• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1355-1363
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• 2017

Recently, distributed compressed video sensing (DCVS) has been actively studied in order to achieve a low complexity video encoder by integrating both compressed sensing and distributed video coding characteristics. Conventionally, a motion compensated block compressed sensing with smoothed projected Landweber (MC-BCS-SPL) has been considered as an effective scheme of DCVS with all compressed sensing frames pursuing the simplest sampling. In this scheme, video frames are separately classified into key frames and WZ frames. However, when reconstructing WZ frame with conventional MC-BCS-SPL scheme at the decoder side, the visual qualities are poor for temporally active video sequences. In this paper, to overcome the drawbacks of the conventional scheme, an enhanced MC-BCS-SPL algorithm is proposed, which corrects the initial image with reference to the key frame using a high correlation between adjacent key frames. The proposed scheme is analyzed with respect to GOP (Group of Pictures) structuring method. Experimental results show that the proposed method performs better than conventional MC-BCS-SPL in rate-distortion.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.27-34
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• 2017

For The Purpose of Pursuing the Simplest Sampling, a Motion Compensated Block Compressed Sensing with Smoothed Projected Landweber (MC-BCS-SPL) has been Studied for an Effective Scheme of Distributed Compressive Video Sensing with all Compressed Sensing (CS) Frames. However, Conventional MC-BCS-SPL Scheme is Very Simple and so it Does not Provide Good Visual Qualities in Reconstructed Wyner-Ziv (WZ) Frames. In this Paper, the Conventional Scheme of MC-BCS-SPL is Modified to Provide Better Visual Qualities in WZ Frames. That is, the Proposed Agorithm is Designed in such a way that the Reference Frame may be Adaptively Selected Based on the Temporal Correlation Between Successive Frames. Several Experimental Results show that the Proposed Algorithm Provides Better Visual Qualities than Conventional Algorithm.

• Journal of electromagnetic engineering and science
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• v.11 no.4
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• pp.250-256
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• 2011

This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology. At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance. In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.

• The Magazine of the IEIE
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• v.41 no.6
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• pp.18-26
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• 2014

신호의 표현과 압축은 밀접하게 연관되어 있다. 만약 매우 효과적인 신호 표현 방식을 찾을 수 있다면 매우 높은 비율로 신호 압축이 가능하다. 또한 효과적인 신호 표현 방식을 통해 우수한 성능과 낮은 복잡도를 갖는 신호 추정 방식을 유도할 수도 있다. 효과적인 신호 표현 방식은 대상 신호 자체의 성질과 관련되어 있다. 영상 신호등 매우 일반적인 신호가 적절한 변환을 통해 산재된 신호(sparse signal)가 될 수 있음을 많은 연구를 통해 볼 수 있다. 이러한 사실이 compressive sensing(CS)의 기반이다. 즉 신호가 어떠한 변환을 통해 산재된 신호로 표현될 수 있다면 매우 적은 수의 샘플로 이러한 신호를 알아낼 수 있다는 것을 Donoho와 Candes 등이 보였고 이것이 가능한 다양한 조건에 등에 대해 연구되었다. CS는 신호 처리에 근본적인 문제인 효과적인 신호 표현 방식에 직접 연관되어 매우 다양한 분야에 적용될 수 있다. 이 논문에서 CS의 기본적인 개념을 소개한 후 CS가 레이더 신호 처리에 어떻게 도움이 될 수 있는지 살펴본다.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.439-445
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• 2016

In this paper, we consider subcarrier-index modulation (SIM) for precoded orthogonal frequency division multiplexing (OFDM) with a few activated subcarriers per user and its generalization to multi-carrier multiple access systems. The resulting multiple access is called sparse index multiple access (SIMA). SIMA can be considered as a combination of multi-carrier code division multiple access (MC-CDMA) and SIM. Thus, SIMA is able to exploit a path diversity gain by (random) spreading over multiple carriers as MC-CDMA. To detect multiple users' signals, a low-complexity detection method is proposed by exploiting the notion of compressive sensing (CS). The derived low-complexity detection method is based on the orthogonal matching pursuit (OMP) algorithm, which is one of greedy algorithms used to estimate sparse signals in CS. From simulation results, we can observe that SIMA can perform better than MC-CDMA when the ratio of the number of users to the number of multi-carrier is low.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.628-630
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• 2016

Compressive sensing can recover an original sparse signal from a few measurements. Its performance is affected by the number of non-zero elements in the signal. The knowledge of partial locations of non-zero elements can improve the recovery performance. In this paper, we apply the partial location knowledge to the multipath matching pursuit. The numerical results show it improves the signal recovery performance and the channel estimation performance in the ITU-VB channel.

• ETRI Journal
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• v.42 no.6
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• pp.815-826
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• 2020

We propose a two-dimensional (2D) scattering-center-extraction (SCE) method using sparse recovery based on the compressive-sensing theory, even with data missing from the received radar cross-section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak-finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point-scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5481-5495
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• 2018

Single pixel imaging technology has developed for years, however the video acquisition on the single pixel camera is not a well-studied problem in computer vision. This work proposes a new scheme for single pixel camera to acquire video data and a new regularization for robust signal recovery algorithm. The method establishes a single pixel video compressive sensing scheme to reconstruct the video clips in spatial domain by recovering the difference of the consecutive frames. Different from traditional data acquisition method works in transform domain, the proposed scheme reconstructs the video frames directly in spatial domain. At the same time, a new regularization called spatial cluster is introduced to improve the performance of signal reconstruction. The regularization derives from the observation that the nonzero coefficients often tend to be clustered in the difference of the consecutive video frames. We implement an experiment platform to illustrate the effectiveness of the proposed algorithm. Numerous experiments show the well performance of video acquisition and frame reconstruction on single pixel camera.

• The Journal of the Acoustical Society of Korea
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• v.39 no.3
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• pp.191-199
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• 2020

Currently, studies on the estimation algorithm based on compressive sensing are actively underway, but to the best of our knowledge, no study on the performance of the Sparse Spectrum Fitting (SpSF) algorithm in nonuniform sensor arrays has been made. This paper deals with the derivation of the compressive sensing based covariance fitting algorithm extended to the frequency domain. In addition, it shows the performance of directon-of-arrival estimation of the frequency domain SpSF algorithm in non-uniform linear sensor array system and the sensor array failure situation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.122-129
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• 2012

As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP), we derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{NK}$ < $\frac{\sqrt{N}}{\sqrt{K}+2\sqrt{N}}$. In addition, we show recovery performance and the reduced number of iteration required to recover the sparse signal.