Journal articles: 'D Estimation'

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Li, Zhengzheng, Yan Zhang, and Scott E. Giangrande. "Rainfall-Rate Estimation Using Gaussian Mixture Parameter Estimator: Training and Validation." Journal of Atmospheric and Oceanic Technology 29, no. 5 (May 1, 2012): 731–44. http://dx.doi.org/10.1175/jtech-d-11-00122.1.

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Abstract This study develops a Gaussian mixture rainfall-rate estimator (GMRE) for polarimetric radar-based rainfall-rate estimation, following a general framework based on the Gaussian mixture model and Bayes least squares estimation for weather radar–based parameter estimations. The advantages of GMRE are 1) it is a minimum variance unbiased estimator; 2) it is a general estimator applicable to different rain regimes in different regions; and 3) it is flexible and may incorporate/exclude different polarimetric radar variables as inputs. This paper also discusses training the GMRE and the sensitivity of performance to mixture number. A large radar and surface gauge observation dataset collected in central Oklahoma during the multiyear Joint Polarization Experiment (JPOLE) field campaign is used to evaluate the GMRE approach. Results indicate that the GMRE approach can outperform existing polarimetric rainfall techniques optimized for this JPOLE dataset in terms of bias and root-mean-square error.

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Trömel, Silke, Matthew R. Kumjian, Alexander V. Ryzhkov, Clemens Simmer, and Malte Diederich. "Backscatter Differential Phase—Estimation and Variability." Journal of Applied Meteorology and Climatology 52, no. 11 (November 2013): 2529–48. http://dx.doi.org/10.1175/jamc-d-13-0124.1.

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AbstractOn the basis of simulations and observations made with polarimetric radars operating at X, C, and S bands, the backscatter differential phase δ has been explored; δ has been identified as an important polarimetric variable that should not be ignored in precipitation estimations that are based on specific differential phase KDP, especially at shorter radar wavelengths. Moreover, δ bears important information about the dominant size of raindrops and wet snowflakes in the melting layer. New methods for estimating δ in rain and in the melting layer are suggested. The method for estimating δ in rain is based on a modified version of the “ZPHI” algorithm and provides reasonably robust estimates of δ and KDP in pure rain except in regions where the total measured differential phase ΦDP behaves erratically, such as areas affected by nonuniform beam filling or low signal-to-noise ratio. The method for estimating δ in the melting layer results in reliable estimates of δ in stratiform precipitation and requires azimuthal averaging of radial profiles of ΦDP at high antenna elevations. Comparisons with large disdrometer datasets collected in Oklahoma and Germany confirm a strong interdependence between δ and differential reflectivity ZDR. Because δ is immune to attenuation, partial beam blockage, and radar miscalibration, the strong correlation between ZDR and δ is of interest for quantitative precipitation estimation: δ and ZDR are differently affected by the particle size distribution (PSD) and thus may complement each other for PSD moment estimation. Furthermore, the magnitude of δ can be utilized as an important calibration parameter for improving microphysical models of the melting layer.

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Xinhua Zhuang and Yan Huang. "Robust 3-D-3-D pose estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 16, no. 8 (1994): 818–24. http://dx.doi.org/10.1109/34.308478.

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Thành, Nguyễn Tường, Lê Văn Hùng, and Phạm Thành Công. "An Evaluation of Pose Estimation in Video of Traditional Martial Arts Presentation." Journal of Research and Development on Information and Communication Technology 2019, no. 2 (December 31, 2019): 114–26. http://dx.doi.org/10.32913/mic-ict-research.v2019.n2.864.

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Preserving, maintaining, and teaching traditional martial arts are very important activities in social life. That helps individuals preserve national culture, exercise, and practice self-defense. However, traditional martial arts have many differentposturesaswellasvariedmovementsofthebodyand body parts. The problem of estimating the actions of human body still has many challenges, such as accuracy, obscurity, and so forth. This paper begins with a review of several methods of 2-D human pose estimation on the RGB images, in which the methods of using the Convolutional Neural Network (CNN) models have outstanding advantages in terms of processing time and accuracy. In this work we built a small dataset and used CNN for estimating keypoints and joints of actions in traditional martial arts videos. Next we applied the measurements (length of joints, deviation angle of joints, and deviation of keypoints) for evaluating pose estimation in 2-D and 3-D spaces. The estimator was trained on the classic MSCOCO Keypoints Challenge dataset, the results were evaluated on a well-known dataset of Martial Arts, Dancing, and Sports dataset. The results were quantitatively evaluated and reported in this paper.

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Amorino, Chiara, and Eulalia Nualart. "Optimal convergence rates for the invariant density estimation of jump-diffusion processes." ESAIM: Probability and Statistics 26 (2022): 126–51. http://dx.doi.org/10.1051/ps/2022001.

