Clean water in the classroom : understanding the importance of water quality

This study’s objective was to answer three research questions related to students’ knowledge and attitudes about water quality and availability issues. It is important to understand what knowledge students have about environmental problems such as these, because today’s students will become the problem solvers of the future. If environmental problems, such as those related to water quality, are ever going to be solved, students must be environmentally literate. Several methods of data collection were used. Surveys were given to both Bolivian and Jackson High School students in order to comparison their initial knowledge and attitudes about water quality issues. To study the effects of instruction, a unit of instruction about water quality issues was then taught to the Jackson High School students to see what impact it would have on their knowledge. In addition, the learning of two different groups of Jackson High School students was compared—one group of general education students and a second group of students that were learning in an inclusion classroom and included special education students and struggling learners form the general education population. Student and teacher journals, a unit test, and postsurvey responses were included in the data set. Results suggested that when comparing Bolivian students and Jackson High School students, Jackson High School students were more knowledgeable concerning clean water infrastructure and its importance, despite the fact that these issues were less relevant to their lives than for their Bolivian counterparts. Although overall, the data suggested that all the Jackson High students showed evidence that the instruction impacted their knowledge, the advanced Biology students appeared to show stronger gains than their peers in an inclusion classroom.

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.