Regularization Method Research Articles

Super-Oscillating Diffractive Optical Spot Generators

The prior discrete Fourier transform (PDFT) is applied to the design of super-oscillating diffractive optical elements with rotational symmetry. Numerical simulations of the filter response are used to demonstrate the potential of the PDFT-based approach, which includes a regularization method for improved numerical and functional stability of the filter design. For coherent monochromatic illumination, the Strehl ratio of spot generators as a function of the spot radius is compared to the theoretical upper bound. It is shown that the performance of the PDFT design varies significantly depending on the aperture function and the encoding as a phase-only diffractive element. Experimental results are in good agreement with simulations and demonstrate the moderate demands to implement super-oscillating diffractive optical elements.

Elastic reweighted sparsity regularized sparse unmixing for hyperspectral image analysis

Sparse unmixing is a crucial component in the analysis of hyperspectral images because of its ability to sparsely estimate abundances with a spectral library of considerable scale. The existing sparse unmixing techniques tend to employ the sparsity from a single perspective, i.e., ℓ1 or ℓ2,1. In this paper, we accurately estimate abundances from element-wise and channel-wise sparsity perspectives, which forms a new elastic reweighted sparse regularized sparse unmixing method, termed ElSpaSU. The proposed ElSpaSU method leverages the abundance matrix simultaneously using ℓ1 and ℓ2,1 sparsity regularizations. To further promote sparsity in the abundances, the proposed ElSpaSU incorporates two types of iterative reweighting weights. We utilize the alternating direction method of multipliers (ADMM) framework to ensure guaranteed convergence in solving the optimization problem. Experimental outcomes from simulated and real hyperspectral images highlight the remarkable efficacy of the suggested ElSpaSU in contrast to competing approaches.

A combined denoising method for Q-factor compensation of poststack seismic data

Attenuation is a main factor limiting the resolution of seismic data. Earth works as a low-pass filter, which has strong attenuation of the high-frequency data. The loss of high-frequency energy can be compensated by the inverse Q filtering strategy. However, this method will also increase the energy of random noise which limits its application. The inverse Q filtering algorithm also needs the Q-factor as the input parameter, which is not easy to obtain. In this paper, we proposed a three-stage process to correct the attenuation of poststack data. In the first stage, a robust structure-oriented filtering is applied to remove random noise while protecting the structure information to avoid high-frequency noise burst. In the second stage, the local centroid frequency shift (LCFS) method is used to estimate the Q factor along the seismic trace. This method combined shaping regularization and centroid frequency shift (CFS) method to improve the robustness and accuracy of Q estimation to some extent. The final stage is to apply a stable inverse Q-filtering. Synthetic and field data examples demonstrate that time-varying Q-value can be accurately estimated by using the local centroid frequency shift (LCFS) method, and the proposed workflow can compensate the attenuation without bursting of high-frequency random noise.

Nonconvex low-rank regularization method for video snapshot compressive imaging

The reconstruction of snapshot compressive imaging (SCI) presents a significant challenge in signal processing. The primary goal of SCI is to employ a low-dimensional sensor to capture high-dimensional data in a compressed form. As a result, compared to traditional compressive sensing, SCI emphasizes capturing structural information and enhancing the reconstruction quality of high-dimensional videos and hyperspectral images. This paper proposes a novel SCI reconstruction method by integrating non-convex regularization approximation in conjunction with rank minimization. Furthermore, we address the characterization of structural information by leveraging nonlocal self-similarity across video frames to improve the reconstruction quality. We also develop an optimization algorithm based on the alternating direction method of multipliers (ADMM) to solve the model and provide a convergence algorithm analysis. Extensive experiments demonstrate that the proposed approach can potentially reconstruct SCI effectively.

Use of electrical impedance tomography for lung volume reconstruction

The article presents a study of the application of electro-impedance tomography (EIT) in diagnosing lung capacity using the Tikhonov regularization method. The possibility of reconstructing the lungs to monitor the degree of air filling was investigated. The experiment included a series of tests using a torso phantom designed to simulate different states of the lungs - from fully inflated to fully deflated. Lung-filling states were manipulated in controlled scenarios to test nine main experimental conditions reflecting different lung-filling states. In addition, the quality of reconstruction was checked using various types of reference backgrounds. The results show significant differences in lung volume reconstructions depending on the lung filling state. The most successful reconstructions, which were obtained using the 'No phantom' background, provided the most explicit visualization of the lungs, reassuring the method's reliability. The experiments confirm the potential of EIT to distinguish between different lung states and reconstruct the degree of lung filling. The study also underscores the need to optimize the reference background to increase the precision of the images, especially for the left lung.

