• ETRI Journal
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• v.41 no.2
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• pp.224-234
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• 2019

This paper proposes a new spectrum forecasting (SF) model to estimate the spectrum demands for future mobile broadband (MBB) services. The model requires five main input metrics, that is, the current available spectrum, site number growth, mobile data traffic growth, average network utilization, and spectrum efficiency growth. Using the proposed SF model, the future MBB spectrum demand for Malaysia in 2020 is forecasted based on the input market data of four major mobile telecommunication operators represented by A-D, which account for approximately 95% of the local mobile market share. Statistical data to generate the five input metrics were obtained from prominent agencies, such as the Malaysian Communications and Multimedia Commission, OpenSignal, Analysys Mason, GSMA, and Huawei. Our forecasting results indicate that by 2020, Malaysia would require approximately 307 MHz of additional spectrum to fulfill the enormous increase in mobile broadband data demands.

• Journal of Communications and Networks
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• v.8 no.2
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• pp.169-174
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• 2006

In this paper, the algorithm of a methodology for the calculation of spectrum requirements was implemented. As well, the influence of traffic distribution ratio among radio access technology groups, spectral efficiency, and flexible spectrum usage (FSU) margin was analyzed in terms of the spectrum requirements, with a view toward for future development of international mobile telecommunication (IMT)-2000 and systems beyond IMT-2000. The calculated spectrum requirement in the maximum spectral efficiency case is reduced by approximately 40% compared to a minimum spectral efficiency case. The effect of the distribution ratio on the required spectrum is smaller than the effect of the spectral efficiency. As the flexible spectrum usage margin increases by 1.0 dB, the total spectrum requirement decreases by 0.9 dB. The required spectrum for the market input parameter, ${\rho}$ = 0.5 is 801.63 MHz, while the required spectrum for ${\rho}$ = 1.0 is 6295.4 MHz. This is equivalent to an increase of 785.32 %.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.2 s.105
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• pp.110-116
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• 2006

In this paper the algorithm of a methodology for the calculation of spectrum requirements was implemented. As well, the influence of traffic distribution ratio among radio access technology groups, spectral efficiency, and flexible spectrum usage(FSU) margin was analyzed in terms of the spectrum requirements, with a view toward for future development of IMT-2000 and systems beyond IMT-2000. The ratio of the spectrum requirement to the traffic distribution ratio is approximately $1\;GHz/20\;\%$, and the spectrum requirement varies from 5 to 9 GHz. As the FSU margin increases by 1.0 dB, the total spectrum requirement decreases by 0.9 dB. The required spectrum for the market input parameter, ${\rho}=0.5$ is 801.63 MHz, while the required spectrum for ${\rho}=1.0$ is 6295.4 MHz. It can be concluded that the market input parameter is the most effective parameter in the calculation of spectrum requirements.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.2
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• pp.216-222
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• 2008

The determination of spectrum charges for the operators is a main issue to efficiently manage the limited radio spectrum resources. In this paper, we propose a model to compute the optimal charges for radio spectrum usage. The objective is to determine that will maximize the spectrum charges, and decision variables of ratios for actual or estimated revenues are considered. The spectrum charges are maximized under satisfying the least profit for operators based on Log-Linear demand function. The parameters of actual sales and minimum profit of operators are analyzed to make an efficient management for radio spectrum. The results show that the spectrum charges increase as the actual sales increase, but it decrease as the required minimum profit of operator increases. It is also observed that the government should increase the ratio for estimated sales if anticipating the poor market in the future, otherwise they should increase the ratio for actual sales to maximize the spectrum charges.

• Journal of Communications and Networks
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• v.14 no.2
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• pp.188-194
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• 2012

Cognitive networks (CNs) are capable of enabling dynamic spectrum allocation, and thus constitute a promising technology for future wireless communication. Whereas, the implementation of CN will lead to the requirement of an increased energy-arrival rate, which is a significant parameter in energy harvesting design of a cognitive user (CU) device. A well-designed spectrum-sensing scheme will lower the energy-arrival rate that is required and enable CNs to self-sustain, which will also help alleviate global warming. In this paper, spectrum sensing in a multi-user cognitive ad hoc network with a wide-band spectrum is considered. Based on the prospective spectrum sensing, we classify CN operation into two modes: Distributed and centralized. In a distributed network, each CU conducts spectrum sensing for its own data transmission, while in a centralized network, there is only one cognitive cluster header which performs spectrum sensing and broadcasts its sensing results to other CUs. Thus, a wide-band spectrum that is divided into multiple sub-channels can be sensed simultaneously in a distributed manner or sequentially in a centralized manner. We consider the energy consumption for spectrum sensing only of an analog-to-digital convertor (ADC). By formulating energy consumption for spectrum sensing in terms of the sub-channel sampling rate and whole-band sensing time, the sampling rate and whole-band sensing time that are optimal for minimizing the total energy consumption within sensing reliability constraints are obtained. A power dissipation model of an ADC, which plays an important role in formulating the energy efficiency problem, is presented. Using AD9051 as an ADC example, our numerical results show that the optimal sensing parameters will achieve a reduction in the energy-arrival rate of up to 97.7% and 50% in a distributed and a centralized network, respectively, when comparing the optimal and worst-case energy consumption for given system settings.