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We aim at estimating the invariant density associated to a stochastic differential equation with jumps in low dimension, which is for d = 1 and d = 2. We consider a class of fully non-linear jump diffusion processes whose invariant density belongs to some Hölder space. Firstly, in dimension one, we show that the kernel density estimator achieves the convergence rate 1/T, which is the optimal rate in the absence of jumps. This improves the convergence rate obtained in Amorino and Gloter [J. Stat. Plann. Inference 213 (2021) 106–129], which depends on the Blumenthal-Getoor index for d = 1 and is equal to (logT)/T for d = 2. Secondly, when the jump and diffusion coefficients are constant and the jumps are finite, we show that is not possible to find an estimator with faster rates of estimation. Indeed, we get some lower bounds with the same rates {1/T, (logT)/T} in the mono and bi-dimensional cases, respectively. Finally, we obtain the asymptotic normality of the estimator in the one-dimensional case for the fully non-linear process.

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Xie, Jinhong, X. Michael Song, Marvin Sirbu, and Qiong Wang. "Kalman Filter Estimation of New Product Diffusion Models." Journal of Marketing Research 34, no. 3 (August 1997): 378–93. http://dx.doi.org/10.1177/002224379703400307.

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The authors introduce a new estimation procedure, Augmented Kalman Filter with Continuous State and Discrete Observations (AKF(C-D)), for estimating diffusion models. This method is directly applicable to differential diffusion models without imposing constraints on the model structure or the nature of the unknown parameters. It provides a systematic way to incorporate prior knowledge about the likely values of unknown parameters and updates the estimates when new data become available. The authors compare AKF(C-D) empirically with five other estimation procedures, demonstrating AKF(C-D)'s superior prediction performance. As an extension to the basic AKF(C-D) approach, they also develop a parallel-filters procedure for estimating diffusion models when there is uncertainty about diffusion model structure or prior distributions of the unknown parameters.

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Zhao, Xiaofeng, and Sixun Huang. "Estimation of Atmospheric Duct Structure Using Radar Sea Clutter." Journal of the Atmospheric Sciences 69, no. 9 (September 1, 2012): 2808–18. http://dx.doi.org/10.1175/jas-d-12-073.1.

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Abstract Retrieving atmospheric refractivity profiles from the sea surface backscattered radar clutter is known as the refractivity-from-clutter (RFC) technique. Because the relationship between refractivity and radar sea clutter is clearly nonlinear and ill posed, it is difficult to get analytical solutions according to current theories. Previous works treat this problem as a model parameter estimation issue and some optimization algorithms are selected to get approximate solutions. Two main factors that limit the accuracy of the estimation are that 1) the refractive environments are described by using some idealized refractivity parameter models that cannot describe the exact information of the refractivity profile, and 2) accurate modeling of the sea surface radar cross section (RCS) is very difficult. Rather than estimating a few model parameters, this paper puts forward possibilities of using the variational adjoint approach to jointly retrieve the every-height refractivity values and sea surface RCS using radar clutter data. The derivation of the adjoint model is accomplished by an analytical transformation of the parabolic equation (PE) in the continuous domain. Numerical simulations including range-independent and range-dependent RCS cases are presented to demonstrate the ability of this method for RFC estimations. Making use of the refractivity retrievals, propagation loss predictions are also presented.

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Chanwimaluang, T., Guoliang Fan, G. G. Yen, and S. R. Fransen. "3-D Retinal Curvature Estimation." IEEE Transactions on Information Technology in Biomedicine 13, no. 6 (November 2009): 997–1005. http://dx.doi.org/10.1109/titb.2009.2027014.

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Wilson, Greg, and H. Tuba Özkan-Haller. "Ensemble-Based Data Assimilation for Estimation of River Depths." Journal of Atmospheric and Oceanic Technology 29, no. 10 (October 1, 2012): 1558–68. http://dx.doi.org/10.1175/jtech-d-12-00014.1.

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Abstract A method is presented for estimating bathymetry in a river, based on observations of depth-averaged velocity during steady flow. The estimator minimizes a cost function that combines known information in the form of a prior estimate and measured data (including measurement noise). State augmentation is used to relate the measured variable (velocity) to the unknown parameter (bathymetry). Specifically, the unknown consists of deviations in depth about a known along-channel mean. Verification of the method is performed using a simple 1D channel geometry as well as for two real-world reaches. In all cases, the verification is based on nominal river depths of 3–10 m, channel widths of 50–100 m, and Froude numbers much less than one. Further tests are performed to assess the usefulness of various observation types and sampling schemes for this type of estimation.

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Hacking, Chris, Nitesh Poona, Nicola Manzan, and Carlos Poblete-Echeverría. "Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation." Sensors 19, no. 17 (August 22, 2019): 3652. http://dx.doi.org/10.3390/s19173652.

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Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one’s (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r2 = 0.950), which outperformed RGB imagery (r2 = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.

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Oliveira, Miguel, Gi-Hyun Lim, Tiago Madeira, Paulo Dias, and Vítor Santos. "Robust Texture Mapping Using RGB-D Cameras." Sensors 21, no. 9 (May 7, 2021): 3248. http://dx.doi.org/10.3390/s21093248.

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The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, capable of producing quality textures even in considerable camera misalignments scenarios. To this end, we argue that use of the depth data from RGB-D images can be an invaluable help to confer such robustness to the texture mapping process. Results show that the complete texture mapping procedure proposed in this paper is able to significantly improve the quality of the produced textured 3D meshes.

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del Álamo, Miguel, and Axel Munk. "Total variation multiscale estimators for linear inverse problems." Information and Inference: A Journal of the IMA 9, no. 4 (March 2, 2020): 961–86. http://dx.doi.org/10.1093/imaiai/iaaa001.

