Mobile devices have become an integral part of our life and provide dozens of useful services to their users. However, usability of mobile devices is hindered by battery lifetime. Energy conservation can extend battery lifetime, however, any energy management policy requires accurate prediction of energy consumption, which is impossible without reliable energy measurement and estimation methods and tools. We present an analysis of the energy measurement methodologies and describe the implementations of the internal (profiling) software (proprietary, custom) and external software-based (Java API, Sensor API, GSM AT) energy measurement methodologies. The methods are applied to measure energy consumption on a variety of mobile devices (laptop PC, PDA, smart phone). A case study of measuring energy consumption on a mobile computer using 3DMark06 benchmarking software is presented
Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.