• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.603-609
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• 2010

This paper introduces an objects recognition algorithm based on SURF(Speeded Up Robust Features) and GP(Genetic Programming) based gaits generation. Combining both methods, a recognition based intelligent walking for quadruped robots is proposed. The gait of quadruped robots is generated by means of symbolic regression for each joint trajectories using GP. A position and size of target object are recognized by SURF which enables high speed feature extraction, and then the distance to the object is calculated. Experiments for objects recognition and autonomous walking for quadruped robots are executed for ODE based Webots simulation and real robot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.119-125
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• 2018

Various techniques for indoor positioning using a smart phone have been studied. Among them, the positioning technology using the acceleration sensor and the gyro sensor built in the smartphone is widely used in conjunction with the WiFi fingerprint technology. The location tracking technology using sensors has been used for a long time, but the performance environment of the smartphone is poor and the user is moving with the smartphone in a certain posture. Therefore, in order to improve the accuracy of location tracking in a smartphone environment, it is necessary to study and develop appropriate algorithms in a mobile environment. In this paper, we analyze the performances of frequency analysis method, maximum sum of minimum acceleration method and adaptive threshold method, which are the user's moving step count detection algorithms, and determine the most accurate method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1106-1113
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• 2013

For patients who have senile mental disorder such as dementia, quantity of excercise and amount of sunlight are important clue for dose and the treatment. Therefore, monitoring health information of daily life is necessary for patients' safety and healthy life. Portable & wearable sensor device and server configuration monitoring data are needed to provide these services for patients. Watch-type device(smart watch) which patients wear and server system are developed in this paper. Smart watch developed includes GPS, accelerometer and illumination sensor, and can obtain real time health information by measuring the position of patients, quantity of exercise and amount of sunlight. Server system includes the sensor data analysis algorithm and web server that doctor and protector can monitor through sensor data acquired from smart watch. The proposed data analysis algorithm acquires quantity of exercise information and detects step count in patients' motion acquired from acceleration sensor and to verify this, the three cases with fast pace, slow pace, and walking pace show 96% of the experimental result. If developed u-Healthcare System for dementia patients is applied, more high-quality medical service can be provided to patients.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.15-20
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• 2023

Until now, Pollard's Rho algorithm has been known as the most efficient way for discrete algebraic problems to decrypt symmetric keys. However, the algorithm is being studied on how to further reduce the complexity of O(${\sqrt{p}}$) performance, along with the disadvantage of having to store the giant stride m=⌈${\sqrt{p}}$⌉ data. This paper proposes an array method for cycle detection in discrete logarithms. The proposed method reduces the number of updates of stack memory by at least 73%. This is done by only updating the array when (x_i<0.5x_i-1)∩(x_i<0.5(p-1)). The proposed array method undergoes the same number of modular calculation as stack method, but significantly reduces the number of updates and the execution time for array through the use of a binary search method.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.