Shape Estimation Research Articles

Estimation of the drift velocity of Equatorial Plasma Bubbles using GNSS and digisonde data

Equatorial Plasma Bubbles (EPBs) play a crucial role in modulating plasma density and electron content within the equatorial ionosphere. In this work, we present an advanced and more robust version of the method developed by \citet{Blanch2018} for detecting EPBs using data from the Global Navigation Satellite System (GNSS). The enhancements introduced in this version significantly improve the EPB detection process, achieving a notable reduction in the false positive rate compared to the previous approach. These refinements include the application of more rigorous statistical techniques to achieve a more accurate fit for the background Total Electron Content (TEC), leading to better characterization of EPBs through improved estimation of disturbance shapes. Applying the capabilities of this new method in a dense network of GNSS sensors, we have developed an interferometric procedure for estimating EPB drift velocities, including both speed and direction. This procedure provides valuable insights into the dynamic behavior of EPBs in the Caribbean region during 2014. Our analysis reveals a predominant eastward propagation pattern of EPBs, closely aligned with modified dip isolines. Furthermore, by integrating the results from the GNSS-based method with quasi co-located digisondes, we applied a conceptual model to estimate EPB velocities along their drift direction. This model has been tested across different geographical sectors and validated through comparisons with results from other independent studies. This cross-verification confirms the reliability of the methods for capturing EPB characteristics. This approach improves the precision of EPB detection and contributes to a deeper understanding of their spatiotemporal dynamics and behavior, providing a valuable framework for characterizing these phenomena in the equatorial ionosphere.

Playing for 3D Human Recovery.

Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress. However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data and obtain five major insights. First, game-playing data is surprisingly effective. A simple frame-based baseline trained on GTA-Human outperforms more sophisticated methods by a large margin. For video-based methods, GTA-Human is even on par with the in-domain training set. Second, we discover that synthetic data provides critical complements to the real data that is typically collected indoor. We highlight that our investigation into domain gap provides explanations for our data mixture strategies that are simple yet useful, which offers new insights to the research community. Third, the scale of the dataset matters. The performance boost is closely related to the additional data available. A systematic study on multiple key factors (such as camera angle and body pose) reveals that the model performance is sensitive to data density. Fourth, the effectiveness of GTA-Human is also attributed to the rich collection of strong supervision labels (SMPL parameters), which are otherwise expensive to acquire in real datasets. Fifth, the benefits of synthetic data extend to larger models such as deeper convolutional neural networks (CNNs) and Transformers, for which a significant impact is also observed. We hope our work could pave the way for scaling up 3D human recovery to the real world.

Precise Inner Radius and Inner Surface Shape Estimation in Finite Conductive Tubes Using Eddy Current Technique

Inner radius inspection is critical in the production of tubes, particularly those with small thicknesses, to ensure structural integrity and performance. Traditional ultrasonic methods often fall short in measuring thin-walled tubes, necessitating the use of eddy current method for its non-contact and highly sensitive capabilities. In this paper, we employ the finite element method (FEM) to calculate the output signal of a vertical surface probe in the vicinity of a finite conductive tube. We address the significant impact of edge effects, which can deteriorate the primary signal from material properties. The probe data are then used to determine the inner radius through a regression method, and an evaluation technique is proposed to inspect the ovality of the inner surface. Our results demonstrate the accuracy and efficiency of the FEM to predict coil impedance and regression methods to evaluate both the inner radius and the shape of the inner surface, even in the presence of noise. This research provides significant contributions to the field of tube inspection, particularly in industries where precision is paramount, such as oil and gas industries, automotive, and medical sectors.

Fusion algorithms on identifying vacant parking spots using vision-based approach

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In densely populated cities, parking space scarcity results in issues like traffic congestion and difficulty finding parking spots. Recent advancements in computer vision have introduced methods to address parking lot management challenges. The availability of public image datasets and rapid growth in deep learning technology has led to vision-based parking management studies, offering advantages over sensor-based systems in comprehensive area coverage, cost reduction, and additional functionalities. This study presents an innovative fusion algorithm that integrates object detection with occupancy state algorithms to accurately identify vacant parking spaces. The employment of the YOLOv7 framework for vehicle instance segmentation, combined with three occupancy algorithms Euclidean distance (ED), intersection over reference (IoR), and intersection over union (IoU) are compared to determine the occupancy state of observed areas. The proposed method is evaluated using the CNRPark-EXT dataset, and its performance is compared with state-of-the-art methods. As a result, the proposed approach demonstrates robustness under varying conditions. It outperforms existing methods in terms of system evaluation performance, achieving accuracies of 98.88%, 97.99%, and 90.04% for ED, IoR, and IoU, respectively. This fusion detection method enhances adaptability and addresses occlusions, emphasizing YOLOv7’s advantages and accurate shape approximation for slot annotation. This study contributes valuable insights for effective parking management systems and has potential usage in the real-world implementation of intelligent transportation systems.</p></div></div></div>

A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting.