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Abstract Even though the statistical theory of linear inverse problems is a well-studied topic, certain relevant cases remain open. Among these is the estimation of functions of bounded variation ($BV$), meaning $L^1$ functions on a $d$-dimensional domain whose weak first derivatives are finite Radon measures. The estimation of $BV$ functions is relevant in many applications, since it involves minimal smoothness assumptions and gives simplified, interpretable cartoonized reconstructions. In this paper, we propose a novel technique for estimating $BV$ functions in an inverse problem setting and provide theoretical guaranties by showing that the proposed estimator is minimax optimal up to logarithms with respect to the $L^q$-risk, for any $q\in [1,\infty )$. This is to the best of our knowledge the first convergence result for $BV$ functions in inverse problems in dimension $d\geq 2$, and it extends the results of Donoho (1995, Appl. Comput. Harmon. Anal., 2, 101–126) in $d=1$. Furthermore, our analysis unravels a novel regime for large $q$ in which the minimax rate is slower than $n^{-1/(d+2\beta +2)}$, where $\beta$ is the degree of ill-posedness: our analysis shows that this slower rate arises from the low smoothness of $BV$ functions. The proposed estimator combines variational regularization techniques with the wavelet-vaguelette decomposition of operators.

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Hacking, Chris, Nitesh Poona, and Carlos Poblete-Echeverria. "Vineyard yield estimation using 2-D proximal sensing: a multitemporal approach." OENO One 54, no. 4 (October 23, 2020): 793–812. http://dx.doi.org/10.20870/oeno-one.2020.54.4.3361.

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Vineyard yield estimation is a fundamental aspect in precision viticulture that enables a better understanding of the inherent variability within a vineyard. Yield estimation conducted early in the growing season provides insightful information to ensure the best fruit quality for the maximum desired yield. Proximal sensing techniques provide non-destructive in situ data acquisition for yield estimation during the growing season. This study aimed to determine the ideal phenological stage for yield estimation using 2-dimensional (2-D) proximal sensing and computer vision techniques in a vertical shoot positioned (VSP) vineyard. To achieve this aim, multitemporal digital imagery was acquired weekly over a 12-week period, with a final acquisition two days prior to harvest. Preceding the multitemporal analysis for yield estimation, an unsupervised k-means clustering (KMC) algorithm was evaluated for image segmentation on the final dataset captured before harvest, yielding bunch-level segmentation accuracies as high as 0.942, with a corresponding F1-score of 0.948. The segmentation yielded a pixel area (cm2), which served as input to a cross-validation model for calculating bunch mass (g). The ‘calculated mass’ was linearly regressed against the ‘actual mass’, indicating the capability for estimating vineyard yield. Results of the multitemporal analysis showed that the final stage of berry ripening was the ideal phenological stage for yield estimation, achieving a global r2 of 0.790.

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Liu, Chao, and Shunian Yin. "An Efficient 2-D DOA Estimation for a Cylindrical Conformal Array with Unknown Mutual Coupling." International Journal of Antennas and Propagation 2018 (October 4, 2018): 1–8. http://dx.doi.org/10.1155/2018/4015980.

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The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms. In this paper, an efficient 2-D direction finding method is developed in the presence of unknown mutual coupling for the uniform cylindrical conformal array (CCA). To avoid the time-consuming two-dimensional spectral peak searching, the 2-D DOA estimation is decoupled and divided into two 1-D DOA estimations. Elevation is first estimated based on a subarray estimation of signal parameters via rotation invariant technique (ESPRIT), and then azimuth is estimated based on the rank reduction (RARE) method by using the elevation estimation result. Consequently, the mutual coupling coefficients can be estimated after getting the DOA estimates. The proposed method can well calibrate the mutual coupling effect of a cylindrical array with a low computational complexity. The final simulation results corroborate our analysis.

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Therrien, C. W., and H. T. El-Shaer. "Multichannel 2-D AR spectrum estimation." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 11 (1989): 1798–800. http://dx.doi.org/10.1109/29.46570.

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Fermüller, Cornelia, and Yiannis Aloimonos. "Tracking facilitates 3-D motion estimation." Biological Cybernetics 67, no. 3 (July 1992): 259–68. http://dx.doi.org/10.1007/bf00204399.

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Kwon, Jaimyoung, and Pravin Varaiya. "Real-Time Estimation of Origin–Destination Matrices with Partial Trajectories from Electronic Toll Collection Tag Data." Transportation Research Record: Journal of the Transportation Research Board 1923, no. 1 (January 2005): 119–26. http://dx.doi.org/10.1177/0361198105192300113.

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The origin–destination (O-D) matrix of a traffic network is usually estimated from link traffic counts combined with a sample survey. Partially observed vehicle trajectories obtained with vehicle reidentification or automatic vehicle identification techniques such as electronic tags provide a new data source for real-time O-D matrix estimation. However, because of incomplete sampling, accurate estimation of O-D matrices from these data is not trivial. A statistical model was developed for such data, and an unbiased estimator of the O-D matrix was derived based on the method of moments. With further exploitation of the sound statistical model, the bootstrap standard error estimate of the O-D matrix estimator was also developed. The algorithm can be computed quickly and performs well under simulation compared with simpler estimators. Applied to data from vehicles with electronic toll collection tags in the San Francisco Bay Area, the algorithm produces a realistic time series of the hourly O-D matrix. The relationship of the proposed estimator with similar methods in the literature was also studied and extension of the methods to general, more complex networks is discussed.

