As online shopping continues to grow in popularity, shoes are increasingly being purchased without being physically tried on. This has resulted in a significant surge in returns, causing both financial and environmental consequences. To tackle this issue, several systems are available to measure foot dimensions accurately either in-store or at home. By obtaining precise foot measurements, individuals can determine their ideal shoe size and prevent unnecessary returns. In order to make such a system as simple as possible for the user, only a single image should be sufficient to measure the foot. To make this possible, point clouds from one side of the foot, which are generated by taking a depth image, are to be used. Since these point clouds represent only one side of the foot, the other side has to be generated. For this purpose, different existing state of the art networks were tested and compared to determine which architecture is best suited for this task. After implementing, re-training on our own dataset and testing the different architectures, it can be concluded that the point/transormer-based network SnowflakeNet is the most efficient to be used for our task.

Due to the increasing trend of online shopping, shoes are more and more often bought without being tried on. This leads to a strong increase in returns, which results in a high financial as well as ecological burden. To prevent this, feet can be measured either in the store or at home by various systems to determine the exact dimensions of the foot and derive an optimal shoe size. In this paper, we want to present an overview of the methods currently available on the market for the measurement of feet. The most important commercial systems are classified according to the underlying basic technology. Subsequently, the most promising methods were implemented and tested. The results of the different methods were finally compared to find out the strengths and weaknesses of each technology. After determining the measurement accuracy of the length and width for each measurement method and also comparing the general shape of the 3D reconstruction with the GT, it can be said that the measurement using a ToF sensor is currently the most robust, the easiest and, among other methods, the most accurate method.

In this work, we introduce a robust multi-view 3D pose estimation method which leverages infra-red intensity image and the depth information from the ToF camera. We use the intensity image as an input to a CNN based 2D key point detector from multiple view ports and forward project them to calculate the 3D joint position. If a keypoint is missing in one of the views we benefit from the depth information from the ToF camera. This results in a robust system which is robust against material reflectivity and occlusion. Our experiments show that the system is able to deliver 3D joint positions with a maximum positional error of only 4 cm independent of the material reflectivity. We also compare our technique with different 3D key point estimation techniques. We compare our system to a state-of-the-art CNN based multi-view depth based pose estimation technique to lift 2D key points from a single image to 3D pose. The maximum euclidean distance from the ground truth and its deviation for each system are evaluated against VICON, a marker based motion capture system.

A novel integrated sensor for the simultaneous measurement of layer refractive index and thickness based on evanescent fields is proposed. The theoretical limits for the accuracy of the sensor were examined for the example of a TiO2 layer. The influence of production tolerance on the accuracy was evaluated. In the experimental part of this work, a sensor chip containing nanowire and nanorib waveguides realized in silicon on insulator technology was used to demonstrate the detection of refractive index and thickness of a TiO2 atomic layer deposition (ALD) layer.

The combination of extreme miniaturization with a high sensitivity and the potential to be integrated in an array form on a chip has made silicon-based photonic microring resonators a very attractive research topic. As biosensors are approaching the nanoscale, analyte mass transfer and bonding kinetics have been ascribed as crucial factors that limit their performance. One solution may be a system that applies dielectrophoretic forces, in addition to microfluidics, to overcome the diffusion limits of conventional biosensors. Dielectrophoresis, which involves the migration of polarized dielectric particles in a non-uniform alternating electric field, has previously been successfully applied to achieve a 1000-fold improved detection efficiency in nanopore sensing and may significantly increase the sensitivity in microring resonator biosensing. In the current work, we designed microring resonators with integrated electrodes next to the sensor surface that may be used to explore the effect of dielectrophoresis. The chip design, including two different electrode configurations, electric field gradient simulations, and the fabrication process flow of a dielectrohoresis-enhanced microring resonator-based sensor, is presented in this paper. Finite element method (FEM) simulations calculated for both electrode configurations revealed ∇E2 values above 1017 V2m−3 around the sensing areas. This is comparable to electric field gradients previously reported for successful interactions with larger molecules, such as proteins and antibodies.

Two types of silicon dual-ring resonator-based high-speed optical modulators are proposed. With two microring resonators cascaded either in series or in parallel, the transmission spectrum evolves from a deep notch to a sharp peak with the resonators operating in a push-pull manner. The frequency chirp of the modulated signals can be highly suppressed by choosing a proper working wavelength.

Methylated dendritic polyglycerol (dPG(OMe)) is proposed as an antifouling coating for the application with silicon on insulator (SOI) bio sensors. Sensors were coated with dPG(OMe). Fibrinogen was used to test the antifouling properties. Using SOI ring resonator sensors for the measurement, a reduction of 87% in the binding of fibrinogen to the silicon surface was shown experimentally.

A novel modulation technique for refractive index measurements using resonant nano-photonic structures is proposed. While results comparable to conventional setups are possible the need for a TLS is replaced by thermo-optical modulation.

Four SOI nano rib ring resonators are coupled to a common bus waveguide. All ring resonators have the same free spectral range and a thermo-optical modulation is used to individually access each ring resonator. The addressing of the rings is shown experimentally and oligonucleotides are used as a biological sample to test the sensors.