• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2478-2484
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• 2010

In this paper, real-time detection methods for Panorama system Key-Points offers. A recent study in PANORAMA system real-time area navigation or DVR to apply such research has recently been actively. The detection of the Key-Point is the most important elements that make up a Panorama system. Not affected by contrast, scale, Orientation must be detected Key-Point. Existing research methods are difficult to use in real-time Because it takes a lot of computation time. Therefore, this paper propose BLOG(BitRate Laplacian Of Gaussian)method for faster time Key-Point Detecting and Through various experiments to detect the Speed, Computation, detection performance is compared against.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2531-2533
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• 2005

The proposed neural network technique is the real time computation method based theory of inter-node diffusion for searching the safety distances from the sudden appearance-objects during the work driving. The main steps of the distance computation using the theory of stereo vision like the eyes of man is following steps. One is the processing for finding the corresponding points of stereo images and the other is the interpolation processing of full image data from nonlinear image data of objects. All of therm request much memory space and time. Therefore the most reliable neural-network algorithm is drived for real-time matching of obejects, which is composed of a dynamic programming algorithm based on sequence matching techniques in moving objects.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.281-283
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• 1993

A new method of EEG spindle waveform detection i s presented. The method combines the signal conditioning in the time-domin and the analysis of local spectrum in the frequency-domain. Fast computation methods, utilizing some effective approximations, are also suggested for the desist and implementation of the filter as well as for the computation of the local spectrum. The presented approach is especially useful for the real-time implementation of the waveform detection system under a general purpose microcomputer environment. The overall detection system is implemented and tested on-line with the total 24 hour data of selected four subjects. The result show the average agreement of 86.7% with the visually inspected result.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.44-51
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• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1189-1195
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• 2004

This paper investigates construction of gene (interaction) networks from gene expression time-series data based on evolutionary computation. To illustrate the proposed approach in a comprehensive way, we first assume an artificial gene network and then compare it with the reconstructed network from the gene expression time-series data generated by the artificial network. Next, we employ real gene expression time-series data (Spellman's yeast data) to construct a gene network by applying the proposed approach. From these experiments, we find that the proposed approach can be used as a useful tool for discovering the structure of a gene network as well as the corresponding relations among genes. The constructed gene network can further provide biologists with information to generate/test new hypotheses and ultimately to unravel the gene functions.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.292-298
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• 2012

It is a real-time system that the system correctness depends not only on the correctness of the logical result of the computation but also on the result delivery time. Real-time Operating System (RTOS) is a software that manages the time of a microprocessor to ensure that the most important code runs first so that it is a good building block to design the real-time system. The real-time performance is achieved by using real-time mechanisms through data communication and synchronization of inter-task communication (ITC) between tasks. Therefore, test on the response time of real-time mechanisms is a good measure to predict the performance of real-time systems. This paper aims to analysis the response characteristics of real-time mechanisms in kernel space for real-time embedded Linux: RTAI and Xenomai. The performance evaluations of real-time mechanism depending on the changes of task periods are conducted. Test metrics are jitter of periodic tasks and response time of real-time mechanisms including semaphore, real-time FIFO, Mailbox and Message queue. The periodicity of tasks is relatively consistent for Xenomai but RTAI reveals smaller jitter as an average result. As for real-time mechanisms, semaphore and message transfer mechanism of Xenomai has a superior response to estimate deterministic real-time task execution. But real-time FIFO in RTAI shows faster response. The results are promising to estimate deterministic real-time task execution in implementing real-time systems using real-time embedded Linux.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2540-2545
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• 2003

We propose a new technique to the design and real-time implementation of an adaptive controller for robotic manipulator based on digital signal processors in this paper. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved direct Lyapunov method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot consisting of two 4-d.o.f. robots at the joint space and cartesian space.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.741-746
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• 2004

We propose a new technique to the design and real-time implementation of an adaptive controller for robotic manipulator based on digital signal processors in this paper. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved direct Lyapunov method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot consisting of two 4-d.o.f. robots at the joint space and cartesian space.

• Journal of IKEEE
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• v.20 no.4
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• pp.420-423
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• 2016

In this paper, we proposed an implementation of panoramic image stitching that operates in real time at the embedded environment by applying ROI based PROSAC algorithm using the directionality of the image. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains unnecessary computation and a lots of data to detect feature points. In this paper, we use the direction of the input image and we proposed the method of reducing the unnecessary computation by using ROI. We use a gyro sensor and an acceleration sensor. Output data from gyro and acceleration sensors can be calibrated by complementary filter. The calibration does not affect the operating time of the proposed image stitching algorithm in embedded environment. Therefore, it is possible to operate in real-time.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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• pp.90-95
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• 2003

For the safety driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.