Our Lives, Our Thoughts and Our Allegiance: New Immigrants and American Industry in 1914

Through comparative analysis of the immigrant labor forces at work in iron mining in northern Minnesota, coal mining in Illinois, and steel milling in the Calumet region of Chicago and Gary, this paper addresses the forms of social distance separating and marginalizing new immigrants from American society and trade unionism that existed in 1914, the year that marked the end point of mass immigration from Eastern and Southern Europe. The “new immigration” was a labor migration that congregated its subjects overwhelmingly in what were called "unskilled" or "semi-skilled" forms of labor. Skilled work was largely, with certain variations, the preserve of "American" or old immigrant workers. This labor gulf separating new immigrants and American workers was hardened by a spatial separateness. New immigrants often lived in what have been called industrial villages—the mining town or location, the factory neighborhood— striking in their isolation and insularity from mainstream society. This separateness and insularity became a major preoccupation for corporate managers, Progressive reformers, and for American trade unions as new immigrants began to engage in major labor struggles leading up to 1914. But among the three industries, only the union of coal miners, the United Mine Workers, enjoyed success in organizing the new immigrants. In the steel mills and the iron mines, the unions were either rooted out or failed to gain a foothold at all. The explanation for these differences is to be found in the different forms of industrial development among the industries studied.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.