• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.194-199
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• 2012

This paper proposes a novel scheme of a gray scale fingerprint image for a high-accuracy capacitive sensor chip. The conventional grayscale image scheme uses a digital-to-analog converter (DAC) of a large-scale layout or charge-pump circuit with high power consumption and complexity by a global clock signal. A modified capacitive detection circuit for the charge sharing scheme is proposed, which uses a down literal circuit (DLC) with a floating-gate metal-oxide semiconductor transistor (FGMOS) based on a neuron model. The detection circuit is designed and simulated in a 3.3 V, 0.35 ${\mu}m$ standard CMOS process. Because the proposed circuit does not need a comparator and peripheral circuits, the pixel layout size can be reduced and the image resolution can be improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.692-695
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• 2011

This paper describes the possibility of a low-power, high-resolution fingerprint sensor chip. A modified capacitive detection circuit of charge sharing scheme is proposed, which reduces the static power dissipation and increases the voltage difference between a ridge and valley more than conventional circuit. The detection circuit is designed and simulated in 3.3V, $0.35{\mu}m$ standard CMOS process, 40MHz condition. The result shows about 35% power dissipation reduction and 90% improvement of difference between a ridge and valley sensing voltage. The proposed circuit is more stable and effective than a typical circuit.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.269-275
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• 2012

In this paper, we proposed a novel location estimation algorithm based on the concept of space-time signature matching in a moving target environment. In contrast to previous fingerprint-based approaches that rely on received signal strength (RSS) information only, the proposed algorithm uses angle, delay, and RSS information from the received signal to form a signature, which in turn is utilized for location estimation. We evaluated the performance of the proposed algorithm in terms of the average probability of error and the average error distance as a function of target movement. Simulation results confirmed the effectiveness of the proposed algorithm for location estimation even in moving target environment.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.1
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• pp.94-106
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• 2020

Recently, the radio navigation method utilizing the GPS(Global Positioning System) satellite information is widely used as the method to measure the position of objects. As GPS applications become wider and fields based on various positioning information emerge, new methods for achieving higher accuracy are required. In the case of autonomous vehicles, the INS(Inertial Navigation System) using the IMU(Inertial Measurement Unit), and the DR(Dead Reckoning) algorithm using the in-vehicle sensor, are used for the purpose of preventing degradation of accuracy of the GPS and to measure the position in the shadow area. However, these positioning methods have many elements of problems due not only to the existence of various shaded areas such as building areas that are continually enlarged, tunnels, underground parking lots and but also to the limitations of accumulation-based location estimation methods that increase in error over time. In this paper, an efficient positioning method in a large underground parking space using Fingerprint method is proposed by placing the AP(Access Points) and directional antennas in the form of four anchors using WLAN, a popular means of wireless communication, for positioning the vehicle in the GPS shadow area. The proposed method is proved to be able to produce unchanged positioning results even in an environment where parked vehicles are moved as time passes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.657-659
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• 2018

In real life, We are using IoT technology directly. IoT technology has been applied in many fields and has developed continuously. Internet of Things is a technology or environment that attaches sensors to objects and sends and receives data through internet in real time. In this paper, if a password is wrong more than three times, the existing password is initialized and randomly changed. Changed password data seeks to realize that push alarms are sent via the app to the user or administrator. We have studied an Indono-based IoT system that can send real-time data to smart phones through WiFi. smart door locks are intended to implement them using Aduino, fingerprint sensor, camera sensor, Wi-Fi shield, Android Studio, and door lock components.

• Smart Structures and Systems
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• v.5 no.2
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• pp.153-171
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• 2009

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.25-30
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• 2019

Magnetic sensors have the disadvantage that their vector values differ depending on the direction. In this paper, we propose a magnetic vector calibration method for geomagnetic-based indoor localization estimates. The fingerprinting technique used in geomagnetic-based indoor localization the position by matching the magnetic field map and the magnetic sensor value. However, since the moving direction of the current user may be different from the moving direction of the person who creates the magnetic field map at the collection time, the sampled magnetic vector may have different values from the vector values recorded in the field map. This may substantially lower the positioning accuracy. To avoid this problem, the existing studies use only the magnitude of magnetic vector, but this reduces the uniqueness of the fingerprint, which may also degrade the positioning accuracy. In this paper we propose a vector calibration algorithm which can adjust the sampled magnetic vector values to the vector direction of the magnetic field map by using the parametric equation of a circle. This can minimize the inaccuracy caused by the direction mismatch.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.65-72
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• 2022

In this paper, a zero trust-based complex biometric authentication was proposed in a passwordless environment. The linkage of FIDO 2.0 (Fast IDENTITY Online) transaction authentication platforms was designed in conjunction with metaverse. In particular, it was applied with the location information of a smart terminal according to a geomagnetic sensor, an accelerator sensor, and biometric information for multi-factor authentication(MFA). At this time, a FIDO transaction authentication platform was presented for adaptive complex authentication with user's environment through complex authentication with secondary authentication based on situational awareness such as illuminance and temperature/humidity. As a result, it is possible to authenticate secondary users based on zero trust with behavior patterns such as fingerprint recognition, iris recognition, face recognition, and voice according to the environment. In addition, it is intended to check the linkage result of the FIDO platform for complex integrated authentication and improve the authentication accuracy of the linkage platform for transaction authentication using FIDO2.0.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.188-194
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• 2004

With a remarkable advance in CMOS (complimentary metal-oxide-semiconductor) process technology, a variety of vision sensors with signal processing circuits for complicated functions are actively being developed. Especially, as the principles of signal processing in human retina have been revealed, a series of vision chips imitating human retina have been reported. Human retina is able to detect the edge and motion of an object effectively. The edge detection among the several functions of the retina is accomplished by the cells called photoreceptor, horizontal cell and bipolar cell. We designed a CMOS vision chip by modeling cells of the retina as hardwares involved in edge and motion detection. The designed vision chip was fabricated using $0.6{\mu}m$ CMOS process and the characteristics were measured. Having reliable output characteristics, this chip can be used at the input stage for many applications, like targe tracking system, fingerprint recognition system, human-friendly robot system and etc.