• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.43-51
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• 2010

Attenuation coefficients of medical ultrasound not only reflect the pathological information of tissues scanned but also provide the quantitative information to compensate the decay of backscattered signals for other medical ultrasound parameters. Based on the frequency-selective attenuation property of human tissues, attenuation estimation methods in spectral domain have difficulties for real-time implementation due to the complexicity while estimation methods in time domain do not achieve the compensation for the diffraction effect effectively. In this paper, we propose the modified VSA method, which compensates the diffraction with reference phantom in time domain, using adaptive bandpass filters with decreasing center frequencies along depths. The adaptive bandpass filtering technique minimizes the distortion of relative echogenicity of wideband transmit pulses and maximizes the signal-to-noise ratio due to the random scattering, especially at deeper depths. Since the filtering center frequencies change according to the accumulated attenuation, the proposed algorithm improves estimation accuracy and precision comparing to the fixed filtering method. Computer simulation and experimental results using tissue-mimicking phantoms demonstrate that the distortion of relative echogenicity is decreased at deeper depths, and the accuracy of attenuation estimation is improved by 5.1% and the standard deviation is decreased by 46.9% for the entire scan depth.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.178-184
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• 2013

In this paper, we propose an efficient parallelization technique of PU-level motion estimation (ME) in the next generation video coding standard, high efficiency video coding (HEVC) to reduce the time complexity of video encoding. It is difficult to encode video in real-time because ME has significant complexity (i.e., 80 percent at the encoder). In order to solve this problem, various techniques have been studied, and among them is the parallelization, which is carefully concerned in algorithm-level ME design. In this regard, merge estimation method using merge estimation region (MER) that enables ME to be designed in parallel has been proposed; but, parallel ME based on MER has still unconsidered problems to be implemented ideally in HEVC test model (HM). Therefore, we propose two strategies to implement stable parallel ME using MER in HM. Through experimental results, the excellence of our proposed methods is shown; the encoding time using the proposed method is reduced by 25.64 percent on average of that of HM which uses sequential ME.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.611-623
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• 2007

The H.264|AVC standard incorporates an intra prediction tool into inter frame coding. However, this leads to excessive amount of increase in encoding time, thus resulting in the difficulty in real-time implementation of software encoders. In this paper, we first propose an early decision on intra prediction coding and a fast intra prediction method using the characteristics of block contents and the context of neighboring block modes for the intra prediction in the inter frame coding of H.264/AVC. Basically, the proposed methods determine a skip condition on whether the $4{\times}4$ intra prediction is to be used in the inter frame coding by considering the content characteristics of each block to be encoded and the context of its neighboring blocks. The performance of our proposed methods is compared with the Joint Model reference software version 11.0 of H.264|AVC. The experimental results show that our proposed methods allow for 41.63% reduction in the total encoding time with negligible amounts of PSNR drops and bitrate increases, compared to the original Joint Model reference software version 11.0.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.38-46
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• 2010

In this paper, an autonomous surveillance-tracking system for Workers monitoring basing on the stereo vision scheme is proposed. That is, analysing the characteristics of the cross-axis camera system through some experiments, a optimized stereo vision system is constructed and using this system an intelligent worker surveillance-tracking system is implemented, in which a target worker moving through the environments can be detected and tracked, and its resultant stereo location coordinates and moving trajectory in the world space also can be extracted. From some experiments on moving target surveillance-tracking, it is analyzed that the target's center location after being tracked is kept to be very low error ratio of 1.82%, 1.11% on average in the horizontal and vertical directions, respectively. And, the error ratio between the calculation and measurement values of the 3D location coordinates of the target person is found to be very low value of 2.5% for the test scenario on average. Accordingly, in this paper, a possibility of practical implementation of the intelligent stereo surveillance system for real-time tracking of a target worker moving through the environments and robust detection of the target's 3D location coordinates and moving trajectory in the real world is finally suggested.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.3
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• pp.68-76
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• 2000

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of conventional techniques such as the pole placement and the optimal control based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Thus, in this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algorithm(RVEGA) was presented for stabilization of an IP system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. And the proposed ENNC was implemented successfully on the ADA-2310 data acquisition board and the 80586 microprocessor in order to stabilize the IP system. Through the simulation and experimental results, we showed that the finally acquired optimal ENNC was very useful in the stabilization control of IP system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.4
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• pp.43-54
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• 2007

