• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.641-646
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• 2011

In this paper, we investigate a power control problem which is very critical in underlay-based spectrum sharing systems. Although an underlay-based spectrum sharing system is more efficient compared to an overlay-based spectrum sharing system in terms of spectral utilization, some practical problems obstruct its commercialization. One of them is a real-time-based power adaptation of secondary transmitters. In the underlay-based spectrum sharing system, it is essential to adapt secondary user's transmit power to interference channel states to secure primary users' communication. Thus, we propose a practical power control scheme for secondary transmitters. The feedback overhead of our proposed scheme is insignificant because it requires one-bit signaling, while the optimal power control scheme requires the perfect information of channel states. In addition, the proposed scheme is robust to feedback delay. We compare the performance of the optimal and proposed schemes in terms of primary user's outage probability and secondary user's throughput. Our simulation results show that the proposed scheme is almost optimal in terms of both primary user's outage probability and secondary user's throughput when the secondary user's transmit power is low. As the secondary user's transmit power increases, the primary user's outage probability of the proposed scheme is degraded compared with the optimal scheme while the secondary user's throughput still approaches that of the optimal scheme. If the feedback delay is considered, however, the proposed scheme approaches the optimal scheme in terms of both the primary user's outage probability and secondary user's throughput regardless of the secondary user's transmit power.

• Annals of Clinical Neurophysiology
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• 제8권2호
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• pp.135-145
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• 2006

Background: Parkinson's disease is movement disorder due to dopaminergic deficiency. It has been noted that cognitive dysfunction also presented on Parkinson's disease patients. But, it is not clear whether such a cognitive dysfunction was a dopaminergic dysfunction or cholinergic dysfunction. Using linear and non-linear analyses, we analysed the effect of cognitive and motor symptom on EEG change. Methods: EEGs were recorded from patients with Parkinson's disease and essential tremor, and normal controls during rest. We calculated the power spectrum, correlation dimension and Lyapunov exponent by using 'Complexity'program. The power spectrum, correlation dimension, and Lyapunov exponent were compared between Parkinson's disease patients and essential tremor patients. Results: Theta power was increased in Parkinson's disease patient group. Correlation dimension was increased in Parkinson's disease patients. Positive correlation was noted between MMSE and correlation dimension, and negative correlation was noted between MMSE and Lyapunov exponent. Lyapunov exponent was decreased in Parkinson's disease patient. Conclusions: We conclude that the state of Parkinson's disease patient is characterized by increased correlation dimension and decreased Lyapunov exponent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• 한국통신학회논문지
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• 제38A권10호
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• pp.898-907
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• 2013

셀룰러 네트워크에서 D2D 통신을 지원하기 위해서는 기존의 셀룰러 통신과 D2D 통신이 셀룰러 자원을 공유하므로 주파수 효율성과 간섭에 대한 고려가 반드시 필요하다. 따라서 D2D 통신을 위한 자원할당은 셀룰러 통신과의 간섭을 최소화하면서 동시에 주파수 재사용에 따른 스펙트럼 이용 효율을 최대화하는 것이 바람직하다. 본 논문에서는 D2D 단말과 기지국이 상호 협력적인 방법으로 D2D 자원을 할당하는 기법을 제안한다. D2D 단말은 스펙트럼 센싱을 통해 셀룰러 단말과 다른 D2D 단말로부터 오는 간섭을 인지하여 최적의 셀룰러 자원을 선택하여 기지국에 보고한다. 기지국은 해당 셀룰러 단말과 D2D 단말이 동일한 자원을 공유하도록 자원할당을 함으로써 간섭의 영향을 최소화한다. 3GPP LTE (Long Term Evolution)-Advanced 환경에서 모의실험을 통해 제안한 자원할당 기법의 성능을 검증한다.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1037-1044
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• 2020

