Data Reconstruction Algorithm Research Articles

High-speed forward-viewing optical coherence tomography probe based on Lissajous sampling and sparse reconstruction.

We present a novel endoscopy probe using optical coherence tomography (OCT) that combines sparse Lissajous scanning and compressed sensing (CS) for faster data collection. This compact probe is only 4 mm in diameter and achieves a large field of view (FOV) of 2.25 mm2 and a 10 mm working distance. Unlike traditional OCT systems that use bulky raster scanning, our design features a dual-axis piezoelectric mechanism for efficient Lissajous pattern scanning. It employs compressive data reconstruction algorithms that minimize data collection requirements for efficient, high-speed imaging. This approach significantly enhances imaging speed by over 40%, substantially improving miniaturization and performance for endoscopic applications.

SHM data compression and reconstruction based on IGWO-OMP algorithm

The long-term operating structural health monitoring (SHM) system generates massive monitoring data, whose transmission and storage are challenging tasks. To address this issue, this study proposed an improved grey wolf optimizer-orthogonal matching pursuit (IGWO-OMP) algorithm for SHM data compression and reconstruction. The effectiveness of the proposed algorithm was validated using four SHM datasets including ultrasonic guided wave (UGW), acceleration, and audio signals. Firstly, the optimization results of IGWO were compared with those of other classical algorithms to verify its superiority in reconstruction accuracy. Secondly, the reconstruction accuracy under various compression factors was investigated and the optimal compression factor was determined. Finally, the relationship between reconstruction accuracy and signal sparsity as well as the performance of IGWO-OMP in dealing with noise-containing signals were discussed. The results demonstrate the generalizability and excellent noise robustness of IGWO-OMP in SHM data compression and reconstruction. Compared with other optimization algorithms, IGWO exhibits the best global optimal solution searching ability and obtains the reconstructed signal closest to the original one. Comprehensively considering the compressed signal length, reconstruction accuracy and computational efficiency, the optimal compression factor is suggested to be 0.2. In IGWO-OMP, the signal reconstruction accuracy is related to its sparsity, the variation of the CCD index for the reconstructed signal with relative sparsity follows an exponential growth relationship.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Application of Ensemble Learning and Feature Selection Models in Energy Expenditure Estimation of Fitness Tracking Devices

The use of fitness tracking devices to monitor energy expenditure (EE) is becoming increasingly popular among consumers. However, the accuracy of EE estimation still needs to be improved due to the lack of accurate data filtering and more efficient data analysis models. To address this issue, we designed an energy expenditure estimation (EEE) model based on smart tracking devices. This paper proposes an algorithm for data reconstruction called PC-DF-RFECV: firstly, Pearson correlation analysis (PC) is used for initial data screening, then Feature Derivation (DF) is applied to reconstruct new features, and finally, the Recursive Feature Elimination algorithm based on cross-validation (RFECV) is employed to select the new features. Furthermore, the EEE model adopts a weighted average (Wei-Voting) strategy to enhance the robustness of the model by integrating the predictions of multiple base learners (XGBoost, LightGBM, Random Forest). During algorithm optimization, a Bayesian hyperparameter optimization technique is employed to fine-tune the model's hyperparameters. Empirical findings indicate that the EEE model, when applied to the dataset collected from motion tracking devices, attains an score of 0.9985, of 1.033, and of 0.865. These results demonstrate superior performance compared to current state-of-the-art research and conventional algorithms including random forests, gradient boosting, and bagging.

