To provide the students with an understanding of various processing algorithms and methods that are applicable to modern sensor systems.
At a glance
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- Dates
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- 13 - 17 Jun 2022
- Duration5 days
- LocationNSWC Crane, Indiana, USA
Course structure
Lectures, tutorials, computer based practical’s; video practicalWhat you will learn
On successful completion of this module a student should be able to:
Knowledge and Understanding
- Describe and analyze the principles, capabilities and limitations of a number of different sensor processing methods, algorithms and schemes
- Critically assess performance of neural network models
- Evaluate computational models for dealing with uncertainty; discern the utility of Bayesian and Fuzzy models
Skills and Other Attributes:
- Compare and contrast different estimation methods and tools when applied to a specific problem and devise a distributed or centralized processing scheme based on this comparison
- Design, develop and implement models in MATLAB
Core content
• Adaptive Signal Processing: Adaptive FIR and spatial filters, error surface, Newton’s method, gradient search method, LMS algorithm, practical examples
• Multi-layer perceptrons: Architecture, Back-propagation algorithm, performance of the algorithm, Unsupervised learning, Hebbian learning, Kohonen maps, Neural network design in Matlab
• Overview of deep learning algorithms
• Sonar signal processing: Beamforming, passive analysis, active processing
• Fuzzy logic: fundamentals, fuzzy associative matrix, fuzzy inference
• Adaptive linear elements: Tapped delay lines, Noise cancellation, Time series prediction,
• Sensor fusion: distributed sensor systems, sensor coverage, distributed vs centralised systems
• Sensor processing: Genetic algorithms, evolutionary algorithms, artificial neural nets
• MATLAB: practical sessions, demonstrating the above concepts in Matlab
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