Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Growing urban health: community gardening in South-East Toronto.

Abstract: SUMMARY This article describes results from an investigation of the health impacts of community gardening, using Toronto, Ontario as a case study. According to community members and local service organizations, these gardens have a number of positive health benefits. However, few studies have explicitly focused on the health impacts of community gardens, and many of those did not ask community gardeners directly about their experiences in community gardening. This article sets out to fill this gap by describing the results of a community-based research project that collected data on the perceived health impacts of community gardening through participant observation, focus groups and in-depth interviews. Results suggest that community gardens were perceived by gardeners to provide numerous health benefits, including improved access to food, improved nutrition, increased physical activity and improved mental health. Community gardens were also seen to promote social health and community cohesion. These benefits were set against a backdrop of insecure land tenure and access, bureaucratic resistance, concerns about soil contamination and a lack of awareness and understanding by community members and decisionmakers. Results also highlight the need for ongoing resources to support gardens in these many roles.

New integration algorithms for estimating motor flux over a wide speed range

Abstract: Three new integration algorithms for motor flux estimation are proposed in this paper. These algorithms are developed for use in high-performance sensorless AC motor drives. The first algorithm is used to elaborate the basic operating principle. The second one is designed for the drives that require a constant air-gap flux during operation. The third algorithm, in which an adaptive controller is used, can have wide industrial applications. The proposed algorithms can effectively solve the problems associated with pure integrators. These algorithms can be used to accurately measure the motor flux including its magnitude and phase angle over a wide speed range (1:100). The provenance of the algorithms is investigated, compared, and verified experimentally.

Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose

Abstract: This paper addresses the challenge of 3D human pose estimation from a single color image. Despite the general success of the end-to-end learning paradigm, top performing approaches employ a two-step solution consisting of a Convolutional Network (ConvNet) for 2D joint localization and a subsequent optimization step to recover 3D pose. In this paper, we identify the representation of 3D pose as a critical issue with current ConvNet approaches and make two important contributions towards validating the value of end-to-end learning for this task. First, we propose a fine discretization of the 3D space around the subject and train a ConvNet to predict per voxel likelihoods for each joint. This creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates. Second, to further improve upon initial estimates, we employ a coarse-to-fine prediction scheme. This step addresses the large dimensionality increase and enables iterative refinement and repeated processing of the image features. The proposed approach outperforms all state-of-the-art methods on standard benchmarks achieving a relative error reduction greater than 30% on average. Additionally, we investigate using our volumetric representation in a related architecture which is suboptimal compared to our end-to-end approach, but is of practical interest, since it enables training when no image with corresponding 3D groundtruth is available, and allows us to present compelling results for in-the-wild images.