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In the course of this thesis, an overview will be given on which way developers can guide
users into acting environmentally friendly without the users realizing they are being
nudged. In the last couple of years, our private and work-life have been more and more
shifted away from reality into a digital context. Since the start of the Covid – 19 pandemic
in 2019, even more aspects of everyday life have been shifted to an online context, one
of them being groceries shopping. Even though online groceries shopping is not yet
common in Germany, there is a trend toward the online purchase of groceries visible.
This can be seen as a possibility to tackle another challenge the world is facing, the
climate crisis. One reason for the climate crisis is mindless consumption and purchasing
of too much food. This paper aims to combine the need for more aware consumption
with the newly rising trend of online supermarkets. Furthermore, a supermarket will be
provided to show if the implementation of environmentally–friendly nudges is technically
possible. To eventually prove the effectiveness of a nudge, it needs to be tested.
Keywords: Nudging, Environment, Online supermarkets
In this document a reliable data streaming mechanism for a TDMA LPWAN application is developed by adapting a link layer solution for power line communication, published at the International Symposium on Power Line Communications and its Applications (ISPLC) 2015. A C++ implementation of the services link layer is provided and demonstrated
working at a packet error rate of 50%.
This work aims to generate synthetic electromyographic (EMG) signals using Generative Adversarial Network (GAN). GANs are considered as one of the most exciting and promising approaches in deep learning [6], offering the possibility to generate artificial data based on real data. GAN consists of two main parts, a discriminator that attempts to differentiate between the generated data and the original data, and a generator that tries to fool the discriminator by generating data which looks like real data, the GAN works by staging a two-player
minimax game between generator and discriminator networks. To achieve the objective of generating realistic artificial electromyographic signals, two different architectures are considered for the generator and the discriminator networks of the GAN model: Long short-term memory (LSTM), which can avoid the long-term dependency problem and remembers information over a long period of time, and convolutional neural network (CNN), which is a powerful tool at automatic feature extraction. Different combinations of CNN and LSTM including hybrid model are experimented within the GAN using the same training data-set. The results and performances of each combination are compared and reviewed. The generated artificial EMG signals can be used to
simulate real muscle activity situations to for example improve muscle signal controlled prostheses using artificial data that may include conditions that does not exist in real data. This method of artificial data generation is not limited to EMG signals, the network can also be used to generate other synthetic biomedical signals such as electroencephalogram (EEG) or electrocardiogram (ECG) that can be practically used for testing algorithms and classifiers.