Computational Challenges of High-Density Biosensor Data Analysis
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (10 May 2024) | Viewed by 3593
Special Issue Editors
Interests: high-density EEG signal processing; brain connectivity analysis; parallel and GPU computing; algorithms and performance optimization
Special Issue Information
Dear Colleagues,
The proliferation of high-density biosensor (EEG, MEG, EMG, ECoG, fNIRS) measurement systems present growing problems in data analysis due to the increases in measurement dataset size, subject/patient population, sampling frequency, etc. Routine analysis is typically performed by scripting environments and algorithms designed for single-core computing systems that cannot fully utilize the capabilities of modern computing systems. The aim of this Special Issue is to explore how the fundamental changes in computer architecture (multi-core revolution, GPU and FPGA accelerators, cloud computing, supercomputers) affect multi-sensor biosignal data processing and what changes are required to fully benefit from these advanced computing environments. This Issue welcomes original research articles and review papers that demonstrate the efficient use of these new technologies in the form of new data processing techniques and analysis algorithms, highly parallel implementations, and novel data analysis system architecture designs that demonstrate efficient use of multi-core, GPU, cloud, and other HPC systems. Case studies of very large dataset analysis studies, implementations using accelerators and/or HPC systems, the performance analysis of advanced signal processing methods, and reports on clinical application experiments are especially welcome.
Dr. Zoltan Juhasz
Dr. Istvan Vassanyi
Guest Editors
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Keywords
- EE
- MEG
- EMG
- fNIRS
- high-density biosensors
- data processing
- multi-core architecture
- GPU
- accelerator
- cloud computing
- big data analysis
- HPC
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