• The Magazine of the IEIE
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• v.38 no.1
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• pp.31-43
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• 2011

The mathematical foundations of the compressive sensing which goes against the common wisdom of data acquisition (the Nyquist-Shannon theorem) is reviewed. The compressive sensing asserts that one can reconstruct images or signals of interest accurately from a number of samples far smaller than the desired resolution of the image (e.g., the number of pixels in the image). The compressive sensing has far reaching implications. It suggests the new data acquisition protocols that translates analog information to digital form with fewer sensors considered necessary.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.534-542
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• 2019

In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Computers and Concrete
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• v.6 no.3
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• pp.225-234
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• 2009

Dredged silt from reservoirs in southern Taiwan was sintered to make lightweight aggregates (LWA), which were then used to produce lightweight aggregate concrete (LWAC).This study aimed to assess the compressive strength and homogeneity of LWAC using ultrasonic-echo sensing. Concrete specimens were prepared using aggregates of four different particle density, namely 800, 1100, 1300 and 2650 kg/$m^3$. The LWAC specimens were cylindrical and a square wall with core specimens drilled. Besides compressive strength test, ultrasonic-echo sensing was employed to examine the ultrasonic pulse velocity and homogeneity of the wall specimens and to explore the relationship between compressive strength and ultrasonic pulse velocity. Results show that LWA, due to its lower relative density, causes bloating, thus resulting in uneven distribution of aggregates and poor homogeneity. LWAC mixtures using LWA of particle density 1300 kg/$m^3$ show the most even distribution of aggregates and hence best homogeneity as well as highest compressive strength of 63.5 MPa. In addition, measurements obtained using ultrasonic-echo sensing and traditional ultrasonic method show little difference, supporting that ultrasonic-echo sensing can indeed perform non-destructive, fast and accurate assessment of LWAC homogeneity.

• Smart Structures and Systems
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• v.20 no.1
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• pp.35-42
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• 2017

Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.522-533
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• 2019

The compressive sensing model for range-Doppler estimation can be expressed as an under-determined linear system y = Ax. To find the solution of the linear system with the compressive sensing method, matrix A should be sufficiently incoherent and x to be sparse. In this paper, we propose a transmission waveform design method that maintains the bandwidth required by the sonar system while lowering the mutual coherence of the matrix A so that the matrix A is incoherent. The proposed method combines two methods of optimizing the sensing matrix with the alternating projection and suppressing unwanted frequency bands using the DFT (Discrete Fourier Transform) matrix. We compare range-Doppler estimation performance of existing waveform LFM(Linear Frequency Modulated) and designed waveform using the matched filter and the compressive sensing method. Simulation shows that the designed transmission waveform has better detection performance than the existing waveform LFM.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.230-231
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• 2014

Compressive sensing is a new sampling technique, which allows to sample a signal under the Nyquist-Shannon sampling rate. For block-based compressive sensing, a hybrid sensing matrix which contains low-frequency patterns in addition to the random Gaussian numbers is good for exploiting typical property of natural images. By noting that MH-BCS-SPL is well known for its good recovery performance, this paper investigates effect of the hybrid sensing matrix on MH-BCS-SPL in the sense of how large portion of low-frequency patterns can provide performance improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.106-108
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• 2017

본 논문에서는 휘어지거나 굴곡진 array인 3차원 conformal array의 beam pattern을 보정하고자 기존의 2차원에서 3차원으로 확장한 interpolation technique과 compressive sensing을 이용하여 3-D uniform rectangular array(3-D URA)에 적용하는 방법을 연구하였다. 시뮬레이션 결과는 compressive sensing이 interpolation technique보다 우수한 특성을 보여준다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1024-1031
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• 2015

Compressive sensing is a new data acquisition method enabling the reconstruction of sparse or compressible signals from a smaller number of measurements than Nyquist rate, as long as the signal is sparse and the measurement is incoherent. In this paper, we consider a simple hypothesis testing in target detection and estimation problems using compressive sensing, where the performance depends on the sparsity level of the signals being detected. We provide theoretical analysis results along with some experiment results.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.1
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• pp.19-27
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• 2014

Compressive sensing is an emerging sampling technique which enables sampling a signal at a much lower rate than the Nyquist rate. In this paper, we propose a novel framework based on Kronecker compressive sensing that provides multi-resolution image reconstruction capability. By exploiting the relationship of the sensing matrices between low and high resolution images, the proposed method can reconstruct both high and low resolution images from a single measurement vector. Furthermore, post-processing using BM3D improves its recovery performance. The experimental results showed that the proposed scheme provides significant gains over the conventional framework with respect to the objective and subjective qualities.