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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.
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%.
In this scientific research, an innovative sensor system is developed to prevent child heatstrokes in vehicles. The system incorporates a 24 GHz Continuous-Wave (CW) radar system, which identifies vital signs of an infant through a 4-by-1 patch antenna array embedded in a specifically designed circuit board. Intelligent signal processing algorithms analyze data generated by the radar chip and execute processing tasks on a robust microcontroller. The child’s respiration
rate can be extracted qualitatively from the data in nearly real-time, enabling the system to differentiate between a child and a mere shopping bag on the seat. In the event of identifying a critical condition, the system transmits this information via a data bus to a central ECU within the vehicle. This ECU is integrated with GSM and GPS connections, allowing communication with the driver or emergency services. The development of the sensor system adheres to existing
automotive industry standards, featuring a cost-effective design intended as a prototype for large-scale production. Through rigorous evaluation across various scenarios, including realworld
situations with children, the sensor system is refined. The continuously reliable function of the developed radar-based sensor system holds the potential to save children’s lives, making
a major contribution to automotive safety.
Efficient and reliable onsite inspection methods are gaining importance as the construc-tion of PV power plants is expanding. For large PV installations, time- and cost-efficient failure detection is essential for optimized operation and maintenance. For this purpose, various optical methods as Infrared thermography (IR), Electroluminescence (EL), Pho-toluminescence (PL) and Ultraviolet Fluorescence (UVF) are employed and under con-stant development. For each method, the camera, and eventually the light source, can be handheld, or mounted on a drone, also called unmanned aircraft vehicle (UAV), to achieve higher throughputs.
IR is the most widely used optical onsite PV inspection method, as many defects can be detected by the thermal radiation (heating) of the defect component. EL and PL reveal further information on the electrical behaviour of the Si-waver. They are also widely used and take the role of a complement to IR, showing electrically active/inactive areas of the semiconductor. On the other hand, UVF focuses on the degradation of the polymeric encapsulant of the Si-cell, most commonly consisting of EVA (ethylene-vinyl acetate). The degradation of the encapsulant can lead to its discoloration, also called yellow-ing/browning, which decreases the transmittance of visual light. UVF patterns can show this yellowing as well as humidity and oxygen entrances, which can lead to effects of corrosion. Both mechanisms (discoloration and corrosion) decrease the performance of the PV cell. The discoloration cannot be directly observed on IR or EL images, as the encapsulant is neither a heat source nor electroconductive. Using IR imagery, severe discoloration might be observed indirectly, as the reduced optical transmittance leads to changes in heat transfer mechanisms concerning the cell and the encapsulant.
Similarly, as long as corrosion does not lead to inactive cell areas or heating, it most likely will not be spotted using EL, PL or IR. So, UVF can fill the niche of inspecting the state of the encapsulant and detecting its defects due to climate impacts in early stages.
While a high number of studies on IR, EL, PL and some on UVF were performed in Europe and the USA, there are not yet many studies about the application of these tech-niques in South America (i.e., in Brazil). UVF mainly depends on climate factors (irradi-ation, temperature, humidity) and the operation time/”age” of the module. The UVF im-agery method has not yet been tested in climate and system conditions of Brazil. Fur-thermore, systems in Brazil are more recently installed. All this can affect differences in the results of UVF imagery applied in Europe, the USA and Brazil.
The present work focuses on the application of UVF imaging on PV power plants in Bra-zil, the creation of an experimental setup and the proposal of proceedings for the data analysis of the acquired images. The aim is to propose a method that is suitable for large-scale inspection.
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