• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.93-102
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• 2010

The FMCW(Frequency Modulation Continuous Wave) radar possesses range-velocity ambiguity to identify the correct combination of beat frequencies for each target in the multi-target situation. It can lead to ghost targets and missing targets, and it can reduce the detection probability. In this pap er, we propose an effective identification algorithm for the correct pairs of beat frequencies and the signal processing hardware architecture to effectively support the algorithm. First, using the correlation of the detected up- and down-beat frequencies and Doppler frequencies, the possible combinations are determined. Then, final pairing algorithm is completed with the power spectrum density of the correlated up- and down-beat frequencies. The proposed hardware processor has the basic architecture consisting of beat-frequency registers, pairing table memory, and decision unit. This method will be useful to improve the radar detection probability and reduce the false alarm rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.264-269
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• 2013

In this paper, low-power heart beat detection algorithm and structure is proposed. The proposed detection algorithm utilizes filter banks to display a specific beat per minute(BPM) from the heart beat signals. Since general filter banks need a lot of computation to calculate a BPM, we propose the filter banks structure using modified CIC(Cascaded Integrator Comb) filters. It is shown that the proposed modified CIC filter banks algorithm can display the BPM from the heart beat signals precisely and efficiently.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2391-2398
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• 2009

Premature ventricular contractions are the most common of all arrhythmias and may cause more serious situation in some patients. Therefore, the detection of this arrhythmia becomes crucial in the early diagnosis and prevention of possible life threatening cardiac diseases. Most of the algorithms detecting PVC reported in literature is not always feasible due to the presence of noise and P wave making the detection difficult, and the process being time consuming and ineffective for real time analysis. To solve this problem, a new approach for the detection of PVC is presented based rhythm analysis and beat matching in this paper. For this purpose, the ECG signals are first processed by the usual preprocessing method and R wave was detected. The algorithm that decides beat type using the rhythm analysis of RR interval and beat matching of QRS width is developed. The performance of R wave and PVC detection is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate sensitivity of 99.74%, positive predictivity of 99.81% and sensitivity of 93.91%, positive predictivity of 96.48% accuracy respectively for R wave and PVC detection.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.731-739
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• 2015

In arrhythmia ECG signal, abnormal beat that has various abnormal shape depending on the generation site and conduction disorders is included and it is very important to diagnose heart disease such as arrhythmia. In this paper, we propose a PVC abnormal beat detection algorithm associated with ventricular disease. The PVC abnormal beat is characterized by distortion of the QRS complex occurs among the components of the ECG signal. Therefore it is possible to detect PVC abnormal beat according to the degree of distortion of the QRS complex. First, quantify the distortion of the QRS complex by using the potential of the R-peak, kurtosis and period. By using the mean and standard deviation, PVC abnormal beat is detected depending on the degree of distortion from the normal beat. The proposed algorithm can detect the average over 98% of the AAMI-V class type abnormal beat associated with ventricular disease in MIT-BIH arrhythmia database.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.4
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• pp.81-88
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• 2001

We present a new digital phase measuring method for heterodyne optical interferometry, which providers high measuring speed up to 6 m/s with a fine displacement resolution of 0.1 nanometer. The key idea is combining two distinctive digital phase measuring techniques with mutually complementary characteristics to earth other one is counting the Doppler shift frequency counting with 20 MHz beat frequency for high-velocity measurement and the other is the synchronous phase demodulation with 2.0 kHz beat frequency for extremely fine displacement resolution. The two techniques are operated in switching mode in accordance wish the object speed in a synchronized way. Experimental results prove that the proposed dual mode phase measuring scheme is realized with a set of relatively simple electronic circuits of beat frequency shifting, heterodyne phase detection. and low-pass filtering.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.203-204
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• 1998

ECG analysis is main techniques for diagnosing heart disease. In recent, some studies have been performed about detection of QT interval. But, it's difficult to detect QT interval because T wave is evasive. In this paper, we have detected peak point and end point of T wave and calculated QT interval. And the result has been compared with the other algorithm after detection of QT interval.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.380-388
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• 2013

Large variation in electrocardiogram (ECG) waveforms continues to present challenges in defining R-wave locations in ECG signals. This research presents a procedure to extract the R-wave locations by forward-backward (FB) algorithm and classify the arrhythmic beat conditions by using RR intervals. The FB algorithm shows forward and backward searching rules from QRS onset and eliminates lower-amplitude signals near the baseline using a statistical process control concept. The proposed algorithm was trained the optimal parameters by using MIT-BIH arrhythmia database (MITDB), and it was verified by actual Holter ECG signals from a local hospital. The signals are classified into normal (N) and three arrhythmia beat types including premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF), and second-degree heart block (BII) beat. This work produces 98.54% accuracy in the detection of R-wave location; 98.68% for N beats; 91.17% for PVC beats; and 87.2% for VF beats in the collected Holter ECG signals, and the results are better than what are reported in literature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2187-2193
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• 2010

A specific radar system can be implemented more easily using the frequency modulated continuous wave comparing with the pulse Doppler radar. It also has the advantage of LPI (low probability of interception) because of the low power and wide bandwidth characteristics. These radars are usually used to cover the short range area and to obtain the high resolution measurements of the target range and velocity information. The transmitted waveform is used in the mixer to demodulate the received echo signal and the resulting beat signal can be obtained. This beat signal is analyzed using the FFT method for the purpose of clutter removal, detection of a target, extraction of velocity and range information, etc. However, for the case of short signal acquisition time, this FFT method can cause the serious leakage effect which disables the detection of weaker echo signals masked by strong side lobes of the clutter. Therefore, in this paper, the weighting window method is analyzed to suppress the strong side lobes while maintaining the proper main lobe width. Also, the results of FFT beat spectrum analysis are shown under various environments.