윈드프로파일러(RWP, radar wind profiler)는 기상 상태와 관계없이 시공간 분해능이 높은 바람장 자료를 제공하며 생산된 바람의 정확도나 품질에 대한 검증이 필수적이다. 기존 정확도 검증 방식은 레윈존데와의 동시 관측을 통해 윈드프로파일러에서 생성된 바람 벡터를 기준 자료로 활용하는 것이다. 본 연구에서는 평균 모멘트 자료로부터 스펙트럼과 원시 시계열 자료를 시뮬레이션하는 알고리즘을 통해 윈드프로파일러의 신호처리 알고리즘을 단계별로 검증하는 방안을 제시하고, LAP-3000의 원시 자료와의 비교를 통해 해당 알고리즘의 가능성을 확인하였다. 기상 신호의 밀도 함수를 모멘트값을 인자로 하는 왜곡된 정규 분포의 밀도 함수로 가정하여 생성하였고, 난수를 통해 시뮬레이션 스펙트럼을 생성하였다. 또한, 난수 위상과 역 이산푸리에 변환으로 간섭 평균된 시뮬레이션 원시 시계열 자료를 생성하고 최종적으로 디리클레 분포(Dirichlet distribution)를 통해 간섭 평균 전 단계의 원시 시계열 자료를 생성하였다.

• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• 제11권2호
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• pp.939-951
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• 2019

A novel empirical mode decomposition strategy based on Fourier transform and band-pass filter techniques, contributing to efficient instantaneous vibration analyses, is developed in this study. Two key improvements are proposed. The first is associated with the adoption of a band-pass filter technique for intrinsic mode function sifting. The primary characteristic of decomposed components is that their bandwidths do not overlap in the frequency domain. The second improvement concerns an attempt to design narrowband constraints as the essential requirements for intrinsic mode function to make it physically meaningful. Because all decomposed components are generated with respect to their intrinsic narrow bandwidth and strict sifting from high to low frequencies successively, they are orthogonal to each other and are thus suitable for an instantaneous frequency analysis. The direct Hilbert spectrum is employed to illustrate the instantaneous time-frequency-energy distribution. Commendable agreement between the illustrations of the proposed direct Hilbert spectrum and the traditional Fourier spectrum was observed. This method provides robust identifications of vibration modes embedded in vibration processes, deemed to be an efficient means to obtain valuable instantaneous information.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.668-670
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• 2010

주파수 변조 방식의 연속 파형을 사용하는 레이다 시스템에서는 이동 목표물 등의 원격탐지를 위하여 각 거리에 따른 변이 주파수 및 추가적인 도플러 스펙트럼의 추정이 필요하다. 그러나 이러한 기저대역 또는 중간주파수 대역의 스펙트럼 추정은 주로 FFT 기법에 의하여 이루어지며 목표물에 대한 수신신호 시간이 비교적 짧은 경우 클러터 등의 강력한 간섭신호의 부엽이 인접 도플러 필터에 누설되어 탐지하고자 하는 신호가 가려지는 문제가 나타나게 된다. 따라서 본 논문에서는 약간의 처리손실을 감수하더라도 부엽의 절대적인 크기를 낮출 수 있는 효과적인 데이터 윈도잉 기법 및 그 결과들을 고찰하고 분석하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.762-766
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• 1998

Mechanical valve is one of the most widely used implantable artificial organs, Since its failure (mechanical failures and thrombosis to name two representative example) means the death of patient, its reliability is very important and early noninvasive detection is essential requirement . This paper will explain the method to detect the thrombosis formation by spectral analysis and neural network. In order quantitatively to distinguish peak of a normal valve from that of a thrombotic valve, a 3 layer backpropagation neural network, which contains 7,000 input nodes, 20 hidden layer and 1output , was employed. The trained neural network can distinguish normal and thrombotic valve with a probability that is higher than 90% . In conclusion, the acoustical spectrum analysis coupled with a neural network algorithm lent itself to the noninvasive monitoring of implanted mechanical valves. This method will be applied to be applied to the performance evaluation of other implantable rtificial organs.

• 대한전자공학회논문지
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• 제25권1호
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• pp.27-32
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• 1988

Electron Spectroscopy for Chemical Analysis(ESCA) allowes the determination of the elemental composition and the bonding state of the surface atomes in the interface between two materials. In the binding energies of ESCA spectrum, there are zero error, voltage scaling error and random error. Accurate analysis of the intensity energy response functions and accurate calibration of the energy scale are essential to use X-ray photoelectron spectron meter. At the results of the calibration of the ESCA spectra in the copper and cordierite (Mg2Al4Si5kO18) interfaces, the errors relative to the copper are -3.03 eV for the zero error -z,-197 ppm for the voltage scaling error -V and 6.9 meV for the random error -R. The method of the calibration is able to apply for the binding energy calibration of the another ESCA spectra.