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Nguyen, Tuong Thanh, Van-Hung Le, Duy-Long Duong, Thanh-Cong Pham, and Dung Le. "3D Human Pose Estimation in Vietnamese Traditional Martial Art Videos." Journal of Advanced Engineering and Computation 3, no. 3 (September 30, 2019): 471. http://dx.doi.org/10.25073/jaec.201933.252.

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Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps preserve national culture, exercise and self-defense for practitioners. However, traditional martial arts have many different postures and activities of the body and body parts are diverse. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, etc. In this paper, we survey several strong studies in the recent years for 3-D human pose estimation. Statistical tables have been compiled for years, typical results of these studies on the Human 3.6m dataset have been summarized. We also present a comparative study for 3-D human pose estimation based on the method that uses a single image. This study based on the methods that use the Convolutional Neural Network (CNN) for 2-D pose estimation, and then using 3-D pose library for mapping the 2-D results into the 3-D space. The CNNs model is trained on the benchmark datasets as MSCOCO Keypoints Challenge dataset [1], Human 3.6m [2], MPII dataset [3], LSP [4], [5], etc. We final publish the dataset of Vietnamese's traditional martial arts in Binh Dinh province for evaluating the 3-D human pose estimation. Quantitative results are presented and evaluated.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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Goldenshluger, A., and O. Lepski. "On adaptive minimax density estimation on $$R^d$$ R d." Probability Theory and Related Fields 159, no. 3-4 (July 6, 2013): 479–543. http://dx.doi.org/10.1007/s00440-013-0512-1.

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Yuga Suseno, Dwi Prabowo, and Tomohito J. Yamada. "The Role of GPS Precipitable Water Vapor and Atmosphere Stability Index in the Statistically Based Rainfall Estimation Using MTSAT Data." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1922–32. http://dx.doi.org/10.1175/jhm-d-12-0128.1.

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Abstract A rainfall estimation method was developed based on the statistical relationships between cloud-top temperature and rainfall rates acquired by both the 10.8-μm channel of the Multi-Functional Transport Satellite (MTSAT) series and the Automated Meteorological Data Acquisition System (AMeDAS) C-band radar, respectively. The method focused on cumulonimbus (Cb) clouds and was developed in the period of June–September 2010 and 2011 over the landmass of Japan and its surrounding area. Total precipitable water vapor (PWV) and atmospheric vertical instability were considered to represent the atmospheric environmental conditions during the development of statistical models. Validations were performed by comparing the estimated values with the observed rainfall derived from the AMeDAS rain gauge network and the Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall estimation product. The results demonstrated that the models that considered the combination of total PWV and atmospheric vertical instability were relatively more sensitive to heavy rainfall than were the models that considered no atmospheric environmental conditions. The use of such combined information indicated a reasonable improvement, especially in terms of the correlation between estimated and observed rainfall. Intercomparison results with the TRMM 3B42 confirmed that MTSAT-based rainfall estimations made by considering atmospheric environmental conditions were more accurate for estimating rainfall generated by Cb cloud.

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Liu, Yinlong, Xuechen Li, Manning Wang, Alois Knoll, Guang Chen, and Zhijian Song. "A Novel Method for the Absolute Pose Problem with Pairwise Constraints." Remote Sensing 11, no. 24 (December 13, 2019): 3007. http://dx.doi.org/10.3390/rs11243007.

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Absolute pose estimation from corrupted point correspondences is typically a problem of estimating parameters from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved exactly faster than O ( N d ) . Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a novel algorithm utilizing global optimization method Branch-and-Bound (BnB) for solving the absolute pose estimation problem. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the BnB algorithm. Lastly, we estimate the translation based on the optimal rotation by using another BnB algorithm. The proposed algorithm has an approximately linear complexity in the number of correspondences at a given outlier ratio. The advantages of our method were demonstrated via thorough testing on both synthetic and real-world data.

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Chang, Wen-Chung, and Van-Toan Pham. "3-D Point Cloud Registration Using Convolutional Neural Networks." Applied Sciences 9, no. 16 (August 9, 2019): 3273. http://dx.doi.org/10.3390/app9163273.

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This paper develops a registration architecture for the purpose of estimating relative pose including the rotation and the translation of an object in terms of a model in 3-D space based on 3-D point clouds captured by a 3-D camera. Particularly, this paper addresses the time-consuming problem of 3-D point cloud registration which is essential for the closed-loop industrial automated assembly systems that demand fixed time for accurate pose estimation. Firstly, two different descriptors are developed in order to extract coarse and detailed features of these point cloud data sets for the purpose of creating training data sets according to diversified orientations. Secondly, in order to guarantee fast pose estimation in fixed time, a seemingly novel registration architecture by employing two consecutive convolutional neural network (CNN) models is proposed. After training, the proposed CNN architecture can estimate the rotation between the model point cloud and a data point cloud, followed by the translation estimation based on computing average values. By covering a smaller range of uncertainty of the orientation compared with a full range of uncertainty covered by the first CNN model, the second CNN model can precisely estimate the orientation of the 3-D point cloud. Finally, the performance of the algorithm proposed in this paper has been validated by experiments in comparison with baseline methods. Based on these results, the proposed algorithm significantly reduces the estimation time while maintaining high precision.

