The Identification of Best Management Practices in a Materials Recovery Facility to Increase Solid Waste Diversion in the Department of Defense (DoD) Installations along the Front Range of Colorado to Satisfy the DoD Solid Waste Diversion Goal of 40 Percent

The five installations operated by the Department of Defense (DoD) in the Front Range region of Colorado do not meet the DoD non-hazardous solid waste diversion goal of 40 percent, further impacting landfills and generating greenhouse gases. This applied capstone project identifies and evaluates best management practices of a Materials Recovery Facility (MRF), qualitatively and quantitatively, to increase solid waste diversion at a DoD MRF. An environmental benefits model quantified the externalities of increasing solid waste diversion at the installations. By implementing best management practices at a MRF, the DoD would divert an additional 1,400 tons of solid waste per year, resulting in the equivalent of 1,502,567 gallons of gasoline being saved, among many benefits presented in this capstone.

Trees as Tools in Environmental Improvement; An Analysis of the Contribution of Vegetation to Energy and Water Conservation in the Front Range of Colorado

The Denver metropolitan area is facing rapid population growth that increases the stress on already limited resources. Research and advanced computer modeling show that trees, especially those in urban areas, have significant environmental benefits. These benefits include air quality improvements, energy savings, greenhouse gas reduction, and possible water conservation. This Capstone Project applies statistical methods to analyze a small data set of residential homes and their energy and water consumption, as a function of their individual landscape. Results indicate that tree shade can influence water conservation, and that irrigation methods can be an influential factor as well. The Capstone is a preliminary analysis for future study to be performed by the Institute for Environmental Solutions in 2007.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Surréalisme Sous-L’Eau : science and surrealism in the early films and writings of Jean Painlevé

A French biologist who moved in Surrealist circles, Jean Painlevé began making films about underwater creatures in 1927, and by 1982 had created over two hundred films on a broad range of natural, scientific, and political subjects. His underwater films remain the most ethereal and poetic works in his oeuvre, and he specifically used cinema to capture the mystery and wonder of nature.