Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review
As a result of several successful applications in computer vision and image processing,
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …
sparse representation (SR) has attracted significant attention in multi-sensor image fusion …
Compressed sensing for wireless communications: Useful tips and tricks
As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …
[書籍][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
Infrared and visible image fusion methods and applications: A survey
Infrared images can distinguish targets from their backgrounds based on the radiation
difference, which works well in all-weather and all-day/night conditions. By contrast, visible …
difference, which works well in all-weather and all-day/night conditions. By contrast, visible …
New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation …
In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …
processing, image processing, computer vision, and pattern recognition. Sparse …
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Multi-task learning aims to improve generalization performance of multiple prediction tasks
by appropriately sharing relevant information across them. In the context of deep neural …
by appropriately sharing relevant information across them. In the context of deep neural …
Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns
Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …
Nearly every downstream control decision is affected by these sensor and actuator …
Spatially sparse precoding in millimeter wave MIMO systems
Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the
microwave signals currently used in most wireless applications and all cellular systems …
microwave signals currently used in most wireless applications and all cellular systems …
[書籍][B] An invitation to compressive sensing
This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …
standard compressive problem studied throughout the book and reveals its ubiquity in many …