Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection.

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tube signals serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering the Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as a trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method, is presented.

Identifying the Sweet Spot of Padel Rackets with a Robot.

Although the vibration of rackets and the location of the sweet spot for players when hitting the ball is crucial, manufacturers do not specify this behavior precisely. This article analyses padel rackets, provides a solution to determine the sweet spot position (SSP), quantifies its behavior, and determines the level of vibration transmitted along the racket handle. The proposed methods serve to locate the SSP without quantifying it. This article demonstrates the development of equipment capable of analyzing the vibration behavior of padel rackets. To do so, it employs a robot that moves along the surface of the padel racket, striking it along its central line. Accelerometers are placed on a movable cradle where rackets are positioned and adjusted. A method for analyzing accelerometer signals to quantify vibration severity is proposed. The SSP and vibration behavior along the central line are determined and quantified. As a result of the study, 225 padel rackets are analyzed and compared. SSP is independent of the padel racket shape, balance, weight, moment of inertia, and padel racket shape (tear, diamond, or round) and is not located at the same position as the center of percussion.

Hardware-in-the-Loop Test Bench for Simulation of Catenary-Pantograph Interaction (CPI) with Linear Camera Measurement.

Catenary-pantograph contact force is generally used for assessment of the current collection quality. A good current collection quality not only increases catenary lifetime but also keeps a stable electric supply and helps to avoid accidents. Low contact forces lead to electric arcs that degrade the catenary, and high contact forces generate excessive wear on the sliding surfaces. Railway track operators require track tests to ensure that catenary-pantograph force remains between safe values. However, a direct measure of the contact force requires an instrumented pantograph which is generally costly and complicated. This paper presents a test bench that allows testing virtual catenaries over real pantographs. Therefore, the contact point force behavior can be tested before the track test to guarantee that the test is passed. Moreover, due to its flexibility, the system can be used for model identification and validation, catenary testing, or contact loss simulation. The test bench also explores using computer vision as an additional sensor for each application. Results show that the system has high precision and flexibility in the available tests.

A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics.

Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g., neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g., the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate, and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics.

Depth-Dependent High Distortion Lens Calibration.

Accurate correction of high distorted images is a very complex problem. Several lens distortion models exist that are adjusted using different techniques. Usually, regardless of the chosen model, a unique distortion model is adjusted to undistort images and the camera-calibration template distance is not considered. Several authors have presented the depth dependency of lens distortion but none of them have treated it with highly distorted images. This paper presents an analysis of the distortion depth dependency in strongly distorted images. The division model that is able to represent high distortion with only one parameter is modified to represent a depth-dependent high distortion lens model. The proposed calibration method obtains more accurate results when compared to existing calibration methods.

Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation.

The goal of this research work is to improve the accuracy of human pose estimation using the Deformation Part Model (DPM) without increasing computational complexity. First, the proposed method seeks to improve pose estimation accuracy by adding the depth channel to DPM, which was formerly defined based only on red-green-blue (RGB) channels, in order to obtain a four-dimensional DPM (4D-DPM). In addition, computational complexity can be controlled by reducing the number of joints by taking it into account in a reduced 4D-DPM. Finally, complete solutions are obtained by solving the omitted joints by using inverse kinematics models. In this context, the main goal of this paper is to analyze the effect on pose estimation timing cost when using dual quaternions to solve the inverse kinematics.

Calibration of a trinocular system formed with wide angle lens cameras.

To obtain 3D information of large areas, wide angle lens cameras are used to reduce the number of cameras as much as possible. However, since images are high distorted, errors in point correspondences increase and 3D information could be erroneous. To increase the number of data from images and to improve the 3D information, trinocular sensors are used. In this paper a calibration method for a trinocular sensor formed with wide angle lens cameras is proposed. First pixels locations in the images are corrected using a set of constraints which define the image formation in a trinocular system. When pixels location are corrected, lens distortion and trifocal tensor is computed.

Accurate calibration with highly distorted images.

Camera calibration is a two-step process where first a linear algebraic approximation is followed by a nonlinear minimization. The nonlinear minimization adjusts the pin-hole and lens distortion models to the calibrating data. Since both models are coupled, nonlinear minimization can converge to a local solution easily. Moreover, nonlinear minimization is poorly conditioned since parameters with different effects in the minimization function are calculated simultaneously (some are in pixels, some in world coordinates, and some are lens distortion parameters). A local solution is adapted to parameters, which minimize the function easily, and the remaining parameters are just adapted to this solution. We propose a calibration method where traditional calibration steps are inverted. First, a nonlinear minimization is done, and after, camera parameters are computed in a linear step. Using projective geometry constraints in a nonlinear minimization process, detected point locations in the images are corrected. The pin-hole and lens distortion models are computed separately with corrected point locations. The proposed method avoids the coupling between both models. Also, the condition of nonlinear minimization increases since points coordinates are computed alone.

Calibration of a wide angle stereoscopic system.

Inaccuracies in the calibration of a stereoscopic system appear with errors in point correspondences between both images and inexact points localization in each image. Errors increase if the stereoscopic system is composed of wide angle lens cameras. We propose a technique where detected points in both images are corrected before estimating the fundamental matrix and the lens distortion models. Since points are corrected first, errors in point correspondences and point localization are avoided. To correct point location in both images, geometrical and epipolar constraints are imposed in a nonlinear minimization problem. Geometrical constraints define the point localization in relation to its neighbors in the same image, and eipolar constraints represent the location of one point referred to its corresponding point in the other image.

Camera calibration under optimal conditions.

Different methods based on photogrammetry or self-calibration exist to calibrate intrinsic and extrinsic camera parameters and also for data pre- and post-processing. From a practical viewpoint, it is quite difficult to decide which calibration method gives accurate results and even whether any data processing is necessary. This paper proposes a set of optimal conditions to resolve the calibration process accurately. The calibration method uses several images of a 2D pattern. Optimal conditions define the number of points and the number of images to resolve the calibration accurately, as well as positions and orientations from where images should be taken.

Lens distortion models evaluation.

Many lens distortion models exist with several variations, and each distortion model is calibrated by using a different technique. If someone wants to correct lens distortion, choosing the right model could represent a very difficult task. Calibration depends on the chosen model, and some methods have unstable results. Normally, the distortion model containing radial, tangential, and prism distortion is used, but it does not represent high distortion accurately. The aim of this paper is to compare different lens distortion models to define the one that obtains better results under some conditions and to explore if some model can represent high and low distortion adequately. Also, we propose a calibration technique to calibrate several models under stable conditions. Since performance is hard conditioned with the calibration technique, the metric lens distortion calibration method is used to calibrate all the evaluated models.