Low Signal-to-noise Ratio Conditions Research Articles

Adaptive stochastic resonance enhanced weak linear frequency modulated signal perception in low signal-to-noise ratio environments

Abstract The linear frequency modulated (LFM) signal has gained extensive utilization in radar, sonar and covert communication system due to its large time-bandwidth product, high-precision distance measurement and low detection probability. The perception of weak LMF signals holds significant importance yet encounters substantial challenges within the increasing complexity of the electromagnetic environments. Consequently, this paper proposes an adaptive stochastic resonance (ASR) enhanced approach for perceiving weak LFM signals in low signal-to-noise ratio (SNR) conditions. This approach initially commences a pioneering study on the quantitative synergistic resonance mechanism among LFM signals, random noise, and nonlinear stochastic resonance (SR) systems. Subsequently, the ASR system’s implementation becomes straightforward through the adaptive adjustment of SR system parameters according to the LFM signal and noise characteristics. This implementation leverages the inherent property of noise energy transfer to signal energy, facilitating the enhancement of ordered weak signals via random noise. Following the enhancement of the output signal by the ASR system, the Wigner–Ville distribution (WVD) transform’s time-frequency concentration property is employed to extract the time-frequency characteristics. Building on the WVD transform, the Radon transform with linear integral projection is applied to further harness the time-frequency domain energy concentration, thereby achieving the effective perception of weak LFM signals. Finally, the effectiveness of the proposed method in improving the perception performance of weak LFM signal in very low SNR conditions is verified through numerical simulations. It is anticipated that this method will have potential application value in fields such as radar, sonar, and communication under complex electromagnetic environments.

DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning.

Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots, and coherent sources. Finally, precise DOA estimation is achieved for multiple sources without prior knowledge of their number. Simulation experiments have shown that compared with the OMP, MUSIC, and CBF algorithms, the proposed method exhibits higher DOA estimation accuracy under conditions of low SNR, small snapshots, multiple sources, and coherent sources. Furthermore, it demonstrates superior resolution when dealing with closely spaced signal sources.

A high-resolution method for direction of arrival estimation based on an improved self-attention module.

The high-resolution direction of arrival (DOA) estimation is a prominent research issue in underwater acoustics. The existing high-resolution methods include subspace methods and sparse representation methods. However, the performance of subspace methods suffers from low signal-to-noise ratio (SNR) and limited snapshots conditions, and the computational complexity of sparse representation methods is too high. The neural network methods are emerging high-resolution methods. However, insufficient support for big data is frequently observed in underwater acoustics, and conventional network structures present challenges in further enhancing performance. To address the aforementioned problems, we propose a neural network method based on an improved self-attention module to achieve high accuracy and robust DOA estimation. First, we design a multi-head self-attention module with large-scale convolutional kernels and residual structures to improve the estimated accuracy. Second, we propose an improved input feature to enhance the robustness to non-uniform noise and unequal-intensity targets. The simulations demonstrate that the proposed method exhibits superior angle resolution compared to sparse representation methods under the same simulation conditions. The proposed method demonstrates exceptional accuracy and robustness in DOA estimation under challenging conditions of low SNR, limited snapshots, and unequal-intensity targets. The experimental results further prove the effectiveness of the proposed method.

A Dual-Stream Deep Learning-Based Acoustic Denoising Model to Enhance Underwater Information Perception

Estimating the line spectra of ship-radiated noise is a crucial remote sensing technique for detecting and recognizing underwater acoustic targets. Improving the signal-to-noise ratio (SNR) makes the low-frequency components of the target signal more prominent. This enhancement aids in the detection of underwater acoustic signals using sonar. Based on the characteristics of low-frequency narrow-band line spectra signals in underwater target radiated noise, we propose a dual-stream deep learning network with frequency characteristics transformation (DS_FCTNet) for line spectra estimation. The dual streams predict amplitude and phase masks separately and use an information exchange module to swap learn features between the amplitude and phase spectra, aiding in better phase information reconstruction and signal denoising. Additionally, a frequency characteristics transformation module is employed to extract convolutional features between channels, obtaining global correlations of the amplitude spectrum and enhancing the ability to learn target signal features. Through experimental analysis on ShipsEar, a dataset of underwater acoustic signals by hydrophones deployed in shallow water, the effectiveness and rationality of different modules within DS_FCTNet are verified.Under low SNR conditions and with unknown ship types, the proposed DS_FCTNet model exhibits the best line spectrum enhancement compared to methods such as SEGAN and DPT_FSNet. Specifically, SDR and SSNR are improved by 14.77 dB and 13.58 dB, respectively, enabling the detection of weaker target signals and laying the foundation for target localization and recognition applications.

