A sparse representation method of 2-D sensory data in wireless sensor networks. Abstract: Sparsity is an important principle in Compressive Sensing(CS). For ...

Assuming the 2-D sensor data can be sparsely represented by a dictionary, a sparsity-based recovery approach by solving for l1 norm minimization is proposed. It ...

Lv et al. A sparse representation method of 2-d sensory data in wireless sensor networks. IEEE International Instrumentation and Measurement Technology ...

Aug 2, 2015 · We introduce an unsupervised neural network to extract an intrinsic sparse coding of the data. The sparse codes are generated at the activation ...

A sparse representation method of 2-D sensory data in wireless sensor networks · Computer Science, Engineering. 2016 IEEE International Instrumentation and…

In general sensor networks, researchers mainly focus on omni-directional sensors and deploying cameras according to the energy consumption, coverage and ...

Missing: sensory | Show results with:sensory

The core idea is that a finite-dimensional signal can be recovered from a small set of linear measurements when the signal is sparse in a basis or a dictionary.

sparse representation is in image compression, where a 2-D ... sensor networks, where a wireless network ... and memory to process high-dimensional sensor data.

This paper has the following contributions: (1) sparse representation is used to detect sensor node anomalies in multilayer sensor networks; (2) by evaluating ...