• Journal of information and communication convergence engineering
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• v.9 no.6
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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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• v.8 no.2
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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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• v.17 no.7
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.10
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• pp.898-907
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• 2013

For D2D communications underlaying cellular networks, it is essential to consider the mutual interference between the existing cellular communications and D2D communications as well as the spectral efficiency, as they need to share the same frequency. Accordingly, a resource allocation scheme should be designed in such a way that minimizes the mutual interference and maximizes the spectrum utilization efficiency at the same time. In this paper, we propose a resource allocation scheme based on cooperation of the base station and D2D terminals. Specifically, a D2D terminal senses the cellular spectrum to recognize the interference condition, chooses the best cellular resource, and reports the information to the base station. The base station allocates D2D resource such that the corresponding D2D link and cellular link share the same resource. The performance of the proposed resource allocation scheme is ated through compu under 3GPP LTE-Advanced scenarios.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1037-1044
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• 2020

Since radar wind profiler (RWP) provides wind field data with high time and space resolution in all weather conditions, their verification of the accuracy and quality is essential. The simultaneous wind measurement from rawinsonde is commonly used to evaluate wind vectors from RWP. In this study, the simulation algorithm which produces the spectrum and raw time series (I/Q) data from the average values of moments is presented as a step-by-step verification method for the signal processing algorithm. The possibility of the simulation algorithm was also confirmed through comparison with the raw data of LAP-3000. The Doppler power spectrum was generated by assuming the density function of the skew-normal distribution and by using the moment values as the parameter. The simulated spectrum was generated through random numbers. In addition, the coherent averaged I/Q data was generated by random phase and inverse discrete Fourier transform, and raw I/Q data was generated through the Dirichlet distribution.

• 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.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.668-670
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• 2010

It is necessary to estimate the range and Doppler shifted spectrum for the extraction of useful information from the return echoes in the frequency modulated continuous wave radar systems used for the remote sending purpose such as detection of moving targets. However, the spectrum estimation using the FFT method causes the very large sidolobes of clutter masking the essential signal information if the acquisition time of an echo signal is pretty short. Therefore, in this paper, the efficient data windowing method is investigated to suppress the strong sidelobe levels of the clutter and results are analyzed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.1
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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.