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There is a growing concern about chronic diseases and other health problems related to diet including obesity and cancer. The need to accurately measure diet (what foods a person consumes) becomes imperative. Dietary intake provides valuable insights for mounting intervention programs for prevention of chronic diseases. Measuring accurate dietary intake is considered to be an open research problem in the nutrition and health fields. In this paper, we describe a novel mobile telephone food record that will provide an accurate account of daily food and nutrient intake. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed. The mobile device provides a unique vehicle for collecting dietary information that reduces the burden on respondents that are obtained using more classical approaches for dietary assessment. We describe our approach to image analysis that includes the segmentation of food items, features used to identify foods, a method for automatic portion estimation, and our overall system architecture for collecting the food intake information.

Abstract-Low-loss optical communication requires light sources at 1.5um wavelengths. Experiments showed without much theoretical guidance that InAs/GaAs quantum dots (QDs) may be tuned to such wavelengths by adjusting the In fraction in an In x Ga 1-x As strain-reducing capping layer (SRCL). In this work systematic multimillion atom electronic structure calculations qualitatively and quantitatively explain for the first time available experimental data. The NEMO 3-D simulations treat strain in a 15 million atom system and electronic structure in a subset of ~9 million atoms using the experimentally given nominal geometries and without any further parameter adjustments the simulations match the nonlinear behavior of experimental data very closely. With the match to experimental data and the availability of internal model quantities significant insight can be gained through mapping to reduced order models and their relative importance. We can also demonstrate that starting from simple models has in the past led to the wrong conclusions. The critical new insight presented here is that the QD changes its shape. The quantitative simulation agreement with experiment without any material or geometry parameter adjustment in a general atomistic tool leads us to believe that the era of nano Technology Computer Aided Design (nano-TCAD) is approaching. NEMO 3-D will be released on nanoHUB.org where the community Manuscript received July 13, 2008. This work was carried out in part at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). Muhammad Usman is funded through Fulbright USAID (Grant ID # 15054783). nanoHUB.org computational resources operated by the Network for Computational Nanotechnology (NCN) funded by the National Science Foundation were used in this work.Copyright (c) 2008 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org .Muhammad Usman is with the Network for Computational Nanotechnology, Electrical Engineering Department, Purdue University West Lafayette, Indiana 47907 USA can duplicate and expand on the results presented here through interactive simulations.

The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial transfer function generation serves as a reasonable base for volumetric rendering, reducing the trial-and-error overhead typically found in transfer function design.

As obesity concerns mount, dietary assessment methods for prevention and intervention are being developed. These methods include recording, cataloging and analyzing daily dietary records to monitor energy and nutrient intakes. Given the ubiquity of mobile devices with built-in cameras, one possible means of improving dietary assessment is through photographing foods and inputting these images into a system that can determine the nutrient content of foods in the images. One of the critical issues in such the image-based dietary assessment tool is the accurate and consistent estimation of food portion sizes. The objective of our study is to automatically estimate food volumes through the use of food specific shape templates. In our system, users capture food images using a mobile phone camera. Based on information (i.e., food name and code) determined through food segmentation and classification of the food images, our system choose a particular food template shape corresponding to each segmented food. Finally, our system reconstructs the three-dimensional properties of the food shape from a single image by extracting feature points in order to size the food shape template. By employing this template-based approach, our system automatically estimates food portion size, providing a consistent method for estimation food volume.