.png){kind=icon}
.png){kind=icon}
Return
Low-Complexity Compression for Sensory Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2014.2387552.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This brief presents a low-complexity mixed-domain data compression solution that is suitable for resource-constrained wireless sensory systems. Data compression reduces the transmission bandwidth of sensor nodes, helping them to save energy and extend their operation time. In the proposed compression solution, the sensor signal is decorrelated in the analog domain and converted to digital using a compressing analog-to-digital converter. The compressing converter is based on a cyclic converter architecture and is able to jointly perform the functions of quantization, signal conversion, and entropy coding in a single circuit. Since data compression is performed as the sensor signal is acquired, there is no need to use a microprocessor or a dedicated circuit to compress the signal, reducing the computational requirements of a sensor node. As a proof of concept, the proposed data compression scheme was implemented using programmable hardware and employed to acquire and compress physiological and speech signals.
Keywords:
Data compression
data conversion
entropy encoding
programmable hardware
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
Organization
No organization information available