In recent years, mobile sensing data are widely used for analyzing human's activities, usage patterns, emotions, health conditions and social relationships. In order to understand and analyze human's behaviors, several frameworks have been proposed to collect mobile sensing data. In this paper we extend previous works and design StarLog, which is a distributed and energy-configurable framework for both mobile data collecting and analyzing. It collects fine-grained sensing data of five categories, reflecting user's locations, activities, interactions with smart phone, social contacts and device setting habits. Data analyses are developed on both client side and server side to understand individual as well as crowd behaviors. Besides, StarLog proposes optional modes for collecting sensory data from GPS, accelerometer, gyroscope and magnetometer to make it configurable for battery concern.
Abstract-Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.
Abstract:Sensors play an important role in the execution of robot tasks as an important component of understanding the world. In this paper, we propose a role-based sensor management framework on ROS (Robot Operating System), in which the role is a set of information needed for robot sensor development. Firstly, we develop a sensor management interface to facilitate the development of sensor applications on robot. And a subscriber module is proposed to shield the details of the sensor driver and the communication between processes, the sensor drivers on the robot are effectively managed. Robot sensors have different executive ability in different situations when robot performing tasks. For example, we know that RGB camera can collect a clear image when the light is strong, while generate a blurred image in weak light environment. Secondly, we propose an environment based sensor role dynamic evaluation mechanism to get the confidence of sensor in a certain environment. The confidence represents the executive ability of sensor when performing tasks. The confidence provides the basis for switching the task schemes. In the mechanism we can dynamically configure the environment information and confidence calculation method. In the experiment phase, we verify the effectiveness of the framework through a robot patrol task. Experiment results show that our framework can effectively manage the robot sensors with a certain environmental adaptability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.