This work presents a novel and effective method for fitting multidimensional ellipsoids (i.e., ellipsoids embedded in [Formula: see text]) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a Bayesian parameter estimate process and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the predictive distribution within the Bayesian framework, i.e., considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring ellipsoid-specific results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the predictive distribution to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable Expectation Maximization (EM) framework. Moreover, we introduce an ε-accelerated technique to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.

Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

PurposeIn autonomous mobile robots, high-level accuracy and precision in 3D perception are required for object detection, shape estimation and obstacle distance measurement. However, the existing methods suffer from limitations like inaccurate point clouds, noise in sensor data and synchronization problems between 2D LiDAR and servomotor. These factors can lead to the wrong perception and also introduce noise during sensor registration. Thus, the purpose of this study is to address these limitations and enhance the perception in autonomous mobile robots.Design/methodology/approachA new sensor mounting structure is developed for 3D mapping by using a 2D LiDAR and servomotor. The proposed method uses a support vector machine regression (SVM-R) technique to optimize the waypoints of the servomotor for the point cloud reconstruction process and to obtain a highly accurate and detailed representation of the environment.FindingsThe study includes an analysis of the SVM-R model, including Linear, radial basis function (RBF) and Polynomial kernel. Results show that the Linear kernel performs better with the lowest 3.67 mean absolute error (MAE), 26.24 mean squared error (MSE) and 5.12 root mean squared error (RMSE) values than the RBF and Polynomial kernels. The 2D to 3D point cloud reconstruction shows that the proposed method with a new sensor mounting structure performs better in perception accuracy and achieves an error of 0.45% in measuring the height of the target objects whereas in previous techniques the error was very large.Originality/valueThe study shows the effectiveness of SVM-R in the 3D point cloud reconstruction process and exhibits remarkable performance for object height measurement. Further, the proposed technique is applicable for future advanced visual applications and has a superior performance over the other conventional methods.

Statistical Inference for Counting Processes Under Shape Heterogeneity.

Proportional rate models are among the most popular methods for analyzing recurrent event data. Although providing a straightforward rate-ratio interpretation of covariate effects, the proportional rate assumption implies that covariates do not modify the shape of the rate function. When the proportionality assumption fails to hold, we propose to characterize covariate effects on the rate function through two types of parameters: the shape parameters and the size parameters. The former allows the covariates to flexibly affect the shape of the rate function, and the latter retains the interpretability of covariate effects on the magnitude of the rate function. To overcome the challenges in simultaneously estimating the two sets of parameters, we propose a conditional pseudolikelihood approach to eliminate the size parameters in shape estimation, followed by an event count projection approach for size estimation. The proposed estimators are asymptotically normal with a root- convergence rate. Simulation studies and an analysis of recurrent hospitalizations using SEER-Medicare data are conducted to illustrate the proposed methods.

Learning-based object's stiffness and shape estimation with confidence level in multi-fingered hand grasping.

When humans grasp an object, they are capable of recognizing its characteristics, such as its stiffness and shape, through the sensation of their hands. They can also determine their level of confidence in the estimated object properties. In this study, we developed a method for multi-fingered hands to estimate both physical and geometric properties, such as the stiffness and shape of an object. Their confidence levels were measured using proprioceptive signals, such as joint angles and velocity. We have developed a learning framework based on probabilistic inference that does not necessitate hyperparameters to maintain equilibrium between the estimation of diverse types of properties. Using this framework, we have implemented recurrent neural networks that estimate the stiffness and shape of grasped objects with their uncertainty in real time. We demonstrated that the trained neural networks are capable of representing the confidence level of estimation that includes the degree of uncertainty and task difficulty in the form of variance and entropy. We believe that this approach will contribute to reliable state estimation. Our approach would also be able to combine with flexible object manipulation and probabilistic inference-based decision making.

