• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Journal of the Optical Society of Korea
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• 제12권3호
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• pp.121-125
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• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• Journal of electromagnetic engineering and science
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• 제10권4호
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• pp.322-327
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• 2010

Spaceborne sparse array radar for ground moving targets indication (GMTI) has outstanding advantage over full array radar for constructing ultra-large aperture. Rapid development of millimeter wave (MMW) technology make it possible for realizing MMW GMTI radar, which is much more sensitive to slow moving ground target. The paper presented the system model of a multi-carrier frequency sparse array MMW radar as well as preliminary simulation results, which showed future application of the system is very promising.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2015년도 추계학술대회
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• pp.1-3
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• 2015

In this paper, we propose a sparse signal reconstruction method referred to as the matching pursuit with a pruning-based tree search (PTS-MP). Two key ingredients of PTS-MP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our simulations, we confirm that PTS-MP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.

• Journal of Information Processing Systems
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• 제11권3호
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• pp.342-353
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• 2015

It is widely accepted that single carrier frequency division multiple access (SC-FDMA) is an excellent candidate for broadband wireless systems. Channel estimation is one of the key challenges in SC-FDMA, since accurate channel estimation can significantly improve equalization at the receiver and, consequently, enhance the communication performances. In this paper, we study the application of compressive sensing for sparse channel estimation in a SC-FDMA system. By skillfully designing pilots, their patterns, and taking advantages of the sparsity of the channel impulse response, the proposed system realizes channel estimation at a low cost. Simulation results show that it can achieve significantly improved performance in a frequency selective fading sparse channel with fewer pilots.

• 한국정보전자통신기술학회논문지
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• 제12권5호
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• pp.521-528
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• 2019

Epilepsy is one of the most prevalent neurological diseases. Electroencephalogram (EEG) signals are widely used for monitoring and diagnosis tool for epileptic seizure. Typically, a huge amount of EEG signals is needed, where they are visually examined by experienced clinicians. In this study, we propose a simple automatic seizure detection framework using intracranial EEG signals. We suggest a sparse approximation based classification (SAC) scheme by solving overdetermined system. L1-norm minimization algorithms are utilized for efficient sparse signal recovery. For evaluation of the proposed scheme, the public EEG dataset obtained by five healthy subjects and five epileptic patients is utilized. The results show that the proposed fast L1-norm minimization based SAC methods achieve the 99.5% classification accuracy which is 1% improved result than the conventional L2 norm based method with negligibly increased execution time (42msec).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.218-223
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• 2021

Pervasive enhancement and required enforcement of the Internet of Things (IoTs) in a distributed massively multiplayer online architecture have effected in massive growth of Big-Data in terms of server over-load. There have been some previous works to overcome the overloading of server works. However, there are lack of considered methods, which is commonly applicable. Therefore, we propose a combing Sparse Auto-Encoder and Load-Balancing, which is ProGReGA for Big-Data of server loads. In the process of Sparse Auto-Encoder, when it comes to selection of the feature-pattern, the less relevant feature-pattern could be eliminated from Big-Data. In relation to Load-Balancing, the alleviated degradation of ProGReGA can take advantage of the less redundant feature-pattern. That means the most relevant of Big-Data representation can work. In the performance evaluation, we can find that the proposed method have become more approachable and stable.

• Communications for Statistical Applications and Methods
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• 제29권1호
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• pp.53-64
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• 2022

High dimensional time series is gaining considerable attention in recent years. The sparse vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) uses adaptive lasso and debiasing procedure in estimation, and showed superb forecasting performance in realized volatilities. This paper extends the sparse VHAR model by considering non-convex penalties such as SCAD and MCP for possible bias reduction from their penalty design. Finite sample performances of three estimation methods are compared through Monte Carlo simulation. Our study shows first that taking into cross-sectional correlations reduces bias. Second, nonconvex penalties performs better when the sample size is small. On the other hand, the adaptive lasso with debiasing performs well as sample size increases. Also, empirical analysis based on 20 multinational realized volatilities is provided.

• Current Optics and Photonics
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• 제6권3호
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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.