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Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG),(More)
Surface Electromyogram (EMG) signals are usually utilized as a control source for multifunction powered prostheses. A challenge that arises with the current demands of such prostheses is the ability to accurately control a large number of individual and combined fingers movements and to do so in a computationally efficient manner. As a response to such a(More)
— Lane detection in urban environments is a challenging task. That is mainly due to the non existence of unique models, poor quality of lane markings due to wear, occlusions due to the presence of traffic and complex road geometry. In this work we present a novel lane detection and tracking algorithm for urban road scenarios based on weak models, which is(More)
Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The(More)
quadrotor microaerial vehicles (MAVs) are simple robotic platforms with regard to their construction. In their basic form, they are no more than two counterrotating propeller pairs attached symmetrically to a rigid crosslike frame, along with the means to control the speed of each individual propeller. This symmetric design has enabled the quadrotor to(More)
Scene classification is a fundamental perception task for environmental understanding in today's robotics. In this paper, we have attempted to exploit the use of popular machine learning technique of deep learning to enhance scene understanding, particularly in robotics applications. As scene images have larger diversity than the iconic object images, it is(More)
Representation of spaces including both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Therefore, in recent years identifying and semantically labeling the environments based on onboard sensors has become an important competency for mobile robots. Supervised learning algorithms have been extensively(More)
Road-boundary detection is an integral and important function in advanced driver-assistance systems and autonomous vehicle navigation systems. A prominent feature of roads in urban, semi-urban, and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is curbs on either side defining the road's(More)
Recent studies on the myoelectric control of powered prosthetics revealed several factors that affect its clinical performance. One of the important factors is the variation in the limb position associated with normal use which can have a substantial impact on the robustness of Electromyogram (EMG) pattern recognition. To solve this problem, we propose in(More)