Compressive sensing: From theory to applications, a survey
S Qaisar, RM Bilal, W Iqbal… - Journal of …, 2013 - ieeexplore.ieee.org
Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …
more efficient way than the established Nyquist sampling theorem. CS has recently gained a …
Compressed data aggregation for energy efficient wireless sensor networks
As a burgeoning technique for signal processing, compressed sensing (CS) is being
increasingly applied to wireless communications. However, little work is done to apply CS to …
increasingly applied to wireless communications. However, little work is done to apply CS to …
Compressed data aggregation: Energy-efficient and high-fidelity data collection
We focus on wireless sensor networks (WSNs) that perform data collection with the objective
of obtaining the whole dataset at the sink (as opposed to a function of the dataset). In this …
of obtaining the whole dataset at the sink (as opposed to a function of the dataset). In this …
Compressive sampling–based data loss recovery for wireless sensor networks used in civil structural health monitoring
In a wireless sensor network, data loss often occurs during the data transmission between
the wireless sensor nodes and the base station. In the wireless sensor network applications …
the wireless sensor nodes and the base station. In the wireless sensor network applications …
An efficient privacy-preserving compressive data gathering scheme in WSNs
Because of the strict energy limitation and the common vulnerability of Wireless Sensor
Networks (WSNs), providing efficient and secure data gathering in WSNs becomes an …
Networks (WSNs), providing efficient and secure data gathering in WSNs becomes an …
Exact Recoverability From Dense Corrupted Observations via -Minimization
This paper confirms a surprising phenomenon first observed by Wright under a different
setting: given m highly corrupted measurements y= A Ω· x*+ e*, where A Ω· is a submatrix …
setting: given m highly corrupted measurements y= A Ω· x*+ e*, where A Ω· is a submatrix …
Resource Allocation for Distributed Multi-Target Tracking in Radar Networks With Missing Data
In this paper, an effective joint measurement selection and power allocation (JMSPA)
scheme is proposed for the distributed multi-target tracking task in radar networks with …
scheme is proposed for the distributed multi-target tracking task in radar networks with …
Restoration of electromechanical admittance signature via solving constrained optimization problems for concrete structural damage identification
H Li, Y Luo, D Ai - Measurement, 2023 - Elsevier
Effective restoration of lossy data is prerequisite to guarantee the reliability of early warning
information for damage diagnosis, considering large amounts of sensor data to be collected …
information for damage diagnosis, considering large amounts of sensor data to be collected …
A data loss recovery technique using compressive sensing for structural health monitoring applications
Abstract Recent developments in Wireless Sensor Networks (WSN) benefited various fields,
among them Structural Health Monitoring (SHM) is an important application of WSNs. Using …
among them Structural Health Monitoring (SHM) is an important application of WSNs. Using …
Applications of compressed sensing in communications networks
This paper presents a tutorial for CS applications in communications networks. The
Shannon's sampling theorem states that to recover a signal, the sampling rate must be as …
Shannon's sampling theorem states that to recover a signal, the sampling rate must be as …