In this paper, the parameters of compressive sensing (CS) is studied. The main objective to evaluate these property, to find the key factor of reconstruct input signal possible by using CS where the minimum number of samples measurement can be utilized. Furthermore, we proposed CS scheme to enhance the effectiveness of wireless capsule endoscopy power consumption. In our proposed algorithm, distributed sensor node is used to capture the image data from WCE and reconstruct the signal in data center. Full paper can be find in http://okadanil.com/publications

1. 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.
1
Winter Conference The Korean Institute of Communication and Information Sciences
Gangwon- South Korea,
22nd January 2015.

2. Wireless Capsule Endoscopy
2
Figure source: http://wens.kumoh.ac.kr/

3. Shannon-Nyquist Sampling
Source: http://www.brainshark.com

4. Compressive Sensing
Signal 𝑥
Sparsity
Measurement
Matrix
Measurement
Vector
Source: Donoho, D.L., "Compressed sensing," Information Theory, IEEE Transactions on, vol.52, no.4, pp.1289-1306, April 2006.
Measurement
Vector
Recover signal
Signal 𝑥
• CS can recover signal and image with fewer sample.
• Reduce power consumption.

5. 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

6. Proposed Scheme
6

7. Simulation Parameters and Result
7
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
𝐼
1
𝑁
𝑗=1
𝑁
𝑥𝑗 − 𝑥𝑗
𝑥𝑗: Actual signal
𝑥𝑗 : Estimation signal
N : Maximum Length of signal
I: Maximum number of iteration

8. Conclusions
• The parametric study of Compressive Sensing is evaluated in
the paper.
• In wireless communication, the channel effect between WCE
and receiver is investigated.
8
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)