Active contour model with improved second-order differential driven term

Driven terms in active contour models (ACMs) play a significant role in edge identification and image segmentation. However, in many existing ACMs, the driven terms are iteratively updated, resulting in slower segmentation speed because the image segmentation time increases with the iteration number. To address this problem, an ACM based on an improved second-order differential driven term (ISDDT) is presented, which can extract the edge information of images. The improved second-order differential driven term is computed only once before the iterations. Therefore, the computational complexity of our presented ACM is reduced, leading to a faster image segmentation speed. In addition, an improved regularization method with mean filtering is presented to improve the robustness of our ISDDT model. As an application, a target contour tracking method is developed based on our ISDDT model. Experimental results show that our ISDDT model segments images with inhomogeneous intensities well. The image segmentation speed of our proposed model has obvious advantages.

Radiation absorption, Hall and ion-slip current – driven MHD convection with spanwise cosinusoidally fluctuating temperature: a perturbative study

This study investigates the impact of radiation absorption, Hall and ion-slip current on magnetohydrodynamic free convective fluid flow past an infinite vertical porous plate with viscous dissipation and chemical reaction in the presence of Spanwise Cosinusoidally fluctuating temperature by time. Its significance extends across a wide array of applications, including advanced heat transfer systems for electronics, optimization of material processing kinetics, and fostering innovation for tailored material properties, with far-reaching implications spanning aerospace and renewable energy sectors. The governing equations, subject to stipulated boundary conditions are analyzed using the multiple regular perturbation method. The results are visually presented to evaluate the influence of various parameters on system dynamics. Graphical representations illustrate the physical significance of parameters including velocity, temperature, concentration, skin friction, mass transfer rate and heat transfer coefficients. The findings reveal that increased values of Hall and ion-slip current effects correlate with acceleration of velocity and skin friction. Moreover, higher radiation absorption parameter values lead to enhancements in velocity, temperature and Nusselt number. Conversely, different chemical reaction rates and Schmidt number values result in declines in velocity, concentration and Sherwood number. Additionally, incremental values of the Eckert number improve velocity and Nusselt number but have a reverse impact on temperature. The findings of this research align with those from prior examinations.

Advancing drinking water safety: Facile fabrication of nanofiltration membrane for enhanced antibiotics removal and efficient water softening

Long-term water consumption with contaminated antibiotics and high levels of hardness threatens the health of humans and animals. In this study, a novel positively charged nanofiltration membrane was designed for the dual purpose of efficiently removing antibiotics and softening hard water. The membrane was fabricated by casting P84 polymer solution onto non-woven fabrics, followed by immersion in the polyethyleneimine (PEI) solution for phase inversion and cross-linking, and finalized interfacial polymerization with trimesoyl chloride. Notably, the rapid immersion of the P84 substrate into PEI drastically reduces preparation time by 95 % compared to the regular method while maximizing PEI storage, resulting in uniform pore sizes and a positively charged surface. The optimal membrane showed a high water permeance of 11.3 L m−2 h−1 bar−1 and exceptional antibiotic rejection, i.e., 99.6 % for enrofloxacin, 99.2 % for ciprofloxacin, and 96.3 % for sulfamethoxazole, as well as a highly effective removal in water hardness (over 96 % of Ca2+ and Mg2+) at 4 bar. Simulated water separation tests showed that the optimal membrane effectively controls hard water salinity within optimum drinking water ranges (Ca2+ ≤ 80 mg/L; Mg2+ ≤ 30 mg/L), providing efficient water softening and antibiotic capture ability. Additionally, the optimal membrane demonstrated a superior anti-bacterial and long-term stability performance. This study has significant potential for improving drinking water supply safety and addressing pressing public health and environmental concerns.