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SHIRAISHI, Soma, Yaokai FENG, and Seiichi UCHIDA. "Skew Estimation by Parts." IEICE Transactions on Information and Systems E96.D, no. 7 (2013): 1503–12. http://dx.doi.org/10.1587/transinf.e96.d.1503.

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McMichael, D. W. "Robust recursive Lp estimation." IEE Proceedings D Control Theory and Applications 137, no. 2 (1990): 67. http://dx.doi.org/10.1049/ip-d.1990.0007.

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Lu, W., and G. Fisher. "Multirate adaptive inferential estimation." IEE Proceedings D Control Theory and Applications 139, no. 2 (1992): 181. http://dx.doi.org/10.1049/ip-d.1992.0025.

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Schnabolk, Charles, and Theodore Raphan. "Modeling 3-D Slow Phase Velocity Estimation During Off-Vertical-Axis Rotation (OVAR)." Journal of Vestibular Research 2, no. 1 (February 1, 1992): 1–14. http://dx.doi.org/10.3233/ves-1992-2101.

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Off-vertical-axis rotation (OVAR) in darkness generates continuous compensatory eye velocity. No model has yet been presented that defines the signal processing necessary to estimate head velocity in three dimensions for arbitrary rotations during OVAR. The present study develops a model capable of estimating all 3 components of head velocity in space accurately. It shows that processing of two patterns of otolith activation, one delayed with respect to the other, for each plane of eye movement is not sufficient. (A pattern in this context is an array of signals emanating from the otoliths. Each component of the array is a signal corresponding to a class of otolith hair cells with a given polarization vector as described by Tou and Gonzalez in 1974.) The key result is that estimation of head velocity in space can be achieved by processing three temporally displaced patterns, each representing a sampling of gravity as the head rotates. A vector cross product of differences between pairs of the sampled gravity vectors implements the estimation. An interesting property of this model is that the component of velocity about the axis of rotation reduces to that derived previously using the pattern estimator model described by Raphan and Schnabolk in 1988 and Fanelli et al in 1990. This study suggests that the central nervous system (CNS) maintains a current as well as 2 delayed representations of gravity at every head orientation during rotation. It also suggests that computing vector cross products and implementing delays may be fundamental operations in the CNS for generating orientation information associated with motion.

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Rodrigues, Gonçalo C., and Ricardo P. Braga. "Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate." Agronomy 11, no. 10 (October 18, 2021): 2077. http://dx.doi.org/10.3390/agronomy11102077.

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This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d−1, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d−1 is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d−1 ≤ MBE < 0.40 mm d−1). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d−1) but with an RMSE never higher than 0.77 mm d−1. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation.

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Cook, Werner E., and J. Scott Greene. "Gridded Monthly Rainfall Estimates Derived from Historical Atoll Observations." Journal of Atmospheric and Oceanic Technology 36, no. 4 (April 2019): 671–87. http://dx.doi.org/10.1175/jtech-d-18-0140.1.

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AbstractTo provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.

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Hoop, B., S. M. Grimes, and M. Drosg. "Estimation of d-2H Breakup Neutron Energy Distributions From d-3He." Nuclear Science and Engineering 188, no. 1 (June 19, 2017): 102–7. http://dx.doi.org/10.1080/00295639.2017.1332892.

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Lorente-Plazas, Raquel, and Joshua P. Hacker. "Observation and Model Bias Estimation in the Presence of Either or Both Sources of Error." Monthly Weather Review 145, no. 7 (July 2017): 2683–96. http://dx.doi.org/10.1175/mwr-d-16-0273.1.

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In numerical weather prediction and in reanalysis, robust approaches for observation bias correction are necessary to approach optimal data assimilation. The success of bias correction can be limited by model errors. Here, simultaneous estimation of observation and model biases, and the model state for an analysis, is explored with ensemble data assimilation and a simple model. The approach is based on parameter estimation using an augmented state in an ensemble adjustment Kalman filter. The observation biases are modeled with a linear term added to the forward operator. A bias is introduced in the forcing term of the model, leading to a model with complex errors that can be used in imperfect-model assimilation experiments. Under a range of model forcing biases and observation biases, accurate observation bias estimation and correction are possible when the model forcing bias is simultaneously estimated and corrected. In the presence of both model error and observation biases, estimating one and ignoring the other harms the assimilation more than not estimating any errors at all, because the biases are not correctly attributed. Neglecting a large model forcing bias while estimating observation biases results in filter divergence; the observation bias parameter absorbs the model forcing bias, and recursively and incorrectly increases the increments. Neglecting observation bias results in suboptimal assimilation, but the model forcing bias parameter estimate remains stable because the model dynamics ensure covariance between the parameter and the model state.

