Method of optimization of the fundamental matrix by technique speeded up robust features application of different stress images

<span>The purpose of determining the fundamental matrix (F) is to define the epipolar geometry and to relate two 2D images of the same scene or video series to find the 3D scenes. The problem we address in this work is the estimation of the localization error and the processing time. We start by comparing the following feature extraction techniques: Harris, features from accelerated segment test (FAST), scale invariant feature transform (SIFT) and speed-up robust features (SURF) with respect to the number of detected points and correct matches by different changes in images. Then, we merged the best chosen by the objective function, which groups the descriptors by different regions in order to calculate ‘F’. Then, we applied the standardized eight-point algorithm which also automatically eliminates the outliers to find the optimal solution ‘F’. The test of our optimization approach is applied on the real images with different scene variations. Our simulation results provided good results in terms of accuracy and the computation time of ‘F’ does not exceed 900 ms, as well as the projection error of maximum 1 pixel, regardless of the modification.</span>

Vision-based kinematic analysis of the Delta robot for object catching

SUMMARY This paper proposes a vision-based kinematic analysis and kinematic parameters identification of the proposed architecture, designed to perform the object catching in the real-time scenario. For performing the inverse kinematics, precise estimation of the link lengths and other parameters needs to be present. Kinematic identification of Delta based upon Model10 implicit model with ten parameters using the iterative least square method is implemented. The loop closure implicit equations have been modelled. In this paper, a vision-based kinematic analysis of the Delta robots to do the catching is discussed. A predefined library of ArUco is used to get a unique solution of the kinematics of the moving platform with respect to the fixed base. The re-projection error while doing the calibration in the vision sensor module is 0.10 pixels. Proposed architecture interfaced with the hardware using the PID controller. Encoders are quadrature and have a resolution of 0.15 degrees embedded in the experimental setup to make the system closed-loop (acting as feedback unit).

fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models

Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice sheet models.

Single-channel speech enhancement based on joint constrained dictionary learning

To improve the performance of speech enhancement in a complex noise environment, a joint constrained dictionary learning method for single-channel speech enhancement is proposed, which solves the “cross projection” problem of signals in the joint dictionary. In the method, the new optimization function not only constrains the sparse representation of the noisy signal in the joint dictionary, and controls the projection error of the speech signal and noise signal on the corresponding sub-dictionary, but also minimizes the cross projection error and the correlation between the sub-dictionaries. In addition, the adjustment factors are introduced to balance the weight of constraint terms to obtain the joint dictionary more discriminatively. When the method is applied to the single-channel speech enhancement, speech components of the noisy signal can be more projected onto the clean speech sub-dictionary of the joint dictionary without being affected by the noise sub-dictionary, which makes the quality and intelligibility of the enhanced speech higher. The experimental results verify that our algorithm has better performance than the speech enhancement algorithm based on discriminative dictionary learning under white noise and colored noise environments in time domain waveform, spectrogram, global signal-to-noise ratio, subjective evaluation of speech quality, and logarithmic spectrum distance.

Optical Inspection Systems for Axisymmetric Parts with Spatial 2D Resolution

The article proposes a solution to the problem of increasing the accuracy of determining the main shaping dimensions of axisymmetric parts through a control system that implements the optical method of spatial resolution. The influence of the projection error of a passive optical system for controlling the geometric parameters of bodies of revolution from the image of its sections, obtained by a digital camera with non-telecentric optics, on the measurement accuracy is shown. Analytical dependencies are derived that describe the features of the transmission of measuring information of a system with non-telecentric optics in order to estimate the projection error. On the basis of the obtained dependences, a method for compensating the projection error of the systems for controlling the geometry of the main shaping surfaces of bodies of revolution has been developed, which makes it possible to increase the accuracy of determining dimensions when using digital cameras with a resolution of 5 megapixels or more, equipped with short-focus lenses. The possibility of implementing the proposed technique is confirmed by the results of experimental studies.

Dependence of the Possible and Allowable Projection Error on the Projection Time Frame

This study addresses the issues of assessing and factoring in the effect of uncertainty growth on the possible performance of projections and their allowable errors. Relying on projects of nuclear power plants and combined cycle power plants as a case study, we assess the dependence of their economic performance indicators on possible changes in the conditions of their future operation in a given year. To assess the effect of the range and nature of input data uncertainty on the projections of the development of regional energy supply systems, we proposed a methodological toolkit that combines optimization with the Monte Carlo simulation. Its application to one of the options for commissioning new power plants in European Russia enabled us to estimate the possible response of the average and marginal cost of electricity in this aggregated region to the broadening of the uncertainty range of the gas price. We note that the assessment and comparison of the possible error of projected indicators with the requirements for their accuracy in making priority investment and other decisions facilitate the justification of the acceptable complexity of employed models and projection methods.

Evaluation of the Circles Measurement and the ABC Classification of Acromioclavicular Joint Injuries

Background: Acromioclavicular joint (ACJ) injuries are common. Despite this, it remains unclear how best to assess, classify, and manage these cases. A simple, reliable, valid, and accurate radiographic parameter to measure ACJ displacement would allow improved consistency of diagnosis and subsequent treatment pathways. Purpose: To evaluate “the circles measurement” and associated “ABC classification” as a tool for assessing ACJ displacement and injury classification. Study Design: Descriptive laboratory study. Methods: The circles measurement is taken from a lateral Alexander radiograph of the shoulder. The measurement is the center-to-center distance between 2 circles drawn to define the lateral extent of the clavicle and the anteromedial extent of the acromion; it is independent of the displacement plane, judging total ACJ displacement in any direction rather than trying to quantify vertical and/or horizontal displacement. When utilized clinically, the circles measurement is a single measurement calculated as the difference between values recorded for the injured and uninjured sides. Validation of the circles measurement was performed using lateral Alexander radiographs (including ±20° projection error in all planes) and computed tomography of standardized ACJ injury simulations. We assessed inter- and intrarater reliability, convergent validity, and discriminant validity of the circles measurement and subsequently generated a classification of ACJ injury based on displacement. Results: Reliability and validity of the circles measurement was excellent throughout. Interrater reliability (ICC [intraclass correlation coefficient] [2,1], 95% CI; n = 78; 4 observers) was 0.976 (0.964-0.985). Intrarater reliability (ICC [2,1]; 95% CI; n = 78; 2 measures) was 0.998 (0.996-0.998). Convergent validity (Pearson correlation coefficient, r) was 0.970 for ideal radiographs and 0.889 with ±20° projection error in all planes. Discriminant validity, with 1-way analysis of variance, showed a P value of <.0001 and effect size ( η2) of 0.960, with the ability to distinguish between the previously defined stable (Rockwood IIIA) and unstable (Rockwood IIIB) injuries. The results permitted objective, statistically sound parameters for the proposed ABC classification system. Conclusion: The circles measurement is a simple, reliable, valid, accurate, and resilient parameter for assessing ACJ displacement and can be used in conjunction with the proposed ABC classification to define ACJ injuries more accurately and objectively than previously described. Clinical Relevance: This novel parameter has the potential to standardize the initial assessment and possibly the subsequent clinical management of ACJ injuries, in addition to providing a standardized measure for future research.