1,720,964 research outputs found
How Well Can Driverless Vehicles Hear? A Gentle Introduction to Auditory Perception for Autonomous and Smart Vehicles
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Short-Term Wireless Connectivity Prediction for Connected Agricultural Vehicles
No full textRobots and autonomous vehicles have been integrated in our life and utilized in a plethora of application scenarios, including intelligent transportation, industrial automation and smart agriculture. Several of the these applications might be functioning in environments where cellular network coverage is low or non-existent. In a case like this, lower bandwidth networks and vehicle-to-vehicle communication can be used to keep the application operating safely, even with less active features. In such settings, disconnection events can be avoided if deteriorating communication links are detected early so that prevention measures can be taken. In this paper we investigate how we can predict if a communication link will be terminated in the near future based on the recent trend of the signal. We propose a deep neural network framework which is executed onboard and we evaluate its performance based on simulation and real word data. The results show that we can predict the termination of a link up to 7 seconds into the future with 72.38% accuracy and 86.38% recall
From Bits of Data to Bits of Knowledge: An On-Board Classification Framework for Wearable Sensing Systems
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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