Qualcomm incorporated (20240248952). ADAPTIVE ACQUISITION FOR COMPRESSED SENSING simplified abstract

ADAPTIVE ACQUISITION FOR COMPRESSED SENSING

Organization Name

qualcomm incorporated

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 20240248952 titled 'ADAPTIVE ACQUISITION FOR COMPRESSED SENSING

Simplified Explanation

This patent application discusses techniques for reinforcement-learning-based compressed sensing, where a subset of elements of a signal tensor is generated using neural networks.

• An observed signal tensor is accessed, and a subset of elements of a sensing matrix is generated based on processing 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.
• The first subset of elements of the reconstructed signal tensor is output.

Key Features and Innovation

• Utilizes reinforcement-learning-based compressed sensing techniques.
• Employs neural networks for generating subsets of elements of signal tensors.
• Enhances signal reconstruction using neural networks.

Potential Applications

• Signal processing in telecommunications.
• Image and video compression.
• Medical imaging.

Problems Solved

• Efficient signal reconstruction from compressed data.
• Improved data compression techniques.
• Enhanced signal processing in various applications.

Benefits

• Faster signal reconstruction.
• Reduced data storage requirements.
• Enhanced signal quality.

Commercial Applications

• Telecommunications industry for efficient data transmission.
• Medical imaging companies for improved diagnostic tools.
• Video streaming services for better compression algorithms.

Prior Art

Further research can be conducted in the field of reinforcement-learning-based compressed sensing to explore existing technologies and techniques.

Frequently Updated Research

Stay updated on advancements in neural network technologies for signal processing and data compression to enhance the efficiency of the proposed techniques.

Questions about Reinforcement-Learning-Based Compressed Sensing

How does this technology improve signal reconstruction compared to traditional methods?

This technology utilizes neural networks to generate subsets of elements of signal tensors, allowing for more efficient and accurate signal reconstruction compared to traditional methods that may not leverage machine learning algorithms.

What are the potential limitations of using neural networks for compressed sensing?

While neural networks can significantly improve signal reconstruction and data compression, they may require extensive computational resources and training data, which could be a limitation in certain applications.

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.