Mountain bike suspension forks have been developed to reduce the accelerations transmitted to the rider. However, the effectiveness of suspension forks has not been systematically investigated. It was the goal of this project to quantify the amount of impact acceleration damping afforded by three stiffness settings of suspension forks compared to rigid mountain bike forks. Seven experienced mountain bike riders gave their informed consent to participate in the study. The subjects coasted down a ramp and impacted a bump at 5.4 m/s located about 2.3 m past the ramp end. Accelerometers were placed on the axle and frame of the bicycle which was fitted with either a rigid fork (FR) or suspension forks set on soft (F1), medium (F3), or firm (F6) stiffness. Bumps were either small (B1), medium (B2) or large (B3). Accelerometer data were telemetered to a computer, sampled at 1000 Hz and smoothed with Butterworth filter with 50 Hz cutoff. Peak acceleration during impact (P1) and landing (P2) as well as the slope of the impact acceleration peak (jerk, J) were extracted from the data and analyzed using a 2 x 3 x 4 repeated measures ANOVA for each of the dependent variables (P1, P2, J), and with linear contrasts as follow-up tests. A significance level of p<.01 was chosen. All forks were found to produce similar impact acceleration (P1) at the axle and frame on the small bump (B1). On larger bumps (B2 and B3), softer suspension forks (F1 and F3) significantly reduced acceleration transmitted to the rider during bump impact (P1), while maintaining significantly higher axle acceleration than other forks (p<.001); Jerk was significantly reduced at the frame compared to the axle for each suspension fork with the larger bumps. Landing impacts (P2) were of similar magnitude for most fork conditions at both the axle and frame. It appears from these data that suspension forks with moderate stiffness may provide the best impact acceleration damping for mountain bikes encountering impacts with characteristics similar to the bumps and velocity used in this study. It is unclear how these results generalize to other conditions encountered while riding. / Graduation date: 1997

Thèses (M.Sc.A.)--Université de Sherbrooke (Canada), 1998. / Titre de l'écran-titre (visionné le 20 juin 2006). Publié aussi en version papier.

Thesis (M.S.) in Forest Resources--University of Maine, 2008. / Includes vita. Includes bibliographical references (leaves 77-82).

Dans cette thèse, nous abordons deux problèmes principaux, à savoir l'évaluation quantitative des algorithmes de segmentation de maillages ainsi que la segmentation de maillages par apprentissage en exploitant le facteur humain.Tout d'abord, nous proposons un benchmark dédié à l'évaluation des algorithmes de segmentation de maillages 3D. Le benchmark inclut un corpus de segmentation vérités-terrains réalisées par des volontaires ainsi qu'une nouvelle métrique de similarité pertinente qui quantifie la cohérence entre ces segmentations vérités-terrains et celles produites automatiquement par un algorithme donné sur les mêmes modèles. De plus, nous menons un ensemble d'expérimentations, y compris une expérimentation subjective, pour respectivement démontrer et valider la pertinence de notre benchmark. Nous proposons ensuite un algorithme de segmentation par apprentissage. Pour cela, l'apprentissage d'une fonction d'arête frontière est effectué, en utilisant plusieurs critères géométriques, à partir d'un ensemble de segmentations vérités-terrains. Cette fonction est ensuite utilisée, à travers à une chaîne de traitement pour segmenter le maillage en entrée. Nous montrons, à travers une série d'expérimentations s'appuyant sur différents benchmarks, les excellentes performances de notre algorithme par rapport à ceux de l'état de l'art. Enfin, nous proposons une application de notre algorithme de segmentation pour l'extraction de squelettes cinématiques pour les maillages 3D dynamiques, et présentons quelques résultats prometteurs. / In this thesis, we address two main problems namely the quantitative evaluation of mesh segmentation algorithms and learning mesh segmentation by exploiting the human factor. First, we propose a benchmark dedicated to the evaluation of mesh segmentation algorithms. The benchmark includes a human-made ground-truth segmentation corpus and a relevant similarity metric that quantifies the consistency between these ground-truth segmentations and automatic ones produced by a given algorithm on the same models. Additionally, we conduct extensive experiments including subjective ones to respectively demonstrate and validate the relevance of our benchmark. Then, we propose a new learning mesh segmentation algorithm. A boundary edge function is learned, using multiple geometric criteria, from a set of human segmented training meshes and then used, through a processing pipeline, to segment any input mesh. We show, through a set of experiments using different benchmarks, the performance superiority of our algorithm over the state-of-the-art. Finally, we propose an application of our segmentation algorithm for kinematic skeleton extraction of dynamic 3D-meshes, and present some early promising results.

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A key component of autonomous outdoor navigation in unstructured environments is the classification of terrain. Recent development in the area of machine learning show promising results in the task of scene segmentation but are limited by the labels used during their supervised training. In this work, we present and evaluate a flexible strategy for terrain classification based on three components: A deep convolutional neural network trained on colour, depth and infrared data which provides feature vectors for image segmentation, a set of exchangeable segmentation engines that operate in this feature space and a novel, air pressure based actuator responsible for distinguishing rigid obstacles from those that only appear as such. Through the use of unsupervised learning we eliminate the need for labeled training data and allow our system to adapt to previously unseen terrain classes. We evaluate the performance of this classification scheme on a mobile robot platform in an environment containing vegetation and trees with a Kinect v2 sensor as low-cost depth camera. Our experiments show that the features generated by our neural network are currently not competitive with state of the art implementations and that our system is not yet ready for real world applications.

Title: Pathfinding within a Hierarchical Navmesh Based on Geometry Analysis Author: Miroslav Chomut Department / Institute: Department of Software and Computer Science Education Supervisor of the master thesis: Mgr. Tomáš Plch, Media and Communications Office Abstract: Pathfinding is a common problem in the computer science dealing with navigation from a starting point to a destination point. Common algorithms today are mostly based on A* search on a graph representation of navigated world. Another common approach is creation of navigation structure of convex navigation meshes and navigating on them. Our goal is to propose pathfinding algorithm on hierarchical navigation meshes, based on the terrain geometry, which benefits from complexity of hierarchical search yet provides paths comparable in length to reference ones. This thesis analyses and describes our proposed approach of navigation including generation of the navigation structure. Keywords: navmesh, pathfinding, A*, hierarchy, terrain analysis, geometry

The goal of this thesis is to develop a procedural terrain height map generator programmable using a Turing complete scripting language. The generator must allow rendering of partial maps by generating arbitrary rectangular region of the map described by any script. The thesis explains why this means the traditional script execution model won't work in this case and proposes a novel two stage model, which executes the scripts in two stages - a simulation stage and a rendering stage. Powered by TCPDF (www.tcpdf.org)

Unity is a game development tool with large userbase. One of the features it is missing, however, is a deeper support for the development of two dimensional games. In particular a tool for editing and generation of two dimensional terrain. The aim of the presented work is to design and develop this tool. First, the problem is analysed and a set of requirements is assembled. Then, the Unity development environment is described in order to be able to use it for implementing the tool. The tool is designed from the programmer's point of view and data representation inside Unity is proposed. Large part of the work is also devoted to describing different algorithms used for terrain generation. Finally, the user interface of the program is designed using the knowledge gained by studying Unity.