parametric study on signal reconstruction in wireless capsule endoscopy using compressive sensing...
TRANSCRIPT
Parametric Study on Signal Reconstruction in Wireless Capsule
Endoscopy using Compressive Sensing
Oka Danil Saputra, Soo Young Shin
Wireless & Emerging Networking System (WENS) Laboratory,
School of Electronic Engineering,
Kumoh National Institute of Technology, Gumi, South Korea.
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Winter Conference The Korean Institute of Communication and Information Sciences Gangwon- South Korea,
22nd January 2015.
Compressive Sensing
Signal 𝑥
Sparsity
MeasurementMatrix
MeasurementVector
Source: Donoho, D.L., "Compressed sensing," Information Theory, IEEE Transactions on, vol.52, no.4, pp.1289-1306, April 2006.
MeasurementVector
Recover signal
Signal 𝑥
• CS can recover signal and image with fewer sample.• Reduce power consumption.
Parametric in Compressive Sensing
x =
Source: Hong Huang, Satyajayant Misra, Wei Tang, Hajar Barani, Hussein Al-Azzawi, "Applications of Compressed Sensing in Communications Networks," May 2013, (http://arxiv.org/abs/1305.3002).
Spark Φ = rank Φ + 1 [1]
The main function of spark is to check the sparsity of input signal noiseless.
The main function of MIP is to check the how many measurement is needed.
The main function of RIP is to check the sparsity of input signal with noise.
amount of noise
𝑘-sparse
N x 1N x N
N x 1 M x 1
N x 1
Proposed Scheme
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Simulation Parameters and Result
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Parameters Value
Length of Signal (N) 100
Modulation type BPSK, QPSK
Iteration 100 times
Noise (n) AWGN
SNR 10 dB
𝑀𝐴𝐸𝑟𝑒𝑐𝑜𝑛𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 =1
𝐼
𝑖=1
𝐼1
𝑁
𝑗=1
𝑁
𝑥𝑗 − 𝑥𝑗
𝑥𝑗: Actual signal
𝑥𝑗 : Estimation signal
N : Maximum Length of signalI: Maximum number of iteration
Conclusions
• The parametric study of Compressive Sensing is evaluated inthe paper.
• In wireless communication, the channel effect between WCEand receiver is investigated.
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This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the Global
IT Talent support program (NIPA-2014-H0904-14-1005)