Likelihood-based Sensor Calibration for Expert-Supported Distributed Learning Algorithms in IoT Systems

An important task in the field of sensor technology is the efficient implementation of adaptation procedures of measurements from one sensor to another sensor of identical design. One idea is to use the estimation of an affine transformation between different systems, which can be improved by the knowledge of experts. This paper presents an improved solution from Glacier Research that was published back in 1973. It is shown that this solution can be adapted for software calibration of sensors, implementation of expert-based adaptation, and federated learning methods. We evaluate our research with simulations and also with real measured data of a multi-sensor board with 8 identical sensors. The results show an improvement for both the simulation and the experiments with real data.

Multi-Scale Anisotropic Fourth-Order Diffusion Improves Ridge and Valley Localization

Ridge and valley enhancing filters are widely used in applications such as vessel detection in medical image computing. When images are degraded by noise or include vessels at different scales, such filters are an essential step for meaningful and stable vessel localization. In this work, we propose a novel multi-scale anisotropic fourth-order diffusion equation that allows us to smooth along vessels, while sharpening them in the orthogonal direction. The proposed filter uses a fourth order diffusion tensor whose eigentensors and eigenvalues are determined from the local Hessian matrix, at a scale that is automatically selected for each pixel. We discuss efficient implementation using a Fast Explicit Diffusion scheme and demonstrate results on synthetic images and vessels in fundus images. Compared to previous isotropic and anisotropic fourth-order filters, as well as established second-order vessel enhancing filters, our newly proposed one better restores the centerlines in all cases.