Christian Kehl

Antonino Armato, Christian Kehl, Sebastian Fischer

Accurate and reliable safety metrics are paramount for functional safety verification of ASICs in automotive systems. Traditional FMEDA (Failure Modes, Effects, and Diagnostic Analysis) metrics, such as SPFM (Single Point Fault Metric) and LFM (Latent Fault Metric), depend on the precision of failure mode distribution (FMD) and diagnostic coverage (DC) estimations. This reliance can often leads to significant, unquantified uncertainties and a dependency on expert judgment, compromising the quality of the safety analysis. This paper proposes a novel approach that introduces error propagation theory into the calculation of FMEDA safety metrics. By quantifying the maximum deviation and providing confidence intervals for SPFM and LFM, our method offers a direct measure of analysis quality. Furthermore, we introduce an Error Importance Identifier (EII) to pinpoint the primary sources of uncertainty, guiding targeted improvements. This approach significantly enhances the transparency and trustworthiness of FMEDA, enabling more robust ASIC safety verification for ISO 26262 compliance, addressing a longstanding open question in the functional safety community.

Wail Mustafa, Christian Kehl, Ulrik Lund Olsen et al.

Spectral computed tomography (CT) is an emerging technology capable of providing high chemical specificity, which is crucial for many applications such as detecting threats in luggage. This type of application requires both fast and high-quality image reconstruction and is often based on sparse-view (few) projections. The conventional filtered back projection (FBP) method is fast but it produces low-quality images dominated by noise and artifacts in sparse-view CT. Iterative methods with, e.g., total variation regularizers can circumvent that but they are computationally expensive, as the computational load proportionally increases with the number of spectral channels. Instead, we propose an approach for fast reconstruction of sparse-view spectral CT data using a U-Net convolutional neural network architecture with multi-channel input and output. The network is trained to output high-quality CT images from FBP input image reconstructions. Our method is fast at run-time and because the internal convolutions are shared between the channels, the computational load increases only at the first and last layers, making it an efficient approach to process spectral data with a large number of channels. We have validated our approach using real CT scans. Our results show qualitatively and quantitatively that our approach outperforms the state-of-the-art iterative methods. Furthermore, the results indicate that the network can exploit the coupling of the channels to enhance the overall quality and robustness.

Christian Kehl, Wail Mustafa, Jan Kehres et al.

Multi-spectral computed tomography is an emerging technology for the non-destructive identification of object materials and the study of their physical properties. Applications of this technology can be found in various scientific and industrial contexts, such as luggage scanning at airports. Material distinction and its identification is challenging, even with spectral x-ray information, due to acquisition noise, tomographic reconstruction artefacts and scanning setup application constraints. We present MUSIC - and open access multi-spectral CT dataset in 2D and 3D - to promote further research in the area of material identification. We demonstrate the value of this dataset on the image analysis challenge of object segmentation purely based on the spectral response of its composing materials. In this context, we compare the segmentation accuracy of fast adaptive mean shift (FAMS) and unconstrained graph cuts on both datasets. We further discuss the impact of reconstruction artefacts and segmentation controls on the achievable results. Dataset, related software packages and further documentation are made available to the imaging community in an open-access manner to promote further data-driven research on the subject

Marius Stärk, Damian Backes, Christian Kehl

In this article an automation system for human-machine-interfaces (HMI) for setpoint adjustment using supervised learning is presented. We use HMIs of multi-modal thermal conditioning systems in passenger cars as example for a complex setpoint selection system. The goal is the reduction of interaction complexity up to full automation. The approach is not limited to climate control applications but can be extended to other setpoint-based HMIs.