• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1502-1509
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• 2006

In this paper, we proposed reliability and capacity enhancement methods for IEEE 802.15.3 HDR-WPAN (High Data Rate-Wireless Personal Area Network) system which is currently getting an interest in home network technology adopting a MIMO technique. We also analyzed performance or the proposed system through a computer simulation. The HDR-WPAN system using V-BLAST algorithm, transmitting the different signal vector to each other's sub-channel, can get the transmission speed of more than 110Mbps using two Tx/Px antenna without bandwidth expansion in TCM-64QAM mode. Also the proposed system has reliability of 104 at $E_b/N_0=35dB$ under the Rayleigh fading channel in case of two Tx/Rx antenna with MMSE algorithm. The HDR-WPAN system adopting V-BLAST method has its drawback which is very complicated to determine the decision-ordering at the receiver. But, the proposed system enhances the transmission capacity and reliability without extra bandwidth expansion by sending data streams to multiple antennas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1306-1312
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• 2017

The route request (RREQ) packet is selectively re-broadcasted in the routing protocols that improve the broadcast storm problem of the ad-hoc on-demand routing protocol (AODV). However, in a low node density scenario, the connectivity of these selective rebroadcast schemes becomes less than that of the AODV. In order to clarify the requirements of these selective re-broadcast routing protocols, it is necessary to investigate the relationship between the node density and the connectivity. In this paper, we drive a probability to preserve the connectivity of the GAODV at an intermediate rebroadcast node. In addition, we present an intuitive method to approximate the end-to-end connectivity of the GAODV. We draw the required node density to guarantee the connectivity of 0.9 and 0.99 through computer simulations, and verify the validity of the derived theoritical connectivity by comparing with the simulation results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1477-1484
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• 2015

This paper proposes a robust method for object tracking based on Kalman filters algorithm and covariance matrix. As a feature of the object to be tracked, covariance matrix ensures the continuity of the moving target tracking in the image frames because the covariance is addressed spatial and statistical properties as well as the correlation properties of the features, despite the changes of the form and shape of the target. However, if object moves faster than operation time, real time tracking is difficult. In order to solve the problem, Kalman filters are used to estimate the area of the moving object and covariance matrices as a feature vector are compared with candidate regions within the estimated Kalman window. The results show that the tracking rate of 96.3% achieved using the proposed method.

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.305-312
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• 2001

Recently, high-dimensional index structures have been required for similarity search in such database applications s multimedia database and data warehousing. In this paper, we propose a new cell-based signature tree, called CS-tree, which supports efficient storage and retrieval on high-dimensional feature vectors. The proposed CS-tree partitions a high-dimensional feature space into a group of cells and represents a feature vector as its corresponding cell signature. By using cell signatures rather than real feature vectors, it is possible to reduce the height of our CS-tree, leading to efficient retrieval performance. In addition, we present a similarity search algorithm for efficiently pruning the search space based on cells. Finally, we compare the performance of our CS-tree with that of the X-tree being considered as an efficient high-dimensional index structure, in terms of insertion time, retrieval time for a k-nearest neighbor query, and storage overhead. It is shown from experimental results that our CS-tree is better on retrieval performance than the X-tree.

• The KIPS Transactions:PartC
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• v.14C no.2
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• pp.191-198
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• 2007

A mobile ad-hoc network (MANET) is an autonomous, infrastructure-less system that consists of mobile nodes. In MANET, on demand routing protocols are usually used because network topology changes frequently. AODV, which is a representative on demand routing protocol, operates using the routing table of each node that includes next hop of a route for forwarding packets. It maintains the established route if there is not an expiration of route or any link break. In the paper, we propose a partially adaptive route maintenance scheme (AODV-PA) based on AODV, which provides dynamic route modification of initial route for selecting the effective route using not only next hop but also next-hop of next-hop (i.e. 2-hop next node) acquired through route discovery process. In addition, the proposed scheme additionally manages the routing table for preventing exceptional link breaks by route modification using HELLO messages. We use NS 2 for the computer simulation and validate that the proposed scheme is better than general AODV in terms of packet delivery ratio, latency, routing overhead.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.77-87
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• 2013

In this paper, we present a novel method for local image descriptor called exact order based descriptor (EOD) which is robust to illumination changes and Gaussian noise. Exact orders of image patch is induced by changing discrete intensity value into k-dimensional continuous vector to resolve the ambiguity of ordering for same intensity pixel value. EOD is generated from overall distribution of exact orders in the patch. The proposed local descriptor is compared with several state-of-the-art descriptors over a number of images. Experimental results show that the proposed method outperforms many state-of-the-art descriptors in the presence of illumination changes, blur and viewpoint change. Also, the proposed method can be used for many computer vision applications such as face recognition, texture recognition and image analysis.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.5
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• pp.383-392
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• 2007

In this paper, we study the prediction of depth and width of a defect in steam generator tube in nuclear power plant using neural network. To this end, we first generate eddy current testing (ECT) signals for 4 defect patterns of SG tube: I-In type, I-Out type, V-In type, and V-Out type. In particular, we generate 400 ECT signals for various widths and depths for each defect type by the numerical analysis program based on finite element modeling. From those generated ECT signals, we extract new feature vectors for the prediction of defect size, which include the angle between the two points where the maximum impedance and half the maximum impedance are achieved. Using the extracted feature vector, multi-layer perceptron with one hidden layer is used to predict the size of defects. Through the computer simulation study, it is shown that the proposed method achieves decent prediction performance in terms of maximum error and mean absolute percentage error (MAPE).

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.229-234
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• 2018

Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.65-77
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• 2004

This paper presents a priori and the local font classification method. The font classification uses ascenders, descenders, and serifs extracted from a word image. The gradient features of those sub-images are extracted, and used as an input to a neural network classifier to produce font classification results. The font classification determines 2 font styles (upright or slant), 3 font groups (serif sans-serif or typewriter), and 7-font names (Postscript fonts such as Avant Garde, Helvetica, Bookman, New Century Schoolbook, Palatine, Times, and Courier). The proposed a priori and local font classification method allows an OCR system consisting of various font-specific character segmentation tools and various mono-font character recognizers. Experiments have shown font classification accuracies reach high performance levels of about 95.4 percent even with severely touching characters. The technique developed for tile selected 7 fonts in this paper can be applied to any other fonts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2633-2648
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• 2015

The problem of visual words' synonymy and ambiguity always exist in the conventional bag of visual words (BoVW) model based object category methods. Besides, the noisy visual words, so-called "visual stop-words" will degrade the semantic resolution of visual dictionary. In view of this, a novel bag of visual words method based on PLSA and chi-square model for object category is proposed. Firstly, Probabilistic Latent Semantic Analysis (PLSA) is used to analyze the semantic co-occurrence probability of visual words, infer the latent semantic topics in images, and get the latent topic distributions induced by the words. Secondly, the KL divergence is adopt to measure the semantic distance between visual words, which can get semantically related homoionym. Then, adaptive soft-assignment strategy is combined to realize the soft mapping between SIFT features and some homoionym. Finally, the chi-square model is introduced to eliminate the "visual stop-words" and reconstruct the visual vocabulary histograms. Moreover, SVM (Support Vector Machine) is applied to accomplish object classification. Experimental results indicated that the synonymy and ambiguity problems of visual words can be overcome effectively. The distinguish ability of visual semantic resolution as well as the object classification performance are substantially boosted compared with the traditional methods.