Robotic arm, 3D vision and geometric matching for an efficient implementation of inverse Patch Transfer Function method

The identification of source velocities remains an important problem in noise control. For this purpose, several methods were developed such as Near-field Acoustic Holography (NAH) or inverse Boundary Elements Method (iBEM). An alternative method, based on the double measurement of pressure and particle velocity fields surrounding the source is presented. This method has been developed in the SILENCE European project framework. In this method, called inverse Patch Transfer Functions method (iPTF), measurement and identification surfaces are divided into elementary areas called patches. Then, source velocities are computed from acoustic field and inversion of impedances matrices obtained by FEM. Theoretically, this method presents two main advantages: it can be applied to sources with complex 3D geometries and measurements can be carried out in a non-anechoic environment, even in the presence of other sources. In the present paper, theoretical background of iPTF is exposed and results are presented on a source with simple geometry (an L-shaped plate) and discussed.

Identification of source velocities on 3D structures in non-anechoic environments: Theoretical background and experimental validation of the inverse patch transfer functions method

Inverse transfer function identification for high-frequency pressure probes using M-sequence pressure generators

We present an algorithm that simultaneously identifies the structure and learns the kinematic model of an articulated body from point cloud data and joint angle data with minimal prior assumptions. The robot arm is represented by a point cloud. The structure is identified by segmenting the point cloud to subsets corresponding to different links according to a minimum matching error criterion that includes spatial continuity. The kinematic model is represented as a set of rigid transformations, which are learned by a stochastic gradient decent-based iterative closest point (ICP) algorithm. The learned transformations are then used to infer the structural relationship between segments. We validate the experiment on both synthetic data and data acquired by a Kinect depth sensor viewing a robot arm moving three of its joints. Our results demonstrate that point cloud can be correctly segmented into different rigid parts; the rigid transformations associated with each part can be learned and the kinematic structure can be correctly inferred. Our proposed approach makes a step toward a unified framework for robot self-discovery and learning forward kinematic models purely with data from depth sensors and joint angle encoders.

At present, 3D vision technology has been more and more applied in the field of livestock breading industry. Three depth cameras are installed in the data acquisition walkway from different views, as the animals pass through the walkway, the depth cameras will get the local point cloud of livestock at the same time. By this method the livestock point cloud exists inevitable noise, as well as the background point cloud including railing and ground. Therefore, the segmentation of livestock point cloud from the original point cloud mixed with noise and background is the important first step of the application of three-dimensional vision in livestock breading industry. This paper proposed using the PointNet++ model to separate the target point cloud from the background point cloud. The improved DBSCAN clustering algorithm is proposed to denoise and segment the data preliminarily, which is helpful to label the data manually. The labeled data are used to train the PointNet++ neural network model to extract target point cloud efficiently and accurately. The experimental results show that the mean Intersection over Union (MIOU) of pig target point cloud segmentation based on single-scale grouping and multi-scale grouping Pointnet++ are 0.932 and 0.940, respectively, which both obtain good segmentation results.

Inverse patch transfer functions (iPTF) method is an efficient technique for sound field reconstruction and separation of arbitrarily shaped sound sources in noisy acoustic environments. By applying Neumann boundary condition to a closed virtual cavity surrounding the source, the Helmholtz integral equation is simplified and can be solved with a Green's function satisfying the Neumann boundary condition. However, in the identification of sparsely distributed sources, ghost sources appear in the result solved by using the iPTF method with classic Tikhonov regularization methods and influence the accuracy of identification. In the present work, an evanescent Green's function with fast convergence of calculation is utilized, and a technique that combines the iPTF method and Fast Iterative Shrinkage-Thresholding Algorithm aimed at improving the performance for the identification of sparsely distributed source is proposed. Then double layer measurements, instead of using expensive p-u probes, are employed to acquire the normal velocity of the hologram surface. In numerical simulations, the normal velocities of two anti-phased piston sources are well reconstructed with high sparsity while a disturbing source is radiating in the sound field within the frequency band of 50 to 1000 Hz. Finally, an experiment on two baffled loudspeakers has been carried out. The results of simulations and experiments indicate that the proposed technique has obviously improved the accuracy of the iPTF method in the identification of sparsely distributed sources for the frequency band from 50 to 1000 Hz.