Deformation Recovery: Localized Learning for Detail-Preserving Deformations

We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that require a global shape encoding, we observe that detail-preserving deformations can be estimated reliably without any global context in certain scenarios. Building on this intuition, we leverage Jacobians defined in a one-ring neighborhood as a coarse representation of the deformation. Using this as the input to our neural network, we apply a series of MLPs combined with feature smoothing to learn the Jacobian corresponding to the detail-preserving deformation, from which the embedding is recovered by the standard Poisson solve. Crucially, by removing the dependence on a global encoding, every point becomes a training example, making the supervision particularly lightweight. Moreover, when trained on a class of shapes, our approach demonstrates remarkable generalization across different object categories. Equipped with this novel network, we explore three main tasks: refining an approximate shape correspondence, unsupervised deformation and mapping, and shape editing. Our code is made available at https://github.com/sentient07/LJN.

Modal Parameters Estimation of Circular Plates Manufactured by FDM Technique Using Vibrometry: A Comparative Study

This paper presents a comparative study focused on a modal parameters estimation of specimens manufactured by the FDM technique using a fixed embedded vibrometer based on the laser Doppler principle and roving hammer-impact method. Part of this paper is devoted to testing a fixed circular plate with a honeycomb infill pattern while varying the number of excitation points (DOFs), the number of analysis lines of fast Fourier transformation (FFT), and the locations or numbers of reference degrees of freedom (REFs). Although these parameters did not significantly affect the values found for the natural frequencies of the structure, there were changes in the estimates of the mode shapes (affected by the low number of DOFs), in the height and sharpness of the peaks of the CMIF functions (caused by the increased number of FFT lines), and in the number of identified modes (influenced by the chosen location(s) of REFs), respectively. Subsequently, the authors compared the results of experimental modal analyses carried out under the same conditions on three circular plates with honeycomb, star, and concentric infill patterns made of PLA. The results confirm that specimens with honeycomb or star infill patterns have a higher stiffness than those with concentric infill patterns. The low values of the damping ratios obtained for each structure indicate a strong response to excitation at or near their natural frequencies.

Ontogenetic scaling of disc width with total length in west African batoids

AbstractMorphological scaling describes changes in the size or shape of one morphological character (e.g. mass, length, width, area etc.) as another increases in size. Understanding how morphological characters scale with body size can shed light on how natural selection influences morphology, and the nature of ecomorphological relationships through ontogeny. Batoids (Elasmobranchii: Batoidea) are a highly specialised lineage of cartilaginous fishes displaying extreme dorsoventral flattening. Despite this, little is known about morphological scaling in batoids compared to sharks. In this study we test the relationship between disc width and total length in five batoid species (Torpedo torpedo, Mobula tarapacana, Fontitrygon margarita, Raja parva, Rhinobatos irvinei) representing four orders that differ in both ecology and morphology, measured from artisanal fisheries in Western and Central Ghana. Whilst a lack of existing ecological data presents some limitations, our results are broadly consistent with ecomorphological theory previously applied to sharks. Moreover, we find that for some lineages (including some myliobatiform taxa) total length may represent a valid proxy for estimating overall body size. This finding has applications for body size and shape estimation in partially processed batoids obtained from fishing camps, and extinct taxa known only from incomplete or fragmented remains.

Involvement of adverse life events in body image distortion in a patient with eating disorders

Adverse life events (ALEs), particularly interpersonal ALEs and those experienced during childhood are associated with adult psychopathology. Altered eating behaviors and body image distortion (BID) have been reported in maltreated children. This study aimed to evaluate the presence of interpersonal ALEs before the age of 13 years in patients with eating disorders (ED) and clarify its relationship with BID. This observational case–control study comprised the ED group, including 79 outpatients with ED, and two control groups, including 20 outpatients with depressive disorder and 41 participants with no history of mental illness. The presence of ALEs was determined using the Traumatic Events Questionnaire. To assess BID, the contouring drawing rate scale was used, and visual BID was distinguished from non-visual BID. Data were collected and processed using Statistical Package for Social Sciences v28. All tests were two-tailed (significance level of 0.05). Patients with ED had a higher proportion of interpersonal ALEs during childhood (P = 0.065). In the ED group, patients with interpersonal ALEs during childhood overestimated their shape (P = 0.021). The non-visual BID was significantly higher in patients with childhood ALEs. In a logistic regression model, the presence of interpersonal ALEs during childhood as an independent variable predicted visual and non-visual BID in patients with ED. During the estimation of body shape, it appears that the negative emotions experienced during childhood are shifted to the body shape itself. Thus, the BID experienced is attributed more to the unresolved emotions of the maltreated individuals than to the social pressures experienced.

