most citedMachine Learning in the Internet of Things for Industry 4.0

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Compression and Inference of Spiking Neural Networks on Resource-Constrained Hardware

Karol C. Jurzec, Tomasz Szydlo, Maciej Wielgosz

Spiking neural networks (SNNs) communicate via discrete spikes in time rather than continuous activations. Their event-driven nature offers advantages for temporal processing and e…

cs.NI2025

TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems

Wojciech Kalka, Ruitao Xue, Kamil Faber +4

Autonomic Computing (AC) is a promising approach for developing intelligent and adaptive self-management systems at the deep network edge. In this paper, we present the problems an…

cs.DB2025

Higher-Order Graph Databases

Maciej Besta, Shriram Chandran, Jakub Cudak +6

Recent advances in graph databases (GDBs) have been driving interest in large-scale analytics, yet current systems fail to support higher-order (HO) interactions beyond first-order…

cs.NI20201 cited

Machine Learning in the Internet of Things for Industry 4.0

Tomasz Szydlo, Joanna Sendorek, Robert Brzoza-Woch +1

Number of IoT devices is constantly increasing which results in greater complexity of computations and high data velocity. One of the approach to process sensor data is dataflow pr…

cs.OH20201 cited

Dataset for anomalies detection in 3D printing

Joanna Sendorek, Tomasz Szydlo, Mateusz Windak +1

Nowadays, Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making a great usage of concepts like smart sensors and big data analysis. IoT de…