• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.398-407
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• 2015

Compressive sensing (CS) has drawn much interest as a novel sampling technique that enables sparse signal to be sampled under the Nyquitst/Shannon rate. By noting that the block-based CS can still keep spatial correlation in measurement domain, this paper proposes to adapt sampling rate of each block in frame according to its characteristic defined by edge information. Specifically, those blocks containing more edges are assigned more measurements utilizing block-wise correlation in measurement domain without knowledge about full sampling frame. For natural image, the proposed adaptive rate allocation shows considerable improvement compared with fixed subrate block-based CS in both terms of objective (up to 3.29 dB gain) and subjective qualities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3498-3511
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• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• ALGAE
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• v.25 no.3
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• pp.141-153
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• 2010

Seagrass epiphyte blooms potentially have important economic and ecological consequences in Tampa Bay, one of the Gulf of Mexico's largest estuaries. As part of a Tampa Bay pilot study to monitor the impact of environmental stresses, precise characterization of epiphyte diversity is required for efficient management of affected resources. Thus, epiphyte diversity may be used as a rational basis for assessment of ecosystem health. In May 2001, epiphytic species encompassing green, brown and red macroalgae were manually collected from dense and sparse seagrass beds of Thalassia testudinum and Syringodium filiforme. A total of 20 macroalgal epiphytes, 2 Chlorophyta, 2 Phaeophyta, and 16 Rhodophyta, were found on T. testudinum and S. filiforme seagrass at the four sampling sites (Bishop Harbor, Cockroach Bay, Feather Sound, and Mariposa Key). The Rhodophyta, represented by 16 species, dominated the numbers of species. Among them, the thin-crusted Hydrolithon farinosum was the most commonly found epiphyte on seagrass leaves. Species number, as well as species frequency of epiphytes, is higher at dense seagrass sites than sparse seagrass sites. Four attachment patterns of epiphytes can be classified according to cortex and rhizoid development: 1) creeping, 2) erect, 3) creeping & erect, and 4) erect & holding. The creeping type is characterized by an encrusting thallus without a rhizoid or holdfast base. Characteristics of the erect type include a filamentous thallus with or without a cortex, and a rhizoid or holdfast base. The creeping and erect type is characterized by a filamentous thallus with a cortex and rhizoid. A filamentous thallus with a cortex, holdfast base, and host holding branch is characteristics of the erect and holdfast attachment type. This study characterized each species found on the seagrass for epiphyte identification.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.455-461
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• 2002

This paper presents a new parametric log line equation of polar form for Hough transform in log-polar plane, in which it can remove the well-known unboundedness problem of Hough parameters. Bolduc's method is used to generate a log-polar image dividing the fovea and periphery from a Cartesian image. Edges of the fovea and periphery are detected by using the Sobel mask and the proposed space-variant gradient mask, and are combined in the log-polar plane. The sampled points that might constitute a log line are quite sparse in a deep peripheral region due to severe under-sampling, which is an inherent property of LPM. To cope with such under-sampling, we determine the values of cumulative cells in Hough space by using the space-variant weighting. In our experiments, the proposed method demonstrates its validity of detecting not only the lines passing through both the fovea and periphery but also the lines in a deep periphery.

• Ocean and Polar Research
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• v.27 no.2
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• pp.189-195
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• 2005

Several models predict large and potentially abrupt ocean circulation changes due to anthropogenic greenhouse-gas emissions. These circulation changes drive-in the models-considerable oceanic oxygen trend. A sound estimate of the observed oxygen trends can hence be a powerful tool to constrain predictions of future changes in oceanic deepwater formation, heat and carbon dioxide uptake. Estimating decadal scale oxygen trends is, however, a nontrivial task and previous studies have come to contradicting conclusions. One key potential problem is that changes in the historical observation network might introduce considerable errors. Here we estimate the likely magnitude of these errors for a subset of the available observations in the Southern Ocean. We test three common data analysis methods south of Australia and focus on the decadal-scale trends between the 1970's and the 1990's. Specifically, we estimate errors due to sparsely sampled observations using a known signal (the time invariant, temporally averaged, World Ocean Atlas 2001) as a negative control. The crossover analysis and the objective analysis methods are for less prone to spatial sampling location biases than the area averaging method. Subject to numerous caveats, we find that errors due to sparse sampling for the area averaging method are on the order of several micro-moles $kg^{-1}$. for the crossover and the objective analysis method, these errors are much smaller. For the analyzed example, the biases due to changes in the spatial design of the historical observation network are relatively small compared to the tends predicted by many model simulations. This raises the possibility to use historic oxygen trends to constrain model simulations, even in sparsely sampled ocean basins.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.121-128
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• 2010

K-means clustering is an iterative algorithm in which items are moved among sets of clusters until the desired set is reached. K-means clustering has been widely used in many applications, such as market research, pattern analysis or recognition, image processing, etc. It can identify dense and sparse regions among data attributes or object attributes. But k-means algorithm requires many hours to get k clusters that we want, because it is more primitive, explorative. In this paper we propose a new method of k-means clustering using a center of gravity for grid-based sample. It is more fast than any traditional clustering method and maintains its accuracy.

• Investigative Magnetic Resonance Imaging
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• v.19 no.1
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.591-600
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• 2015

The efficiency of a powertrain system of hybrid vehicle is highly dependent on the design and control of the hybrid powertrain system. In other words, the optimal design of the powertrain systems is coupled with optimal control of the powertrain system. Therefore, the solution of an optimal design problem for hybrid vehicles is computationally and timely very expensive. For example, dynamic programming, which is a recursive optimization method, is usually used to evaluate the best fuel economy of certain hybrid vehicle design, and, thus, the evaluation takes tens of minutes to several hours. This research aims to accelerate the speed of efficiency evaluation of hybrid vehicles. We suggest a mathematical treat and a methodological treat to reduce the computational load. The mathematical treat is that the dynamics of system is discretized with sparse sampling time without loss of energy balance. The methodological treat is that the efficiency of the hybrid vehicle is inferred by characteristic loss evaluation that is computationally inexpensive. With the suggested methodology, evaluating a design candidate of hybrid powertrain system is taken few minutes, which was taken several hours when dynamic programming is used.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.381-389
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• 2022

Studies examining the interplay between brand transgression and brand forgiveness is notably sparse especially in the context of Southeast Asian banking customers. The purpose of this research is to add to the existing literature by examining the impact of brand transgression, which is represented by negative past experience image incongruence, and corporate wrongdoing on brand forgiveness among Islamic banking customers in Malaysia. The increasing surge in interest in unfavorable brand relationships has sparked concerns about its impact on brand forgiveness. As a result, this theoretical argument, which lacks empirical proof, has to be statistically tested. The current study was conducted utilizing a non-probability purposive sampling technique among clients in the Klang Valley who had poor experiences with Islamic banking services. Data analysis included descriptive statistics, exploratory factor analysis, and multiple regression on a total of 211 valid replies. The findings show that two elements of brand transgression, image inconsistency, and corporate wrongdoing, have a major impact on brand forgiveness. However, the other dimension namely negative past experience was found to be non-significant to brand forgiveness. Research implications and directions for future studies are also discussed in this paper.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.651-664
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• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.