• Transactions of the Korean Society for Noise and Vibration Engineering
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• 제25권11호
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• pp.787-797
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• 2015

In this paper, a cochlea-inspired artificial filter bank(CAFB) was developed in order to efficiently acquire dynamic response of structure, and it was also evaluated via dynamic response experiments. To sort out signals containing significant modal information from all the dynamic responses of structure, it was made to adopt a band-pass filter optimizing algorithm(BOA) and a peak-picking algorithm (PPA). Optimally designed on the basis of El-centro and Kobe earthquake signals, it was then embedded into the wireless multi-measurement system(WiMMS). In order to evaluate the performance of the developed CAFB, a vibration test was conducted using the El-centro and Kobe earthquake signals, and structural responses of a two-span bridge were obtained and analyzed simultaneously by both a wired measurement system and a CAFB-based WiMMS. The test results showed that the compressed dynamic responses acquired by the CAFB-based WiMMS matched with those of the wired system, and they included significant modal information of the two-span bridge. Therefore this study showed that the developed CAFB could be used as a new, economic, and efficient measurement device for wireless sensor networks(WSNs) based real time structural health monitoring because it could reconstruct the whole dynamic response using the compressed dynamic response with significant modal information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of Korea Society of Industrial Information Systems
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• 제23권6호
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• pp.47-58
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• 2018

Recently, remote sensing video applications have become increasingly important in many wireless networks. Distributed compressive video sensing (DCVS) framework in these applications has been studied to reduce encoding complexity and to simultaneously capture and compress video data. Specially, a motion compensated block compressed sensing with smoothed projected Landweber (MC-BCS-SPL) has been actively researched for one useful algorithm of DCVS schemes, However, conventional MC-BCS-SPL schemes do not provide good visual qualities in reconstructed Wyner-Ziv (WZ) frames. In this paper, the conventional schemes of MC-BCS-SPL are described and then upgraded to provide better visual qualities in WZ frames by introducing reliability estimate between adjacent key frames and by constructing efficiently motion-compensated interpolated frames. Through experimental results, it is shown that the proposed algorithm is effective in providing better visual qualities than conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• 제48권11호
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• pp.67-72
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• 2011

In this paper, we investigate the energy detector to detect a primary user. And employ the compressed sensing method to get the lower sampling rate than Nyquist sampling rate. In more wide bandwidth we using the small samples than Nyquist sampling rate samples to recover original signal. we investigate the performance of energy detector with compressive sensing method under suzuki channel. The performance is investigated by simulation and compared to that of conventional energy detector.

• Journal of Communications and Networks
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• 제18권1호
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• pp.95-104
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• 2016

Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.

• The Journal of the Acoustical Society of Korea
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• 제36권6호
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• pp.394-400
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• 2017

This paper shows the extension of SpSF (Sparse Spectrum Fitting) algorithm, which is one of covariance matrix fitting-based DOA (Direction-of-Arrival) estimation algorithms, from the time domain to the frequency domain, and presents that SpSF can be implemented in the frequency domain. The superiority of the SpSF algorithm has been demonstrated by comparing DOA estimation performance with the performance of Conventional DOA estimation algorithm in the frequency domain for sinusoidal incident signals.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제40권12호
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• pp.2476-2484
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• 2015

Narrow band signal estimation and broad band signal estimation can be used to detect the ship-radiated noise. The broad band signal estimation method to detect the ship-radiated noise is called DEMON (Detection of Envelop Modulation On Noise). This paper proposes a new DEMON algorithm applying compressed sensing algorithm to filter bank and autocorrelation. We show the proposed algorithm estimates the hidden period in the wide band signal better than the conventional DEMON algorithm and the recently proposed filter-bank based DEMON algorithm. Especially we show that the proposed algorithm needs shorter data length than the conventional DEMON algorithm.

• Investigative Magnetic Resonance Imaging
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• 제19권1호
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• pp.19-30
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• 2015

Purpose: A new compressed sensing technique by iterative truncation of small transformed coefficients (ITSC) is proposed for fast cardiac CINE MRI. Materials and Methods: The proposed reconstruction is composed of two processes: truncation of the small transformed coefficients in the r-f domain, and restoration of the measured data in the k-t domain. The two processes are sequentially applied iteratively until the reconstructed images converge, with the assumption that the cardiac CINE images are inherently sparse in the r-f domain. A novel sampling strategy to reduce the normalized mean square error of the reconstructed images is proposed. Results: The technique shows the least normalized mean square error among the four methods under comparison (zero filling, view sharing, k-t FOCUSS, and ITSC). Application of ITSC for multi-slice cardiac CINE imaging was tested with the number of slices of 2 to 8 in a single breath-hold, to demonstrate the clinical usefulness of the technique. Conclusion: Reconstructed images with the compression factors of 3-4 appear very close to the images without compression. Furthermore the proposed algorithm is computationally efficient and is stable without using matrix inversion during the reconstruction.

• Journal of Communications and Networks
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• 제15권3호
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• pp.283-291
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• 2013

In this paper, we propose new multiuser detectors (MUDs) based on compressed sensing approaches for the large-scale multiple antenna systems equipped with dozens of low-power antennas. We consider the scenarios where the number of receiver antennas is smaller than the total number of users, but the number of active users is relatively small. This prior information motivates sparsity-embracing MUDs such as sparsity-embracing linear/nonlinear MUDs where the detection of active users and their symbol detection are employed. In addition, sparsity-embracing MUDs with maximum a posteriori probability criterion (MAP-MUDs) are presented. They jointly detect active users and their symbols by exploiting the probability of user activity, and it can be solved efficiently by introducing convex relaxing senses. Furthermore, it is shown that sparsity-embracing MUDs exploiting common users' activity across multiple symbols, i.e., frame-by-frame, can be considered to improve performance. Also, in multiple multiple-input and multiple-output networks with aggressive frequency reuse, we propose the interference cancellation strategy for the proposed sparsity-embracing MUDs. That first cancels out the interference induced by adjacent networks and then recovers the desired users' information by exploiting the low user activity. In simulation studies for binary phase shift keying modulation, numerical evidences establish the effectiveness of our proposed MUDs exploiting low user activity, as compared with the conventional MUD.

• Journal of Satellite, Information and Communications
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• 제9권4호
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• pp.104-110
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• 2014

This paper considers two reconstruction schemes of structured-sparse signals, turbo inference and Markov chain Monte Carlo (MCMC) inference, in compressed sensing(CS) technique that is recently getting an important issue for an efficient video wireless transmission system using multi-copter as an unmanned aerial vehicle. Proposed reconstruction algorithms are setting importance on reduction of image data sizes, fast reconstruction speed and errorless reconstruction. As a result of experimentation with twenty kinds of images, we can find turbo reconstruction algorithm based on loopy belief propagation(BP) has more excellent performances than MCMC algorithm based on Gibbs sampling as aspects of average reconstruction computation time, normalized mean squared error(NMSE) values.