Measurement Matrix Research Articles

Image compressed sensing using a random orthogonal structured discrete Fourier transform measurement matrix

Image compressed sensing using a random orthogonal structured discrete Fourier transform measurement matrix

Response reconstruction based on measurement matrix optimization in compressed sensing for structural health monitoring

Structural health monitoring (SHM) data have a large volume, increasing the cost of data storage and transmission and the difficulties of structural parameter identification. The compressed sensing (CS) theory provides a signal acquisition and analysis strategy. Signal reconstruction using limited measurements and CS has attracted significant interest. However, the dynamic responses obtained from civil engineering structures contain noise, resulting in sparse samples and reducing the signal reconstruction accuracy. Therefore, we propose an optimization algorithm for the measurement matrix integrating the Karhunen-Loeve transform (KLT) and approximate QR decomposition (KLT-QR) to improve the accuracy of dynamic response reconstruction of SHM data. The KLT reduces the correlation between the measurement matrix and the sparse basis. The approximate QR decomposition is used to improve the independence between the column vectors of the measurement matrix, optimizing the measurement matrix. The experimental results for a laboratory steel beam indicate that the proposed KLT-QR algorithm outperforms three other algorithms regarding the accuracy of dynamic response reconstruction (acceleration, displacement, and strain), especially at high compression ratios. The acceleration responses from the Ji’an Bridge are utilized to verify the advantages of the proposed algorithm. The results demonstrate that the KLT-QR algorithm has the highest accuracy of reconstructing the vibration signals and yields better Fourier spectra than the conventional Gaussian measurement matrix.

Pre-transplant Screening for MMP-9 in Allogeneic HSCT Candidates

Purpose: InflammaDry® (Quidel Eye Health, San Diego, California), an FDA-approved point-of-care commercial test, measures matrix metalloproteinase-9 (MMP-9) levels in the tear film. MMP-9 is an inflammatory biomarker that is elevated in response to ocular surface stress, particularly observed in ocular graft-versus-host disease (oGVHD). The purpose of this study is to assess the prevalence of MMP-9 positivity and a score >4 on the OSDI-6 questionnaire in patients before allogeneic hematopoietic stem cell transplant (HSCT). Methods: A prospective, observational, cross-sectional single center pilot study was conducted among 23 patients (46 eyes) undergoing planned allogenic HSCT. InflammaDry® results, OSDI-6 questionnaire results, and development of oGVHD were collected. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and chi-square test were calculated. Results: InflammaDry® demonstrated high sensitivity (1.0) but low specificity (0.1429) for oGVHD development. The PPV was 0.25, while the NPV was 1.0. No statistical significance was found between InflammaDry® result and development of oGVHD (p-value> 0.05). Conclusion: InflammaDry® is not an effective tool for detecting the onset or predicting the risk of developing oGVHD. A significant percentage of patients exhibited ocular inflammation before allogeneic HSCT, suggesting that initiating prophylactic treatment could be valuable in reducing oGVHD development.

Constructing HairDB to facilitate exposome research using human hair

This study introduces HairDB, an online database serving as a comprehensive repository of hair-related chemicals for exposome research. HairDB was created via an integrative approach. It first extracted 4,184 unique hair-related chemicals through text mining of over 34 million PubMed abstracts and 5.2 million PubMed Central articles, followed by manual data checking. HairDB also applied an artificial intelligence-enabled search to discover organic aerosol biomarkers in literature. A set of 768 chemicals used in hair-related products was then curated through a combination of manual searches and data extraction from the Cosmetic Ingredient Database (CosIng) of the European Union. From manually reading review papers, 29 organic aerosol biomarkers were extracted. Furthermore, 3,679 known exposure chemicals extracted from the Toxin and Toxin Target Database (T3DB) were incorporated in HairDB to represent the possible environmental exposures detected on hair surfaces. The comprehensive set of chemicals captured in HairDB represents the current knowledge of what can be found in and on hair. HairDB was constructed as a user-friendly web interface, allowing easy exploration of hair-related chemicals and tailored for annotating mass spectrometry-based hair exposomics data. The development of HairDB marks an important step forward in using hair as a biological matrix for chemical exposure measurement, facilitating the adoption of hair for exposome research. HairDB is publicly available at https://www.hairdb.ca/.