Abstract 2329: Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data

Abstract Background: Spatially resolved transcriptomics (ST) has enabled a variety of cancer research on the heterogeneity of tumor microenvironment. However, when identifying cancer boundaries based on ST, the limited resolution of ST such as Visium hinders the accurate identification of cancerous regions. To address these issues, we have developed a novel approach for accurately classifying regions of the tumor by using deep image prior (DIP) algorithm to convert Visium data into high-resolution images. Methods: We proposed SuperST, which utilizes deep image prior to transform low-resolution ST data into high-resolution images, providing more accurate spatial marker expression patterns. This method was used to define cancer regions by combining with a pre-trained deep learning algorithm that recognizes spatial patterns. ST data of hepatocellular carcinoma (HCC) patients were used to apply SuperST. Firstly, 400 spatially variable genes were identified. SuperST leverages the deep image prior (DIP) algorithm and a U-Net neural network to transform ST data into high-resolution ones without needing ground-truth high-resolution data. The final high-resolution images of gene expression data were used to extract 512-dimensional feature vectors for each gene via a pre-trained convolutional neural network model (VGG16). Subsequently, we apply K-means clustering to these features to identify transcriptionally distinct tissue regions considering spatial information and compared with the human-labeled cancer regions. Results: The cancer region detection using spatial gene expression data and our algorithm yielded results that substantially align with the human annotations on the H&E images. Furthermore, compared to the cancer definition obtained using the CopyKat and SPACET algorithms, our results were more spatially consistent for cancerous regions as well as providing high-resolution image-level definition of cancer region margin. This indicates that our algorithm better utilizes spatial information and more effectively overcomes the low resolution of ST data. Conclusion: This research demonstrates the effective application of our SuperST algorithm to the challenging problem of cancer region detection only using spatial gene expression data. It suggests that our algorithm could be utilized for a variety of problems in spatial biology in cancer research. Citation Format: Jeongbin Park, Seungho Cook, Dongjoo Lee, Jinyeong Choi, Seongjin Yoo, Hyung-Jun Im, Daeseung Lee, Hongyoon Choi. Cancer region definition using spatial gene expression patterns by super resolution reconstruction algorithm for spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2329.

Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification

BackgroundTraditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets. New methodTo solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification. ResultsThe improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets. ConclusionsThe proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.

Pixel-Level Clustering Algorithm for Data Reconstruction in ILC

Pixel-Level Clustering Algorithm for Data Reconstruction in ILC

Transfer learning based remaining useful life prediction of lithium-ion battery considering capacity regeneration phenomenon

Lithium-ion battery (LIB) has been widely used in various energy storage systems, and the accurate remaining useful life (RUL) prediction for LIB is critical to ensure the normal operation of system. However, the capacity regeneration (CR) phenomenon caused by the non-working state of LIB will seriously affect the capacity degradation trajectory of LIB, thus leading to an inaccurate RUL prediction result. To solve this problem, this paper proposes a non-parametric CR detection algorithm to detect the CR phenomenon online and quantitatively correct the prediction errors caused by different CR phenomena. In addition, a data reconstruction algorithm and a parameter update scheme are proposed to solve the problem of unit-to-unit difference among LIBs. Finally, the long and short term memory neural network-based transfer learning model is applied to predict the RUL of target LIB. The effectiveness of the proposed model is verified by the real LIB dataset of NASA. Compared with some existing RUL prediction models considering CR phenomenon, the proposed model exhibits higher accuracy and better universality.

A data reconstruction-based Monte Carlo method for remaining useful life prediction of lithium-ion battery with few historical samples

Lithium-ion battery (LIB) has been widely used in many energy storage systems, and the accurate remaining useful life (RUL) prediction of LIB is essential to ensure the safe operation of these systems. However, for the LIB installed in a new system, there are only few historical samples available for RUL prediction, which makes it difficult to build accurate RUL prediction model for LIB. Therefore, this paper proposes a data reconstruction-based Monte Carlo method to solve the problems caused by few historical samples. First, a Wiener process-based data reconstruction algorithm is used to reconstruct historical dataset, providing sufficient prior information for the Monte Carlo method. Then, Bayesian theory and a parameter update scheme are proposed to update the reconstructed dataset and the corresponding parameter distributions, so that the training data of RUL prediction model can be closer to the data of target LIB. Finally, long short-term memory neural network is applied for RUL prediction. The effectiveness of our model is verified by two real LIB datasets. Compared with some existing RUL prediction models of LIB, the proposed model has better generality and higher prediction accuracy under the condition of few historical samples.

