18475995. ADAPTIVE ACQUISITION FOR COMPRESSED SENSING simplified abstract (QUALCOMM Incorporated)
Contents
ADAPTIVE ACQUISITION FOR COMPRESSED SENSING
Organization Name
Inventor(s)
Gianluigi Silvestri of Nijmegen (NL)
Fabio Valerio Massoli of Amsterdam (NL)
Tribhuvanesh Orekondy of Biel (CH)
Arash Behboodi of Amsterdam (NL)
Joseph Binamira Soriaga of San Diego CA (US)
ADAPTIVE ACQUISITION FOR COMPRESSED SENSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18475995 titled 'ADAPTIVE ACQUISITION FOR COMPRESSED SENSING
Simplified Explanation: This patent application discusses techniques for reinforcement-learning-based compressed sensing. It involves accessing a signal tensor, generating subsets of elements using neural networks, and outputting reconstructed signal tensors.
Key Features and Innovation:
- Reinforcement-learning-based compressed sensing
- Accessing signal tensor
- Generating subsets of elements using neural networks
- Outputting reconstructed signal tensors
Potential Applications: This technology can be applied in various fields such as image processing, medical imaging, and signal processing where compressed sensing is used.
Problems Solved: This technology addresses the challenge of efficiently reconstructing signals from compressed data in a neural network-based approach.
Benefits:
- Improved signal reconstruction accuracy
- Efficient compressed sensing
- Neural network-based processing
Commercial Applications: Potential commercial applications include data compression, image and video processing, medical imaging, and telecommunications.
Prior Art: Prior research in compressed sensing, neural networks, and signal processing can provide valuable insights into related technologies.
Frequently Updated Research: Stay updated on advancements in neural network-based compressed sensing and signal processing for the latest developments in the field.
Questions about Reinforcement Learning-Based Compressed Sensing: 1. How does reinforcement learning enhance compressed sensing techniques? 2. What are the key advantages of using neural networks in signal reconstruction?
Original Abstract Submitted
Certain aspects of the present disclosure provide techniques and apparatus for reinforcement-learning-based compressed sensing. An observed signal tensor comprising a plurality of elements is accessed, and a subset of elements of a sensing matrix is generated based on processing, from among the plurality of elements, a subset of elements of the observed signal tensor using an acquisition neural network. A subset of elements of a reconstructed signal tensor is generated based on processing a second subset of elements of the observed signal tensor and the subset of elements of the sensing matrix using a reconstruction neural network. At least the first subset of elements of the reconstructed signal tensor is output.