ILN-SSR: Improved Logarithmic Norm and Sparse Structure Refinement for Infrared Small Target Detection

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds.

Defibrillator shocks and their effect on objective asian patient outcomes: results of the painfree SST clinical trial

Abstract Background The implantable cardioverter defibrillator (ICD) reduces mortality in patients at risk of sudden cardiac death. However, the effect of ICD shock on device-measured activity is unknown. The aim of this analysis was to analyze the acute and long-term effects of ICD shock on objective behavioral data. Methods The PainFree SST study was a prospective, multicenter, global study that enrolled 2770 patients including 225 from from Asia (181 from Japan, 25 from India, and 19 from Malaysia). Patient physical activity was determined from the device built-in accelerometer. The device translates the accelerometer output into a count of active minutes, which is stored as a daily total value in device memory for a rolling window of 425 days. Obviously, activity is measured only after device implantation. Early after implant, activity is reduced due to the procedure and thus not representative. Therefore, no baseline can be reported for activity. The follow-up activity data was analyzed with a linear mixed model, with a random effect for patient and an auto-regressive correlation matrix for subsequent measurements of individual patients. Results Figure 1 shows average daily activity between 90 days before and 90 days after a shock. The dashed vertical line indicates the day of shock. Both patients from Asia and from other countries show a drop in activity after a shock with recovery in the subsequent 30-60 days. The curve for Asian patients is more variable than the curve for other countries due to the lower number of patients (there were 33 Asian patients with shocks contributing data). Figure 2 shows average daily activity before and after ATP therapy. Any post shock measurement is excluded. Contrary to the temporary reduction in activity around a shock (Figure 1), ATP therapies do not seem to have an impact on activity. In patients from the other countries, activity is significantly reduced when patients are hospitalized (-66.3 minutes/day, p<0.0001), but not in Asian patients (-5.1 minutes/day, p=0.71). Both in Asian and other countries, activity is significantly reduced after an ICD shock (Asian: -47.5 minutes/day, p=0.0002; Other countries: -21.5 minutes/day, p<0.0001). The effect of shock for Asian is greater than the other countries (p=0.054). In the month after the shock there is a significant recovery, +34.8 minutes/day in Asian patients (p=0.0077) and +7.7 minutes/day in patients from other countries (p=0.031). The recovery is trended to be bigger in Asian patients (p=0.058). Conclusions ICD shocks have a lasting adverse impact on objective, device-measured physical activity, particularly among Asian individuals. Successful management of patients with an ICD necessitates specific attention to clinically relevant behavioral outcomes, aiming to expedite recovery and facilitate a return to activities of daily living, with a particular focus on the Asian population.

Isolation of Methane from Ambient Water and Preparation for Source-Diagnostic Natural Abundance Radiocarbon Analysis.

A key challenge in climate change research is apportioning the greenhouse gas methane (CH4) between various natural and anthropogenic sources. Isotopic source fingerprinting of CH4 releases, particularly with radiocarbon analysis, is a promising approach. Here, we establish an analytical protocol for preparing CH4 from seawater and other aqueous matrices for high-precision natural abundance radiocarbon measurement. Methane is stripped from water in the optionally field-operated system (STRIPS), followed by shore-based purification and conversion to carbon dioxide (CO2) in the CH4 Isotope Preparation System (CHIPS) to allow Accelerator Mass Spectrometry analysis. The blank (±1σ) of the combined STRIPS and CHIPS is low (0.67 ± 0.12 μg C), allowing natural sample sizes down to 10 μg C-CH4 (i.e., 30 L samples of 40 nM CH4). The full-system yield is >90% for both CH4-spiked seawater and ambient samples from CH4 hotspots in the Baltic Sea and the Arctic Ocean. Furthermore, the radiocarbon isotope signal of CH4 remains constant through the multistage processing in the STRIPS and the CHIPS. The developed method thus allows for in-field sampling and sample size reduction followed by precise and CH4-specific radiocarbon analysis. This enables powerful source apportionment of CH4 emitted from aquatic systems from the tropics to the polar regions.

