Application of Signal Processing and Computational Intelligence in High-Energy Calorimeter Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 11
Special Issue Editors
Interests: signal estimation; signal detection; digital signal processing; machine learning; pattern recognition; high-energy calorimetry
Interests: optimization; parameter estimation; computational modeling; signal reconstruction; artificial intelligence; high-energy calorimeter systems
Special Issue Information
Dear Colleagues,
High-energy physics experiments rely on calorimeter systems to provide better measurements of the subproducts of particle collisions. In modern HEP experiments, calorimeter readout electronics need to cope with increases in the data rate and complexity of the experiments through the use of advanced digital signal processing (DSP) techniques and computational intelligence approaches.
In this Special Issue, we invite submissions of theoretical and experimental studies, as well as comprehensive review and survey papers, exploring cutting-edge research and recent advances in the following topics related to high-energy calorimetry:
- Data acquisition strategies;
- Signal conditioning circuits;
- Noise estimation methods;
- Signal estimation and detection;
- Optimal filtering methods;
- Nonlinear strategies for signal estimation;
- Linear and nonlinear computational modeling;
- Statistical signal processing;
- Optimization methods;
- Neural network systems;
- Artificial intelligence applications;
- FPGA solutions for signal processing;
- Real-time applications.
Dr. Bernardo Peralva
Dr. Gustavo Barbosa Libotte
Guest Editors
Manuscript Submission Information
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Keywords
- data acquisition strategies
- signal conditioning circuits
- noise estimation methods
- signal estimation and detection
- optimal filtering methods
- nonlinear strategies for signal estimation
- linear and nonlinear computational modeling
- statistical signal processing
- optimization methods
- neural network systems
- artificial intelligence applications
- FPGA solutions for signal processing
- real-time applications
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