• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1768-1769
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• 2011

The nonlinear estimation and the pseudo-linear estimation are used to treat the target localization in sensor network which provides range difference of arrival (RDOA) measurements. It is known that the nonlinear estimation has sensitive problem for the initial estimate and the pseudo-linear estimation has a large estimation error. The QCLS method is the typical estimator of the methods for pseudo-linear estimation. However the estimate by using the QCLS method includes the estimation error because the first stage of two estimation processes of the QCLS method causes the biased estimation error. Therefore we propose a instrumental variables(IV) method for minimizing the estimation error of the first stage. The simulation shows that the performance of the proposed method is superior to the QCLS method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.4 s.316
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• pp.1-7
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• 2007

When the sensor geometry is poor, the user position estimate obtained by of GN (Gauss-Newton) method is often diverged in radio navigation. In other to avoid divergence problem QCLS (Quadratic Correction Least Square) method using TDOA (Time Difference of Arrival) measurements is introduced, but the estimation error is somewhat large. This paper presents the modified QCLS method using weighted least square. Since the weighting matrix is influenced by the unknown user position, two-step approach is employed in the proposed method. The weighting matrix is estimated in the first step using least square, and then find user position is obtained using weighted least square. Simulation results show that the performance of the proposed method is superior to the conventional QCLS all over the workspace.

• Applied Science and Convergence Technology
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• v.26 no.5
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• pp.139-142
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• 2017

In this paper, InP-based InGaAs/InAlAs quantum cascade lasers(QCLs) providing nearly zero emission wavelength mismatch between the measured emission wavelength and the designed transition wavelength of QCLs is presented. The zero emission wavelength mismatch of QCLs influenced by both the accurate compositions and thicknesses of the low-pressure metal-organic vapor-phase epitaxy(MOVPE) grown InGaAs and InAlAs layers throughout the core and the abrupt composition transitions between InGaAs and InAlAs layers. The abrupt interfaces between InGaAs and InAlAs layers have been achieved throughout the core structure by means of controlling individually purged vent/run valves of a closed coupled showerhead reactor. In addition, maintaining substrate temperature constant during InGaAs/InAlAs core growth was a partial factor of uniformity improvement of QCLs. These approaches for reducing the possible discrepancies between the designed and MOVPE grown epitaxial structures could lead to improvement of QCL performance.

• Proceedings of the Optical Society of Korea Conference
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• 2009.10a
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• pp.105-106
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• 2009

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1717_1718
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• 2009

본 논문에서는 실시간으로 물류를 운반하는 차량의 위치추적을 위한 TDOA 기반의 알고리즘을 다루고 있다. Taylor-Series 방법과 QCLS 방법에 대한 모의실험을 수행하였으며, 이를 통해 RTLS에서 물류 관리를 위한 위치 추적 알고리즘으로 활용할 수 있음을 확인하였다.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.25-25
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• 2010

The spectroscopy over a region from 3 to 17 ${\mu}m$ based on the tuneable diode lasers (TDLAS) is the most powerful technique for in situ studies of the diagnostics of small molecules. The increasing interest in small molecules especially containing carbon, oxygen, hydrogen, and fluorine containing ones can be fulfilled by TDLAS at 0.0001 cm-1 resolution, because most of these compounds are infrared active. TDLAS provides a means of determining the absolute concentrations of the ground states of stable and transient molecular species, which can be employed for the time dependent studies in sub micro second scale. Information about gas temperature and population densities can also be derived from TDLAS measurements. Collisional energy transfer between the small molecules can be studied with TDLAS. Also, a variety of free radicals and molecular ions have been detected by TDLAS. Since plasmas with molecular feed gases are used in many applications, there are new applications in industrial field. Recently, the development of quantum cascade lasers (QCLs) offers an attractive new option for TDLAS.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.76-84
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• 2013

In the passive emitter localization using instantaneous TDOA (time difference of arrival) and FDOA (frequency difference of arrival) measurements, the estimation accuracy can be improved by collecting additional measurements. To achieve this goal, it is required to increase the number of the sensors. However, in electronic warfare environment, a large number of sensors cause the loss of military strength due to high probability of intercept. Also, the additional processes should be considered such as the data link and the clock synchronization between the sensors. Hence, in this paper, the passive localization of a stationary emitter is presented by using the successive TDOA and FDOA measurements from two moving sensors. In this case, since an independent pair of sensors is added in the data set at every instant of measurement, each pair of sensors does not share the common reference sensor. Therefore, the QCLS (quadratic correction least squares) methods cannot be applied, in which all pairs of sensor should include the common reference sensor. For this reason, a Gauss-Newton algorithm is adopted to solve the non-linear least square problem. In addition, to show the performance of the proposed method, we compare the RMSE (root mean square error) of the estimates with CRLB (Cramer-Rao lower bound) and derived the CEP (circular error probable) planes to analyze the expected estimation performance on the 2-dimensional space.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.