Efficacy of modal curvature damage detection in various pre-damage data assumptions and modal identification techniques

The efficacy of modal curvature approach for damage localization is discussed in the paper in the context of input data. Three modal identification methods, i.e., Eigensystem Realization Algorithm (ERA), Natural Excitation Technique with ERA (NExT-ERA) and Covariance Driven Stochastic Subspace Identification (SSI-Cov), and four methods of determining baseline data, i.e., real measurement of the undamaged state, analytical function, Finite Element (FE) model and approximation of current experimental mode shape, are considered. Practical conclusions are formulated based on analysis of two cases. The first is a laboratory beam with a notch and the second is a stonemasonry historic lighthouse with modern restoration in its upper part. The analysis shows that NExT-ERA and SSI-Cov in combination with approximation of current mode shape provide high efficacy in damage localization alongside relatively straightforward determination of baseline data. It proves that the construction of advanced FE models of a structure can be replaced with a much simpler method of baseline data acquisition. Furthermore, the research shows the structural mode shapes identified with ERA may not always indicate the presence of damage.

Shape Preserving Approximation of Periodic Functions: Conclusion

AbstractWe give here the final results about the validity of Jackson-type estimates in comonotone approximation of $$2\pi $$ 2 π -periodic functions by trigonometric polynomials ($$q=1$$ q = 1 ). For coconvex ($$q=2$$ q = 2 ) and the so called co-q-monotone, $$q>2$$ q > 2 , approximations, everything is known by now. Thus, this paper concludes the research on Jackson type estimates of Shape Preserving Approximation of periodic functions by trigonometric polynomials. It is interesting to point out that the results for comonotone approximation of a periodic function are substantially different than the analogous results for comonotone approximation of a continuous function on a finite interval, by algebraic polynomials.

Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views

Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views

Effects of diesel pilot-injection strategy on a methanol/diesel dual-direct injection engine

To verify the effectiveness of dual-direct injection technology in enhancing the performance of methanol/diesel dual-fuel engines and the positive impacts of multiple-injection strategy on the in-cylinder mixture formation and combustion process, an experiment was conducted on a modified methanol/diesel dual-direct engine test platform in this paper. The research aimed to explore the effects of the diesel pilot-injection strategy, especially from the two aspects of diesel pilot-injection timing and diesel pilot-injection duration, on the operating range, combustion characteristics, performance and emissions. Results indicate that the pilot-injection strategy reduces the maximum pressure and pressure rise rate of the engine, alleviating the rough combustion in the cylinder and broaden the operating range. As the diesel pilot-injection timing advances, the in-cylinder pressure curve during the combustion process shifts leftward as a whole, and the pressure rise rate curve gradually becomes an approximate single-peak shape. The ignition delay almost increases linearly and CA50 first decreases and then increases with the advance of pilot-injection timing. When the pilot-injection duration increases, the ignition delay shortens and CA50 advances. Additionally, the diesel pilot-injection strategy can optimize engine emissions. As the pilot-injection timing advances and the duration increases, the HC, CO and soot emissions decrease, while the NOx emission increases. Using the pilot-injection strategy, the highest indicated thermal efficiency of 42.94 % is obtained at the diesel ignition injection timing and duration of −28 °CA aTDC and 0.25 ms respectively, which is 7.54 % relatively higher than that under a single injection strategy. The research results show that the pilot-injection strategy better adjusts the distribution of high reactivity and low reactivity fuels in the cylinder, which is beneficial for improving the combustion and performance of the methanol/diesel dual-fuel engine.

Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking

BackgroundCalibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.MethodsThis study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing “gold standard” muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to “gold standard” muscle activations and forces.ResultsOn average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle–tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to “gold standard” calibrated model parameter values.ConclusionsThese findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network.

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant's canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

Kernel-inspired algorithm to transform transmission electron microscopy images into discrete dipole approximation geometries.

In this work, we present a code that transforms 2D transmission electron microscopy images into 3D geometries for discrete dipole approximation simulations in DDSCAT 7.3.3 based on Python 3.11 and OpenCV 4.8.1. This allows for the extrapolation of experimental sample images into ready-to-use simulation geometries. The advantage is that the geometry reflects complex shapes instead of approximations of basic shapes like spheres, cylinders, or cubes. The underlying algorithm to extrapolate 2D images to 3D structures is inspired by the working principle of kernels used in image processing. To showcase the code, the absorption spectrum of deposited gold nanoparticles was simulated and compared with experimental values. Apart from a small systematic shift of the simulated spectrum, it is in excellent agreement with the experiment.

Zonal shape reconstruction for Shack-Hartmann sensors and deflectometry

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error.Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption.In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell.Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.