection. These findings enable dietary management and blood lipid monitoring, and therefore hold crucial implications for the early prevention of lipid-related disorders and diseases.

High-accuracy reconstruction of Stokes vectors via spatially modulated polarimetry using deep learning at low light field

Polarization measurement is generally performed in scenes with a low signal-to-noise ratio (SNR) such as remote sensing and biological tissue detection. The spatially modulated polarimeter can satisfy the real-time measurement requirements in low SNR scenes by establishing the mapping between photon spatial distribution and polarization information. However, accurately measuring the polarization state under low-light illumination becomes highly challenging owing to the interference of background noise. In this paper, a deep learning method is proposed and applied to the high-accuracy reconstruction of polarization information at low light field. A reinforced two-layer deep convolutional neural network is designed to respectively extract global and local features of noise in this method. Accurate photon spatial distribution can be obtained by fusing and processing these features. Experimental results illustrate the excellent accuracy achieved by the proposed method with a maximum average value of the absolute measured error below 0.04. More importantly, the proposed method is well-performed for the reconstruction of Stokes vectors at low light fields of various levels without requiring changes to the model, enhancing its practicality and simplicity.

Low-noise fluorescent detection of cardiac troponin I in human serum based on surface acoustic wave separation

Acute myocardial infarction (AMI) is a life-threatening disease when sudden blockage of coronary artery occurs. As the most specific biomarker, cardiac troponin I (cTnI) is usually checked separately to diagnose or eliminate AMI, and achieving the accurate detection of cTnI is of great significance to patients’ life and health. Compared with other methods, fluorescent detection has the advantages of simple operation, high sensitivity and wide applicability. However, due to the strong fluorescence interference of biological molecules in body fluids, it is often difficult to obtain high sensitivity. In order to solve this problem, in this study, surface acoustic wave separation is designed to purify the target to achieve more sensitive detection performance of fluorescent detection. Specifically, the interference of background noise is almost completely removed on a microfluidic chip by isolating microbeads through acoustic radiation force, on which the biomarkers are captured by the immobilized detection probe. And then, the concentration of cTnI in human serum is detected by the fluorescence intensity change of the isolated functionalized beads. By this way, the detection limit of our biosensor calculated by 3σ/K method is 44 pg/mL and 0.34 ng/mL in PBS buffer and human serum respectively. Finally, the reliability of this method has been validated by comparison with clinical tests from the nephelometric analyzer in hospital.

Оценка точности цифровой обработки сильно зашумленных сигналов с помощью эвристических алгоритмов

Heuristic algorithms are often used as an alternative when solving problems of high computational complexity or lacking an exact solution, allowing to quickly obtain the desired result. Usually, they do not have a strict mathematical justification, but their application is justified in terms of practicality. Formally, algorithms that use approximate methods can be classified as heuristic. However, when applying them, the problem of determinism lack is often arises, which does not always allow one to evaluate the solution obtained accuracy. The paper considers a methodical approach to assessing the accuracy of heuristic algorithms designed to determine the useful signal shape and parameters on the strong noise component background. It is based on the method of analogy and consists in modeling an artificial signal with given parameters and a background noise interference similar in its characteristics to additive white Gaussian noise. In this case, the noise component is formed by software using a pseudo-random number sequence generator. Such generators are included in the packages of almost all high-level programming languages built-in functions. A comparative analysis of the real and artificial noise characteristics is presented, that shown the problem solving by numerical modeling possibility. The results of accuracy estimation in determining the artificial signal parameters, that is separated from the noise component using piecewise linear approximation and averaging heuristic algorithms, are obtained. The problem of empirical data smoothing with the discrete signal equivalent replacement by a quadratic functions whose parameters provide a piecewise parabolic approximation its shape is also considered. This procedure eliminates the residual signal bounce that inevitably occurs as a result of linearization and allows further recording at any sampling rate. Thus, the proposed approach allows us to quantify the accuracy of heuristic algorithms used in determining the expected signal parameters.