A high-quality visual image encryption algorithm utilizing the conservative chaotic system and adaptive embedding

Regarding the issue of encrypted images in the channel attracting the attention of attackers and making them vulnerable to attacks. This paper proposes a high-quality visual image encryption algorithm utilizing the conservative chaotic system, two-dimensional compressive sensing, optimization local of binary patterns, and adaptive embedding method. Firstly, a conservative chaotic system with excellent performance and resistance to reconstruction attacks is proposed. Secondly, the pseudo-random sequences generated by the chaotic system dynamically generate measurement matrices, which are optimized before compressing the image. Then, during the encryption process, by utilizing the newly proposed composite DNA computing rules, chaotic sequences dynamically encode and compute image information, which enriches the coding criteria and improves the security of encryption algorithms. Finally, in the information embedding stage, the newly proposed OLBP algorithm can identify important textured and non-textured regions of the host image, prioritize embedding non-textured regions, and then embed textured regions, which can improve the amount of information embedding and reduce damage to the host image. Experimental simulation and analysis exhibit that the encryption algorithm has high security, strong robustness and high efficiency. Meanwhile the imperceptible analysis of the steganographic images is exceed 52 dB, so the algorithm presents a high-quality visual effect of imperceptibility.

B-182 Development and validation of an LC-MS/MS assay for simultaneous measurement of serum androstenedione and 17-hydroxyprogesterone in serum

Abstract Background Androstenedione and 17-hydroxyprogesterone are measured via immunoassays or high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS) in clinical laboratories. Immunoassays have limited sensitivity and specificity for certain steroid hormones testing. We aimed to develop and validate a sensitive, accurate, and specific in-house LC-MS/MS assay to simultaneously measure both 17-hydroxyprogesterone and androstenedione in serum. Methods 13C-labeled androstenedione and 17-hydroxyprogesterone internal standards were added to calibrator, control, and patient samples. A hexane/ethyl acetate solution was used in a liquid-liquid extraction procedure. The organic layer was then removed and dried followed by reconstituting the extracts in 2.5 mM ammonium formate in 50% methanol. The HPLC mobile phase gradient began with 47%, 2.5 mM ammonium formate in water and 53%, 2.5 mM ammonium formate in methanol. This progressed to 56% of the methanol phase over three minutes. The mixtures were separated though an Agilent 1260 Infinity HPLC system with a Poroshell 120 EC-C18 analytical column (2.1 × 50 mm × 2.7 µm). The separated HPLC eluents then entered an Agilent 6460C QQQ mass spectrometer in positive ion mode through an electrospray ionization source where protonated androstenedione (m/z = 287.2) with the protonated androstenedione-[2, 3, 4-13C3] internal standard (m/z = 290.2) and protonated 17-hydroxyprogesterone (m/z = 331.2) with the protonated 17-hydroxyprogesterone-[2, 3, 4-13C3] (m/z = 334.2) were mass-selected for collision-induced dissociation. For androstenedione, respective quantifier and qualifier m/z values of 287.2 > 97.1 and 287.2 > 109.1 were used while respective quantifier and qualifier m/z values of 331.2 > 97.1 and 331.2 > 109.1 were used for 17-hydroxyprogesterone. Validation studies were performed including imprecision, lower limit of quantitation (LLOQ), analytical measurement range (AMR), interference, specificity, carryover, extraction recovery, matrix effect, and comparison with a reference laboratory LC-MS/MS method. The total allowable error limits used in this study were 23.5% for androstenedione and 30.2% for 17-hydroxyprogesterone. Results Total imprecision results across three concentrations yielded a maximum CV of 4.6 % for androstenedione and 6.5% for 17-hydroxyprogesteron. The AMRs of androstenedione and 17-hydroxyprogesterone were 0.2 to 13.4 and 0.1 to 22.0 ng/mL without significant carryovers. No significant interferences were observed from 11-deoxycorticosterone (700 ng/dL), testosterone (2500 ng/dL), DHEA (1000 ng/mL), bilirubin (30.1 mg/dL), hemoglobin (1032 mg/dL), or triglycerides (1858 mg/dL). The extraction recoveries of androstenedione and 17-hydroxyprogesterone were 98% and 81%, respectively. No significant matrix effects were identified. In comparison to the reference lab LC-MS/MS method, the in-house LC-MS/MS assay demonstrated a 0.523% bias for androstenedione and a -3.77% bias for 17-hydroxyprogesterone with both exhibiting excellent correlations with the reference lab measurements. More specifically, our results (as a function of values obtained from the reference lab) followed the relationship of y = 1.039x - 0.092 (R = 0.9951) for androstenedione and y = 0.957x + 0.01 (R = 0.9974) for 17-hydroxyprogesterone. Conclusions An LC-MS/MS assay is developed for simultaneous detection of serum androstenedione and 17-hydroxyprogesterone. The assay demonstrates acceptable imprecision, AMR, and accuracy. No significant carryover and interference from other structurally similar steroid hormones and other common interferents are identified.