Telescope imaging beyond the Rayleigh limit in extremely low SNR

Abstract The Rayleigh limit and low Signal-to-Noise Ratio (SNR) scenarios pose significant limitations to optical imaging systems used in remote sensing, infrared thermal imaging, and space domain awareness. In this study, we introduce a Stochastic Sub-Rayleigh Imaging (SSRI) algorithm to localize point objects and estimate their positions, brightnesses, and number in low SNR conditions, even below the Rayleigh limit. Our algorithm adopts a maximum likelihood approach and exploits the Poisson distribution of incoming photons to overcome the Rayleigh limit in low SNR conditions. In our experimental validation, which closely mirrors practical scenarios, we focus on conditions with closely spaced sources within the sub-Rayleigh limit (0.49-1.00R) and weak signals (SNR less than 1.2). We use the Jaccard index and Jaccard efficiency as a figure of merit to quantify imaging performance in the sub-Rayleigh region. Our approach consistently outperforms established algorithms such as Richardson-Lucy and CLEAN by 4X in the low SNR, sub-Rayleigh regime. Our SSRI algorithm allows existing telescope-based optical/infrared imaging systems to overcome the extreme limit of sub-Rayleigh, low SNR source distributions, potentially impacting a wide range of fields, including passive thermal imaging, remote sensing, and space domain awareness.

Accurate gain method for ground-penetrating radar signals based on stationary wavelet packet transform

In this study, we propose an accurate gain method for ground-penetrating radar (GPR) signals based on the characteristics of refined time-frequency analysis and translation invariance offered by the Stationary Wavelet Packet Transform (SWPT), combined with the conventional signal gain approach. This method aims to address the issue of low signal resolution resulting from the direct gain processing of GPR signals with a low signal-to-noise ratio (SNR). Specifically, the GPR signals are initially decomposed into appropriate wavelet packet coefficients using SWPT, wherein only those coefficients with high SNR undergo gain processing, followed by reconstruction of the signals through SWPT. By employing accurate gain processing on low SNR GPR signals acquired during concrete crack detection tests, we have confirmed that the proposed method effectively distinguishes the target reflected signals from most noise, thereby achieving accurate amplification of the desired reflected signals and significantly enhancing the GPR signals resolution under low SNR conditions.

Enhancing radio signal classification under low SNR conditions using deep residual networks with channel attention mechanisms

This paper delves into the advancement of deep residual networks (ResNets) integrated with channel attention mechanisms for the classification of radio signals under conditions of low Signal-to-Noise Ratio (SNR). Utilizing an expansive dataset of radio signals, this paper introduces a novel architecture, MyResNet1, that combines residual learning with channel-wise attention, allowing the model to concentrate on essential features for precise classification. My, investigations exhibit notable improvements in classification accuracy, especially in challenging low SNR scenarios, highlighting the potential of attention-augmented deep residual networks in radio signal processing. Furthermore, this studyexplores various optimization strategies, including data augmentation and regularization techniques, to enhance the models performance and robustness. My findings contribute significantly to cognitive radio technologies and illuminate the potential of deep learning in sophisticated signal classification tasks, aligned with recent explorations in automatic modulation recognition (AMR) through deep learning and autoencoder-based methodologies for enhancing I/Q channel interactions.

