• The Pure and Applied Mathematics
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• v.6 no.2
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• pp.79-86
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• 1999

A Boolean function generates a binary sequence which is frequently used in a stream cipher. There are number of critical concepts which a Boolean function, as a key stream generator in a stream cipher, satisfies. These are nonlinearity, correlation immunity, balancedness, SAC (strictly avalanche criterion), PC (propagation criterion) and so on. In this paper we construct an algorithm for finding the correlation immune order of a Boolean function, and check how long to find the correlation immune order of a given Boolean function in our algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.468-473
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• 1998

An intelligent multimode target tracking algorithm using fuzzy logic is presented. Multimode tracking represents a synergistic approach that utilizes a variety of tracking techniques(centroid, correlation, etc.) to overcome the limitations inherent in any single-mode tracker. The design challenge for this type of multimode tracker is the data fusion algorithm. designs for this algorithm are based on heuristic rather than analytical approaches. A correlation-tracking algorithm seeks to align the incoming target image with a reference in age of the target, but has a critical problem, so called drift phenomenon. In this paper we will suggest a robust correlation tracker with gradient preprocessor combined by centroid algorithm to overcome the drift problem.

• ETRI Journal
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• v.43 no.1
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.651-658
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• 2002

Although least mean square(LMS) algorithm is known to one of the most popular algorithm in adaptive signal processing because of the simplicity and the small computation, the choice of the step size reflects a tradeoff between the misadjustment and the speed of adaptation. In this paper, we present a new variable step size LMS algorithm, so-called ECLMS(Estimated correlation LMS), using the correlation between reference input and error signal of adaptive filter. The proposed algorithm updates each weight of filter by different step size at same sample time. We applied this algorithm to adaptive multiple-notch filter. Simulation results are presented to compare the performance of the proposed algorithm with the usual LMS algorithm and another variable step algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.331-343
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• 2009

Many types of sensor data exhibit strong correlation in both space and time. Both temporal and spatial suppressions provide opportunities for reducing the energy cost of sensor data collection. Unfortunately, existing clustering algorithms are difficult to utilize the spatial or temporal opportunities, because they just organize clusters based on the distribution of sensor nodes or the network topology but not on the correlation of sensor data. In this paper, we propose a novel clustering algorithm based on the correlation of sensor data. We modify the advertisement sub-phase and TDMA schedule scheme to organize clusters by adjacent sensor nodes which have similar readings. Also, we propose a spatio-temporal suppression scheme for our clustering algorithm. In order to show the superiority of our clustering algorithm, we compare it with the existing suppression algorithms in terms of the lifetime of the sensor network and the size of data which have been collected in the base station. As a result, our experimental results show that the size of data is reduced and the whole network lifetime is prolonged.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.69-72
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• 2000

In CDMA systems, the performance of the typical multipath searcher degrades much according as the signal to noise ratio becomes low. In this paper, multipath searcher algorithm is proposed based on sliding window to overcome this drawback. In searcher systems, correlation values between incoming and local PN sequences are used to acquire multipath components. Therefore more accurate distributions of correlation values obtained through this proposed algorithm enables to get higher detection probability. In computer simulations, it is verified that proposed algorithm has better performances in Rayleigh fading channel and Gaussian channel.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.87-90
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• 1995

In this paper, we propose a new deinterlacing algorithm based on weighted wide vector correlations. This algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but not limited to. By having wide vector correlations, visually annoying artifacts caused by interlacing, such as a serrate line, line crawling, a line flicker, and a large area flicker, can be remarkably reduced, since the use of wide vector correlation increases the detectability of edges in various orientations.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.305-320
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• 2017

The selection of subset size is of great importance to the accuracy of digital image correlation (DIC). In the traditional DIC, a constant subset size is used for computing the entire image, which overlooks the differences among local speckle patterns of the image. Besides, it is very laborious to find the optimal global subset size of a speckle image. In this paper, a self-adaptive and bidirectional dynamic subset selection (SBDSS) algorithm is proposed to make the subset sizes vary according to their local speckle patterns, which ensures that every subset size is suitable and optimal. The sum of subset intensity variation (${\eta}$) is defined as the assessment criterion to quantify the subset information. Both the threshold and initial guess of subset size in the SBDSS algorithm are self-adaptive to different images. To analyze the performance of the proposed algorithm, both numerical and laboratory experiments were performed. In the numerical experiments, images with different speckle distribution, different deformation and noise were calculated by both the traditional DIC and the proposed algorithm. The results demonstrate that the proposed algorithm achieves higher accuracy than the traditional DIC. Laboratory experiments performed on a substrate also demonstrate that the proposed algorithm is effective in selecting appropriate subset size for each point.

• Current Optics and Photonics
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• v.4 no.4
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• pp.317-325
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• 2020

Accurate estimation of spatial frequencies and phases for illumination patterns are essential to reconstructing super-resolution images in structured illumination microscopy (SIM). In this manuscript, we propose the improved component cross-correlation (ICC) algorithm, which is based on optimization of the cross-correlation values of the overlapping information between various spectral components. Compared to other algorithms for spatial-frequency and phase determination, the results calculated by the ICC algorithm are more accurate when the modulation depths of the illumination patterns are low. Moreover, the ICC algorithm is able to calculate the spatial frequencies and phases simultaneously. Simulation results indicate that even if the modulation depth is lower than 0.1, the ICC algorithm still estimates the parameters precisely; the images reconstructed by the ICC algorithm are much clearer than those reconstructed by other algorithms. In experiments, our home-built SIM system was used to image bovine pulmonary artery endothelial (BPAE) cells. Drawing support from the ICC algorithm, super-resolution images were reconstructed without artifacts.