• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.11
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• pp.1029-1043
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• 2013

We introduce Approximate Message Passing (AMP) algorithm which is one of the efficient recovery algorithms in Compressive Sensing (CS) area. Recently, AMP algorithm has gained a lot of attention due to its good performance and yet simple structure. This paper provides not only a understanding of the AMP algorithm but its relationship with a classical (Sum-Product) Message Passing (MP) algorithm. Numerical experiments show that the AMP algorithm outperforms the classical MP algorithms in terms of time and phase transition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.2
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• pp.366-385
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• 2013

In this paper, we propose a compressed sensing (CS) based Wyner-Ziv (WZ) codec using motion-aligned auto regressive model (MAAR) based side information (SI) extrapolation to improve the compression performance of low-delay distributed video coding (DVC). In the CS based WZ codec, the WZ frame is divided into small blocks and CS measurements of each block are acquired at the encoder, and a specific CS reconstruction algorithm is proposed to correct errors in the SI using CS measurements at the decoder. In order to generate high quality SI, a MAAR model is introduced to improve the inaccurate motion field in auto regressive (AR) model, and the Tikhonov regularization on MAAR coefficients and overlapped block based interpolation are performed to reduce block effects and errors from over-fitting. Simulation experiments show that our proposed CS based WZ codec associated with MAAR based SI generation achieves better results compared to other SI extrapolation methods.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.63-68
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• 2014

This paper proposed a novel compressed sensing (CS) technique for an efficient video transmission of multi-copter. The proposed scheme is focused on reduction of the amount of data based on CS technology. First, we describe basic principle of Spectrum sensing. And then we compare AMP(Approximate Message Passing) with CoSaMP(Compressive Sampling Matched Pursuit) through mathematical analysis and simulation results. They are evaluated in terms of calculation time and complexity, then the promising algorithm is suggestd for multicopter operation. The result of experiment in this paper shows that AMP algorithm is more efficient than CoSaMP algorithm when it comes to calculation time and image error probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4645-4664
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• 2019

It is significant to construct deterministic measurement matrices with easy hardware implementation, good sensing performance and good cryptographic property for practical compressed sensing (CS) applications. In this paper, a deterministic construction method of bipolar chaotic measurement matrices is presented based on binary sequence family (BSF) and Chebyshev chaotic sequence. The column vectors of these matrices are the sequences of BSF, where 1 is substituted with -1 and 0 is with 1. The proposed matrices, which exploit the pseudo-randomness of Chebyshev sequence, are sensitive to the initial state. The performance of proposed matrices is analyzed from the perspective of coherence. Theoretical analysis and simulation experiments show that the proposed matrices have limited influence on the recovery accuracy in different initial states and they outperform their Gaussian and Bernoulli counterparts in recovery accuracy. The proposed matrices can make the hardware implement easy by means of linear feedback shift register (LFSR) structures and numeric converter, which is conducive to practical CS.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.5
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• pp.59-65
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• 2012

In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.114-124
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• 2019

Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.

• The Magazine of the IEIE
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• v.38 no.1
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• pp.56-67
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• 2011

As a result of quickly growing data, a digital transmission system is required to deal with the challenge of acquiring signals at a very high sampling rate, Fortunately, the CS (Compressed Sensing or Compressive Sensing) theory, a new concept based on theoretical results of signal reconstruction, can be employed to exploit the sparsity of the received signals. Then, they can be adequately reconstructed from a set of their random projections, leading to dramatic reduction in the sampling rate and in the use of ADC (Analog-to-Digital Converter) resources. The goal of this article is provide an overview of the basic CS theory and to survey some important compressed sensing applications in wireless communications.

• Current Optics and Photonics
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• v.6 no.3
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• pp.288-296
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• 2022

We propose and experimentally demonstrate a novel photonic compressive sensing (CS) scheme for acquiring sparse radio frequency signals with adjustable compression ratio in this paper. The sparse signal to be measured and a pseudo-random binary sequence are modulated on consecutively connected chirped pulses. The modulated pulses are compressed into short pulses after propagating through a dispersive element. A programmable optical filter based on spatial light modulator is used to realize spectral segmentation and demultiplexing. After spectral segmentation, the compressed pulses are transformed into several sub-pulses and each of them corresponds to a measurement in CS. The major advantage of the proposed scheme lies in its adjustable compression ratio, which enables the system adaptive to the sparse signals with variable sparsity levels and bandwidths. Experimental demonstration and further simulation results are presented to verify the feasibility and potential of the approach.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.199-207
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• 2021

This paper deals with accurate image reconstruction of gamma camera using a coded-aperture mask based on pixel-type CsI(Tl) scintillator coupled with silicon photomultipliers (SiPMs) array. Coded-aperture imaging (CAI) system typically has a smaller effective viewing angle than Compton camera. Thus, if the position of the gamma source to be searched is out of the fully-coded field-of-view (FCFOV) region of the CAI system, artifacts can be generated when the image is reconstructed by using the conventional cross-correlation (CC) method. In this work, we propose an effective method for more accurate reconstruction in CAI considering the source distribution of partially-coded field-of-view (PCFOV) in the reconstruction in attempt to overcome this drawback. We employed an iterative algorithm based on compressed-sensing (CS) and compared the reconstruction quality with that of the CC algorithm. Both algorithms were implemented and performed a systematic Monte Carlo simulation to demonstrate the possiblilty of the proposed method. The reconstructed image qualities were quantitatively evaluated in sense of the root mean square error (RMSE) and the peak signal-to-noise ratio (PSNR). Our simulation results indicate that the proposed method provides more accurate location information of the simulated gamma source than the CC-based method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1199-1212
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• 2019

This paper proposes two wideband spectrum sensing approaches: (i) method A, the cumulative sum (CUSUM) algorithm with short-time Fourier transform, taking advantage of the time-frequency analysis for wideband spectrum. (ii)method B, the quickest spectrum sensing with short-time Fourier transform and compressed sensing, shortening the time of perception and improving the speed of spectrum access or exit. Moreover, method B can take advantage of the sparsity of wideband signals, sampling in the sub-Nyquist rate, and it is more suitable for wideband spectrum sensing. Simulation results show that method A significantly outperforms the single serial CUSUM detection for small SNRs, while method B is substantially better than the block detection based spectrum sensing in small probability of the false alarm.