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This paper is a summary of the creation and data usage of an autonomous model vehicle which was recreated in CarMaker[1]. The simulation data come to use when following students of this semester project will develop algorithms and simulations which are impractical to test or train in real life. This paper starts with a summary of the background information around the model vehicle and the software CarMaker[1], then reproduces the construction process, digs deeper in ROS[6] and finishes with the extraction of the data.
A real time object detection system is an essential core feature of any autonomous driving car. With the variety of models and data sets available, there is great opportunity to train a model that meets the high requirements of autonomous vehicle. In this paper we want to present an object detection model for traffic signs and lights implemented on a NVIDIA Xavier board [7] using the robot operating system (ROS) [9]. Furthermore we explain how to create and evaluate a data set as well as to test the accuracy and performance of the implemented detector. In regards to pattern recognition with neural networks, the performance and accuracy must also be weighed up. The goal is to achieve one without sacrificing the other. Considering all these aspects, we decided to train a YOLOv4 Standard and a YOLOv4 Tiny configuration [2]. While the accuracy of the YOLOv4 network exhibited a very high accuracy rate on high and mediocre resolutions but the performance is too poor to be used in a real time object detection system. On the other hand, the YOLOv4 tiny network reached the strived for performance even on the highest resolution scales tested, but at the cost of accuracy.
The enormous power consumption of Bitcoin has led to undifferentiated discussions in science and practice about the sustainability of blockchain and distributed ledger technology in general. However, blockchain technology is far from homogeneous - not only with regard to its applications, which now go far beyond cryptocurrencies and have reached businesses and the public sector, but also with regard to its technical characteristics and, in particular, its power consumption. This paper summarizes the status quo of the power consumption of various implementations of blockchain technology, with special emphasis on the recent 'Bitcoin Halving' and so-called 'zk-rollups'. We argue that although Bitcoin and other proof-of-work blockchains do indeed consume a lot of power, alternative blockchain solutions with significantly lower power consumption are already available today, and new promising concepts are being tested that could further reduce in particular the power consumption of large blockchain networks in the near future. From this we conclude that although the criticism of Bitcoin's power consumption is legitimate, it should not be used to derive an energy problem of blockchain technology in general. In many cases in which processes can be digitised or improved with the help of more energy-efficient blockchain variants, one can even expect net energy savings.