arXiv subjects

Fabian Portner

Publications and source records attributed to Fabian Portner.

3 recordsLinked to original sources

arXiv2026

WiFlow: Estimating Optical Flow using WiFi Channel State Information

Knowing where and how fast objects are moving within a scene is important across various domains. Usually, cameras are used to capture the data necessary for this task, but adding cameras often raises privacy concerns, and the quality of captured frames is heavily influenced by lighting conditions. In this work, we explore using WiFi channel state information (CSI) instead of camera frames for optical flow estimation. We propose WiFlow, a CSI based flow estimator, a preprocessor evaluation for CSI, and three model architectures that offer different trade-offs between accuracy and complexity. Further, we create the first dataset for training and evaluating CSI-based optical flow estimators, and our experiments provide insights into key design elements for this task. Code and data are available at https://visinf.github.io/wiflow.

cs.CV↗

arXiv2026

Same Signal, Different Story: Demystifying Receiver Effects in Wi-Fi Channel State Information

Wi-Fi sensing has emerged as a versatile tool for tasks such as localization, gesture recognition, and vital-sign monitoring, enabling applications from smart environments to personalized healthcare. However, sensing accuracy often significantly degrades when pretrained models are deployed across different commodity receivers. We present the first systematic comparison of Channel State Information (CSI) across diverse Commercial Off-The-Shelf Wi-Fi sensing platforms. Using a unified experimental setup delivering precisely precoded signals simultaneously to multiple receivers, we isolate receiver-specific variability. We find that dominant cross-device differences arise from Automatic Gain Control and consistent subcarrier nonlinearities. We propose a simple gain-alignment preprocessing step, recovering most of the lost accuracy (up to 75%) in cross-device Human Activity Recognition model deployments. Without preprocessing, model accuracy sharply drops-effectively breaking practical deployments. Additional analyses reveal measurable inherent differences in receiver faithfulness, sensitivity and noise. While these receiver-induced differences do not significantly affect robust sensing tasks such as Human Activity Recognition, they become relevant in scenarios demanding high precision (e.g., single-shot time of flight). Our findings demonstrate that cross-device variability in CSI is real but manageable, and we provide tools and guidelines for robust, hardware-agnostic Wi-Fi sensing.

eess.SP↗

arXiv2019

Tomonaga-Luttinger liquid in a box: electrons confined within MoS$_2$ mirror twin boundaries

Two- or three-dimensional metals are usually well described by weakly interacting, fermionic quasiparticles. This concept breaks down in one dimension due to strong Coulomb interactions. There, low-energy electronic excitations are expected to be bosonic collective modes, which fractionalize into independent spin and charge density waves. Experimental research on one-dimensional metals is still hampered by their difficult realization, their limited accessibility to measurements, and by competing or obscuring effects such as Peierls distortions or zero bias anomalies. Here we overcome these difficulties by constructing a well-isolated, one-dimensional metal of finite length present in MoS$_2$ mirror twin boundaries. Using scanning tunneling spectroscopy we measure the single-particle density of the interacting electron system as a function of energy and position in the 1D box. Comparison to theoretical modeling provides unambiguous evidence that we are observing spin-charge separation in real space.

cond-mat.str-el↗