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Deep learning for compressive sensing: a ubiquitous systems perspective
Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …
sampling rate, potentially bringing context-awareness to a wider range of devices …
An improved sample complexity for rank-1 matrix sensing
Matrix sensing is a problem in signal processing and machine learning that involves
recovering a low-rank matrix from a set of linear measurements. The goal is to reconstruct …
recovering a low-rank matrix from a set of linear measurements. The goal is to reconstruct …
Compressive sensing based distributed data storage for mobile crowdsensing
Mobile crowdsensing systems typically operate centralized cloud storage management, and
the environment data sensed by the participants are usually uploaded to certain central …
the environment data sensed by the participants are usually uploaded to certain central …
Metrics for evaluating the efficiency of compressing sensing techniques
Compressive sensing has been receiving a great deal of interest from researchers in many
areas because of its ability in speeding up data acquisition. This framework allows fast …
areas because of its ability in speeding up data acquisition. This framework allows fast …
Compressive sensing and paillier cryptosystem based secure data collection in WSN
Due to the technological advancements and smart deployment, wireless sensor networks
(WSNs) receive much attention in numerous real-time application fields. The stringent …
(WSNs) receive much attention in numerous real-time application fields. The stringent …
Compressive learning in communication systems: a neural network receiver for detecting compressed signals in OFDM systems
Nowadays, the development of efficient communication system is necessary for future
networks. Compressive sensing was proposed as a technique to save storage and energy …
networks. Compressive sensing was proposed as a technique to save storage and energy …
Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing
With the widespread utilization of sensors and the rapid development of Mobile Crowd
Sensing (MCS), the framework of distributed data storage, which makes use of the limited …
Sensing (MCS), the framework of distributed data storage, which makes use of the limited …
Sequential edge detection using joint hierarchical Bayesian learning
This paper introduces a new sparse Bayesian learning (SBL) algorithm that jointly recovers
a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new …
a temporal sequence of edge maps from noisy and under-sampled Fourier data. The new …
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference--A Ubiquitous Systems Perspective
Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate,
potentially bringing context-awareness to a wider range of devices. Nevertheless, practical …
potentially bringing context-awareness to a wider range of devices. Nevertheless, practical …
Block sparse variational Bayes regression using matrix variate distributions with application to SSVEP detection
Due to the nonsparse representation, the use of compressed sensing (CS) for physiological
signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We …
signals, such as a multichannel electroencephalogram (EEG), has been a challenge. We …