• Smart Structures and Systems
• /
• v.25 no.2
• /
• pp.123-133
• /
• 2020

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.461-484
• /
• 2021

Compressed sensing in image reconstruction has attracted attention and many studies are proposed. As we know, adding prior knowledge about the distribution of the support on the original signal to CS can improve the quality of reconstruction. However, it is still difficult for a recovery framework adjusts its strategy for exploiting the prior knowledge efficiently according to the current estimated signals in serial iterations. With the theory of information geometry, we propose an adaptive strategy based on the current estimated signal in each iteration of the recovery. We also improve the performance of existing algorithms through the adaptive strategy for exploiting the prior knowledge according to the current estimated signal. Simulations are presented to validate the results. In the end, we also show the application of the model in the image.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39A no.12
• /
• pp.764-770
• /
• 2014

It is expected that the techniques of optical imaging through turbid media enables non-invasive imaging through human skin and biological tissues. In recent years, many researches have shown that imaging through turbid media can be made possible by measuring the transmission matrix (TM) of the turbid medium and utilizing it for image recovery. However, this TM based image recovery requires a huge amount of data acquisition and post signal processing of them. Very recently, there were new results that this problem of huge data acquisition and processing can be resolved by using the compressed sensing (CS) framework. CS is a relatively new signal acquisition and reconstruction framework which makes possible to recover the signal of interest correctly with significantly smaller number of signal measurements. In this paper, the TM-based image recovery in imaging through turbid media is reviewed and the recent progress made by using CS is introduced.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.3
• /
• pp.2141-2145
• /
• 2015

A new broadcasting signal transmission, which can save its channel bandwidth using compressive sensing(CS), is proposed in this paper. A new compression technique, which uses two dimensional discrete wavelet transform technique, is proposed to get high sparsity of multimedia image. A L1 minimization technique based on orthogonal matching pursuit is also introduced in order to reconstruct the compressed multimedia image. The CS enables us to save the channel bandwidth of wired and wireless broadcasting signal because various transmitted data are compressed using it. A $256{\times}256$ gray-scale image with compression rato of 20 %, which is sampled by 10 Gs/s, was transmitted to an optical receiver through 20-km optical transmission and then was reconstructed successfully using L1 minimization (bit error rate of $10^{-12}$ at the received optical power of -12.2 dB).

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.6
• /
• pp.167-173
• /
• 2016

Compressed Sensing(CS) is the theory that can recover signals which are sampled below the Nyquist sampling rate to original analog signals. In this paper, we propose the estimation algorithm of ultrasonic attenuation coefficients in the frequency domain using CS. While most estimation algorithms transform the time-domain signals into the frequency-domain using the Fourier transform, the proposed method directly utilize the spectral information in the recovery process by the basis matrix without the completely recovered signals in the time domain. We apply three transform bases for sparsifying and estimate the attenuation coefficients using the Centroid Downshift method with Dual-reference diffraction compensation technique. The estimation accuracy and execution time are compared for each basis matrix. Computer simulation results show that the DCT basis matrix exhibits less than 0.35% estimation error for the compressive ratio of 50% and about 6% average error for the compressive ratio of 70%. The proposed method which directly extracts frequency information from the CS signals can be extended to estimating for other ultrasonic parameters in the Quantitative Ultrasound (QUS) Analysis.

• Progress in Medical Physics
• /
• v.27 no.1
• /
• pp.1-7
• /
• 2016

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.4
• /
• pp.43-52
• /
• 2021

IoT applications are quickly spread in many fields. Blockchain methods(BC), defined as a distributed sharing mechanism, offer excellent support for IoT evolution. The BC provides a secure way for communication between IoT devices. However, the IoT environments are threatened by hacker attacks and malicious intrusions. The IoT applications security are faced with three challenges: intrusions and attacks detection, secure communication, and compressed storage information. This paper proposed a system based on double-blockchain to improve the communication transactions' safety and enhance the information compression method for the stored data. Information security is enhanced by using an Ellipse Curve Cryptography(ECC) considered in a double-blockchain case. The data compression is ensured by the Compressed Sensing(CS) method. The conducted experimentation reveals that the proposed method is more accurate in security and storage performance than previous related works.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.119-126
• /
• 2021

The Internet of Things (IoT) applications are quickly spread in many fields. Blockchain methods (BC), defined as a distributed sharing mechanism, offer excellent support for IoT evolution. The BC provides a secure way for communication between IoT devices. However, the IoT environments are threatened by hacker attacks and malicious intrusions. The IoT applications security are faced with three challenges: intrusions and attacks detection, secure communication, and compressed storage information. This paper proposed a system based on double-blockchain to improve the communication transactions' safety and enhance the information compression method for the stored data. Information security is enhanced by using an Ellipse Curve Cryptography (ECC) considered in a double-blockchain case. The data compression is ensured by the Compressed Sensing (CS) method. The conducted experimentation reveals that the proposed method is more accurate in security and storage performance than previous related works.

• Journal of Communications and Networks
• /
• v.12 no.4
• /
• pp.317-329
• /
• 2010

In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.4160-4176
• /
• 2015

Data compression like image and video compression has come a long way since the introduction of Compressive Sensing (CS) which compresses sparse signals such as images, videos etc. to very few samples i.e. M < N measurements. At the receiver end, a robust and efficient recovery algorithm estimates the original image or video. Many prominent algorithms solve least squares problem (LSP) iteratively in order to reconstruct the signal hence consuming more processing time. In this paper non-iterative threshold based recovery algorithm (NITRA) is proposed for the recovery of images and videos without solving LSP, claiming reduced complexity and better reconstruction quality. The elapsed time for images and videos using NITRA is in ㎲ range which is 100 times less than other existing algorithms. The peak signal to noise ratio (PSNR) is above 30 dB, structural similarity (SSIM) and structural content (SC) are of 99%.