• Journal of the Korean Institute of Navigation
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• v.6 no.2
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• pp.13-33
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• 1982

The NAVSTAR/GPS is a satellite-based navigation system that will provide extremely accurate three-dimensional position and velocity information to users anywhere on or near the earth. This system has been studied primarly for military use and thus the results of studies are seldom obtainable for civilian use up to date. Expecting this system to be opened to civilian use in the future, this paper aims to the collection of basic data of the PN phase modulated communication system adopted in the GPS system by computer simulation. The results of studies are as follows; 1) The PN phase modulation of the speed of 106b/s can be simulated by Fourier summation, the spectrum region which can be restricted is in the region of 0.5-1.5 MHz and spectrum number required is over than 270. 2) By computer simulation, it is verified that optimum r.f filter bandwidth is about 0.3 MHz for the case of $10^6$b/s PN codes phase modulated in 1.575GHz carrier. 3) In case that PN rate to data rate is between 10 and 100, the allowable minimum SNR to demodulate data signal correctly by optimum LPF is about 0.3. It is also verified that the larger the frequency ratio of PN code to the data code, the lower the allowable minimum SNR required.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.7
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• pp.528-539
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• 2017

Since IoT is expected to enable hyper-connected society to be realized with the advent of the $4^{th}$ Industrial Revolution, many advanced foreign countries as well as United Kingdom identified IoT spectrum policy as one of the first priority in spectrum management in order to cope with the frequency demands required for the promotion of IoT service. In Korea, the frequencies of 110 MHz bandwidth has been also supplied additionaly for IoT in July 2016 in order to activate a promising new IoT industry based on wireless communication, which has great potential for future industrial growth. Therefore, in this paper, we propose spectrum policy and the research and innovation trends on IoT to promote IoT industry by analyzing the major communication network, the key challenge technologies and the spectrum policy framework of foreign countries.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.2
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• pp.62-67
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• 2010

Swells occurred on the coast of the East Sea on February 24, 2008 caused a loss of three lives and also damaged several west coasts of Japan. The recent increase of swell intensity with number of accidents demands more accurate forecasting of swells in terms of time and location. The swells occurred in February 2008 are hindcasted using SWAN model to examine the accuracy of the model for future forecasting. The model results are compared with ReWW3 data as well as measurement wave data and specially, wave spectrum is analysed by comparing with observed spectrum at two wave stations located in the east coast of Korea. The SWAN model shows similar results with observation data in terms of significant wave heights and swell arrival time but the shapes of wave spectrum are different between model and in-situ measurement data. For further improvement of swell forecasting, more comparison and analysis with observed wave spectrum is necessary and wave directional spectrum data are required to study on the characteristics of swells in the East Sea.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.3
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• pp.105-120
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• 2020

Although autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, patients with ASD frequently manifest atypical sensory behaviors. Recently, atypical sensory perception in ASD has received much attention, yet little is known about its cause or neurobiology. Herein, we review the findings from neuroimaging studies related to visual perception in ASD. Specifically, we examined the neural underpinnings of visual detection, motion perception, and face processing in ASD. Results from neuroimaging studies indicate that atypical visual perception in ASD may be influenced by attention or higher order cognitive mechanisms, and atypical face perception may be affected by disrupted social brain network. However, there is considerable evidence for atypical early visual processing in ASD. It is likely that visual perceptual abnormalities are independent of deficits of social functions or cognition. Importantly, atypical visual perception in ASD may enhance difficulties in dealing with complex and subtle social stimuli, or improve outstanding abilities in certain fields in individuals with Savant syndrome. Thus, future research is required to elucidate the characteristics and neurobiology of autistic visual perception to effectively apply these findings in the interventions of ASD.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.179-186
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• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.