Unmanned Aerial Vehicle-Based Compressed Data Acquisition for Environmental Monitoring in WSNs.

With the increasing concerns for the environment, the amount of the data monitored by wireless sensor networks (WSNs) is becoming larger and the energy required for data transmission is greater. However, sensor nodes have limited storage capacity and battery power. The WSNs are faced with the challenge of handling larger data volumes while minimizing energy consumption for transmission. To address this issue, this paper employs data compression technology to eliminate redundant information in the environmental data, thereby reducing energy consumption of sensor nodes. Additionally, an unmanned aerial vehicle (UAV)-assisted compressed data acquisition algorithm is put forward. In this algorithm, compressive sensing (CS) is introduced to decrease the amount of data in the network and the UAV serves as a mobile aerial base station for efficient data gathering. Based on CS theory, the UAV selectively collects measurements from a subset of sensor nodes along a route planned using the optimized greedy algorithm with variation and insertion strategies. Once the UAV returns, the sink node reconstructs sensory data from these measurements using the reconstruction algorithms. Extensive experiments are conducted to verify the performance of this algorithm. Experimental results show that the proposed algorithm has lower energy consumption compared to other approaches. Furthermore, we employ different data reconstruction algorithms to recover data and discover that the data can be better reconstructed in a shorter time.

MAxSIM: multi-angle-crossing structured illumination microscopy with height-controlled mirror for 3D topological mapping of live cells

Mapping 3D plasma membrane topology in live cells can bring unprecedented insights into cell biology. Widefield-based super-resolution methods such as 3D-structured illumination microscopy (3D-SIM) can achieve twice the axial ( ~ 300 nm) and lateral ( ~ 100 nm) resolution of widefield microscopy in real time in live cells. However, twice-resolution enhancement cannot sufficiently visualize nanoscale fine structures of the plasma membrane. Axial interferometry methods including fluorescence light interference contrast microscopy and its derivatives (e.g., scanning angle interference microscopy) can determine nanoscale axial locations of proteins on and near the plasma membrane. Thus, by combining super-resolution lateral imaging of 2D-SIM with axial interferometry, we developed multi-angle-crossing structured illumination microscopy (MAxSIM) to generate multiple incident angles by fast, optoelectronic creation of diffraction patterns. Axial localization accuracy can be enhanced by placing cells on a bottom glass substrate, locating a custom height-controlled mirror (HCM) at a fixed axial position above the glass substrate, and optimizing the height reconstruction algorithm for noisy experimental data. The HCM also enables imaging of both the apical and basal surfaces of a cell. MAxSIM with HCM offers high-fidelity nanoscale 3D topological mapping of cell plasma membranes with near-real-time ( ~ 0.5 Hz) imaging of live cells and 3D single-molecule tracking.

Research into CUP-VISAR velocity reconstruction based on weighted DRUNet and total variation joint optimization.

This Letter proposes a CUP-VISAR data reconstruction algorithm for laser-driven inertial confinement fusion (ICF) research. The algorithm combines weighted deep residual U-Net (DRUNet) and joint optimization with total variation (TV) to improve shockwave velocity fringe image reconstruction. The simulation results demonstrate that the proposed algorithm outperforms the ADMM-TV and enhanced 3D total variation (E-3DTV) algorithms, enhancing the quality of the reconstructed images and thereby improving the accuracy of velocity field calculations. Furthermore, it addresses the challenges of the high compression ratio caused by the diagnostic requirements of the larger number of sampling frames in the CUP-VISAR system and the issues of aliasing within a large encoding aperture. The proposed algorithm demonstrates good robustness to noise, ensuring reliable reconstruction even under Gaussian noise with a relative intensity of 0.05. This algorithm contributes to ICF diagnostics in complex environmental conditions and has theoretical significance and practical application value for achieving controlled thermonuclear fusion.