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Lim, S., R. Cifelli, V. Chandrasekar, and S. Y. Matrosov. "Precipitation Classification and Quantification Using X-Band Dual-Polarization Weather Radar: Application in the Hydrometeorology Testbed." Journal of Atmospheric and Oceanic Technology 30, no. 9 (September 1, 2013): 2108–20. http://dx.doi.org/10.1175/jtech-d-12-00123.1.

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Abstract This paper presents new methods for rainfall estimation from X-band dual-polarization radar observations along with advanced techniques for quality control, hydrometeor classification, and estimation of specific differential phase. Data collected from the Hydrometeorology Testbed (HMT) in orographic terrain of California are used to demonstrate the methodology. The quality control and hydrometeor classification are specifically developed for X-band applications, which use a “fuzzy logic” technique constructed from the magnitude of the copolar correlation coefficient and the texture of differential propagation phase. In addition, an improved specific differential phase retrieval and rainfall estimation method are also applied. The specific differential phase estimation is done for both the melting region and rain region, where it uses a conventional filtering method for the melting region and a self-consistency-based method that distributes the total differential phase consistent with the reflectivity factor for the rain region. Based on the specific differential phase, rainfall estimations were computed using data obtained from the NOAA polarimetric X-band radar for hydrometeorology (HYDROX) and evaluated using HMT rain gauge observations. The results show that the methodology works well at capturing the high-frequency rainfall variations for the events analyzed herein and can be useful for mountainous terrain applications.

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Liao, Wenjing, Mauro Maggioni, and Stefano Vigogna. "Multiscale regression on unknown manifolds." Mathematics in Engineering 4, no. 4 (2022): 1–25. http://dx.doi.org/10.3934/mine.2022028.

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<abstract><p>We consider the regression problem of estimating functions on $ \mathbb{R}^D $ but supported on a $ d $-dimensional manifold $ \mathcal{M} ~~\subset \mathbb{R}^D $ with $ d \ll D $. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on $ \mathcal{M} $ at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension $ d $, instead of an unknown manifold embedded in $ \mathbb{R}^D $. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in $ D $ and exponential in $ d $. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.</p></abstract>

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Tao, Yumeng, Kuolin Hsu, Alexander Ihler, Xiaogang Gao, and Soroosh Sorooshian. "A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information." Journal of Hydrometeorology 19, no. 2 (February 1, 2018): 393–408. http://dx.doi.org/10.1175/jhm-d-17-0077.1.

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Abstract Compared to ground precipitation measurements, satellite-based precipitation estimation products have the advantage of global coverage and high spatiotemporal resolutions. However, the accuracy of satellite-based precipitation products is still insufficient to serve many weather, climate, and hydrologic applications at high resolutions. In this paper, the authors develop a state-of-the-art deep learning framework for precipitation estimation using bispectral satellite information, infrared (IR), and water vapor (WV) channels. Specifically, a two-stage framework for precipitation estimation from bispectral information is designed, consisting of an initial rain/no-rain (R/NR) binary classification, followed by a second stage estimating the nonzero precipitation amount. In the first stage, the model aims to eliminate the large fraction of NR pixels and to delineate precipitation regions precisely. In the second stage, the model aims to estimate the pointwise precipitation amount accurately while preserving its heavily skewed distribution. Stacked denoising autoencoders (SDAEs), a commonly used deep learning method, are applied in both stages. Performance is evaluated along a number of common performance measures, including both R/NR and real-valued precipitation accuracy, and compared with an operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). For R/NR binary classification, the proposed two-stage model outperforms PERSIANN-CCS by 32.56% in the critical success index (CSI). For real-valued precipitation estimation, the two-stage model is 23.40% lower in average bias, is 44.52% lower in average mean squared error, and has a 27.21% higher correlation coefficient. Hence, the two-stage deep learning framework has the potential to serve as a more accurate and more reliable satellite-based precipitation estimation product. The authors also provide some future directions for development of satellite-based precipitation estimation products in both incorporating auxiliary information and improving retrieval algorithms.

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Germain, Christian, Rémy Blanc, Marc Donias, Olivier Lavialle, Jean-Pierre Da Costa, and Pierre Baylou. "ESTIMATING THE SECTION ELEVATION ANGLE OF CUBES ON A CUBIC MESH. APPLICATION TO NICKEL MICROSTRUCTURE SIZE ESTIMATION." Image Analysis & Stereology 24, no. 3 (May 3, 2011): 127. http://dx.doi.org/10.5566/ias.v24.p127-134.

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This paper discusses two new image analysis methods for estimating the elevation angle of the section plane of a material. These methods are applicable to materials such as nickel base superalloys, the microstructure of which shows cubes arranged on a cubic regular grid. 3-D models were proposed that help interpret the section images and validate our approach. Our first method operates in the Fourier domain, and is based on the estimation of the spatial frequencies of the network of lines observed on the section. The second method is based on the average distance measured between hazy areas. Both methods are independent. Applied to synthetic images or to real material samples, they produce comparable estimations. The values of the elevation angle allow us to cancel the bias associated with the estimation of the material pattern dimensions.

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Andriotis, Antonis N., and Madhu Menon. "Estimation of sp–d exchange constants revisited." Journal of Physics: Condensed Matter 33, no. 13 (February 1, 2021): 130001. http://dx.doi.org/10.1088/1361-648x/abdb12.

