Robust hovering controller for uncertain multirotor micro aerial vehicles (MAVS) in gps-denied environments: IMAGE-BASED

This paper proposes an image-based robust hovering controller for multirotor micro aerial vehicles (MAVs) in GPS-denied environments. The proposed controller is robust against the effects of multiple uncertainties in angular dynamics of vehicle which contain external disturbances, nonlinear dynamics, coupling, and parametric uncertainties. Based on visual features extracted from the image, the proposed controller is capable of controlling the pose (position and orientation) of the multirotor relative to the fixed-target. The proposed controller scheme consists of two parts: a spherical image-based visual servoing (IBVS) and a robust flight controller for velocity and attitude control loops. A robust compensator based on a second order robust filter is utilized in the robust flight control design to improve the robustness of the multirotor when subject to multiple uncertainties. Compared to other methods, the proposed method is robust against multiple uncertainties and does not need to keep the features in the field of view. The simulation results prove the effectiveness and robustness of the proposed controller

Smartphones and Biometrics: Gait and Activity Recognition

User authentication is a means of identifying the user and verifying that the user is allowed access to services or objects and is a very central step in many applications. People pass through various types of authentication services in their day-to-day activities. For example, to log on to a computer the user is required to know a secret password. Similarly, when turning on a mobile phone the user has to know a PIN code or a touch pattern. Some person authentication methods are based on human physiological or behavioural characteristics, such as fingerprints, face, or voice. Authentication methods differ in their strengths and weaknesses. PIN codes and passwords have to be remembered and gloves have to be removed before fingerprint authentication. Security and usability are essential factors in person authentication. Usability relates to the unobtrusiveness, user-convenience, and human-friendliness of the authentication method. Security is related to the robustness of the authentication method and vulnerability against attacks. Recent advances in microelectronic chip development allow user authentication based on gait (the way a person walks), using small, light, and low-cost sensors. One of the benefits of this is that unobtrusive person authentication through gait recognition is now possible by using mobile smart phones. Optimization of performance and a strong focus on security, while not ignoring usability, will lead to an increased protection of information on smart mobile devices through the use of gait recognition. The general aim of the research described in this thesis was to protect smart mobile devices against unauthorized access by using gait recognition based on the data collected from the sensors embedded in these devices. The effort was not only to develop new innovative algorithms to improve performance in gait recognition, but also to develop awareness on the usability of this method by focusing on activity recognition and continuous authentication, as well as assuring security against deliberate attackers

Gait Recognition using Time-of-Flight Sensor

This is the copy of the book chapter originally published in: Brömme, A., Busch, Ch. (ed.)(2011). Lecture Notes in Informatics, BIOSIG 2011, Proceedings - International Conference of the Biometrics Special Interest Group; 8.-9. September 2011 in Darmstadt. Bonn: Gesellschaft für Informatik. Reprinted with permission from Gesellschaft für Informatik

Covid-19 and E-Learning: An Exploratory Analysis of Research Topics and Interests in E-Learning During the Pandemic

E-learning has gained further importance and the amount of e-learning research and applications has increased exponentially during the COVID-19 pandemic. Therefore, it is critical to examine trends and interests in e-learning research and applications during the pandemic period. This paper aims to identify trends and research interests in e-learning articles related to COVID-19 pandemic. Consistent with this aim, a semantic content analysis was conducted on 3562 peer-reviewed journal articles published since the beginning of the COVID-19 pandemic, using the N-gram model and Latent Dirichlet Allocation (LDA) topic modeling approach. Findings of the study revealed the high-frequency bigrams such as “online learn”, “online education”, “online teach” and “distance learn”, as well as trigrams such as “higher education institution”, “emergency remote teach”, “education online learn” and “online teach learn”. Moreover, the LDA topic modeling analysis revealed 42 topics. The topics of “Learning Needs”, “Higher Education” and “Social Impact” respectively were the most focused topics. These topics also revealed concepts, dimensions, methods, tools, technologies, applications, measurement and evaluation models, which are the focal points of e-learning field during the pandemic. The findings of the study are expected to provide insights to researchers and future studies.publishedVersio