Robust reconstruction of single-cell RNA-seq data with iterative gene weight updates.

Single-cell RNA-sequencing technologies have greatly enhanced our understanding of heterogeneous cell populations and underlying regulatory processes. However, structural (spatial or temporal) relations between cells are lost during cell dissociation. These relations are crucial for identifying associated biological processes. Many existing tissue-reconstruction algorithms use prior information about subsets of genes that are informative with respect to the structure or process to be reconstructed. When such information is not available, and in the general case when the input genes code for multiple processes, including being susceptible to noise, biological reconstruction is often computationally challenging. We propose an algorithm that iteratively identifies manifold-informative genes using existing reconstruction algorithms for single-cell RNA-seq data as subroutine. We show that our algorithm improves the quality of tissue reconstruction for diverse synthetic and real scRNA-seq data, including data from the mammalian intestinal epithelium and liver lobules. The code and data for benchmarking are available at github.com/syq2012/iterative_weight_update_for_reconstruction.

An image encryption scheme based on chaotic system and compressed sensing for multiple application scenarios

In this paper, we propose a novel image encryption scheme based on a chaotic system and compressed sensing for multiple application scenarios. It is a dedicated scheme for JPEG format that makes full use of the characteristics of DCT data. The proposed scheme generates a key from a plain image with random noise, which is then used to generate the parameters needed in the scheme through using a new chaotic system structure. This structure employs data segmentation for the DCT data of a plain image and processes sparse data and non-sparse data separately: it permutes non-sparse data, and applies permutation and compressed sensing to sparse data. By merging the processed data and performing efficient confusion, we obtain the final encrypted data. The reverse operation is image decryption, and partial decryption can be performed quickly since sparse data is not required. Such partial decryption can be used for multiple applications, such as copyright protection platforms and machine learning based on private data. Simulation experiments and a performance analysis show that the key generation method and efficient confusion of our scheme can improve the security of image encryption. Furthermore, the data segmentation and reconstruction algorithm based on a cyclic matrix can greatly improve decryption efficiency.

A high sensitivity Cherenkov detector for prompt gamma timing and time imaging

We recently proposed a new approach for the real-time monitoring of particle therapy treatments with the goal of achieving high sensitivities on the particle range measurement already at limited counting statistics. This method extends the Prompt Gamma (PG) timing technique to obtain the PG vertex distribution from the exclusive measurement of particle Time-Of-Flight (TOF). It was previously shown, through Monte Carlo simulation, that an original data reconstruction algorithm (Prompt Gamma Time Imaging) allows to combine the response of multiple detectors placed around the target. The sensitivity of this technique depends on both the system time resolution and the beam intensity. At reduced intensities (Single Proton Regime—SPR), a millimetric proton range sensitivity can be achieved, provided the overall PG plus proton TOF can be measured with a 235 ps (FWHM) time resolution. At nominal beam intensities, a sensitivity of a few mm can still be obtained by increasing the number of incident protons included in the monitoring procedure. In this work we focus on the experimental feasibility of PGTI in SPR through the development of a multi-channel, Cherenkov-based PG detector with a targeted time resolution of 235 ps (FWHM): the TOF Imaging ARrAy (TIARA). Since PG emission is a rare phenomenon, TIARA design is led by the concomitant optimisation of its detection efficiency and Signal to Noise Ratio (SNR). The PG module that we developed is composed of a small PbF_{2} crystal coupled to a silicon photoMultiplier to provide the time stamp of the PG. This module is currently read in time coincidence with a diamond-based beam monitor placed upstream the target/patient to measure the proton time of arrival. TIARA will be eventually composed of 30 identical modules uniformly arranged around the target. The absence of a collimation system and the use of Cherenkov radiators are both crucial to increase the detection efficiency and the SNR, respectively. A first prototype of the TIARA block detector was tested with 63 MeV protons delivered from a cyclotron: a time resolution of 276 ps (FWHM) was obtained, resulting in a proton range sensitivity of 4 mm at 2sigma with the acquisition of only 600 PGs. A second prototype was also evaluated with 148 MeV protons delivered from a synchro-cyclotron obtaining a time resolution below 167 ps (FWHM) for the gamma detector. Moreover, using two identical PG modules, it was shown that a uniform sensitivity on the PG profiles would be achievable by combining the response of gamma detectors uniformly distributed around the target. This work provides the experimental proof-of-concept for the development of a high sensitivity detector that can be used to monitor particle therapy treatments and potentially act in real-time if the irradiation does not comply to treatment plan.

