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B.J.H. van Nifterik
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Radar micro-Doppler signatures are powerful indicators of target movements and activities, enabling the extraction of valuable information about various objects' internal and external dynamics. Consequently, classifying these signatures has become crucial in numerous applications, ranging from target recognition in surveillance, to biomedical sensing and interaction with smart sensors.
In this thesis, an evaluation of classification performances for a wide variety of orthogonal moments, when applied to micro-Doppler classification problems, is presented. A pipeline is proposed to evaluate all moments commonly used in image processing, but not routinely employed in radar-based classification.
The evaluation results are compared with other state-of-the-art classification approaches, such as using micro-Doppler signatures directly as the input of Convolutional Neural Networks. The influence of noise in the data on the classification performance is also shown.
The classification results demonstrate the different moments' capabilities with a variety of publicly available datasets containing human micro-Doppler signatures, resulting in a very well performing classification pipeline for this type of classification problem, and novel insights into the potential of these moments for radar classification problems. ...
In this thesis, an evaluation of classification performances for a wide variety of orthogonal moments, when applied to micro-Doppler classification problems, is presented. A pipeline is proposed to evaluate all moments commonly used in image processing, but not routinely employed in radar-based classification.
The evaluation results are compared with other state-of-the-art classification approaches, such as using micro-Doppler signatures directly as the input of Convolutional Neural Networks. The influence of noise in the data on the classification performance is also shown.
The classification results demonstrate the different moments' capabilities with a variety of publicly available datasets containing human micro-Doppler signatures, resulting in a very well performing classification pipeline for this type of classification problem, and novel insights into the potential of these moments for radar classification problems. ...
Radar micro-Doppler signatures are powerful indicators of target movements and activities, enabling the extraction of valuable information about various objects' internal and external dynamics. Consequently, classifying these signatures has become crucial in numerous applications, ranging from target recognition in surveillance, to biomedical sensing and interaction with smart sensors.
In this thesis, an evaluation of classification performances for a wide variety of orthogonal moments, when applied to micro-Doppler classification problems, is presented. A pipeline is proposed to evaluate all moments commonly used in image processing, but not routinely employed in radar-based classification.
The evaluation results are compared with other state-of-the-art classification approaches, such as using micro-Doppler signatures directly as the input of Convolutional Neural Networks. The influence of noise in the data on the classification performance is also shown.
The classification results demonstrate the different moments' capabilities with a variety of publicly available datasets containing human micro-Doppler signatures, resulting in a very well performing classification pipeline for this type of classification problem, and novel insights into the potential of these moments for radar classification problems.
In this thesis, an evaluation of classification performances for a wide variety of orthogonal moments, when applied to micro-Doppler classification problems, is presented. A pipeline is proposed to evaluate all moments commonly used in image processing, but not routinely employed in radar-based classification.
The evaluation results are compared with other state-of-the-art classification approaches, such as using micro-Doppler signatures directly as the input of Convolutional Neural Networks. The influence of noise in the data on the classification performance is also shown.
The classification results demonstrate the different moments' capabilities with a variety of publicly available datasets containing human micro-Doppler signatures, resulting in a very well performing classification pipeline for this type of classification problem, and novel insights into the potential of these moments for radar classification problems.
Measurement Light Bulb
Light source and camera synchronization
Bachelor thesis
(2019)
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Jurgen Wervers, Bob van Nifterik, Elmar Eisemann, Markus Billeter, Nestor Ziliotto Salamon
This document describes the design process the prototype of a measurement light bulb which is able to project spherical harmonics of orders 0-1-2. Photographs of these projections can be combined to represent many light distributions. The measurement light bulb can be used in the field of research focused on the computation of the illumination impact of lighting, more specifically, simulating different light sources. Our prototype consists of a laser beam rotating over two axes, which allows the device to project onto a sphere around itself. The device can be controlled wirelessly using Bluetooth. The lamp can project spherical harmonics in a resolution of 4° and 256 monochrome light levels. The time one projection takes is about one second, which allows for quick measurements. The dimensions and the weight of the lamp are such that it is portable. At this stage, the prototype can only operate in a dark environment, due to the use of a low powered laser. In this document, the focus will be on the light source, its corresponding drivers and the camera synchronisation.
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This document describes the design process the prototype of a measurement light bulb which is able to project spherical harmonics of orders 0-1-2. Photographs of these projections can be combined to represent many light distributions. The measurement light bulb can be used in the field of research focused on the computation of the illumination impact of lighting, more specifically, simulating different light sources. Our prototype consists of a laser beam rotating over two axes, which allows the device to project onto a sphere around itself. The device can be controlled wirelessly using Bluetooth. The lamp can project spherical harmonics in a resolution of 4° and 256 monochrome light levels. The time one projection takes is about one second, which allows for quick measurements. The dimensions and the weight of the lamp are such that it is portable. At this stage, the prototype can only operate in a dark environment, due to the use of a low powered laser. In this document, the focus will be on the light source, its corresponding drivers and the camera synchronisation.