• Korean Journal of Remote Sensing
• /
• v.22 no.5
• /
• pp.337-350
• /
• 2006

The characteristics of neighborhood correlation images for change detection were explored at different spatial resolution scales. Bi-temporal QuickBird datasets of Las Vegas, NV were used for the high spatial resolution image analysis, while bi-temporal Landsat $TM/ETM^{+}$ datasets of Suwon, South Korea were used for the mid spatial resolution analysis. The neighborhood correlation images consisting of three variables (correlation, slope, and intercept) were evaluated and compared between the two scales for change detection. The neighborhood correlation images created using the Landsat datasets resulted in somewhat different patterns from those using the QuickBird high spatial resolution imagery due to several reasons such as the impact of mixed pixels. Then, automated binary change detection was also performed using the single and multiple neighborhood correlation image variables for both spatial resolution image scales.

• Journal of Korean Association for Spatial Structures
• /
• v.23 no.2
• /
• pp.4-7
• /
• 2023

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.15 no.2
• /
• pp.255-266
• /
• 2015

In this paper, a low-complexity and low-power soft output multiple input multiple output (MIMO) symbol detector is proposed for mobile devices with two transmit and two receive antennas. The proposed symbol detector can support both the spatial multiplexing mode and spatial diversity mode in single hardware and shows the optimal maximum likelihood (ML) performance. By applying a multi-stage pipeline structure and using a complex multiplier based on the polar-coordinate, the complexity of the proposed architecture is dramatically decreased. Also, by applying a clock-gating scheme to the internal modules for MIMO modes, the power consumption is also reduced. The proposed symbol detector was designed using a hardware description language (HDL) and implemented using a 65nm CMOS standard cell library. With the proposed architecture, the proposed MIMO detector takes up an area of approximately $0.31mm^2$ with 183K equivalent gates and achieves a 150Mbps throughput. Also, the power estimation results show that the proposed MIMO detector can reduce the power consumption by a maximum of 85% for the various test cases.

• Journal of information and communication convergence engineering
• /
• v.13 no.1
• /
• pp.1-6
• /
• 2015

In this study, we consider visible light communication in an indoor line-of-sight environment. It has been proved that among the multiple input multiple output (MIMO) techniques, spatial modulation (SM) performs better than repetition coding (RC) and spatial multiplexing (SMP). On the basis of a combination of SM and pulse amplitude modulation (PAM), here, we propose an enhanced SM algorithm to improve the bit error rate. Traditional SM activates only one light-emitting diode (LED) at one time, and the proposed enhanced SM activates two LEDs at one time and reduces the intensity levels of PAM by half. Under the condition of a highly correlated channel, power imbalance is used to improve the algorithm performance. The comparison between the two schemes is implemented at the same signal-to-noise ratio. The simulation results illustrate that the enhanced SM outperforms the traditional SM in both highly correlated and lowly correlated channels. Furthermore, the proposed enhanced SM scheme can increase the transmission rate in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.9
• /
• pp.1573-1595
• /
• 2011

In this paper, the outage performance of multiuser multiple-input single-output (MISO) systems exploiting joint spatial and multiuser diversities is investigated for Rayleigh fading channels with outdated feedback. First, we derive closed-form exact outage probabilities for the joint diversity schemes that combine user scheduling with different spatial diversity techniques, including: 1) transmit maximum-ratio combining (TMRC); 2) transmit antenna selection (TAS); and 3) orthogonal space-time block coding (OSTBC). Then the asymptotic outage probabilities are analyzed to gain more insights into the effect of feedback delay. It is observed that with outdated feedback, the asymptotic diversity order of the multiuser OSTBC (M-OSTBC) scheme is equal to the number of transmit antennas at the base station, while that of the multiuser TMRC (M-TMRC) and the multiuser TAS (M-TAS) schemes reduce to one. Further by comparing the asymptotic outage probabilities, it is found that the M-TMRC scheme outperforms the M-TAS scheme, and the M-OSTBC scheme can perform best in the outage regime of practical interest when the feedback delay is large. Theoretical analysis is verified by simulation results.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1209-1219
• /
• 2019

For consistent vegetation monitoring, it is necessary to generate time-series vegetation index datasets at fine temporal and spatial scales by fusing the complementary characteristics between temporal and spatial scales of multiple satellite data. In this study, we quantitatively and qualitatively analyzed the prediction accuracy of time-series change information extracted from spatio-temporal fusion models of multiple satellite data for vegetation monitoring. As for the spatio-temporal fusion models, we applied two models that have been widely employed to vegetation monitoring, including a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). To quantitatively evaluate the prediction accuracy, we first generated simulated data sets from MODIS data with fine temporal scales and then used them as inputs for the spatio-temporal fusion models. We observed from the comparative experiment that ESTARFM showed better prediction performance than STARFM, but the prediction performance for the two models became degraded as the difference between the prediction date and the simultaneous acquisition date of the input data increased. This result indicates that multiple data acquired close to the prediction date should be used to improve the prediction accuracy. When considering the limited availability of optical images, it is necessary to develop an advanced spatio-temporal model that can reflect the suggestions of this study for vegetation monitoring.

• Journal of information and communication convergence engineering
• /
• v.13 no.2
• /
• pp.105-112
• /
• 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 Smart Tourism
• /
• v.1 no.4
• /
• pp.3-7
• /
• 2021

Although Geographic Information Systems (GISs) have commonly been employed as powerful tools for manipulating and displaying spatial data in community-based tourism, a variety of GIS functions still lack the capabilities required to assist multiple decision makers to come to consensual decisions. In this study, I propose an alternative approach: spatial multicriteria decision analysis (SMCDA) that could reflect diverse decision makers' preferences by integrating GISs and multicriteria decision analysis (MCDA). I review the small number of case studies that have employed SMCDA, with a focus on the roles of GISs and MCDA. The methodological integration of GISs and MCDA into multi-spatial decision support systems offers the potential to implement participatory decision-making to solve complex spatial problems in community-based tourism planning, development, and management.

• Journal of Korea Water Resources Association
• /
• v.45 no.9
• /
• pp.875-885
• /
• 2012

In this study, the total precipitation amount in Jeju Island was estimated with the simulated precipitation for ungauged stations missing precipitation data using the spatial precipitation analysis. The missing data were generated through the modified multiple linear regression in this study, and the analysis of spatial precipitation was conducted with the PRISM(Parameter-elevation Regression on Independent Slope Model). The generated data with modified multiple linear regression model have similar pattern with original data. Thus, the model in this study shows good applicability to estimate the missing data. The difference of annual average precipitation between Case 1 (original data) and Case 2 (modified data) appears very small ratio which is about 1.5%. However, the difference of annual average precipitation according to elevation shows the large ratio up to 37.4%. As the results, the method of estimating missing data in this study would be useful to calculate the total precipitation amount at the low station density area and the places with the high spatial variation of precipitation.

• Journal of Biomedical Engineering Research
• /
• v.31 no.6
• /
• pp.456-463
• /
• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.