Sebastian Schmale

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Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission(More)
The aim of this study is to present the first compression and reconstruction methodology based on patch ordering inpainting algorithm for monitoring neural activity. This novel in-painting approach is especially important for the technical realization of implantable neural measurement systems (NMS) since they are subject to strict resource limitations as(More)
In this paper the first low-latency architecture design and hardware implementation for structure-based inpainting to detect and complete isophotes in brain activity recording is presented. This novel mask-based compression and inpainting-based reconstruction methodology for correlated neural signals is especially important for the realization of(More)
This paper presents the first hardware architecture for compressing and reconstructing correlated neural signals using structure-based inpainting. This novel methodology is especially important for the realization of implantable neural measurement systems (NMS), which are subject to strict constraints in terms of area and energy consumption. Such an implant(More)
Orthogonal Matching Pursuit (OMP) is a greedy algorithm well-known for its applications to Compressed Sensing. For this work it serves as a toy problem of a rapid digital design flow based on high-level synthesis (HLS). HLS facilitates extensive design space exploration in connection with a data type-agnostic programming methodology. Nonetheless, some(More)
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