Robert Weigel

Edward J. Oughton, Evan Alexander Peters, Dennies Bor et al.

Space weather risk assessment is constrained by the lack of available asset information needed to model Geomagnetically Induced Currents (GICs) in electricity transmission infrastructure. We propose a reproducible method that enables risk analysts to collect their own open-source substation data. Utilizing an innovative web-browser platform for annotation, we convert OpenStreetMap substation locations to high-resolution, component-level mappings of electricity transmission assets. For example, we convert an initial 1,313 high-voltage (>115 kV) substations to 52,273 substation components via Google Earth APIs utilizing low-altitude, satellite, and streetview imagery. Approximately 41,642 substation components (79.6%) connect to the highest substation voltage levels (>345 kV) and are potentially susceptible to GICs, with 7,949 identified transformers. Compared to the OpenStreetMap baseline, this approach provides detailed insights on voltage levels, line capacities, and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority region, comparing May 2024 results with the UIUC150 synthetic network and with measured ground GICs at 13 monitoring devices. Importantly, the two open-source networks produce 95th-percentile peak ground GIC values within 4% of each other, and the modeled time series broadly capture the temporal morphology of the storm at the monitoring sites. This method shows promise for spatially explicit GIC screening and regional nowcasting without requiring access to operator data.

Michael Stephan, Thomas Stadelmayer, Avik Santra et al.

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs

Ahmed Shaaban, Zeineb Chaabouni, Maximilian Strobel et al.

Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range information. Following detection, a simple Goertzel algorithm is employed to extract five key features, eliminating the need for a second fast Fourier transform. These features are then fed into a recurrent neural network, achieving an accuracy of 98.21% for classifying five gestures. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods

Souvik Hazra, Hao Feng, Gamze Naz Kiprit et al.

Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such as their ability to work in low illumination, harsh weather conditions, and being low-cost and compact, making them highly preferable for a gesture recognition solution. However, most literature work focuses on solutions with a limited range that is lower than a meter. We propose a novel architecture for a long-range (1m - 2m) gesture recognition solution that leverages a point cloud-based cross-learning approach from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning better representations while suppressing noise. We use a variant of Dynamic Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships between the points at a local and global level and to model the temporal dynamics a Bi-LSTM network is employed. In the experimental results section, we demonstrate our model's overall accuracy of 98.4% for five gestures and its generalization capability.

Lan Shi, Christopher Soell, Andreas Baenisch et al.

A concept for a novel CMOS image sensor suited for analog image pre-processing is presented in this paper. As an example, an image restoration algorithm for reducing image noise is applied as image pre-processing in the analog domain. To supply low-latency data input for analog image preprocessing, the proposed concept for a CMOS image sensor offers a new sensor signal acquisition method in 2D. In comparison to image pre-processing in the digital domain, the proposed analog image pre-processing promises an improved image quality. Furthermore, the image noise at the stage of analog sensor signal acquisition can be used to select the most effective restoration algorithm applied to the analog circuit due to image processing prior to the A/D converter.