Modulation recognition of underwater acoustic communication signals based on neural architecture search

Modulation recognition of underwater communication signals is a critical aspect of underwater information confrontation. However, the current deep learning-based methods for underwater communication modulation recognition often mimic neural network architectures used in image processing and speech processing, tending to perform poorly in low signal-to-noise ratio (SNR) conditions. This paper introduces a novel approach by utilizing neural architecture search (NAS) method. By automatically searching for neural architectures that are well-suited for modulation recognition in underwater environment, our proposed method improves the classification performance particularly in low SNR conditions. Furthermore, we have also proposed a recognition method based on attention mechanism and feature fusion, which substantially enhances the accuracy of identifying phase-modulated signals. Numerical simulation and experimental data are used to demonstrate performance of proposed methods.

Noncooperative Spectrum Sensing Strategy Based on Recurrence Quantification Analysis in the Context of the Cognitive Radio

This paper addresses the problem of noncooperative spectrum sensing in very low signal-to-noise ratio (SNR) conditions. In our approach, detecting an unoccupied bandwidth consists of detecting the presence or absence of a communication signal on this bandwidth. Digital communication signals may contain hidden periodicities, so we use Recurrence Quantification Analysis (RQA) to reveal the hidden periodicities. RQA is very sensitive and offers reliable estimation of the phase space dimension m or the time delay τ. In view of the limitations of the algorithms proposed in the literature, we have proposed a new algorithm to simultaneously estimate the optimal values of m and τ. The new proposed optimal values allow the state reconstruction of the observed signal and then the estimation of the distance matrix. This distance matrix has particular properties that we have exploited to propose a Recurrence-Analysis-based Detector (RAD). The RAD can detect a communication signal in a very low SNR condition. Using Receiver Operating Characteristic curves, our experimental results corroborate the robustness of our proposed algorithm compared with classic widely used algorithms.

Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing

The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis.

Underwater Coherent Source Direction-of-Arrival Estimation Method Based on PGR-SubspaceNet

In the field of underwater acoustics, the signal-to-noise ratio (SNR) is generally low, and the underwater environment is complex and variable, making target azimuth estimation highly challenging. Traditional model-based subspace methods exhibit significant performance degradation when dealing with coherent sources, low SNR, and small snapshot data. To overcome these limitations, an improved model based on SubspaceNet, called PConv-GAM Residual SubspaceNet (PGR-SubspaceNet), is proposed. This model embeds the global attention mechanism (GAM) into residual blocks that fuse PConv convolution, making it possible to capture richer cross-channel and positional information. This enhancement helps the model learn signal features in complex underwater conditions. Simulation results demonstrate that the underwater target azimuth estimation method based on PGR-SubspaceNet exhibits lower root mean square periodic error (RMSPE) values when handling different numbers of narrowband coherent sources. Under low SNR and limited snapshot conditions, its RMSPE values are significantly better than those of traditional methods and SubspaceNet-based enhanced subspace methods. PGR-SubspaceNet extracts more features, further improving the accuracy of direction-of-arrival estimation. Preliminary experiments in a pool validate the effectiveness and feasibility of the underwater target azimuth estimation method based on PGR-SubspaceNet.

Automatic Modulation Recognition Method Based on Phase Transformation and Deep Residual Shrinkage Network

Automatic Modulation Recognition (AMR) is currently a research hotspot, and research under low Signal-to-Noise Ratio (SNR) conditions still poses certain challenges. This paper proposes an AMR method based on phase transformation and deep residual shrinkage network to improve recognition accuracy. Firstly, the raw I/Q data from the benchmark dataset RML2016.10a are used as the input. Then, an end-to-end modulation recognition is performed using the model. Phase transformation is used to correct the raw I/Q data and reduce the interference of phase shift on modulation recognition. Convolutional neural network (CNN) and Gate Recurrent Unit (GRU) extract the spatial and temporal features of the modulation signal, respectively. The improved deep residual shrinkage network is added after CNN to eliminate unimportant features through soft thresholding. Finally, the proposed model is trained and tested. The experimental results show that the proposed model notably reduces the number of parameters compared to other models, effectively improving the recognition accuracy under low SNR conditions. The average recognition accuracy reaches 62.46%, and the highest recognition accuracy reaches 92.41%.

