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- 摘要
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- 实验方案
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Hyperspectral and thermal temperature estimation during laser cladding
摘要： Although there is no doubt about the tremendous industrial potential of metal additive manufacturing techniques such as laser metal deposition, the technology still has some intrinsic quality challenges to overcome before reaching its industrial maturity. Noncontact in situ monitoring of the temperature evolution of the workpiece could provide the necessary information to implement an automated closed-loop process control system and optimize the manufacturing process, providing a robust solution to these issues. However, measuring absolute temperatures is not self-evident: wavelength-dependent emissivity values vary between solid, liquid, and mushy metallic regions, requiring spectral information and dedicated postprocessing to relate the amount of emitted infrared radiation to the material temperature. This paper compares the temperature estimation results obtained from a visible and near-infrared hyperspectral line camera and a conventional short-wave infrared (SWIR) thermal camera during the laser melting and cladding of a 316L steel sample. Both methods show agreeing results for the temperature distribution inside the melt pool, with the SWIR camera extending the temperature measurements beyond the melt pool boundaries into the solid region.
关键词： temperature estimation,laser cladding,hyperspectral imaging,additive manufacturing,thermal monitoring
更新于2025-11-28 14:24:20
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Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation
摘要： To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with M sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of M2 signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is used to estimate one dimension angle. By a joint covariance matrix, another dimension angle is estimated, and the estimated angles from two dimensions can be automatically paired. The simulation results show that the number of DOA signals that can be estimated by the proposed two-parallel nested arrays is much larger than the number of sensors. The proposed two-dimensional DOA estimation algorithm has excellent estimation performance.
关键词： decoupled estimation,direction of arrival estimation,degrees of freedom,sparse Bayesian learning,sparse arrays
更新于2025-09-23 15:23:52
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Dense Descriptors for Optical Flow Estimation: A Comparative Study
摘要： Estimating the displacements of intensity patterns between sequential frames is a very well-studied problem, which is usually referred to as optical flow estimation. The first assumption among many of the methods in the field is the brightness constancy during movements of pixels between frames. This assumption is proven to be not true in general, and therefore, the use of photometric invariant constraints has been studied in the past. One other solution can be sought by use of structural descriptors rather than pixels for estimating the optical flow. Unlike sparse feature detection/description techniques and since the problem of optical flow estimation tries to find a dense flow field, a dense structural representation of individual pixels and their neighbors is computed and then used for matching and optical flow estimation. Here, a comparative study is carried out by extending the framework of SIFT-flow to include more dense descriptors, and comprehensive comparisons are given. Overall, the work can be considered as a baseline for stimulating more interest in the use of dense descriptors for optical flow estimation.
关键词： feature descriptors,optical flow estimation,dense descriptors
更新于2025-09-23 15:23:52
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Experimental verification of turbidity tolerance of stereo-vision-based 3D pose estimation system
摘要： This paper presents the verification of the turbidity tolerance of a stereo-vision-based 3D pose estimation system for underwater docking applications. To the best of the authors’ knowledge, no studies have yet been conducted on 3D pose (position and orientation) estimation against turbidity for underwater vehicles. Therefore, the effect of turbidity on the 3D pose estimation performance of underwater vehicles and a method of operating under turbid conditions were studied in this work. A 3D pose estimation method using the real-time multi-step genetic algorithm (RM-GA) proposed by the authors in the previous works shows robust pose estimation performance against changing environmental conditions. This paper discusses how and why the RM-GA is well suited to effective 3D pose estimation, even when turbid conditions disturb visual servoing. The experimental results confirm the performance of the proposed 3D pose estimation system under different levels of turbidity. To demonstrate the practical usefulness of the RM-GA, docking experiments were conducted in a turbid pool and a real sea environment to verify the performance and tolerance of the proposed system under turbid conditions. The experimental results verify the robustness of the system against turbidity, presenting a possible solution to a major problem in the field of robotics.
关键词： Robustness against turbidity,Real-time multi-step genetic algorithm,Sea docking,3D pose estimation,Stereo-vision,Visual servoing
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Simulation Based Approach to Estimating the Three Dimensional Structure of the Harvard Forest with Multi-Modal Remote Sensing
摘要： Tracking carbon as it enters and exits each stage of the carbon cycle is necessary to help build understanding of the cycle's mechanics and its effect on climate. Satellite and airplane-based remote sensing technologies have shown promising results in aiding in human understanding of our planet, including vegetative areas. The Harvard Forest has been studied in various ways over the course of the last century. In particular, synthetic aperture radar, LiDAR, and passive optical sensors have each been used to study the Harvard Forest. Employing a form of data fusion, we present an approach to estimate a forest stand's mean canopy height and biomass for each component tree species while employing minimal ground measurements. We present an approach where a database of simulated forest stands is generated containing both homogeneous stands and heterogeneous stands with up to four tree species present in a given stand. Each simulated stand is compared to an input stand on a number of criteria and a figure of similarity is calculated. In the case that a simulated stand isn't found with a figure of similarity below a set threshold, an iterative process is employed to modify the most similar stand to improve the factor of similarity by modifying the stand's species composition, tree densities, heights, and biomasses. A simulated stand, either pre-existing or developed dynamically will be considered a reasonable representation of the physical forest stand and the 3-D structure of the simulated stand will be reported as an estimate for that of the physical forest stand. This method relies heavily on our sensor simulators, including our fractal-based tree geometry generator, as well as SAR, IfSAR, LiDAR, and Optical simulators. We have previously investigated the ability of our method to differentiate between coniferous and deciduous trees in the same forest stand. We propose to extend this to a maximum of four different tree species, and to validate our approach in the Harvard Forest, a heavily studied region in central Massachusetts.