Adaptive compressed learning boosts both efficiency and utility of differentially private federated learning

In the federated learning (FL) research field, current research is confronted with several pivotal challenges, e.g., data privacy, model utility and communication efficiency. Furthermore, these challenges are further amplified by statistical data heterogeneous in the FL system. Thus, a novel Communication-efficient and Utility-assured Gaussian differential privacy-based Personalized Federated Adaptive Compressed Learning method, called CUG-PFACL, is proposed. Specifically, an end-to-end local adaptive compressed learning strategy is designed, including three crucial modules, namely the measurement matrix, the personalized compressed data transformation and the local model. Especially, jointly training the measurement matrix module and the personalized compressed data transformation module can mitigate the inherent statistical heterogeneity while preserving all important characteristics of the compressed private data of each local client, and alleviate the additional heterogeneity induced by Gaussian differential privacy in each global communication round. Numerous experimental simulation and comparisons demonstrate that CUG-PFACL has three notable advantages: data privacy guarantee, enhanced personalized model utility and high-efficient communication.

Reversible image hiding algorithm based on adaptive embedding mechanism

A reversible blind image hiding algorithm (ReBIHA) with help of an adaptive embedding mechanism and a compressive sensing technology is presented in this paper, of which can effectively hide the secret image through a natural meaningful carrier image. In the first phase, a new key generation model (KeyGM) is built by combining random numbers and the plaintext, followed by a newly designed four dimensional chaotic map (NewCM) with stronger pseudo-randomness. Then, the generated chaotic sequence is taken to produce a measurement matrix for compressive sensing. In the second phase, the plain image is sparsely processed, confused, encrypted and compressed to get measurements. Afterward, these measurements are subjected to diffusion and binary exchange to achieve a middle cipher image (MCI). In the third stage, a meaningful carrier image is randomly selected, and integer wavelet transform (IWT) is applied to it, getting one low-frequency and three high-frequency coefficients. Then, the pixels of MCI are subjected by splitting and symmetrically shifting transformation for three element matrices. Finally, these matrices are respectively embedded into the high-frequency coefficients through an adaptive mechanism to find replaceable elements, and a final carrier image hiding secrets (CiHS) can be achieved after performing an inverse IWT. The experimental results show that the proposed algorithm can avoid the extra transmission of original carrier image. Moreover, peak signal-to-noise ratio (PSNR) of reconstructed image can surpass 32 dB at compression ratio 0.5. Especially, the proposed adaptive embedding mechanism can not only achieve blind extraction of secrets well, but also ensure the visual quality for the original carrier image.

CMQ Matrix for Occupational Impact Measurement 4.0

This article presents its own original methodology to measure the occupational impact of the digitalization of companies, or occupational impact 4.0, by measuring 10 indicators obtained in research carried out by the author on the effects of Industry 4.0 on occupations and jobs. Drawing on different research traditions, the matrix of indicators measures the occupational impact, individually in each job, connecting the substantive content of the work (the tasks and activities carried out in the workplace) with the specific organization of work that takes place in each company or industrial plant because of its digital transformation programs.

Two-dimensional hyperchaos-based encryption and compression algorithm for agricultural UAV-captured planar images.

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.

