团队与科研
skAInet Edge-Compute 由德国波鸿的一支嵌入式工程师与物理学家小团队打造——它源自真实的科研难题。
团队
Stephan Bökelmann
系统架构师
Stephan 于 2014 年加入 Auto-Intern，并与 Odin Holmes 一起将公司的工业过程诊断业务发展为 skAInet。他负责整体系统设计——从载板与 FPGA/Zynq 数据采集通路到 Yocto Linux 镜像——目前正在波鸿鲁尔大学攻读实验强子物理（PANDA/FAIR）博士学位。他在 maxclerkwell.tech 上撰写关于嵌入式、FPGA 与 DAQ 的文章。
亦称 Stephan Boekelmann、MaxClerkwell、施泓杰——并非演员 Stefan Bockelmann，亦与英国机构 ClerksWell 无关。
Odin Holmes
硬件设计与实现
Odin 于 2001 年共同创立 Auto-Intern，拥有 20 余年裸机嵌入式系统开发经验。他负责 Edge-Compute 硬件与固件的设计与实现，创建了 Kvasir 寄存器抽象库，担任 ISO C++ 委员会嵌入式工作组（SG14）主席，共同创办了 emBO++ 大会，并曾在 CppCon、C++Now、Meeting C++ 与 C++ Europe 上发表演讲。
Tabea Bökelmann
用户交互
Tabea 是物理学家兼计算机科学家，自 2017 年起效力于 Auto-Intern。她负责塑造人们与 Edge-Compute 及其测量设备的交互方式——从软件与数据层面，到她在 25square 气象传感器网络等现场项目中主导设计的传感器硬件。
René Glitza
分析、学习与人工智能
René 在 skAInet 领导预测性维护项目，并在波鸿鲁尔大学通信声学研究所研究面向声学传感器网络的隐私保护联邦学习。他拥有嵌入式系统硕士学位，也是 NexuFed AI 的成员。
Philipp Lehmann
网络安全
Philipp 负责 Edge-Compute 的安全事务——经过加固的 Yocto Linux 镜像、有据可查的 SBOM，以及确保原始数据始终留在 LAN 侧（除非您另有决定）的网络架构。
论文发表
团队发表的同行评审成果，为 Edge-Compute 提供了理论与实践支撑——从传感器网络上的联邦学习，到河流监测以及 GSI/FAIR 的探测器物理。每条记录均链接到与之相关的应用案例。
- autowerkstatt4null: An Off-Board-Diagnostics Ecosystem for Car-WorkshopsS. Bökelmann, R. Glitza, M. Huang, O. Holmes, L. Jakubczyk, T. Röthemeyer · arXiv preprint 2608.26911, 2026A modular measurement platform, a secure data-exchange hub, and asynchronous "federated diagnostics" for independent workshops — the same architecture pattern the Edge-Compute follows.
- Never Touch a Running SystemS. Bökelmann · Poster, Data Science Ruhrgebiet 2021 (figshare), 2022A generalized framework for condition-monitoring systems: data acquisition, conservation, and distribution as separate concerns.
- Microfabricated Electrochemical Sensors as a Sentinel System to Detect Biofilms in River SystemsM. Neubauer, S. Bökelmann, O. Holmes, C. Foreman, S. Warnat · ECS Meeting Abstracts MA2025-02, 2025Impedance-spectroscopy sensor arrays deployed in the Clark Fork River, driven by a custom data-acquisition station every 15 minutes; impedance rise tracks biofilm growth over 600 hours.
- PANDA Phase OnePANDA Collaboration (incl. S. Bökelmann) · Eur. Phys. J. A 57, 184, 2021The physics programme of the start-up PANDA detector at the HESR storage ring of FAIR.
- Precision resonance energy scans with the PANDA experiment at FAIRPANDA Collaboration (incl. S. Bökelmann) · Eur. Phys. J. A 55, 42, 2019Energy scans at the HESR — precisely the kind of measurement whose accuracy depends on the luminosity detector.
- Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID DataR. Glitza, L. Becker, R. Martin · IEEE ICASSP 2026, 2026Server- and client-side RL agents tune aggregation weights and personalization in federated learning — robust even with adversarial clients.
- Unsupervised Anomalous Sound Detection Using Loss-Weighted Clustered Federated Pre-TrainingR. Glitza, L. Becker, R. Martin · IEEE MLSP 2024, 2024Clustered federated pre-training for machine anomalous-sound detection with fewer false positives.
- First-Shot Anomalous Sound Detection with Frozen General Audio Encoders and Distance-Based Back-EndsR. Glitza et al. · DCASE 2026 Challenge, Task 2 technical report, 2026Training-free anomalous-sound detection for machine condition monitoring using frozen audio encoders and kNN/GMM back-ends.
- Clustering-based Wake Word Detection in Privacy-aware Acoustic Sensor NetworksT. Koppelmann, L. Becker, A. Nelus, R. Glitza, L. Schönherr, R. Martin · Interspeech 2022, 2022Privacy-preserving processing in distributed acoustic sensor networks.
- Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic EnvironmentsA. Nelus, R. Glitza, R. Martin · EUSIPCO 2021, 2021Federated clustering of sensor nodes without sharing raw audio — the basis for NexuFed-style deployments.
- Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated LearningA. Nelus, R. Glitza, R. Martin · IEEE ICASSP 2021, 2021Unsupervised federated learning across a network of edge sensor nodes.