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Lee, Jin Han, Guoxuan Zhang, and Il Hong Suh. "Motion Estimation Using 3-D Straight Lines." Journal of Korea Robotics Society 11, no. 4 (November 30, 2016): 300–309. http://dx.doi.org/10.7746/jkros.2016.11.4.300.

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Bampis, Christos, Gowri Somanath, Oscar Nestares, and Jiajie Yao. "Panoramic Background Estimation from RGB-D Videos." Electronic Imaging 2017, no. 15 (January 29, 2017): 14–19. http://dx.doi.org/10.2352/issn.2470-1173.2017.15.dpmi-064.

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Anikin, Igor V. "Estimation of Nucleon D-Term in QCD." Particles 2, no. 3 (June 28, 2019): 357–64. http://dx.doi.org/10.3390/particles2030022.

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Using the light-cone sum rules at leading order, we present an approach to perform the preliminary upper estimation for the nucleon gravitational form factor D ( t ) (D-term contribution). Comparison with the experimental data and with the results of different models is discussed.

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39

Porter, W., and J. Aravena. "State estimation in discrete m-D systems." IEEE Transactions on Automatic Control 31, no. 3 (March 1986): 280–83. http://dx.doi.org/10.1109/tac.1986.1104249.

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Cheng, L., W. N. N. Hung, G. Yang, and X. Song. "Congestion Estimation for 3-D Circuit Architectures." IEEE Transactions on Circuits and Systems II: Express Briefs 51, no. 12 (December 2004): 655–59. http://dx.doi.org/10.1109/tcsii.2004.838548.

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Gharavi, Hamid, and Shaoshuai Gao. "3-D Motion Estimation Using Range Data." IEEE Transactions on Intelligent Transportation Systems 8, no. 1 (March 2007): 133–43. http://dx.doi.org/10.1109/tits.2006.883112.

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42

Hakl, H., E. Davies, and W. Roux. "Aircraft Height Estimation using 2-D Radar." Defence Science Journal 60, no. 1 (January 24, 2010): 100–105. http://dx.doi.org/10.14429/dsj.60.116.

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Rani, Dr A. Sujatha. "Confusions in Vitamin D Estimation and Interpretation." IOSR Journal of Pharmacy and Biological Sciences 9, no. 3 (2014): 14–20. http://dx.doi.org/10.9790/3008-09331420.

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44

Yagel, Roni, Daniel Cohen, and Arie Kaufman. "Normal estimation in 3 D discrete space." Visual Computer 8, no. 5-6 (September 1992): 278–91. http://dx.doi.org/10.1007/bf01897115.

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Gupta, Naresh C., and Laveen N. Kanal. "3-D motion estimation from motion field." Artificial Intelligence 78, no. 1-2 (October 1995): 45–86. http://dx.doi.org/10.1016/0004-3702(95)00031-3.

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Mitrishkin, Yuri V., Valerii I. Kruzhkov, and Pavel S. Korenev. "Methodology of Plasma Shape Reachability Area Estimation in D-Shaped Tokamaks." Mathematics 10, no. 23 (December 5, 2022): 4605. http://dx.doi.org/10.3390/math10234605.

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This paper suggests and develops a new methodology of estimation for a multivariable reachability region of a plasma separatrix shape on the divertor phase of a plasma discharge in D-shaped tokamaks. The methodology is applied to a spherical Globus-M/M2 tokamak, including the estimation of a controllability region of a vertical unstable plasma position on the basis of the experimental data. An assessment of the controllability region and the reachability region of the plasma is important for the design of tokamak poloidal field coils and the synthesis of a plasma magnetic control system. When designing a D-shaped tokamak, it is necessary to avoid the small controllability region of the vertically unstable plasma, because such cases occur in practice at a restricted voltage on a horizon field coil. To make the estimations mentioned above robust, PID-controllers for vertical and horizontal plasma position control were designed using the Quantitative Feedback Theory approach, which stabilizes the system and provides satisfactory control indexes (stability margins, setting time, overshoot) during plasma discharges. The controllers were tested on a series of plasma models and nonlinear models of current inverters in auto-oscillation mode as actuators for plasma position control. The estimations were made on these models, taking into account limitations on control actions, i.e., voltages on poloidal field coils. This research is the first step in the design of the plasma shape feedback control system for the operation of the Globus-M2 spherical tokamak. The developed methodology may be used in the design of poloidal field coil systems in tokamak projects in order to avoid weak achievability and controllability regions in magnetic plasma control. It was found that there is a strong cross-influence from the PF-coils currents and the CC current on the plasma shape; hence, these coils should be used to control the plasma shape simultaneously.

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Dumedah, Gift, and Jeffrey P. Walker. "Evaluation of Model Parameter Convergence when Using Data Assimilation for Soil Moisture Estimation." Journal of Hydrometeorology 15, no. 1 (February 1, 2014): 359–75. http://dx.doi.org/10.1175/jhm-d-12-0175.1.