Evaluating the impact of occupational noise exposure on workplace fatal and nonfatal injuries in the U.S. (2006–2020)

ObjectivesThis study assessed the relationship between occupational noise exposure and the incidence of workplace fatal injury (FI) and nonfatal injury (NFI) in the United States from 2006 to 2020. It also examined whether distinct occupational and industrial clusters based on noise exposure characteristics demonstrated varying risks for FI and NFI. MethodsAn ecological study design was utilized, employing data from the U.S. Bureau of Labor Statistics for FI and NFI and demographic data, the U.S. Census Bureau for occupation/industry classification code lists, and the U.S./Canada Occupational Noise Job Exposure Matrix for noise measurements. We examined four noise metrics as predictors of FI and NFI rates: mean Time-Weighted Average (TWA), maximum TWA, standard deviation of TWA, and percentage of work shifts exceeding 85 or 90 dBA for 619 occupation-years and 591 industry-years. K-means clustering was used to identify clusters of noise exposure characteristics. Mixed-effects negative binomial regression examined the relationship between the noise characteristics and FI/NFI rates separately for occupation and industry. ResultsAmong occupations, we found significant associations between increased FI rates and higher mean TWA (IRR: 1.06, 95% CI: 1.01–1.12) and maximum TWA (IRR: 1.10, 95% CI: 1.07–1.14), as well as TWA exceedance (IRR: 1.04, 95% CI: 1.01–1.07). Increased rates of NFI were found to be significantly associated with maximum TWA (IRR: 1.06, 95% CI: 1.04–1.09) and TWA exceedance (IRR: 1.03, 95% CI: 1.01–1.05). In addition, occupations with both higher exposure variability (IRR with FI rate: 1.49, 95% CI: 1.23–1.80; IRR with NFI rate: 1.40, 95% CI: 1.14–1.73) and higher level of sustained exposure (IRR with FI rate: 1.27, 95% CI: 1.12–1.44; IRR with NFI rate: 1.21, 95% CI: 1.05–1.39) were associated with higher rates of FI and NFI compared to occupations with low noise exposure. Among industries, significant associations between increased NFI rates and higher mean TWA (IRR: 1.05, 95% CI: 1.02–1.08) and maximum TWA (IRR: 1.06, 95% CI: 1.04–1.08) were observed. Unlike the occupation-specific analysis, industries with higher exposure variability and higher sustained exposures did not display significantly higher FI/NFI rates compared to industries with low exposure. ConclusionsThe results suggest that occupational noise exposure may be an independent risk factor for workplace FIs/NFIs, particularly for workplaces with highly variable noise exposures. The study highlights the importance of comprehensive occupational noise assessments.

Sparse recovery using expanders via hard thresholding algorithm

Expanders play an important role in combinatorial compressed sensing. Via expanders measurements, we propose the expander normalized heavy ball hard thresholding algorithm (ENHB-HT) based on expander iterative hard thresholding (E-IHT) algorithm. We provide convergence analysis of ENHB-HT, and it turns out that ENHB-HT can recover an s-sparse signal if the measurement matrix A∈{0,1}m×n satisfies some mild conditions. Numerical experiments are simulated to support our two main theorems which describe the convergence rate and the accuracy of the proposed algorithm. Simulations are also performed to compare the performance of ENHB-HT and several existing algorithms under different types of noise, the empirical results demonstrate that our algorithm outperform a few existing ones in the presence of outliers.

Enhancing Compressed Sensing with Graph Structural Constraints: A Novel Approach to Active Learning in Measurement Matrices

Compressed sensing on the graph, signals can be approximated by the graph and with the nodes containing information, so compressed sensing can collect information distributed on nodes or links. Also, compressed sensing on the graph becomes important due to the high cost of examining parameters one by one and the unavailability of information on some of them directly in the graph. In this article, by using the idea of ​​active learning and random walking, a method has been introduced to improve the construction of the measurement matrix in the field of the graph, so that information from the graph that is used in the construction of the measurement matrix (assuming that the measurement matrix is ​​underdetermined and non-horizontal) is introduced by the random walk method. They may be missed, identified, and, after observation, inserted into the measurement matrix, resulting in a stronger recovery of the original signal. To test this method, firstly, from the data set containing five hundred and ninety as the initial signal, the measurement matrix is ​​constructed with two random walking methods and the proposed method, and the output vector is obtained from it, then the initial thin signal is received with two recovery algorithms, convex optimization and model is recovered and finally calculates the amount of error and the degree of similarity of the four recovered signals compared to the original signal and from their comparison, it is clear that the recovery of the thin signal from the matrix made by the proposed method and the recovery with the convex optimization algorithm has the highest The degree of similarity and the lowest amount of error with the original signal is compared to the other three recovered signals.

