- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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On image restoration from random sampling noisy frequency data with regularization
摘要： Consider the image restoration using random sampling noisy frequency data by total variation regularization. By exploring image sparsity property under wavelet expansion, we establish an optimization model with two regularizing terms specifying image sparsity and edge preservation on the restored image. The choice strategy for the regularizing parameters is rigorously set up together with corresponding error estimate on the restored image. The cost functional with data-fitting in the frequency domain is minimized using the Bregman iteration scheme. By deriving the gradient of the cost functional explicitly, the minimizer of the cost functional at each Bregman step is also generated by an inner iteration process with Tikhonov regularization, which is implemented stably and efficiently due to the special structure of the regularizing iterative matrix. Numerical tests are given to show the validity of the proposed scheme.
关键词： Image restoration,iteration,numerics,total variation,wavelet sparsity,error estimate
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Real-time Path Planning Based on IRRT Algorithm in 3D Environment
摘要： This paper presents a real-time path planning method for unmanned aerial vehicles(UAVs) navigation in 3D environment. In this study, 3D point clouds are processed into octomap, which is the octree-based 3D map representation of environment and is a probabilistic representation of occupancy including free and unknown areas. Rapidly-exploring random tree is improved by introducing the random probability and storing relatively optimal path with a certain number of iterations, called iteration rapidly-exploring random tree, to find a collision-free path in the free areas. Down-sampling and curve fitting by Bezier curve are performed to smooth path that satisfy the constraints of flight trajectory of UAVs. Experimental results verify the proposed algorithm and prove that it is valid.
关键词： octree,real-time,planning,iteration rapidly-exploring random tree(IRRT),Bezier curve,path
更新于2025-09-23 15:22:29
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XCT image reconstruction by a modified superiorized iteration and theoretical analysis
摘要： In this paper, we propose an improved iteration superiorization method for X-ray computed tomography image reconstruction. We simplify the classic superiorized iteration by removing two constraints imposed on the perturbation. A novel method is proposed to determine the perturbation amount and direction for the superiorized iteration simultaneously. Some theoretical properties (convergence for instance) of the superiorized iteration sequence with the proposed perturbation are analysed. We present a general proof for the convergence of ART-like iterations with summable perturbations. In addition, we prove the convergence of simultaneous iterations without the summable perturbation assumption. Experiments on simulated and real data not only verify the theoretical result but also show that the proposed algorithm is superior to the classic superiorized iteration and can reconstruct desirable images.
关键词： XCT image reconstruction,superiorization of iteration,proximity perturbation,algebraic method
更新于2025-09-23 15:22:29
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A Synthesizable Constant Tuning Gain Technique for Wideband LC-VCO Design
摘要： In this paper, an iterative method is proposed for the optimization of wideband voltage-controlled oscillators (VCOs). The proposed method attempts to improve two significant performance metrics of wideband VCOs, i.e., the non-uniform band distribution and the non-constant tuning gain KVCO. To address these non-ideality issues, we introduce the deviation as a prior knowledge for calibration in each iteration step. The optimal capacitor array weighting plan can be asymptotically approached through multiple iterations. To demonstrate the efficacy of the proposed method, we use it to implement a 1-to-2.4-GHz LC-VCO and achieve a frequency tuning range of 82.4%. The optimized LC-tank involves 64 mixed cells, and each cell contains 2-bit binary-weighted switching capacitors, leading to 256 tuning bands. The LC-VCO with the synthesized tank achieves uniform frequency steps around 5 MHz with the relative error within ±4.4%. Meanwhile, the tuning gain KVCO is around 10 MHz/V with the relative error within ±3.2%. For this LC-VCO design, each iteration step costs about 55.9 s. After 7 iterations, we approach the convergent solution where both the relative errors of the frequency steps and the tuning gains satisfy the given specifications.
关键词： Constant tuning gain,Constant frequency step,Automatic synthesis,Iteration,Wideband voltage-controlled oscillator (VCO)
更新于2025-09-23 15:21:21
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Optical bright and dark soliton solutions for coupled nonlinear Schr??dinger (CNLS) equations by the variational iteration method
摘要： This paper studies optical bright and dark soliton solutions for coupled nonlinear Schr¨odinger (CNLS) equations in the anomalous and dispersive regimes. We operate the variational iteration method (VIM) to derive a set of diverse types of bright and dark optical soliton solutions. The VIM gives the solution in a rapidly convergent series without any need for physical restrictions or changing the nonlinearity issue. The presented analysis shows the pertinent features of the VIM method in handling nonlinear evolution equations.
关键词： optical solitons,variational iteration method.,Coupled nonlinear Schr¨odinger equation
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - CAOL 2019 Contents
摘要： Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that 1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; 2) the proposed regularized DPIE is effective if we need many embeddings; 3) we show how to efficiently orthogonalize DPIE if one needs; and 4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. Such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.