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Abstract Data assimilation (DA) methods are commonly used for finding a compromise between imperfect observations and uncertain model predictions. The estimation of model states and parameters has been widely recognized, but the convergence of estimated parameters has not been thoroughly investigated. The distribution of model state and parameter values is closely linked to convergence, which in turn impacts the ultimate estimation accuracy of DA methods. This demonstration study examines the robustness and convergence of model parameters for the ensemble Kalman filter (EnKF) and the evolutionary data assimilation (EDA) in the context of the Soil Moisture and Ocean Salinity (SMOS) soil moisture assimilation into the Joint UK Land Environment Simulator in the Yanco area in southeast Australia. The results show high soil moisture estimation accuracy for the EnKF and EDA methods when compared with the open loop estimates during evaluation and validation stages. The level of convergence was quantified for each model parameter in the EDA approach to illustrate its potential in the retrieval of variables that were not directly observed. The EDA was found to have a higher estimation accuracy than the EnKF when its updated members were evaluated against the SMOS level 2 soil moisture. However, the EnKF and EDA estimations are comparable when their forward soil moisture estimates were validated against SMOS soil moisture outside the assimilation time period. This suggests that parameter convergence does not significantly influence soil moisture estimation accuracy for the EnKF. However, the EDA has the advantage of simultaneously determining the convergence of model parameters while providing comparably higher accuracy for soil moisture estimates.

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Liu, Sheng, Yuan Feng, Kang Shen, Yangqing Wang, and Shengyong Chen. "An RGB-D-Based Cross-Field of View Pose Estimation System for a Free Flight Target in a Wind Tunnel." Complexity 2018 (December 2, 2018): 1–9. http://dx.doi.org/10.1155/2018/7358491.

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Estimating the real-time pose of a free flight aircraft in a complex wind tunnel environment is extremely difficult. Due to the high dynamic testing environment, complicated illumination condition, and the unpredictable motion of target, most general pose estimating methods will fail. In this paper, we introduce a cross-field of view (FOV) real-time pose estimation system, which provides high precision pose estimation of the free flight aircraft in the wind tunnel environment. Multiview live RGB-D streams are used in the system as input to ensure the measurement area can be fully covered. First, a multimodal initialization method is developed to measure the spatial relationship between the RGB-D camera and the aircraft. Based on all the input multimodal information, a so-called cross-FOV model is proposed to recognize the dominating sensor and accurately extract the foreground region in an automatic manner. Second, we develop an RGB-D-based pose estimation method for a single target, by which the 3D sparse points and the pose of the target can be simultaneously obtained in real time. Many experiments have been conducted, and an RGB-D image simulation based on 3D modeling is implemented to verify the effectiveness of our algorithm. Both the real scene’s and simulation scene’s experimental results demonstrate the effectiveness of our method.

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Anagnostou, Marios N., John Kalogiros, Frank S. Marzano, Emmanouil N. Anagnostou, Mario Montopoli, and Errico Piccioti. "Performance Evaluation of a New Dual-Polarization Microphysical Algorithm Based on Long-Term X-Band Radar and Disdrometer Observations." Journal of Hydrometeorology 14, no. 2 (April 1, 2013): 560–76. http://dx.doi.org/10.1175/jhm-d-12-057.1.

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Abstract Accurate estimation of precipitation at high spatial and temporal resolution of weather radars is an open problem in hydrometeorological applications. The use of dual polarization gives the advantage of multiparameter measurements using orthogonal polarization states. These measurements carry significant information, useful for estimating rain-path signal attenuation, drop size distribution (DSD), and rainfall rate. This study evaluates a new self-consistent with optimal parameterization attenuation correction and rain microphysics estimation algorithm (named SCOP-ME). Long-term X-band dual-polarization measurements and disdrometer DSD parameter data, acquired in Athens, Greece, have been used to quantitatively and qualitatively compare SCOP-ME retrievals of median volume diameter D0 and intercept parameter NW with two existing rain microphysical estimation algorithms and the SCOP-ME retrievals of rain rate with three available radar rainfall estimation algorithms. Error statistics for rain rate estimation, in terms of relative mean and root-mean-square error and efficiency, show that the SCOP-ME has low relative error if compared to the other three methods, which systematically underestimate rainfall. The SCOP-ME rain microphysics algorithm also shows a lower relative error statistic when compared to the other two microphysical algorithms. However, measurement noise or other signal degradation effects can significantly affect the estimation of the DSD intercept parameter from the three different algorithms used in this study. Rainfall rate estimates with SCOP-ME mostly depend on the median volume diameter, which is estimated much more efficiently than the intercept parameter. Comparisons based on the long-term dataset are relatively insensitive to path-integrated attenuation variability and rainfall rates, providing relatively accurate retrievals of the DSD parameters when compared to the other two algorithms.

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Pateiro-López, Beatriz, and Alberto Rodríguez-Casal. "Length and surface area estimation under smoothness restrictions." Advances in Applied Probability 40, no. 02 (June 2008): 348–58. http://dx.doi.org/10.1017/s000186780000255x.

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The problem of estimating the Minkowski content L 0(G) of a body G ⊂ ℝ d is considered. For d = 2, the Minkowski content represents the boundary length of G. It is assumed that a ball of radius r can roll inside and outside the boundary of G. We use this shape restriction to propose a new estimator for L 0(G). This estimator is based on the information provided by a random sample, taken on a square containing G, in which we know whether a sample point is in G or not. We obtain the almost sure convergence rate for the proposed estimator.

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Journal articles on the topic 'D Estimation'

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