A numerical procedure is presented for the solution of the inverse heat conduction problem. The method finds the desired heat flux in either the frequency domain through the inverse discrete transfer function, or in the time domain through the two-sided convolution with the discrete inverse impulse response function. The method is shown to be well conditioned, in the sense that it never yields heat fluxes oscillating with increasing amplitudes; and for unperturbed data, it does not require stabilization or regularization as the time step is decreased or the spatial discretization is refined. This discretization is carried out using the finite-element method. However, the method is also suitable for finite differences or any other discretization procedure. A series of numerical examples illustrate the accuracy and efficiency of the proposed method.

A practical neural network design method for the identification of both the direct transfer function and inverse transfer function of an object plant is proposed. As a practical application of the direct transfer function identifier, a nonlinear plant simulator is also proposed. Simulated and experimental results for a second-order plant show that identification can be satisfactorily achieved and that neural network identifiers can represent nonlinear plant characteristics very well. The characteristics of a neural network direct controller with a feedback control loop, which uses the learning results of the inverse transfer function identifier, is also proposed and confirmed. >

With the increasing use of commodity RGB-D cameras for computer vision, robotics, mixed and augmented reality and other areas, it is of significant practical interest to calibrate the relative pose between a depth (D) camera and an RGB camera in these typ

Direct immersogeometric fluid flow and heat transfer analysis of objects represented by point clouds

Reverse engineering is an approach for constructing geometric models from a physical part through dimensional measurement and surface modeling. In most applications, the method to reconstructed model from point clouds data which is called NURBS. The accuracy of surface reconstruction depends on number of point clouds data, more point clouds data that make calculations more accurate, but it makes to slowly compute. A tangent function method is a new algorithm for reduction point cloud data had presented in this paper. A tangent function finds turning point and selection point to decrease matrix inversion size in NURBS curve and surface fitting equation while maintaining the accuracy.

Learning on (3D) point clouds is vital for a broad range of emerging applications such as autonomous driving, robot perception, augmented reality, gaming, and security. Such needs have increased recently due to the prevalence of 3D sensors such as LiDAR, 3D camera, and RGB-D. Point clouds consist of thousands to millions of points; They contain rich information and are complementary to the traditional 2D cameras that we have been working on for years in the multimedia (or vision) community. 3D learning algorithms on point cloud data are new, and exciting, for numerous core problems such as 3D classification, detection, semantic segmentation, and face recognition. Covers the requirements of point cloud data, the background of capturing the data, 3D representations, emerging applications, core problems, state-of-the art learning algorithms, and future research opportunities.

Fractional calculus is a well-known tool that improves the closed loop performance of processes when compared to integer order approaches. The study presents a generalization of the popular Internal Model Control (IMC) strategy for fractional order processes of any complexity. The controller is based on the inverse fractional order transfer function to which a fractional order filter is added. The velocity of a scalable nanorobot operating in submersed environments is the chosen process to exemplify the proposed control strategy. The Fractional Order Internal Model Control (FOIMC) is developed for the fractional dynamics of the robot. The validity of method is proved through simulations regarding reference tracking, disturbance rejection and robustness to gain variations.

Increasing speech intelligibility and naturalness in noise based on concepts of modulation spectrum and modulation transfer function

We propose a novel low-complexity artificial neural network (ANN)-based nonlinear equalizer (NLE) for coherent optical orthogonal frequency-division multiplexing (CO-OFDM) and compare it with the recent inverse Volterra-series transfer function (IVSTF)-based NLE over up to 1000 km of uncompensated links. Demonstration of ANN-NLE at 80-Gb/s CO-OFDM using 16-quadrature amplitude modulation reveals a Q -factor improvement after 1000-km transmission of 3 and 1 dB with respect to the linear equalization and IVSTF-NLE, respectively.