关键词： Approximated spectral analysis,power iteration
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Macao, Macao (2019.12.1-2019.12.4)] 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - A Single-Stage Flyback LED Driver Based On Energy Distribution Without Electrolytic Capacitor
摘要： The lp (0 < p < 1) regularization has attracted a great attention in the compressive sensing field, because it can obtain sparser solutions than the well-known l1 regularization. Recently, we developed an approximate general analytic thresholding representation for any lp regularization with 0 < p < 1. The derived thresholding representations are exact for the well-known soft-threshold filtering for l1 regularization and the hard-threshold filtering for l0 regularization. Because the lp regularization is a nonconvex problem, an iterative algorithm can only converge to local optima instead of the global optimum. In this paper, we propose an alternating iteration algorithm for computed tomography reconstruction in a thresholding form based on our general analytic thresholding representation for better convergent properties. The alternating iteration algorithm alternatively minimizes one l1 and one lp (0 < p < 1) regularized objective functions. While the lp regularization can help to find a sparser solution, the l1 regularization can help to monitor the solution not away from the global optimum. Both numerical simulations and phantom experiments are performed to evaluate the proposed alternating iteration algorithm. Compared with the lp (0 < p < 1) regularization using a single p, the proposed alternating iteration algorithm reduces more data measurements for accurate reconstruction and is more robust for projection noise.
关键词： image reconstruction,Compressive sensing,least square solution,computed tomography,alternating iteration,lp regularization
更新于2025-09-23 15:19:57
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An Efficient Semi-Analytical Solution of a One-Dimensional Curvature Equation that Describes the Human Corneal Shape
摘要： In this paper, a numerical approach is proposed to find a semi analytical solution for a prescribed anisotropic mean curvature equation modeling the human corneal shape. The method is based on an integral operator that is constructed in terms of Green’s function coupled with the implementation of Picard’s or Mann’s fixed point iteration schemes. Using the contraction principle, it will be shown that the method is convergent for both fixed point iteration schemes. Numerical examples will be presented to demonstrate the applicability, efficiency, and high accuracy of the proposed method.
关键词： curvature equation,fixed point iteration,semi-analytical solution,Green’s function
更新于2025-09-19 17:15:36
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[IEEE 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC) - Solan, India (2019.10.10-2019.10.12)] 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC) - Technical Survey and review on MPPT techniques to attain Maximum Power of Photovoltaic system
摘要： The lp (0 < p < 1) regularization has attracted a great attention in the compressive sensing field, because it can obtain sparser solutions than the well-known l1 regularization. Recently, we developed an approximate general analytic thresholding representation for any lp regularization with 0 < p < 1. The derived thresholding representations are exact for the well-known soft-threshold filtering for l1 regularization and the hard-threshold filtering for l0 regularization. Because the lp regularization is a nonconvex problem, an iterative algorithm can only converge to local optima instead of the global optimum. In this paper, we propose an alternating iteration algorithm for computed tomography reconstruction in a thresholding form based on our general analytic thresholding representation for better convergent properties. The alternating iteration algorithm alternatively minimizes one l1 and one lp (0 < p < 1) regularized objective functions. While the lp regularization can help to find a sparser solution, the l1 regularization can help to monitor the solution not away from the global optimum. Both numerical simulations and phantom experiments are performed to evaluate the proposed alternating iteration algorithm. Compared with the lp (0 < p < 1) regularization using a single p, the proposed alternating iteration algorithm reduces more data measurements for accurate reconstruction and is more robust for projection noise.
关键词： Compressive sensing,lp regularization,least square solution,image reconstruction,alternating iteration,computed tomography
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Marked improvement of the photoresponsivity of BaSi <sub/>2</sub> light absorbers by increasing growth temperature and three-step growth method
摘要： The lp (0 < p < 1) regularization has attracted a great attention in the compressive sensing field, because it can obtain sparser solutions than the well-known l1 regularization. Recently, we developed an approximate general analytic thresholding representation for any lp regularization with 0 < p < 1. The derived thresholding representations are exact for the well-known soft-threshold filtering for l1 regularization and the hard-threshold filtering for l0 regularization. Because the lp regularization is a nonconvex problem, an iterative algorithm can only converge to local optima instead of the global optimum. In this paper, we propose an alternating iteration algorithm for computed tomography reconstruction in a thresholding form based on our general analytic thresholding representation for better convergent properties. The alternating iteration algorithm alternatively minimizes one l1 and one lp (0 < p < 1) regularized objective functions. While the lp regularization can help to find a sparser solution, the l1 regularization can help to monitor the solution not away from the global optimum. Both numerical simulations and phantom experiments are performed to evaluate the proposed alternating iteration algorithm. Compared with the lp (0 < p < 1) regularization using a single p, the proposed alternating iteration algorithm reduces more data measurements for accurate reconstruction and is more robust for projection noise.
关键词： Compressive sensing,lp regularization,least square solution,image reconstruction,alternating iteration,computed tomography
更新于2025-09-19 17:13:59