Second-order Spatial Measures Low Overlap Rate Point Cloud Registration Algorithm Based On FPFH Features1

When the sensor dynamically collects point cloud data for object or map reconstruction, the registration effect is poor and reconstruction application is difficult with a too low overlap rate of the collected point cloud data. The reason is that the objects are covered, the sensor rotation angle is too large and the speed of movement is too fast. Because of these problems, this paper proposes a point cloud registration algorithm based on FPFH feature matching, combined with second-order spatial measures. Firstly, using the FPFH feature extraction algorithm, the features of each point are extracted, and then feature matching is performed to generate the set of feature point pairs. Secondly, the second-order spatial measure is used to calculate the set of feature point pairs to obtain the second-order spatial measure matrix scores and sort them. Finally, the dichotomy method is used to find the appropriate second-order spatial measure scores for distinguishing the inner points (points in the overlap region) from the outer points (points that do not belong to the overlap region as well as the mismatched points and some disturbances). The contrast experiments between this algorithm and three common point cloud registration algorithms, FPFH-ICP, 4PCS-ICP, and NDT-ICP, on the Stanford dataset and 3DMatch dataset shows that the registration accuracy of the other algorithms decreases significantly with a low overlap rate. But this algorithm still has a high registration accuracy and is less affected by outliers than the other algorithms. Besides, this algorithm can still maintain a good registration effect on different data sets.

An Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis

AbstractThe current proofreading algorithms for action regulation mainly recover the 3D structure and action information of non-rigid objects from image sequences by factorization. Most of algorithms assume that the camera model is an affine model. This assumption only holds if the size and depth of the object change very little relative to the distance from the object to the camera, which is in the case of fixed-shape basis. When the object is very close to the camera, this assumption causes a large reconstruction error. This paper solves this problem by the intelligent proofreading algorithms for remote skiing teaching actions based on variable shape basis. Firstly, the improved Retinex algorithm is used to enhance the multi-frame video images of skiing actions to make the action details more prominent. Then, measurement matrix is calculated after eliminating the translation vector by coordinate transformation. Under the condition of rank constraint, the measurement matrix is decomposed by singular value decomposition algorithm, and the correct shape basis structure of 3D action features can be obtained by using the variable shape basis. Finally, by randomly initializing a parameter, the optimized parameter and the least square algorithm are used to optimize the randomly initialized parameter further. The iteration until the convergence of the objective function can be used to calculate the deformation degree of the actions. The test results show that this algorithm improves the proofreading accuracy of action regulation in skiing teaching, and the proofreading results of various uploaded sliding actions are correct, which can be applied to remote skiing teaching and community learning.

Hybrid Block Chaotic Compressive Sensing and Chaotic Scrambling Algorithms for Color Image Encryption System

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks.

Risk assessment of karst water inrush in tunnel engineering based on improved game theory and uncertainty measure theory

The sudden inrush of water poses a serious threat to the safety of workers during tunnel construction in the karst region of southwest China. To mitigate this risk, a model is proposed to assess the risk of water surge through a tunnel by combining improved game theory with uncertainty measure theory. Eight indicators of risk were extracted based on the solubility of rock, its geological structure, capacity for surface catchment, and hydrogeological factors, and were incorporated into the proposed model. The subjective weights of these indicators were obtained using the analytic hierarchy process, while their objective weights were calculated through the entropy weighting method and the criteria importance through intercriteria correlation method. An improved game theory-based method of combinatorial weighting was then used to construct the corresponding weight vectors. Single-indicator measurement functions and multi-indicator measurement matrices were utilized to classify and evaluate the indicators of the risk of a surge in water level based on a confidence criterion. The proposed method was applied to five typical karst sections of the Yanjin Tunnel of the Chongqing–Kunming High-speed Railway Project, and the method was validated by comparing the recorded and estimated inflow volume ranges during the project’s construction, showing consistency with the actual evaluation results. This proposed model thus offers a practical tool for assessing the risk of water inrush in karst tunnels.