162,830 research outputs found

    The Tennis Ball Problem

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    Stereoscopic vision is a well-established phenomenon: biological evolution showed its utility in ancient times. In this workshop, we have examined some subtleties and limitations in applying this old concept to an entirely new application: with modern technology, we attempt to track the position of an early segment of a flying object, and then extrapolate its later trajectory

    A solution to the tennis ball problem

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    We present a complete solution to the so-called tennis ball problem, which is equivalent to counting lattice paths in the plane that use North and East steps and lie between certain boundaries. The solution takes the form of explicit expressions for the corresponding generating functions. Our method is based on the properties of Tutte polynomials of matroids associated to lattice paths. We also show how the same method provides a solution to a wide generalization of the problem.Comment: 9 pages, Late

    The Tennis Ball Problem

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    AbstractMallows and Shapiro, (J. Integer Sequences2 (1999)) have recently considered what they dubbed the problem of balls on the lawn. Our object is to explore a natural generalization, the s-tennis ball problem, which reduces to that considered by Mallows and Shapiro in the case s=2. We show how this generalization is connected with s-ary trees, and employ the notion of generating trees to obtain a solution expressed in terms of generating functions

    Automatic annotation of tennis games: An integration of audio, vision, and learning

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    Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

    P2_3 Tennis Ball Tunnelling

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    This paper aims to calculate the probability of a tennis ball quantum tunnelling through a tennis racket. This is done by treating the tennis ball as a single particle, and making the potential barrier equal to the energy required to break the strings classically. The probability of tunnelling through a racket is found to be 3.6exp(-2.9x10^31). This very low probability matched with what would be expected. The paper also briefly discusses the problem of decoherence when applying quantum mechanics to macroscopic systems

    A solution to the tennis ball problem

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    We present a complete solution to the so-called tennis ball problem, which is equivalent to counting the number of lattice paths in the plane that use North and East steps and lie between certain boundaries. The solution takes the form of explicit expressions for the corresponding generating functions. Our method is based on the properties of Tutte polynomials of matroids associated to lattice paths. We also show how the same method provides a solution to a wide generalization of the problem.Postprint (published version

    On sphere-filling ropes

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    What is the longest rope on the unit sphere? Intuition tells us that the answer to this packing problem depends on the rope's thickness. For a countably infinite number of prescribed thickness values we construct and classify all solution curves. The simplest ones are similar to the seamlines of a tennis ball, others exhibit a striking resemblance to Turing patterns in chemistry, or to ordered phases of long elastic rods stuffed into spherical shells.Comment: 15 pages, 8 figure

    Intelligent Tennis Robot Based on a Deep Neural Network

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    In this paper, an improved you only look once (YOLOv3) algorithm is proposed to make the detection effect better and improve the performance of a tennis ball detection robot. The depth-separable convolution network is combined with the original YOLOv3 and the residual block is added to extract the features of the object. The feature map output by the residual block is merged with the target detection layer through the shortcut layer to improve the network structure of YOLOv3. Both the original model and the improved model are trained by the same tennis ball data set. The results show that the recall is improved from 67.70% to 75.41% and the precision is 88.33%, which outperforms the original 77.18%. The recognition speed of the model is increased by half and the weight is reduced by half after training. All these features provide a great convenience for the application of the deep neural network in embedded devices. Our goal is that the robot is capable of picking up more tennis balls as soon as possible. Inspired by the maximum clique problem (MCP), the pointer network (Ptr-Net) and backtracking algorithm (BA) are utilized to make the robot find the place with the highest concentration of tennis balls. According to the training results, when the number of tennis balls is less than 45, the accuracy of determining the concentration of tennis balls can be as high as 80%.</jats:p
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