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A. Kiste
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Screen-to-Screen is presented in this thesis, as a potential new method for very close-range communications. This thesis demonstrates the possible viability of using LED arrays as transceivers with LED-to-LED links for very close-range communications between two similar screens. The system presented is composed of two 4 × 6 LED arrays constructed from discrete LED-RGBs and off-the-shelf parts, driven by two Arduinos. These are designed with receive circuitry on the red LEDs, allowing the boards to be receivers for red-to-red LED links between the two boards. With this, algorithms and methods for communicating between the two screens are developed and tested for viability, focusing on the handling of misalignment between the two screens.
In order to evaluate the viability of the system, tests are conducted with varying alignments. The system is tested in the ideal case, where every LED aligns with an equivalent LED on the other side, and a gross achievable data rate of 87 kbit/s is found. At the same time, it is found that translating the screens while maintaining the same rotation can drop this rate to zero. On the other hand, by rotating the screens relative to each other at 45◦, data-rates of 56 kbit/s are achievable with little sensitivity to translation.
Testing the amplitude modulation used in the system showed that there may be correlations between transmitters that are unaccounted for by the calibration system. Thus, it is likely that the real achievable data-rates for this system may be much higher through better algorithms.
The contribution of this thesis to the field of visible light communication lies in the demonstration of a new type of very close-range system that utilizes MIMO methods and LED-to-LED links. Future research could expand upon this work through improved calibration and modulation algorithms, and implementation with a dedicated LED matrix. ...
In order to evaluate the viability of the system, tests are conducted with varying alignments. The system is tested in the ideal case, where every LED aligns with an equivalent LED on the other side, and a gross achievable data rate of 87 kbit/s is found. At the same time, it is found that translating the screens while maintaining the same rotation can drop this rate to zero. On the other hand, by rotating the screens relative to each other at 45◦, data-rates of 56 kbit/s are achievable with little sensitivity to translation.
Testing the amplitude modulation used in the system showed that there may be correlations between transmitters that are unaccounted for by the calibration system. Thus, it is likely that the real achievable data-rates for this system may be much higher through better algorithms.
The contribution of this thesis to the field of visible light communication lies in the demonstration of a new type of very close-range system that utilizes MIMO methods and LED-to-LED links. Future research could expand upon this work through improved calibration and modulation algorithms, and implementation with a dedicated LED matrix. ...
Screen-to-Screen is presented in this thesis, as a potential new method for very close-range communications. This thesis demonstrates the possible viability of using LED arrays as transceivers with LED-to-LED links for very close-range communications between two similar screens. The system presented is composed of two 4 × 6 LED arrays constructed from discrete LED-RGBs and off-the-shelf parts, driven by two Arduinos. These are designed with receive circuitry on the red LEDs, allowing the boards to be receivers for red-to-red LED links between the two boards. With this, algorithms and methods for communicating between the two screens are developed and tested for viability, focusing on the handling of misalignment between the two screens.
In order to evaluate the viability of the system, tests are conducted with varying alignments. The system is tested in the ideal case, where every LED aligns with an equivalent LED on the other side, and a gross achievable data rate of 87 kbit/s is found. At the same time, it is found that translating the screens while maintaining the same rotation can drop this rate to zero. On the other hand, by rotating the screens relative to each other at 45◦, data-rates of 56 kbit/s are achievable with little sensitivity to translation.
Testing the amplitude modulation used in the system showed that there may be correlations between transmitters that are unaccounted for by the calibration system. Thus, it is likely that the real achievable data-rates for this system may be much higher through better algorithms.
The contribution of this thesis to the field of visible light communication lies in the demonstration of a new type of very close-range system that utilizes MIMO methods and LED-to-LED links. Future research could expand upon this work through improved calibration and modulation algorithms, and implementation with a dedicated LED matrix.
In order to evaluate the viability of the system, tests are conducted with varying alignments. The system is tested in the ideal case, where every LED aligns with an equivalent LED on the other side, and a gross achievable data rate of 87 kbit/s is found. At the same time, it is found that translating the screens while maintaining the same rotation can drop this rate to zero. On the other hand, by rotating the screens relative to each other at 45◦, data-rates of 56 kbit/s are achievable with little sensitivity to translation.
Testing the amplitude modulation used in the system showed that there may be correlations between transmitters that are unaccounted for by the calibration system. Thus, it is likely that the real achievable data-rates for this system may be much higher through better algorithms.
The contribution of this thesis to the field of visible light communication lies in the demonstration of a new type of very close-range system that utilizes MIMO methods and LED-to-LED links. Future research could expand upon this work through improved calibration and modulation algorithms, and implementation with a dedicated LED matrix.
Bachelor thesis
(2024)
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A. Kiste, E.A. Skorobogatova, G. van der Veen, J.A. Poot, M. Popławski, N. Al-Bayaty de Ridder, S. Dragotă, S.C.M. Loogman, T.E. Simula, V.M. Iliescu, M.T.H. Brown, E.J. van den Bos, M. Lourenço Baptista
Learning Reduced Order Mappings of Navier-Stokes
An Investigation of Generalization on the Viscosity Parameter
Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during engineering processes. PCA-net is a neural network approach that reduces the dimensionality of the input and output data for PDEs in order to allow mapping from a high-dimensional input and output function with a fully connected neural network through the use of Principal Component Analysis (PCA). In this paper, PCA-net is applied to Navier-Stokes with varying viscosities to test the generalization of PCA-net on viscosity parameters. Training is done on four discrete viscosities, while testing is done on continuous viscosities, extrapolating and interpolating around the training set. Results shows good performance on low viscosities, both with interpolation and extrapolation. Mid-to-high viscosity interpolation shows lesser performance, with high viscosity extrapolation diverging to great error. Omitting high viscosities, performance over varying viscosities is close to that shown by previous research.
...
Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during engineering processes. PCA-net is a neural network approach that reduces the dimensionality of the input and output data for PDEs in order to allow mapping from a high-dimensional input and output function with a fully connected neural network through the use of Principal Component Analysis (PCA). In this paper, PCA-net is applied to Navier-Stokes with varying viscosities to test the generalization of PCA-net on viscosity parameters. Training is done on four discrete viscosities, while testing is done on continuous viscosities, extrapolating and interpolating around the training set. Results shows good performance on low viscosities, both with interpolation and extrapolation. Mid-to-high viscosity interpolation shows lesser performance, with high viscosity extrapolation diverging to great error. Omitting high viscosities, performance over varying viscosities is close to that shown by previous research.