Ward identities in a two-dimensional gravitational model: anomalous amplitude revisited using a completely regularization-independent mathematical strategy

We present a detailed investigation of the anomalous gravitational amplitude in a simple two-dimensional model with Weyl fermions. We employ a mathematical strategy that completely avoids any regularization prescription for handling divergent perturbative amplitudes. This strategy relies solely on the validity of the linearity of the integration operation and avoids modifying the amplitudes during intermediate calculations, unlike studies using regularization methods. Additionally, we adopt arbitrary routings for internal loop momenta, representing the most general analysis scenario. As expected, we show that surface terms play a crucial role in both preserving the symmetry properties of the amplitude and ensuring the mathematical consistency of the results. Notably, our final perturbative amplitude can be converted into the form obtained using any specific regularization prescription. We consider three common scenarios, one of which recovers the traditional results for gravitational anomalies. However, we demonstrate that this scenario inevitably breaks the linearity of integration, leading to an undesirable mathematical situation. This clean and transparent conclusion, enabled by the general nature of our strategy, would not be apparent in similar studies using regularization techniques.

Entanglement entropy via double-cone regularization

This paper proposes an alternative regularization method for handling the ultraviolet behavior of entanglement entropy. Utilizing an iε prescription in the Euclidean double cone geometry, it accurately reproduces the universal behavior of entanglement entropy. The method is demonstrated in the free boson theory in arbitrary dimensions and two-dimensional conformal field theories. The findings highlight the effectiveness of the iε regularization method in addressing ultraviolet issues in quantum field theory and gravity, suggesting potential applications to other calculable quantities. Published by the American Physical Society 2024

Exploring the Feasibility and Educational Suggestions for Integrating OBE Educational Concepts into Higher Vocational English Courses

In response to the English curriculum standards of higher vocational colleges in both society and the nation, this study explores the feasibility of applying Outcome-Based Education (OBE) concepts into higher vocational English courses. A teaching experiment was conducted with 161 students selected from 4 parallel classes at a public higher vocational college in Guangdong Province. The intervention involved teachers in the experimental class utilizing the "Design Plan for the Application of OBE Educational Concepts in Hotel English Courses" provided by the researchers, while the control class proceeded with regular teaching methods. Following an 8-week intervention period, it was found that students in the experimental class demonstrated significantly higher academic performance compared to those in the control class, indicating the positive impact of integrating OBE educational concepts into higher vocational English courses. At the same time, researchers randomly selected 8 students for semi-structured interviews. The results of the interviews revealed that students in the experimental class responded positively and demonstrated recognition and acceptance of the OBE teaching method. Conclusion: The application of the OBE educational concept in higher vocational English courses is deemed feasible. As a result, the researchers have provided educational recommendations for English teachers in higher vocational schools, aiming to foster innovation and enhance educational quality in the field.

A modified iteratively regularized Landweber iteration method: Hölder stability and convergence rates

Abstract In this paper, we formulate the modified iteratively regularized Landweber iteration method in Banach spaces to solve the inverse problems for which the forward operator may be smooth or non-smooth. We study the convergence analysis of the modified method for both the perturbed as well as unperturbed data by utilizing the Hölder stability estimates. In the presence of perturbed data, we terminate the method via a discrepancy principle and show that it is in fact a convergence regularization method that terminates within a few iterations. In the presence of unperturbed data, we show that the iterates converge to the exact solution. Additionally, we deduce the convergence rates in the presence of perturbed as well as unperturbed data. Finally, we discuss two inverse problems on which the method is applicable.

CNN- based intrusion detection software for network operating system environment

Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzing the natural behavior of the system by building a special convolutional network to achieve this goal. The classification and detection results of the proposed convolutional neural network were compared with the regular machine learning method (SVM), with feature selection by correlation for both methods. Same datasets were used to train and test each of CNN and SVM. Some metrics were determined to evaluate the performance of classification and prediction models for a specific type of regular attacks, DoS and BOT attacks, where both SVM and CNN obtained an accuracy of 85.58% and 95.59%, respectively.

