• Journal of electromagnetic engineering and science
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• v.9 no.3
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• pp.130-140
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• 2009

The design theory and experimental results of a true random noise radar system are presented in this paper. Target range information can be extracted precisely by correlation processing between the delayed reference and the signal received from a target, and the velocity information by the Doppler processing with successive correlation data. A K-band noise radar system was designed using random FM noise signal, and the characteristics of the fabricated system were examined with laboratory and outdoor experiments. A C-band random FM noise signal was generated by applying a low-frequency white Gaussian noise source to VCO(Voltage Controlled Oscillator), and a K-band Tx noise signal with 100 MHz bandwidth was obtained by using a following frequency multiplier. Two modified wave-guide horn arrays were designed and fabricated, and used for the Tx and Rx antennas. The required amount of Tx/Rx isolation was attained by using a coupling cancellation circuit as well as keeping them apart with predetermined spacing. A double down-conversion scheme was used in the Rx and reference channels, respectively, for easy post processing such as correlation and Doppler processing. The implemented noise radar performance was examined with a moving bicycle and a very high-speed target with a velocity of 150 m/s. The results extracted by the Matlab simulation using the logging data were found to be in a reasonable agreement with the expected results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.619-628
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• 2019

In this paper, we designed a beta ray sensor for a true random number generator. Instead of biasing the gate of the PMOS feedback transistor to a DC voltage, the current flowing through the PMOS feedback transistor is mirrored through a current bias circuit designed to be insensitive to PVT fluctuations, thereby minimizing fluctuations in the signal voltage of the CSA. In addition, by using the constant current supplied by the BGR (Bandgap Reference) circuit, the signal voltage is charged to the VCOM voltage level, thereby reducing the change in charge time to enable high-speed sensing. The beta ray sensor designed with 0.18㎛ CMOS process shows that the minimum signal voltage and maximum signal voltage of the CSA circuit which are resulted from corner simulation are 205mV and 303mV, respectively. and the minimum and maximum widths of the pulses generated by comparing the output signal through the pulse shaper with the threshold voltage (VTHR) voltage of the comparator, were 0.592㎲ and 1.247㎲, respectively. resulting in high-speed detection of 100kHz. Thus, it is designed to count up to 100 kilo pulses per second.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• Speech Sciences
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• v.10 no.1
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• pp.95-105
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• 2003

This paper describes a new parameter for voice activity detection which serves as a front-end part for automatic speech recognition systems. The new parameter called run-ratio is derived from the runs test statistic which is used in the statistical test for randomness of a given sequence. The run-ratio parameter has the property that the values of the parameter for the random sequence are about 1. To apply the run-ratio parameter into the voice activity detection method, it is assumed that the samples of an inputted audio signal should be converted to binary sequences of positive and negative values. Then, the silence region in the audio signal can be regarded as random sequences so that their values of the run-ratio would be about 1. The run-ratio for the voiced region has far lower values than 1 and for fricative sounds higher values than 1. Therefore, the parameter can discriminate speech signals from the background sounds by using the newly derived run-ratio parameter. The proposed voice activity detector outperformed the conventional energy-based detector in the sense of error mean and variance, small deviation from true speech boundaries, and low chance of missing real utterances

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.11-18
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• 2022

A physically unclonable function (PUF) is a circuit that generates random numbers by exploiting natural variation. Since it utilizes variations, which cannot be fully controlled, it can be used to generate true random numbers, but environment change may distort the output. In this paper, we propose a PUF with a confidence signal. We designed a PUF that exploits the difference of the time constant of the circuit and verified that different PUFs generate distinct outputs and the same PUF keeps generating similar outputs regardless of the temperature change. Compared to the existing technique, which employs an error correction code, the proposed technique offers the same level of reliability at the 700 times smaller overhead.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.338-347
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• 2021

In this paper, the beta-ray sensor circuit used in the true random number generator was designed using DB HiTek's 0.18㎛ CMOS process. The CSA circuit proposed a circuit having a function of selecting a PMOS feedback resistor and an NMOS feedback resistor, and a function of selecting a feedback capacitor of 50fF and 100fF. And for the pulse shaper circuit, a CR-RC2 pulse shaper circuit using a non-inverting amplifier was used. Since the OPAMP circuit used in this paper uses single power instead of dual power, we proposed a circuit in which the resistor of the CR circuit and one node of the capacitor of the RC circuit are connected to VCOM instead of GND. And since the output signal of the pulse shaper does not increase monotonically, even if the output signal of the comparator circuit generates multiple consecutive pulses, the monostable multivibrator circuit is used to prevent signal distortion. In addition, the CSA input terminal, VIN, and the beta-ray sensor output terminal are placed on the top and bottom of the silicon chip to reduce capacitive coupling noise between PCB traces.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.403-411
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• 2020

Since the analog circuit of the beta ray sensor circuit for the true random number generator and the power and ground line used in the comparator circuit are shared with each other, the power generated by the digital switching of the comparator circuit and the voltage drop at the ground line was the cause of the decreasein the output signal voltage drop at the analog circuit including CSA (Charge Sensitive Amplifier). Therefore, in this paper, the output signal voltage of the analog circuit including the CSAcircuit is reduced by separating the power and ground line used in the comparator circuit, which is the source of digital switching noise, from the power and ground line of the analog circuit. In addition, in the voltage-to-voltage converter circuit that converts VREF (=1.195V) voltage to VREF_VCOM and VREF_VTHR voltage, there was a problem that the VREF_VCOM and VREF_VTHR voltages decrease because the driving current flowing through each current mirror varies due to channel length modulation effect at a high voltage VDD of 5.5V when the drain voltage of the PMOS current mirror is different when driving the IREF through the PMOS current mirror. Therefore, in this paper, since the PMOS diode is added to the PMOS current mirror of the voltage-to-voltage converter circuit, the voltages of VREF_VCOM and VREF_VTHR do not go down at a high voltage of 5.5V.

• Wind and Structures
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• v.14 no.4
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• pp.337-357
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• 2011

The collection of wind speed time series by means of digital data loggers occurs in many domains, including civil engineering, environmental sciences and wind turbine technology. Since averaging intervals are often significantly larger than typical system time scales, the information lost has to be recovered in order to reconstruct the true dynamics of the system. In the present work we present a simple algorithm capable of generating a real-time wind speed time series from data logger records containing the average, maximum, and minimum values of the wind speed in a fixed interval, as well as the standard deviation. The signal is generated from a generalized random Fourier series. The spectrum can be matched to any desired theoretical or measured frequency distribution. Extreme values are specified through a postprocessing step based on the concept of constrained simulation. Applications of the algorithm to 10-min wind speed records logged at a test site at 60 m height above the ground show that the recorded 10-min values can be reproduced by the simulated time series to a high degree of accuracy.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.