A New Extended Target Detection Method Based on the Maximum Eigenvalue of the Hermitian Matrix

In the field of radar target detection, the conventional approach is to employ the range profile energy accumulation method for detecting extended targets. However, this method becomes ineffective when dealing with non-stationary and non-uniform radar clutter scenarios, as well as long-distance targets with weak radar cross sections (RCSs). In such cases, the signal-to-noise ratio (SNR) of the target echo is severely degraded, rendering the energy accumulation detection algorithm unreliable. To address this issue, this paper presents a new extended target detection method based on the maximum eigenvalue of the Hermitian matrix. This method utilizes a detection model that incorporates observed data and employs the likelihood ratio test (LRT) theory to derive the maximum eigenvalue detector at low SNR. Specifically, the detector constructs a matrix using a sliding window block with the available data and then computes the maximum eigenvalue of the covariance matrix. Subsequently, the maximum eigenvalue matrix is transformed into a one-dimensional eigenvalue image, enabling extended target detection through analogy with the energy accumulation detection method. Furthermore, this paper analyzes the proposed extended target detection method from both theoretical and experimental perspectives, validating it through field-measured data. The results obtained from the measured data demonstrate that the method effectively enhances the SNR in low SNR conditions, thereby improving target detection performance. Additionally, the method exhibits robustness across different scattering center targets.

Improving ranging performance of SiPM LiDAR in low SNR conditions through an echo processing method and laser pulse modulation

Due to its numerous advantages such as high gain and low operating bias, the silicon photomultiplier (SiPM) holds great potential in LiDAR applications. However, it is more jittery at weak echoes and more sensitive to ambient light, making its ranging performance at low signal-to-noise ratios (SNRs) severely deteriorated. To enhance the ranging performance of SiPM LiDAR under low SNR, a novel echo processing method, to the best of our knowledge, was proposed based on the statistical property of SiPM responses and validated under relatively intensive sunlight (>50klx) using a self-developed LiDAR system. At the same time, laser pulse width modulation and multi-pulse laser emission are used in ranging experiments to maximize the advantages of this method. It has shown that increasing the laser pulse width within a certain range can improve ranging performance, and that emitting multiple laser pulses improves ranging performance more significantly. Utilizing a three-pulse laser with a peak power of only 3.2 W, a target 122 m away was ranged with a precision of 6.53 cm with only five accumulations.

SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC

Automatic modulation classification (AMC) aims to blindly recognize the modulation type of a received signal in wireless systems. It is also a critical component of non-cooperative communication systems after the detection of the presence of a signal. In this paper, we introduce a robust approach, termed DET-AMC (joint Detection and Automatic Modulation Classification), employing Convolutional Neural Networks (CNNs) trained via transfer learning methodology. The main advantage of our approach is its ability to handle a wide range of modulation types, including 10 different schemes generated in Gnuradio and their detection using the same model. Through extensive experimentation, we evaluate the performance of our light CNN-based DET-AMC method across varying signal-to-noise ratio (SNR) levels, as well as in the presence of phase noise and frequency offset. We find that the CNN’s learned features, obtained through transfer learning, exhibit robustness, particularly in low SNR and various challenging conditions, leading to accurate modulation classification. In general, our approach outperforms existing methods by using the effectiveness of deep learning in capturing relevant discriminative features. Additionally, our model offers a robust solution for join detection and AMC by achieving an accurate probability of detection and modulation classification without the need for manual feature engineering or the consideration of frequency offset, phase noise or noise estimation. Our model achieves 100% accuracy for synthetic and real data at an SNR equal to -10 dB for detection, and 100% and 98% for classification of synthetic and real signals at −4 dB, respectively.

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

Cascaded Plane Wave Ultrasound for Blood Velocity Vector Imaging in the Carotid Artery.

