Antonis G. Dimitriou, Stavroula Siachalou, Emmanouil Tsardoulias and Loukas Petrou
"Robotics Meets RFID for Simultaneous Localization (of Robots and Objects) and Mapping (SLAM) – A Joined Problem"
Charpter:7, pp. -, John Wiley & Sons, Inc., 2020 Feb
Localization of wirelessly powered devices is essential for many applications related to the Internet of Things and Ubiquitous Computing. The chapter is focused on deploying a moving robotic platform, i.e. a robot, which hosts radio frequency identification (RFID) equipment and aims to locate passive RFID tags attached on objects in the surrounding area. The robot hosts additional sensors, namely lidar and depth cameras, enabling it to perform SLAM – simultaneous localization (of its own location) and mapping of any (including previously unknown) area. Furthermore, it can avoid obstacles, including people and perform and update path planning. Thanks to its movement, the robot collects a huge amount of data related to received signal strength information (RSSI) and phase information of each tag, realizing the concept of a “virtual antenna array”; i.e. a moving antenna at multiple locations. The antenna‐equipped robot behaves similarly to a synthetic‐aperture radar. The main application is continuous inventorying and localization; focusing on warehouse management, large retail stores, libraries, etc. The main advantage of the robotic approach versus static‐reader‐antenna deployments arises from the equivalent cost‐reduction per square meter of target area, since a single robot can circulate continuously around any area, whereas a fixed RFID‐network would necessitate for infrastructure costs analogous to the size of the area. Another advantage is the huge amount of data from different locations (of the robot) available to be exploited for more accurate RFID localization. Compared to a fixed installation, the disadvantage is that the robot does not cover the entire area simultaneously. Depending on the size of the target area and the desired inventorying update rate, additional robots could be deployed. In this chapter, the localization problem is presented and linked to practical applications. Representative prior‐art is analyzed and discussed. The SLAM problem is also discussed, while related state‐of‐the‐art is presented. Moreover, experimental results by an actual robot are demonstrated. A robot collects phase and RSSI measurements by RFID tags. It is shown that positioning accuracy is affected by both robotics' SLAM accuracy as well as the disruption of tags' backscattered signal due to fading. Finally, techniques to improve the system are discussed.
Valasia Dimaridou, Alexandros-Charalampos Kyprianidis, Michail Papamichail, Themistoklis Diamantopoulos and Andreas Symeonidis
Charpter:1, pp. 25, Springer, 2018 Jan
Nowadays, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. This practice is in principle cost-effective, however it may also lead to low quality software products, if the components to be reused exhibit low quality. Thus, several approaches have been developed to measure the quality of software components. Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by developers. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. Neural networks are thus applied to estimate the final quality score given metrics from these axes. Preliminary evaluation indicates that our approach effectively estimates software quality at both class and package levels.