Characteristic-free resolutions of Weyl and Specht modules

We construct explicit resolutions of Weyl modules by divided powers and of co-Specht modules by permutational modules. We also prove a conjecture of Boltje-Hartmann on resolutions of co-Specht modules.Comment: 31 page

Generalized straightening laws for products of determinants

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1997.Includes bibliographical references (p. 157-159).by Brian David Taylor.Ph.D

Resolutions of three-rowed skew- and almost skew-shapes in characteristic zero

We fi nd an explicit description of the terms and boundary maps for the three-rowed skew-shape and almost skew-shape in charac- teristic zero

Enhancing the mechanical efficiency of skilled rowing through shortened feedback cycles

In elite level rowing competition, the average velocities of medallists differ by less than 1 % over 2000 m. Nations place sporting excellence in high regard and this magnifies the importance of success. As a result, sports science and technology is increasingly used to achieve marginal performance gains. This research considers how to advance biomechanical analysis and skills training provision with a particular focus on the technical and practical delivery of real-time feedback to coaches and athletes, thereby shortening the amount of time between feedback cycles. Underpinning any biomechanical feedback intervention, validated determinants of performance are required. Previous research revealed that, while gross biomechanical measures such as athlete power, stroke rate and stroke length have previously been used as key determinants of performance, elite athletes are nowadays performing within expected ranges and therefore it is no longer possible to easily differentiate crews using these measures alone. This thesis describes workshops held with elite coaches to investigate biomechanical efficiency where the outcomes led to a focus on how a boat accelerates and decelerates during a stroke and hence how the boat's velocity fluctuates. Novel metrics are proposed to quantify aspects of a stroke cycle and used to analyse an elite data set, collected using a standardised protocol. It is shown that individual elite rowers can be successfully differentiated and benchmark values of performance are presented. Consideration of previous research suggests that there is currently no suitably functional and flexible biomechanical real-time feedback system to deliver complex skills training in rowing. Therefore, this thesis describes the research that has led to the development and evaluation of new technology to deliver visual and audible interfaces that support the delivery of concurrent and terminal feedback in water and land-based environments. Coaches and athletes were involved throughout the design process to optimise system suitability and encourage adoption. The technology empowers a coach to intricately manipulate feedback provision, thereby promoting motor control and learning theory best practice. Novel insights relevant to designing interactive systems for use within an elite sporting population are also discussed. This research presents an end-to-end strategy for the applied delivery of real-time feedback to skilled rowers bringing together engineering and social science disciplines. A land-based case series reveals that while statistically significant skill learning was not achieved, participants acquired sport specific technical awareness and heightened motivation as a result of the skills training intervention. Existing motor learning literature was tested as part of the study with a key finding being the lack of support for audible display of stroke acceleration through frequency modulation. Study limitations were identified that explain the lack of an effect of skills training on rower efficiency. The study also acted as a validation of the use of a land-based simulator to monitor and manipulate stroke velocity and a validation of the candidate feedback interfaces that had been implemented. As of result of this work, rowing coaches are able to evaluate their athletes in a novel way, achieving a deeper appreciation of their biomechanical efficiency. Upon identifying athletes with a need for technical development, coaches can intervene with the proposed methodology of skill development making use of the new technologies developed to deliver performance gains. This methodology would achieve enhanced validity through a deeper understanding of the reliability of the new metrics and their relationship to boat speed. Future attempts to test for skill learning should build upon the findings made in this work and, in due course, technology and theory should combine to deliver terminal feedback training during water-based rowing

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single off-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics) achieving a stereo omnidirectional system (SOS). The second approach relies on expanding the visual features from the scene into higher dimensionalities to track the pose of a conventional camera in a photogrammetric fashion. The first goal has many interdependent challenges, which we address as part of this thesis: SOS design, projection model, adequate calibration procedure, and application to VO. We show several practical advantages for the single-camera SOS due to its complete 360-degree stereo views, that other conventional 3D sensors lack due to their limited field of view. Since our omnidirectional stereo (omnistereo) views are captured by a single camera, a truly instantaneous pair of panoramic images is possible for 3D perception tasks. Finally, we address the VO problem as a direct multichannel tracking approach, which increases the pose estimation accuracy of the baseline method (i.e., using only grayscale or color information) under the photometric error minimization as the heart of the “direct” tracking algorithm. Currently, this solution has been tested on standard monocular cameras, but it could also be applied to an SOS. We believe the challenges that we attempted to solve have not been considered previously with the level of detail needed for successfully performing VO with a single camera as the ultimate goal in both real-life and simulated scenes

An integrated study of earth resources in the state of California using remote sensing techniques

The author has identified the following significant results. The effects on estimates of monthly volume runoff were determined separately for each of the following parameters: precipitation, evapotranspiration, lower zone and upper zone tension water capacity, imperviousness of the watershed, and percent of the watershed occupied by riparian vegetation, streams, and lakes. The most sensitive and critical parameters were found to be precipitation during the entire year and springtime evapotranspiration

License Plate Recognition using Convolutional Neural Networks Trained on Synthetic Images

In this thesis, we propose a license plate recognition system and study the feasibility of using synthetic training samples to train convolutional neural networks for a practical application. First we develop a modular framework for synthetic license plate generation; to generate different license plate types (or other objects) only the first module needs to be adapted. The other modules apply variations to the training samples such as background, occlusions, camera perspective projection, object noise and camera acquisition noise, with the aim to achieve enough variation of the object that the trained networks will also recognize real objects of the same class. Then we design two convolutional neural networks of low-complexity for license plate detection and character recognition. Both are designed for simultaneous classification and localization by branching the networks into a classification and a regression branch and are trained end-to-end simultaneously over both branches, on only our synthetic training samples. To recognize real license plates, we design a pipeline for scale invariant license plate detection with a scale pyramid and a fully convolutional application of the license plate detection network in order to detect any number of license plates and of any scale in an image. Before character classification is applied, potential plate regions are un-skewed based on the detected plate location in order to achieve an as optimal representation of the characters as possible. The character classification is also performed with a fully convolutional sweep to simultaneously find all characters at once. Both the plate and the character stages apply a refinement classification where initial classifications are first centered and rescaled. We show that this simple, yet effective trick greatly improves the accuracy of our classifications, and at a small increase of complexity. To our knowledge, this trick has not been exploited before. To show the effectiveness of our system we first apply it on a dataset of photos of Italian license plates to evaluate the different stages of our system and which effect the classification thresholds have on the accuracy. We also find robust training parameters and thresholds that are reliable for classification without any need for calibration on a validation set of real annotated samples (which may not always be available) and achieve a balanced precision and recall on the set of Italian license plates, both in excess of 98%. Finally, to show that our system generalizes to new plate types, we compare our system to two reference system on a dataset of Taiwanese license plates. For this, we only modify the first module of the synthetic plate generation algorithm to produce Taiwanese license plates and adjust parameters regarding plate dimensions, then we train our networks and apply the classification pipeline, using the robust parameters, on the Taiwanese reference dataset. We achieve state-of-the-art performance on plate detection (99.86% precision and 99.1% recall), single character detection (99.6%) and full license reading (98.7%)