Cascaded dual-polarity waves (CDWs) imaging increases the signal-to-noise ratio (SNR) by transmitting trains of pulses with different polarity order, which are combined via decoding afterward. This potentially enables velocity vector imaging (VVI) in more challenging SNR conditions. However, the motion of blood in between the trains will influence the decoding process. In this work, the use of CDW for blood VVI is evaluated for the first time. Dual-angle, plane wave (PW) ultrasound, CDW-coded, and noncoded conventional PW (cPW), was acquired using a 7.8 MHz linear array at a pulse repetition frequency (PRF) of 8 kHz. CDW-channel data were decoded prior to beamforming and cross correlation-based compound speckle tracking for VVI. Simulations of single scatterer motion show a high dependence of amplitude gain on the velocity magnitude and direction for CDW-coded transmissions. Both simulations and experiments of parabolic flow show increased SNRs for CDW imaging. As a result, CDW outperforms cPW VVI in low SNR conditions, based on both bias and standard deviation (SD). Quantitative linear regression and qualitative analyses of simulated realistic carotid artery blood flow show a similar performance of CDW and cPW for high SNR (14 dB) conditions. However, reducing the SNR to 6 dB, results in a root-mean-squared error 2.7× larger for cPW versus CDW, and an R2 of 0.4 versus 0.9. Initial in vivo evaluation of a healthy carotid artery shows increased SNR and more reliable velocity estimates for CDW versus cPW. In conclusion, this work demonstrates that CDW imaging facilitates improved VVI of deeper located carotid arteries.

Effective time-series Data Augmentation with Analytic Wavelets for bearing fault diagnosis

In the realm of rotary machine maintenance, rolling bearings emerge as crucial yet frequently vulnerable components. Ensuring their operational integrity is pivotal for the overall safety and performance of the machinery. Traditional diagnostic methods have utilized a variety of signal processing techniques, ranging from blind source separation to wavelet analysis, and more recently, deep neural networks (DNNs). However, a significant challenge in this domain is the scarcity of labeled failure data in real-world applications, leading to a reliance on data augmentation (DA) techniques to enhance the quality and quantity of the training data. Compounding this problem is the use of DA techniques tailored for images, which are not suitable for the time–frequency representations. This study proposes an innovative Data Augmentation with Analytic Wavelets (DAAW) approach, tailored specifically for time-series (TS) data classification in bearing fault diagnosis. The essence of DAAW lies in its ability to generate synthetic scalogram samples that closely mirror the properties of original samples. The proposed DAAW is directly applied at the input stage of a learning model by successively adjusting predefined parameters to generate scalograms, without the need for auxiliary DA algorithms. This technique is versatile in generating synthetic time–frequency data samples, and the parameterization enables it to be tailored to different time series datasets. Through experimentation with publicly available datasets, we show that combining a simple Convolutional Neural Network (CNN) with the proposed approach results in improved performance. It outperforms existing methods, achieving a mean classification accuracy of 99.49%. The critical distance for average accuracy, as determined by the Nemenyi post-hoc test, is 0.4619. Our results also show enhanced performance with accuracy rates of 90.14%, 78.94%, and 62.86% even under low signal-to-noise ratio (SNR) conditions, using 80%, 60%, and 50% of the dataset, respectively, for model training. Notably, its efficacy remains robust even under challenging low SNR conditions. The proposed DAAW significantly improves classification accuracy in bearing fault diagnosis, simultaneously obviating the need for proxy DA tasks.

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

Effects of diagram plane on neural network based modulation recognition

Modulation recognition using deep learning presents challenges in effectively distinguishing high-order modulation schemes while maintaining a balance between complexity and recognition accuracy. In this study, we curate a comprehensive dataset in the rθ plane, encompassing eight distinct modulation schemes. Leveraging hyperparameter optimization and transfer learning, we explore the capabilities of various CNN-based architectures, including MobileNetV2, ResNet50V2, ResNet101V2, InceptionV3, ResNet152V2, Xception, and InceptionResNetV2, for the classification of modulation schemes. The simulation results demonstrate that with signal-to-noise ratio (SNR) values exceeding 5 dB, all models exhibit classification accuracies surpassing 50% and approach near-perfect accuracy at an SNR value of 20 dB. However, under low SNR conditions, such as 5 dB, the recognition accuracies of all models, except for ResNet152V2 and InceptionV3, show minimal variation. As the SNR increases by 5 dB from −5 dB to 20 dB, ResNet152V2 and InceptionV3 demonstrate remarkable classification accuracy improvements, exceeding 40%, 30%, 30%, 10%, and 15%, respectively. In contrast, the other models do not exhibit such robust responsiveness in accuracy enhancements. The remarkable performance improvements are achieved by fine-tuning pre-trained models through these processes.