• Journal of Integrative Natural Science
• /
• v.5 no.4
• /
• pp.246-252
• /
• 2012

Multivariate cumulative sum (CUSUM) control charts for simultaneously monitoring both means and variances under multivariate normal process are investigated. Performances of multivariate CUSUM schemes are evaluated for matched fixed sampling interval (FSI) and variable sampling interval (VSI) features in terms of average time to signal (ATS), average number of samples to signal (ANSS). Multivariate Shewhart charts are also considered to compare the properties of multivariate CUSUM charts. Numerical results show that presented CUSUM charts are more efficient than the corresponding Shewhart chart for small or moderate shifts and VSI feature with two sampling intervals is more efficient than FSI feature. When small changes in the production process have occurred, CUSUM chart with small reference values will be recommended in terms of the time to signal.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.5
• /
• pp.1030-1037
• /
• 2009

SAW Sensor is greatly developed today and Reader Platform which uses SAW Sensor for temperature or pressure is required to use TDS method for low power and high speed processing. For to use this Platform, high speed timer is required to measure a short interval between reference signal and reflectior's signal. This paper proposes that platform receive SAW Sensor's signals and transform digital signal through comparator. Next the transformed signal is measured by Timer Platform and the measured interval is displayed with time. This paper proposes method of measurment of time with nano sec unit.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.538-540
• /
• 2004

Electrical currents generated by human heart activities create magnetic fields represented by MCG(MagnetoCardioGram). Since an MCG signal acquisition system requires precise and stable operation, the system adopts hundreds of SQUID(Superconducting QUantum Interface Device) sensors for signal acquisition. Such a system requires fast real-time data acquisition in a required sampling interval, i.e., 1 mili-second for each sensor. This paper presents designed hardware to acquire data from 256-channel analog signal with 1 ksamples/sec speed, using 12-bit 8-channel ADC devices, SPI interfaces, parallel interfaces, 8-bit microprocessors, and a DSP processor. We implemented SPI interface between ADCs and a microprocessor, parallel interfaces between microprocessors. Our result concludes that the data collection can be done in $168{\mu}sec$ time-interval for 256 SQUID sensors, which can be interpreted to 6 ksamples/sec speed.

• Journal of Sensor Science and Technology
• /
• v.20 no.6
• /
• pp.382-387
• /
• 2011

Whether a person is feeling sleepy or reasonably awake is important safety information in many areas, such as humans operating in traffic or in heavy industry. The changes of body signals have been mostly researched by looking at electroencephalogram(EEG) signals but more and more other medical signals are being examined. In our study, an electrocardiogram(ECG) signal is measured at a sampling rate of 100 Hz and used to try to distinguish the possible differences in signal between the two states: awake and drowsy. Practical tests are conducted using a wireless sensor node connected to a wearable ECG sensor, and an ECG signal is transmitted wirelessly to a base station connected to a server PC. Through the QRS complex in the ECG analysis it is possible to obtain much information that is helpful for diagnosing different types of cardiovascular disease. A program is made with MATLAB for digital signal filtering and graphing as well as recognizing the parts of the QRS complex within the signal. Drowsiness detection is performed by evaluating the R peaks, R-R interval, interval between R and S peaks and the duration of the QRS complex..

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.4
• /
• pp.859-866
• /
• 2012

Some switching properties of multivariate control charts are investigated when the interval between two consecutive sample selections is not fixed but changes according to the result of the previous sample observation. Many articles showed that the performances of variable sampling interval control charts are more efficient than those of fixed sampling interval control charts in terms of average run length (ARL) and average time to signal (ATS). Unfortunately, the ARL and the ATS do not provide any information on how frequent a switch is being made. We evaluate several switching properties of two sampling interval Shewhart and CUSUM procedures for controlling mean vector of correlated quality variables.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.19-21
• /
• 2009

The reflectometry for locating the fault at a cable is the same as a problem estimating the time delay between the incident and the reflected signals. In this paper, we propose a method for estimating the time delay between the two signals. The proposed method is based on the modeling of the Gaussian enveloped linear chirp signal in the Gaussian noise environment. The phase and the instantaneous frequency of the received signal are estimated by linear Kalman filtering. From the estimated instantaneous frequency, we can measure the time interval between the center frequencies of the incident and the reflected signals. The time interval is the same as the time delay between the incident and the reflected signals. In a simulation assuming that the cable has open fault at the end of the cable, the proposed method showed a good result in estimating the time delay.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.7
• /
• pp.85-91
• /
• 2016

Typically, radar systems change the pulse repetition interval of their modulated signal in order to avoid detection. On the other hand the radar-signal detection system tries to detect the modulation pattern. The histogram or auto-correlation methods are usually used to detect the PRI pattern of the radar signal. However these methods tend to lost the sequence information of the PRI pulses. This paper proposes a PRI-sequence detection algorithm based on the finite-state machine that could detect not only the PRI pattern but also their sequence.

• Journal of the Korean Data and Information Science Society
• /
• v.15 no.4
• /
• pp.1003-1010
• /
• 2004

Properties of the variable sampling interval(VSI) CUSUM chart for monitoring dispersion matrix of related quality characteristics are investigated. Performances of the proposed charts are evaluated for matched fixed sampling interval(FSI) and VSI charts in terms of average time to signal(ATS) and average number of samples to signal (ANSS). Average number of swiches(ANSW) of the proposed VSI Shewhart and CUSUM charts are also investigated.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.151-159
• /
• 2004

Properties of the variable sampling interval(VSI) control charts for monitoring dispersion matrix of related quality characteristics are investigated. Performances of the proposed charts are evaluated for matched fixed sampling interval(FSI) and VSI charts in terms of average time to signal(ATS) and average number of samples to signal (ANSS). Average number of swiches(ANSW) of the proposed VSI charts are also investigated.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.11a
• /
• pp.444-447
• /
• 2004

A heart diagnosis system adopts hundreds of Superconducting Quantum Interface Device(SQUID) sensors for precision MCG(Magnetocardiogram) or MEG(Magnetoencephalogram) signal acquisitions. This system requires correct and real-time data acquisition from the sensors in a required sampling interval, i.e., 1 mili-second. This paper presents our hardware design and test results, to acquire data from 256 channel analog signal with 1-ksample/sec speed, using 12-bit 8-channel ADC devices, SPI interfaces, parallel interfaces, and 8-bit microprocessors. We chose to implement parallel data transfer between microprocessors as an effective way of achieving such data collection. Our result concludes that the data collection can be done in 250 ${\mu}sec$ time-interval.