• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.241-247
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• 2015

Cognitive radio has been attracting increased attention as an effective approach to improving spectrum efficiency. One component of cognitive radio, spectrum sensing, has an important relationship with the performance of cognitive radio. In this paper, after a summary and analysis of the existing spectrum-sensing algorithms, we report that the existing eigenvalue-based semi-blind detection algorithm and blind detection algorithm have not made full use of the eigenvalues of the received signals. Applying multi-antenna systems to cognitive users, we design a variety of spectrum-sensing algorithms based on the joint distribution of the eigenvalues of the received signal. Simulation results validate that the proposed algorithms in this paper are able to detect whether the signal of the primary user exists or not with high probability of detection in an environment with a low signal-to-noise ratio. Compared with traditional algorithms, the new algorithms have the advantages of high detection performance and strong robustness

• Journal of Satellite, Information and Communications
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• v.2 no.2
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• pp.53-58
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• 2007

The Satellite Mobile Communication System holds several advantages, such as wide coverage that guarantees the communication in a huge area. It is suitable in the ocean and forest and especially in emergency situation. However, the licensed frequency is not always occupied within all coverage and all the time. The actual utilization rate is relatively low compared to other wireless communications such as cellular systems. There are a large amount of white spaces in its coverage. Therefore, it is necessary to consider introducing additional services such as data communication, in order to increase the spectrum utilization as well as the revenue of the Satellite service provider. In this paper, we first analyze the possibility to implement new services in the licensed band of satellite mobile phone by its provider. Then we address the most significant issue for the implementation of current service, which is how to accurately detect the satellite mobile terminals. Finally, we suggest two new possible solutions namely, eigenvalue detection based methods to find out the existence of transmitted signal from the satellite mobile terminals.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.74-79
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• 2012

Scalar quantification of the angular prediction error covariance matrix is considered for characterizing tracking performances in phased array radar tracking. Specifically, the maximum eigenvalue and the trace of the covariance matrix are examined in terms of consistency in parameterizing the probability of detection, taking antenna beam-pointing losses into account, and it is shown numerically that the latter is more consistent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4625-4641
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• 2016

The performances of two commonly used feature detectors for wireless energy-harvesting cognitive radio systems are compared with the energy detector under energy causality and collision constraints. The optimal sensing duration is obtained by analyzing the effect of the detection threshold on the average throughput and collision probability. Numerical examples show that the covariance detector has the optimal sensing duration depending on an appropriate choice of the detection threshold, but no optimal sensing duration exists for the ratio of average energy to minimum eigenvalue detector.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.3
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• pp.218-224
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• 2011

A modal method is presented for the investigation of the effects of measurement errors in damage detection for dynamic structural systems. The structural modifications to the baseline system result in the response changes of the perturbed structure, which are measured to determine a unique system in the inverse problem of damage detection. If the numerical modal data are exact, mathematical programming techniques can be applied to obtain the accurate structural changes. In practice, however, the associated measurement errors are unavoidable, to some extent, and cause significant deviations from the correct perturbed system because of the intrinsic instability of eigenvalue problem. Hence, a self-equilibrating inverse system is allowed to drift in the close neighborhood of the measured data. A numerical example shows that iterative procedures can be used to search for the damaged structural elements. A small set of selected degrees of freedom is employed for practical applicability and computational efficiency.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.117-121
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• 1991

본 논문은 견인선(tow-ship)에서 발생하는 자체 잡음을 제거하여 수중 신호처리 시스템에서 표적 탐지(target detection)와 표적 식별(target identification) 등의 성능 향상을 위하여 표적 방향으로 형성된 빔의 출력을 원시 입력신호(primary input)로 사용하고 견인선 방향으로 형성된 빔의 출력을 참고 입력신호(reference input)로 사용한 적응 잡음 제거기(adaptive noise canceller)에 대해 연구하였다. 잡음 제거를 위해 사용되는 계수들은 LMS(Least Mean Square) 알고리듬을 이용하여 조정하였다. 컴퓨터 시뮬레이션을 통하여 TDL(Tapped-Delay Line) 구조와 LAT(LATtice) 구조를 갖는 적응 잡음 제거기 성능을 여러 가지 환경에서 비교, 관찰하였다. 두 알고리듬을 사용할 경우, 자체 잡음이 어떠한 형태로 나타나더라도 제거시킬 수 있음을 보여 주었으나 고유값 분포율(eigenvalue spread ratio)이 큰 경우에는 LMS-LAT가 LMS-TDL보다 수렴 속도뿐만 아니라 성능면에서도 우수함을 보였다.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.1-14
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• 2020

Antenna selection is a method to enhance the performance of spatial multiplexing multiple-input multiple-output (MIMO) systems, which can achieve the diversity order of the full MIMO systems. Although various selection criteria have been studied in the literature, they should be adjusted to the detection operation implemented at the receiver. In this paper, antenna selection methods that optimize the post-processing signal-to-noise ratio (SNR) and eigenvalue are considered for the lattice reduction (LR)-based receiver. To develop a complexity-efficient antenna selection algorithm, the incremental selection strategy is adopted. Moreover, for improvement of performance, an additional iterative selection method is presented in combination with an incremental strategy.