• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.196-199
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• 2004

This paper presents a structure of the searcher using the space diversity in array antenna system operating in the DS/CDMA signal environments. The new technique exploits the fact that the In-phase and Quadrature components of interferers can respectively be viewed as independent Gaussian noise at each antenna element in most practical CDMA (Code Division Multiple Access) signal environments. The proposed PN acquisition scheme is a single dwell serial PN acquisition system consisting of two stage, that is, the searching stage and the verification stage. The searching stage correlates the received signals with the local PN oscilator for obtaining the synchronous energy at the entire uncertainty region. The verification stage compares the searching energy with the optimal threshold, which is pre-designed in the Lock-Detector, and decides whether the acquisition is successful or fail. In this paper, we analyzed the relationship of both diversity order and the mean acquisition time. In general, It is known that the mean acquisition time decreases significantly as the number of antenna elements increases. But, the enhancement of the performance is saturated in terms of PN acquisition scheme. Therefore, to decrease the mean acquisition time, we must design the optimal array antenna system by considering the operating SNR range of the receiver, the detection probability, and the false alarm probability. The performance of the proposed acquisition scheme is analyzed in frequency-selective Rayleigh fading channels. In this paper, the effect of the number of antenna elements on acquisition scheme is considered in terms of the detection probability, false alarm probability. and the mean acquisition time.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.671-677
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• 2012

A black-box program was implemented in order to monitor abnormal symptoms of human body irregularly occurring during sleep. The system consists of sensor probing body signals, auxiliary devices such as the alarm, lamp, network camera, and signal monitoring computer. Various types of sensors, PPG, ECG, EEG, temperature, respiration sensor, G-sensor, and microphone were used to more exactly identify the causes of abnormal symptoms. If a symptom occurs, the system records the patient's condition to provide information being utilized in the treatment. The sensors are attached on some locations of body being proper to check a specific type of abnormal reaction. Based on the normal range and type of measurement data, criteria of signal levels were set to distinguish abnormal reaction. An abnormal signal being probed, the program starts to operate the lamp, alarm, and network camera at the same time and stores the signal and video data.

• Spatial Information Research
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• v.19 no.3
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• pp.83-93
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• 2011

Since urban facilities have a signigicant meaning that represents the degree of development of nations and cities, it is very important to effectively manage and operate the facilities. In this paper, we propose a context-awareness system for managing urban underground facilities intelligently and develop its algorithm and prototype. The algorithm of the suggested system includes the period from the point when various sensors set up in USN environments sense abnormal signals and make alarms to the point when the context-awareness system analyzes the alarm and sends the analysis results to integrated platform and related modules. We then implement the prototype of the proposed context awareness system and verify the effectiveness of the system by performing unit tests. Our developed prototype will become the basis of actual system development. We expect that the suggested system will be used as a good reference model of related systems managing various types of urban facilities.

• Transactions on Electrical and Electronic Materials
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• v.8 no.6
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• pp.293-298
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• 2007

The aged are faced with increasing risk for falls. The aged have more fragile bones than others. When falls occur, it is important to detect this emergency state because such events often lead to more serious illness or even death. A implementation of PDA system, for detection of emergency situation, was developed using 3-axis accelerometer in this paper as follows. The signals were acquired from the 3-axis accelerometer, and then transmitted to the PDA through a Bluetooth module. This system can classify human activity, and also detect an emergency state like falls. When the fall occurs, the system generates the alarm on the PDA. If a subject does not respond to the alarm, the system determines whether the current situation is an emergency state or not, and then sends some information to the emergency center in the case of an urgent situation. Three different studies were conducted on 12 experimental subjects, with results indicating a good accuracy. The first study was performed to detect the posture change of human daily activity. The second study was performed to detect the correct direction of fall. The third study was conducted to check the classification of the daily physical activity. Each test lasted at least 1 min. in the third study. The output of the acceleration signal was compared and evaluated by changing various postures after attaching a 3-axis accelerometer module on the chest. The newly developed system has some important features such as portability, convenience and low cost. One of the main advantages of this system is that it is available at home healthcare environment. Another important feature lies in its low cost of manufacture. The implemented system can detect the fall accurately, so it will be widely used in emergency situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.456-463
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• 2021

In this paper, Ultra Wide-Band(UWB) radar sensor is employed to detect flying drones and avoid collision in dense clutter environments. UWB signal is preferred when high resolution range measurement is required for moving targets. However, the time varying motion of flying drones may increase clutter noises in return signals and deteriorates the target detection performance, which lead to the performance degradation of anti-collision radars. We adopt a dynamic clutter suppression algorithm to estimate the time-varying distances to the moving drones with enhanced accuracy. A modified Constant False Alarm Rate(CFAR) is developed using an adaptive filter algorithm to suppress clutter while the false detection performance is well maintained. For this purpose, a velocity dependent CFAR algorithm is implemented to eliminate the clutter noise against dynamic target motions. Experiments are performed against flying drones having arbitrary trajectories to verify the performance improvement.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.197-202
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• 2023

This paper presents the design method and electrical characteristics of a high-efficiency power amplifier for a 50 Watts class repeater applied to a 5G system and used in in-building, subway, and tunnel. GaN was used for the termination transistor of the power amplifier designed here, and intermodulation signals were removed using DPD to satisfy linearity. In addition, in order to handle various requirements such as amplifier gain control and alarm processing required in the 5G system, the microprocessor is designed to exist inside the power amplifier. The amplifier manufactured to confirm the electrical performance of the power amplifier satisfying these conditions satisfied 46.5 dBm and the overall efficiency of the amplifier was 37%, and it was confirmed that it satisfied various alarm conditions and electrical characteristics required by telecommunication companies.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.262-269
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• 2023

In this paper, we propose a Linear Prediction (LP) analysis-based feature for detecting Snapping Shrimp (SS) Noise (SSN) in underwater acoustic data. SS is a species that creates high amplitude signals in shallow, warm waters, and its frequent and loud sound is a major source of noise. The proposed feature takes advantage of the characteristic of SSN, which is sudden and rapidly disappearing, by using LP analysis to detect the exact noise interval and reduce the effects of SSN. The error between the predicted and measured value is large and results in effective SSN detection. To further improve performance, a constant false alarm rate detector is incorporated into the proposed feature. Our evaluation shows that the proposed methods outperform the state-of-the-art MultiLayer-Wavelet Packet Decomposition (ML-WPD) in terms of receiver operating characteristic curve and Area Under the Curve (AUC), with the LP analysis-based feature achieving a higher AUC by 0.12 on average and lower computational complexity.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.185-190
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• 2020

The rapid aging is increasing as the shortage of medical facilities and the resulting of decline in the quality of public health. In order to ease the burden of rising medical expenses, advanced medical institutions are expanding their remote medical care to lower the cost of services. U-healthcare detects the changes in physical and chemical phenomena occurring in the human body and converts them into electrical signals that can be processed and feeds back to the results through analytical and visualization processes to select only the desired information from the measured signals. The service is provided through a process of providing an alarm to a user. However, traditional biometric methods of attaching sensors directly to the body can be annoying and rejected in daily life. Therefore, there is a need for a method of continuously measuring biometric information without causing inconvenience to daily life. In this paper, we propose an IR-UWB-based non-contact and non-responsive respiratory measurement system that can continuously monitor biological information without any inconveniences to daily life.