1,619 Results Found

This paper presents an innovative method for designing finite impulse response (FIR) filters. The method optimizes the desired frequency response attributes while simultaneously increasing the sparsity of the filter coefficients. Traditional FIR filt...

This paper presents a new l1-RLS method to estimate a sparse impulse response estimation. A new regularization factor calculation method is proposed for l1-RLS that requires no information of the true channel response in advance. In addition, we also...

Blind deconvolution of light microscopy images could improve the ability of distinguishing cell-level substances. In this study, we investigated the blind deconvolution framework for a light microscope image, which combines the benefits of bi-l0-l2-n...

L0 sparse methods are not widespread in Direction-Of-Arrival (DOA) estimation yet, despite their potential superiority over classical methods in difficult scenarios. This comes from the difficulties encountered for global optimization on hill-climbin...

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help...

Regularization techniques are critical in the development of machine learning models. Complex models, such as neural networks, are particularly prone to overfitting and to performing poorly on the training data. L1 regularization is the most extreme...

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an i...

Sparse-signal recovery in noisy conditions is a problem that can be solved with current compressive-sensing (CS) technology. Although current algorithms based on L 1 regularization can solve this problem, the L 1 regularization mechan...

Atmospheric turbulence significantly degrades image quality. A blind image deblurring algorithm is needed, and a favorable image prior is the key to solving this problem. However, the general sparse priors support blurry images instead of explicit im...

In this paper, a novel smooth group L1/2 (SGL1/2) regularization method is proposed for pruning hidden nodes of the fully connected layer in convolution neural networks. Usually, the selection of nodes and weights is based on experience, and the conv...

Both L 1 / 2...

Although wavelet neural networks (WNNs) combine the expressive capability of neural models with multiscale localization, there are currently few theoretical guarantees for their training. We investigate the weight decay (L2 regularization) optimizati...

This article investigates the inverse problem of estimating the weight function using boundary observations in a distributed-order time-fractional diffusion equation. We propose a method based on L2 regularization to convert the inverse problem into...

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first...

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and...

Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link...

The accurate identification of kinematic parameters is crucial for improving the positioning accuracy of industrial robots, particularly in advanced manufacturing and automation. However, limited measurement space in practical applications often lead...

Restricted by the ill-posed antenna measurement matrix, the conventional smoothed L0 norm algorithm (SL0) fails to enable direct real aperture radar angular super-resolution imaging. This paper proposes a modified smoothed L0 norm (MSL0) algorithm to...

Small target detection is a critical step in remotely infrared searching and guiding applications. However, previously proposed algorithms would exhibit performance deterioration in the presence of complex background. It is attributed to two main rea...

Hyperspectral image (HSI) change detection (CD) is an important technology for identifying surface changes using multi-temporal HSIs. Nevertheless, the high dimensionality of HSIs presents significant challenges for CD tasks, including issues such as...

The sparsity regularization based on the L1 norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduc...

Remote-sensing data are used extensively to monitor water quality parameters such as clarity, temperature, and chlorophyll-a (chl-a) content. This is generally achieved by collecting in situ data coincident with satellite data collections and then cr...

As a novel learning algorithm for feedforward neural networks, the twin extreme learning machine (TELM) boasts advantages such as simple structure, few parameters, low complexity, and excellent generalization performance. However, it employs the squa...

The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. H...

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estim...

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can...

To address the limitations of Stochastic Configured Networks (SCNs) in wind speed prediction, specifically insufficient regularization capability and a high risk of overfitting, this paper proposes a novel Regularized Stochastic Configured Network (R...

Various types of heterogeneous observations can be combined within a parameter estimation process using spherical radial basis functions (SRBFs) for regional gravity field refinement. In this process, regularization is in most cases inevitable, and c...

We consider the free boundary problem of MHD in the multi-dimensional case. This problem describes the motion of two incompressible fluids separated by a closed interface under the action of a magnetic field. This problem is overdetermined, and we fi...

Fluid simulation can be automatically interpolated by using data-driven fluid simulations based on a space-time deformation. In this paper, we propose a novel data-driven fluid simulation scheme with the L0 based optical flow deformation method by ma...

The existence of multiple reflections brings difficulty to seismic data processing and interpretation in seismic reflection exploration. Parabolic Radon transform is widely used in multiple attenuation because it is easily implemented, highly robust...

In this paper, we consider the motion of incompressible magnetohydrodynamics (MHD) with resistivity in a domain bounded by a free surface. An electromagnetic field generated by some currents in an external domain keeps an MHD flow in a bounded domain...

Canonical correlation analysis (CCA) has been used for the steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) for a long time. However, the reference signal of CCA is relatively simple and lacks subject-specific informa...

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph-regulariz...

Multi-circular SAR(MCSAR) can obtain holographic three-dimensional (3D) images of interesting observation targets, which is a significant research field at present. For anisotropic scatterers, the multi-circular SAR incoherent 3D imaging strategy com...

Fast-Field-Cycling (FFC) Nuclear Magnetic Resonance (NMR) relaxometry is a powerful, non-destructive magnetic resonance technique that enables, among other things, the investigation of slow molecular dynamics at low magnetic field intensities. FFC-NM...

Seismic inversion is a process of imaging or predicting the spatial and physical properties of underground strata. The most commonly used one is sparse-spike seismic inversion with sparse regularization. There are many effective methods to solve spar...

Recent a few years have witnessed the rapid expansion of the peer-to-peer lending marketplace. As a new field of investment and a novel channel of financing, it has drawn extensive attention throughout the world. Many investors have shown great enthu...

Nano-logic magnetic structures are of great interest for spintronic applications. While the methods used for developing arrays of magnetic L10-phase dots are, in most cases, based on deposition followed by annealing at high temperatures, usually arou...

We investigate spatial moduli of non-differentiability for the fourth-order linearized Kuramoto–Sivashinsky (L-KS) SPDEs and their gradient, driven by the space-time white noise in one-to-three dimensional spaces. We use the underlying explicit kerne...

The corrosion of grounding grids can result in a grounding accident of a power system, and much attentions has been concentrated on the method used to detect a corrosion fault in a grounding grid, in which the methods of voltage measurement and magne...

In this paper, we propose a crosstalk correction method for color filter array (CFA) image sensors based on Lp-regularized multi-channel deconvolution. Most imaging systems with CFA exhibit a crosstalk phenomenon caused by the physical limitations of...

How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerome...

Aiming at the problems of synthetic-aperture radar (SAR), such as high sampling rate and vulnerability to noise interference in imaging, a sparse reconstruction algorithm based on approximate observation and L1/2 threshold iteration is proposed in th...

The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of t...

Mesoporous silica materials are the subjects for relaxometric NMR studies in which we obtain information on the properties of molecules in confined geometries. The signal analysis in such investigations is generally carried out with the help of the I...

In this paper, we proposed a logistic regression model with lp,q regularization that could give a group sparse solution. The model could be applied to variable-selection problems with sparse group structures. In the context of big data, the solutions...

In the rapidly evolving field of multi-omics data analysis, understanding the stability of feature selection is critical for reliable biomarker discovery and clinical applications. This study investigates the stability of feature-selection methods ac...

Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-...

In order to develop the building blocks for future biosensing and spintronic applications, an engraving technique using electron beam lithography is employed in order to develop nanomagnetic pre-patterned structures with logic potential. The paper de...