The aim of this study is to analyze factors that affecting consumers to drink coffee, based on two case studies in University of Gävle in Sweden and Zhuhai College of Jilin University in China.The study was conducted with mixed research methods. A questionnaire survey was designed as a pre-study to get a general idea and the interviews were designed based on the result of survey in order to gain more detailed information and explanations from the respondents. All the data are shown by tables or diagrams or histograms. University students in two different culture participated in survey and interviews, a comparison perspective for cross culture was mentioned in this study. As for Result & Conclusions, both external factors (culture, price and people around) as well as internal factors (personality, psychological influence, learning and knowledge) impact the consumer‟s behaviors that are shown in different consumption characteristics. Take all in account, these factors could have an effect on university students to drink (or not drink) coffee in Jilin University and University of Gävle. And Contribution & implication, the comparison of coffee drink between Chinese culture and Swedish culture is the most attractive contributions, supplementing the related study. Price issue was considered as the important factor that affecting the consumer to choose coffee, thus suggesting the managers to focus on the coffee price. In addition, the effective way to explore coffee market in China or Sweden is to know better of the consumers. Suggestions for future research, other consumer groups are missing given the study only focus on the university students. Also tested with more sample sizes of survey andIIinterviews are recommended in the future research so as to make it even valid and reliable.

The goal of this master's thesis is to design and implement a system for recognition of home appliances based on their power consumption characteristics. This system should identify the individual home appliances from measurements of the total household consumption. The acquired data could be used for statistics of usage of a particular appliance and subsequent detection of errors or non-standard behavior of the measured device. An important part of my work is a design and hardware implementation of a unit for measuring and a system for processing the measured signal. The first version of my project uses pulse output of an electrometer to measure the energy. This method does not provide a sufficient sample rate but it's a quick way to obtain data for processing and analysis. The second version monitors the power consumption with a multi-purpose AC converter which measures active and reactive power with the desired sample rate. The data is then processed and recognized by two classifiers - HMM and KNN.