Graph Clustering: a graph-based clustering algorithm for the electromagnetic calorimeter in LHCb

The recent upgrade of the LHCb experiment pushes data processing rates up to 40 Tbit/s. Out of the whole reconstruction sequence, one of the most time consuming algorithms is the calorimeter data reconstruction. It aims at performing a clustering of the readout cells from the detector that belong to the same particle in order to measure its energy and position. This article presents a new algorithm for the calorimeter data reconstruction that makes use of graph data structures to optimise the clustering process, that will be denoted Graph Clustering. It outperforms the previously used method by 65.4% in terms of computational time on average, with an equivalent efficiency and resolution. The implementation of the Graph Clustering method is detailed in this article, together with its performance results inside the LHCb framework using simulation data.

Three-Dimensional Seismic Data Reconstruction Based on Fully Connected Tensor Network Decomposition

Rank-reduction approaches assume that seismic data in the frequency-space domain is of low-rank after a specific pre-transformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted project onto the convex set (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix, and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods such as CANDECOMP/PARAFAC (CP) and Tucker decomposition cannot establish the connections between different factors, and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.

Optimal Seismic Sensor Placement Based on Reinforcement Learning Approach: An Example of OBN Acquisition Design

Seismic acquisition costs are directly associated with the number of sensors used in the survey. Limiting the number of sensors in a seismic survey can be beneficial, especially when sensors are expensive to purchase, deploy, and maintain. This work explores an optimal design method for ocean bottom node (OBN) detector deployment. The proposed method is based on a reinforcement learning approach. We assume access to an initial dataset over the area of study. These data are used to extract an over-complete pre-learned basis library via the proper orthogonal decomposition method, which leads to a fast least-squares seismic data reconstruction algorithm. Then, the sensor selection procedure entails using Q-learning to find the sensor configuration that maximizes the reconstruction quality.

Large scale medical image online three-dimensional reconstruction based on WebGL using four tier client server architecture

The development of medical device technology has led to the rapid growth of medical imaging data. The reconstruction from two-dimensional images to three-dimensional volume visualization not only shows the location and shape of lesions from multiple views but also provides intuitive simulation for surgical treatment. However, the three-dimensional reconstruction process requires the high performance execution of image data acquisition and reconstruction algorithms, which limits the application to equipments with limited resources. Therefore, it is difficult to apply on many online scenarios, and mobile devices cannot meet high-performance hardware and software requirements. This paper proposes an online medical image rendering and real-time three-dimensional (3D) visualization method based on Web Graphics Library (WebGL). This method is based on a four-tier client-server architecture and uses the method of medical image data synchronization to reconstruct at both sides of the client and the server. The reconstruction method is designed to achieve the dual requirements of reconstruction speed and quality. The real-time 3D reconstruction visualization of large-scale medical images is tested in real environments. During the interaction with the reconstruction model, users can obtain the reconstructed results in real-time and observe and analyze it from all angles. The proposed four-tier client-server architecture will provide instant visual feedback and interactive information for many medical practitioners in collaborative therapy and tele-medicine applications. The experiments also show that the method of online 3D image reconstruction is applied in clinical practice on large scale image data while maintaining high reconstruction speed and quality.

Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining.