• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.114-121
• /
• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.49 no.5
• /
• pp.44-50
• /
• 2012

ECMA-392 is the first CR (cognitive radio) specification for personal/portable devices in TV white space. In ECMA-392, in addition to the long preamble of 2 OFDM symbols, 4 pilot sub-carriers per OFDM symbol are repeatedly transmitted with the period of 13 OFDM symbols. In this paper, we consider channel estimation schemes using both long preamble and pilot sub-carriers: we propose channel estimation schemes in which a first order recursive filter is used, and present a method for reducing channel estimation delay. Computer simulation results indicate that the proposed schemes perform well over slow Rayleigh fading channels.

• Journal of Korea Multimedia Society
• /
• v.18 no.10
• /
• pp.1205-1215
• /
• 2015

Efficient multi-view coding techniques are needed to reduce the complexity of multi-view video which increases in proportion to the number of cameras. To reduce the complexity and maintain image quality and bit-rates, an motion estimation method and temporal prediction structure are proposed in this paper. The proposed motion estimation method exploits the characteristic of motion vector distribution and the motion direction and motion size of the block to place search points and decide the search patten adaptively. And the proposed prediction structure divides every GOP to decide the maximum index of hierarchical B layer and the number of pictures of each B layer. Experiment results show that the complexity reduction of the proposed temporal prediction structure and motion estimation method over hierarchical B pictures prediction structure and TZ search method which are used in JMVC(Joint Multi-view Video Coding) reference model can be up to 45∼70% while maintaining similar video quality and bit rates.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.10 no.5
• /
• pp.139-145
• /
• 2010

In this paper, we propose robust cell ID detection algorithm and SNR estimation algorithm applicable to mobile base station, which can be operated independently. The proposed cell ID estimation uses signal subspace to estimate cell IDs used in cell. The proposed SNR estimation algorithm uses number of noise subspace vectors and the corresponding eigen-vectors. Through the computer simulations, we showed that performance of the proposed cell ID detection and SNR estimation algorithms are superior to existing correlation based algorithms. Also we showed that the proposed algorithm is suitable to fast moving channel in high background noise and strong interference signal.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.7 no.2
• /
• pp.426-434
• /
• 2015

In this paper we proposed a dada-aided Doppler estimation method for underwater acoustic communication. The training sequence is non-dedicate, hence it can be designed for Doppler estimation as well as channel equalization. We assume the channel has been equalized and consider only flat-fading channel. First, based on the training symbols the theoretical received sequence is composed. Next the least square principle is applied to build the objective function, which minimizes the error between the composed and the actual received signal. Then an iterative approach is applied to solve the least square problem. The proposed approach involves an outer loop and inner loop, which resolve the channel gain and Doppler coefficient, respectively. The theoretical performance bound, i.e. the Cramer-Rao Lower Bound (CRLB) of estimation is also derived. Computer simulations results show that the proposed algorithm achieves the CRLB in medium to high SNR cases.

• Journal of Positioning, Navigation, and Timing
• /
• v.3 no.1
• /
• pp.1-10
• /
• 2014

Recently, monitoring of two-body ground vehicles carrying extremely hazardous materials has been considered as one of the most important national issues. This issue induces large cost in terms of national economy and social benefit. To monitor and counteract accidents promptly, an efficient methodology is required. For accident monitoring, GPS can be utilized in most cases. However, it is widely known that GPS cannot provide sufficient continuity in urban cannons and tunnels. To complement the weakness of GPS, this paper proposes an accident monitoring method based on a monocular vision sensor. The proposed method estimates angular acceleration from a sequence of image frames captured by a monocular vision sensor. The possibility of using angular acceleration is investigated to determine the occurrence of accidents such as jackknifing and rollover. By an experiment based on actual measurements, the feasibility of the proposed method is evaluated.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.1
• /
• pp.1-11
• /
• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Journal of Broadcast Engineering
• /
• v.10 no.4 s.29
• /
• pp.576-586
• /
• 2005

This paper presents a new estimation and representation of a disparity map for stereo-based video communication systems. Several pixel-based and block-based algorithms have been proposed to estimate the disparity map. While the pixel-based algorithms can achieve high accuracy in computing the disparity map, they require a lost of bits to represent the disparity information. The bit rate can be reduced by the block-based algorithm, sacrificing the representation accuracy. In this paper, the block enclosing a distinct edge is divided into two regions and the disparity of each region is set to that of a neighboring block. The proposed algorithm employs accumulated histograms and a neural network to classify a type of a block. In this paper, we proved that the proposed algorithm is more effective than the conventional algorithms in estimating and representing disparity maps through several experiments.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2018.01a
• /
• pp.87-88
• /
• 2018

본 논문에서는 IIoT 환경에서 중요한 비정상적 데이터 수집을 위한 노드 우선순위 선정 모델을 제안하였다. 제안하는 모델은 비정상적 데이터 수집과 다른 노드로 부터의 정상 데이터의 수집 격차를 적절히 조절하기 위하여 Fair and Delay-aware Cross-layer(FDRX) 기법과 데이터 Classification 기법을 이용, 데이터의 긴급성과 네트워크 환경을 분석하여 노드를 평가한다. 이를 통하여 IIoT 환경에서의 데이터 분석에 중요한 비정상적 데이터를 원활하게 수집하면서도 다른 노드와의 전송 격차를 줄일 수 있을 것으로 기대된다.

• Proceedings of the KIPE Conference
• /
• 2017.07a
• /
• pp.56-57
• /
• 2017

본 논문에서는 배터리의 노화에 따른 내부 저항 및 전압변동(Differential Voltage; DV)곡선 변화를 실시간으로 추정하는 SOH (State of Health) 알고리즘을 개발한다. 개발된 알고리즘은 정확한 내부 저항 추정을 위해 동작 및 측정 환경에 따른 고주파 통과 필터의 최적 설계 방안을 제안하며 동적 전류 특성을 고려한 DV곡선의 온라인 업데이트 로직을 이용한다. 알고리즘의 정확도는 단전지 시험 결과를 기반으로 시뮬레이션을 통해 검증한다.