This paper presents design and verification of a face detection hardware for real time application. Face detection algorithm detects rough face position based on already acquired feature parameter data. The hardware is composed of five main modules: Integral Image Calculator, Feature Coordinate Calculator, Feature Difference Calculator, Cascade Calculator, and Window Detection. It also includes on-chip Integral Image memory and Feature Parameter memory. The face detection hardware was verified by using S3C2440A CPU of Samsung Electronics, Virtex4LX100 FPGA of Xilinx, and a CCD Camera module. Our design uses 3,251 LUTs of Xilinx FPGA and takes about 1.96${\sim}$0.13 sec for face detection depending on sliding-window step size, when synthesized for Virtex4LX100 FPGA. When synthesized on Magnachip 0.25um ASIC library, it uses about 410,000 gates (Combinational area about 345,000 gates, Noncombinational area about 65,000 gates) and takes less than 0.5 sec for face realtime detection. This size and performance shows that it is adequate to use for embedded system applications. It has been fabricated as a real chip as a part of XF1201 chip and proven to work.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.393-404
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• 2015

Gravity model has had the major problem that the model explains the characteristics of travel behavior with only deterrence factors such as travel time or cost. In modern society, travel behavior can be affected not only deterrence factors but also zonal characteristics or transportation service. Therefore, those features have to be considered to estimate the future travel demand accurately. In this regard, there are two primary aims of this study: 1. to identify the characteristics of inter-zonal travel, 2. to develop the new type of calibration method. By employing accessibility variable which can explain the manifold pattern of trip, we define the zonal travel behavior newly. Furthermore, we suggest 2-phase calibration method, since existing calibration method cannot find the optimum solution when organizing the deterrence function with the new variables. The new method proceeds with 2 steps; step 1.estimating deterrence parameter, step 2. finding balancing factors. The validation results with RMSE, E-norm, C.R show that this study model explains the inter-zonal travel pattern adequately and estimate the O/D pairs precisely than existing gravity model. Especially, the problem with estimation of short distance trip is overcomed. In conclusion, it is possible to draw the conclusion that this study suggests the possibility of improvement for trip distribution model.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.1
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• pp.66-77
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• 2006

In this paper, we researched the searching and tracking locations of a home resident using ON/OFF switches and sensors and designed a remote monitoring system. As an implementation environment, this system is developed on the base of the distributed object group framework we have developed from previous works. In order to trace the moving locations of a home resident, we firstly showed a home structure which attaches ON/OFF switches and sensors to home appliances and indoor facilities being fixed in home. Whenever a home resident opens/closes these objects, the signals operated from ON/OFF switches and sensors are sent to a home server system. In this time, the real locations of ON/OFF switches and sensors that the signals are being occurred must be the current location that he/she stays. A home server system provides the functionalities that map the real location of a resident in home to virtual location designed on remote desk-tops or terminals like PDAs, and that construct a healthcare database consisted of moving patterns, moving ranges, momentum for analyzing the given searching locations and times Finally, this system provides these information for remotely monitoring services.

• Journal of the Korea Society for Simulation
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• v.22 no.1
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• pp.9-20
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• 2013

Defense M&S for weapons effectiveness is a realistic way to support virtual warfare similar to real warfare. As the war paradigm becomes platform-centric to network-centric, people try to utilize smartphones as the source of sensor, and command/control data in the simulation-based weapons effectiveness analysis. However, there have been limited researches on integrating smartphones into the weapon simulators, partly due to high modeling cost - modeling cost to accomodate client-server architecture, and re-engineering cost to adapt the simulator on various devices and platforms -, lack of efficient mechanisms to exchange large amount of simulation data, and low-level of security. In this paper, we design and implement Smartphone Adaptor to utilize smartphones for the simulationbased weapons effectiveness analysis. Smartphone Adaptor automatically sends sensor information, GPS and motion data of a client's smartphone to a simulator and receives simulation results from the simulator on the server. Also, we make it possible for data to be transferred safely and quickly through JSON and SEED. Smartphone Adaptor is applied to OpenSIM (Open simulation engine for Interoperable Models) which is an integrated simulation environment for weapons effectiveness analysis, under development of our research team. In this paper, we will show Smartphone Adaptor can be used effectively in constructing a Live simulation, with an example of a chemical simulator.