关键词： Harvard Forest,Forest Parameter Estimation,IfSAR,Heterogeneous Forests,SAR,LiDAR
更新于2025-09-23 15:23:52
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DeeptransMap: a considerably deep transmission estimation network for single image dehazing
摘要： Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.
关键词： Feature learning,Deep convolutional neural networks (CNNs),Image dehazing,Transmission estimation
更新于2025-09-23 15:23:52
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A novel dynamical approach in continuous cuffless blood pressure estimation based on ECG and PPG signals
摘要： Continuous cu?ess blood pressure (BP) monitoring has attracted much interest in ?nding the ideal treatment of diseases and the prevention of premature death. This paper presents a novel dynamical method, based on pulse transit time (PTT) and photoplethysmogram intensity ratio (PIR), for the continuous cu?ess BP estimation. By taking the advantages of both the modeling and the prediction approaches, the proposed framework e?ectively estimates diastolic BP (DBP), mean BP (BP), and systolic BP (SBP). Adding past states of the cardiopulmonary system as well as present states of the cardiac system to our model caused two main improvements. First, high accuracy of the method in the beat to beat BP estimation. Second, notwithstanding noticeable BP changes, the performance of the model is preserved over time. The experimental setup includes comparative studies on a large, standard dataset. Moreover, the proposed method outperformed the most recent and cited algorithms with improved accuracy.
关键词： Cu?ess blood pressure estimation,Taken’s theorem,Multivariate adaptive regression spline,Pulse transit time,Photoplethysmogram intensity ratio
更新于2025-09-23 15:23:52
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SNR analysis and estimation for efficient phase noise mitigation in millimetre-wave SC-FDE systems
摘要： This study demonstrates a signal-to-noise ratio (SNR) analysis and estimation algorithm for efficient phase noise mitigation that can be practically applied to single-carrier frequency-domain-equalisation (SC-FDE) systems that operate in millimetre-wave bands. First, the effect of phase noise in SC-FDE systems is investigated on each of the packet reception processes, namely, channel estimation, SNR estimation, and data-field reception. According to the analysis, an SNR estimation algorithm is proposed. The performance of minimum-mean-square-error equalisation and conventional phase noise mitigation algorithm can be enhanced using the proposed SNR estimation. The effectiveness of the proposed analysis and SNR estimation algorithm is verified through the link-level simulation. Compared with the conventional SNR estimation and the iterative phase noise mitigation algorithms, the proposed algorithm provides a lower packet-error rate without any iterative decoding process.
关键词： SNR analysis,phase noise mitigation,millimetre-wave,channel estimation,SC-FDE systems,packet-error rate
更新于2025-09-23 15:23:52
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Low-rank and sparse matrix decomposition with background position estimation for hyperspectral anomaly detection
摘要： Hyperspectral anomaly detection (AD) has attracted much attention over the last 20 years. It distinguishes pixels with significant spectral differences from the background without any prior knowledge. The low-rank and sparse matrix decomposition (LRaSMD)-based detector has been applied to AD, where the anomaly value is measured by Euclidean distance based on the sparse component. However, the background interference in sparse component seriously increases the false alarm rate and influences the detection of real anomalies. In this paper, a novel AD method based on LRaSMD and background position estimation is proposed, which aims to suppress background interference in the sparse component for a better separation between background and anomalies. Firstly, the original sparse matrix is obtained using the traditional LRaSMD method. Secondly, the abundance maps are constructed by the sequential maximum angel convex cone (SMACC) endmember extraction model. Thirdly, considering that the anomalies occupy only a few pixels with a low probability, the coordinate positions of background pixels are estimated through these abundance maps. Finally, the spectra corresponding to these positions in the original sparse matrix are replaced with zero vectors, and the final anomaly value is calculated based on the improved sparse matrix. The proposed method achieves an outstanding performance by considering both the spectral and spatial characteristics of anomalies. Experimental results on synthetic and real-world hyperspectral datasets demonstrate the superiority of the proposed method compared with several state-of-the-art AD detectors.
关键词： Anomaly detection,Background estimation,Low-rank and sparse matrix decomposition,Hyperspectral imagery,Endmember extraction
更新于2025-09-23 15:23:52
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A novel natural image noise level estimation based on flat patches and local statistics
摘要： This paper proposes a high-precision algorithm for noise level estimation. Different from existing algorithms, we present a new noise level estimation algorithm by linearly combining the overestimated and underestimated results using combinatorial coefficients that can be tailored to the problem at hand. The algorithm has two distinct features: it avoids the underestimation of noise level estimation algorithms that employ the minimum eigenvalue and demonstrates higher accuracy and robustness for a large range of visual content and noise conditions. The experimental results that are obtained in this study demonstrate that the proposed algorithm is effective for various scenes with various noise levels. The software release of the proposed algorithm is available online at https://ww2.mathworks.cn/matlabcentral/fileexchange/64519-natural-image-noise-level-estimation-based-on-flat-patches-and-local-statistics.
关键词： Covariance matrix,Eigenvalue,Flat patches,Gaussian noise,Noise level estimation
更新于2025-09-23 15:23:52