• ETRI Journal
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• 제41권2호
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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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• 제8권2호
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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 %.

• 한국전자파학회논문지
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• 제17권2호
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• pp.110-116
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• 2006

본 논문에서는 4세대 이동통신 주파수 소요량 산출 방법론을 구현하고, 주파수 소요량의 변화에 큰 영향을 미치는 RATG간 트래픽 배분을, 주파수 효율성, FSU 이득과 시장 데이터 입력 파라미터 ${\rho}$에 따른 주파수 소요량의 변화를 시뮬레이션 하였다. 트래픽 배분율에 따른 주파수 소요량 변화는 $1\;GHz/20\;\%$정도였으며, 주파수 효율성에 따른 변화는 $5{\sim}9\;GHz$였다. 그리고 FSU 이득이 1 dB 증가하면 주파수 소요량은 약 0.9 dB 정도 감소하였다. 시장 데이터 입력 파라미터 ${\rho}=0.5$ 일 때 주파수 소요량은 801.63 MHz, ${\rho}=1.0$ 일 때는 6295.4 MHz로, 시장 데이터 입력 파라미터가 주파수 소요량에 미치는 영향이 가장 크다.

• 대한산업공학회지
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• 제34권2호
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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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• 제14권2호
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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.

• 한국항해학회지
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• 제6권2호
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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.

• 한국전자파학회논문지
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• 제28권7호
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• pp.528-539
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• 2017

4차 산업혁명 도래와 함께 초연결 사회를 실현할 중추 기술로 IoT가 급부상하고 있으며, IoT 투자 촉진 및 서비스 활성화를 위하여 영국을 비롯한 선진외국에서는 IoT 서비스 수요에 대응하는 주파수 관리정책을 최우선 순위 중의 하나로 추진하고 있다. 국내에서도 2016년 7월에 IoT 관련 유망 신산업 활성화를 위해 IoT 용도로 110 MHz 폭 주파수를 신규로 추가 공급하였다. 그리하여 본 논문에서는 IoT 통신 네트워크의 미래 발전방향과 기술개발 방향을 제시하고, 선진외국의 IoT 주파수 정책을 분석하여 국내의 IoT 서비스를 활성화하기 위한 스펙트럼 정책을 제안하고자 한다.

• 한국해양학회지:바다
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• 제15권2호
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• pp.62-67
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• 2010

2008년 2월 24일 동해안에 출현한 너울은 3명의 인명 사고를 발생시켰으며 또한 일본의 서쪽 해안에서도 피해가 나타났다. 최근에 그 강도가 점점 강해지는 동해의 너울에 의한 사고를 줄이기 위해서는 향후 그 발생 시간과 위치에 대한 보다 정확한 예보가 필요하다. 본 연구에서는 동해에서의 파랑 예보의 정확성을 검증하기 위하여 SWAN 파랑 모델을 이용하여 2008년 2월 발생한 동해의 너울을 예측하고 검증하였다. 모델 결과는 파랑 관측 자료뿐만 아니라 한국기상연구소가 ReWW3를 사용하여 생산한 자료와도 비교 하였으며 특히 동해안의 두개의 파랑 관측소에서 획득한 주파수 스펙트럼과의 비교 분석도 수행하였다. 그 결과 파고 분포와 발생 시간은 유사하게 나타났지만 주파수 스펙트럼의 형태에 있어서는 차이가 나타났다. 너울에 대한 보다 정확한 예보를 위해서는 보다 많은 현장 관측 자료와의 비교 분석이 필요하며 특히 동해에서의 너울 특성 연구를 위해서는 파향 스펙트럼 관측 자료가 필요하다.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제31권3호
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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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• 제23권4호
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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.