A single‐channel transmitarray antenna with abilities of super resolution and digital beamforming

AbstractA single‐channel transmitarray antenna with abilities of super resolution and digital beamforming (DBF) is proposed. The amplitude and phase distributions on the array aperture are reconstructed through beam switching with a single channel. By finding and solving relations between distributions and received signals of beams, aperture distributions can be reconstructed. The ill‐conditioned problem caused by limited scanning range and angular interval is solved using the regularization method. The obtained distributions can then be delivered to super‐resolution algorithm to obtain super resolution, or used to produce shaped beams by DBF. On the basis of the proposed theory, a bifocal beam‐reconfigurable transmitarray is designed and tested as a single‐channel DBF antenna without special and additional hardware design. It has the outstanding advantages of low cost, simple structure, low energy consumption, and high working frequency.

NeRF-Art: Text-Driven Neural Radiance Fields Stylization.

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially in simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency.

On the Identification of Control Failures by the Dynamic Regularization Method

On the Identification of Control Failures by the Dynamic Regularization Method

Clean affinity matrix induced hyper-Laplacian regularization for unsupervised multi-view feature selection

Most previous unsupervised multi-view feature selection (UMFS) methods have achieved appealing performance by exploring the consistency among multiple views. However, they have the following shortcomings: (1) They often fail to consider the potential inconsistency that might be caused by view-specific characteristics from the perspective of sparsity. (2) The previously learned hyper-graph might be affected by noise, thereby reducing the quality of the generated graph. To tackle these issues, this paper proposes a clean affinity matrix induced hyper-Laplacian regularization (CAHR) method for UMFS. Firstly, the initial affinity matrix is decomposed into the consistent and inconsistent parts, then a novel diversity penalty term is introduced to enforce the sparsity of the inconsistent part across views, thereby making the consistent part be cleaner. Secondly, a unified affinity matrix is generated by fusing the consistent factors of the initial affinity matrix in a self-weighted manner, thereby considering the consistency of multi-view data. Based on the unified affinity matrix, a hyper-Laplacian matrix is further constructed, which can maintain high-order manifold structure of data. Finally, a loss function is designed to find the best mapping for feature selection. Comprehensive experiments demonstrate that the proposed method significantly outperforms several state-of-the-art UMFS methods.

Identifying retinopathy in optical coherence tomography images with less labeled data via contrastive graph regularization.

Retinopathy detection using optical coherence tomography (OCT) images has greatly advanced with computer vision but traditionally requires extensive annotated data, which is time-consuming and expensive. To address this issue, we propose a novel contrastive graph regularization method for detecting retinopathies with less labeled OCT images. This method combines class prediction probabilities and embedded image representations for training, where the two representations interact and co-evolve within the same training framework. Specifically, we leverage memory smoothing constraints to improve pseudo-labels, which are aggregated by nearby samples in the embedding space, effectively reducing overfitting to incorrect pseudo-labels. Our method, using only 80 labeled OCT images, outperforms existing methods on two widely used OCT datasets, with classification accuracy exceeding 0.96 and an Area Under the Curve (AUC) value of 0.998. Additionally, compared to human experts, our method achieves expert-level performance with only 80 labeled images and surpasses most experts with just 160 labeled images.

Non-convex sparse regularization via convex optimization for blade tip timing

Blade Tip Timing (BTT), an emerging technology poised to replace strain gauges, enables contactless measurement of rotor blade vibration. However, the blade vibration signals measured by BTT systems often suffer from significant undersampling. Sparse reconstruction methods are instrumental in addressing the challenge of undersampled signal reconstruction. However, traditional approaches grounded in ℓ1 regularization tend to underestimate the amplitude of the true solution. This underestimation is particularly pronounced in the resonance state of the rotor blade, hindering effective prediction of the blade’s operational state. To overcome this limitation, this paper introduces a novel non-convex regularized BTT model, employing a non-convex penalty term with convex-preserving properties to achieve nearly unbiased reconstruction accuracy. Additionally, we propose a new threshold iteration algorithm designed for the swift solution of this model. The accuracy and robustness of the proposed method in identifying the multimodal vibration parameters of rotor blades are validated through simulations and experiments. Comparatively, the proposed method closely aligns with the Orthogonal Matching Pursuit (OMP) method in recognizing blade multimodal vibration amplitude, showcasing significant improvement over the ℓ1 regularization method. Furthermore, it demonstrates lower sensitivity to changes in BTT probe layout when compared to the OMP and ℓ1 regularization methods.

Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.

We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC. Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME. Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively. Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.