• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.77-78
• /
• 2015

Image Binarization is essential process in the digital image processing for the read out of a one-dimensional barcode. Local threshold method is suitable for binarization of a bar code. However, It has problem that processing time is slower than other binarization algorithm. Also, It's results not appropriate If the image has a noise. In this paper, we propose the modification method for solve these problems. Proposed algorithm help to improve the speed of local thresholding method using average image. Also, we proposed a high frequency filter to one-dimensional barcode for improvement quality of binary image.

• The KIPS Transactions:PartC
• /
• v.13C no.1 s.104
• /
• pp.75-82
• /
• 2006

In this paper, a space-frequency coded orthogonal frequency division multiplexing (SFC-OFDM) scheme with dual Viterbi decoder is proposed and analyzed. Here, two independent half-rate OFDM symbols are generated after convolutional coding of the binary source code. A dual Viterbi decoder is exploited to decode the demodulated sequences independently in the receiver, and their path metrics are compared. Accordingly, the recovered binary data in the proposed scheme are composed of the combination of the sequences having larger path metrics while those in a conventional system are simply the output of single Viterbi decoder. As a result, the proposed SFC-OFDM scheme has a better performance than the conventional one for all signal-to-noise power ratio.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.40 no.8
• /
• pp.589-597
• /
• 2003

Visual cryptography made it possible to decrypt thresholding scheme with not digital system but human vision system. This method, however, has some limit in it. Optical visual cryptography was proposed which used laser instead of human eyesight. As a result, it was possible to adapt cryptography to optical system. However, it also had some difficulties because it did not overcome the existing problem of visual cryptography completely. These problems occurred in the process of transferring data processing system from visual to optics. Therefore, it is appropriate to approach these problems in terms of optics. This paper analyzes the level of noise and the security characteristics for optical visual cryptography in terms of frequency based on joint transform correlator.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.4072-4091
• /
• 2015

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

• Journal of Information Processing Systems
• /
• v.15 no.3
• /
• pp.682-693
• /
• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.

• Journal of Positioning, Navigation, and Timing
• /
• v.11 no.3
• /
• pp.147-156
• /
• 2022

In this paper, we propose Time-Division-Multiplexing Tertiary Offset Carrier (TDMTOC), a novel GNSS modulation based on Tertiary Offset Carrier (TOC) modulation. The TDMTOC modulation multiplexes two three-level signals (i.e., -1, 0, and 1) while crossing over time, and is a type of TOC modulation designed specifically for signal multiplexing. The proposed modulation generates TDMTOC subcarriers of two different phases by simply combining two Binary Offset Carrier (BOC) subcarriers by addition or subtraction. TDMTOC has better correlation and spectral properties than conventional BPSK, BOC, and MBOC modulation techniques, and has good power and spectral efficiency since it can multiplex signals without power loss similar to time division multiplexing. To prove this, we introduce the multiplexing process of TDMTOC, and compare TDMTOC with Binary Phase Shift Keying (BPSK), BOC, Composite BOC (CBOC), and Time Multiplexed BOC (TMBOC) that are currently serviced in GNSS by simulations of various aspects. Through the simulation results, we prove that TDMTOC has better correlation property than modulations currently used in GNSS, less intersystem interference due to its wide spectrum property, and robustness in multipath and noise channel environments.

• Journal of Positioning, Navigation, and Timing
• /
• v.13 no.1
• /
• pp.1-13
• /
• 2024

In this paper, we investigate the signal design of six (USA, EU, Russia, China, Japan, and India) countries for Global Navigation Satellite Systems (GNSS). Recently, a navigation satellite system that is capable of high-precision and reliable Positioning, Navigation, Timing (PNT) services has been developed. Prior to system design, a survey of the signal design for other GNSS systems should precede to ensure compatibility and interoperability with other GNSS. The signal design includes carrier frequency, Pseudorandom Noise (PRN) code, modulation, navigation service, etc. Specifically, GNSS is allocated L1, L2, and L5 bands, with recent additions of the L6 and S bands. GNSS uses PRN code (such as Gold, Weil, etc) to distinguish satellites that transmit signals simultaneously on the same frequency band. For modulation, both Binary Phase Shift Keying (BPSK) and Binary Offset Carrier (BOC) have been widely used to avoid collision in the frequency spectrum, and alternating BOCs are adopted to distinguish pilot and data components. Through the survey of other GNSS' signal designs, we provide insights for guiding the design of new satellite navigation systems.

• Journal of Astronomy and Space Sciences
• /
• v.41 no.3
• /
• pp.149-158
• /
• 2024

This study developed a machine learning-based methodology to classify gravitational wave (GW) signals from black hol-eneutron star (BH-NS) mergers by combining convolutional neural network (CNN) with conditional information for feature extraction. The model was trained and validated on a dataset of simulated GW signals injected to Gaussian noise to mimic real world signals. We considered all three types of merger: binary black hole (BBH), binary neutron star (BNS) and neutron starblack hole (NSBH). We achieved up to 96% correct classification of GW signals sources. Incorporating our novel conditional information approach improved classification accuracy by 10% compared to standard time series training. Additionally, to show the effectiveness of our method, we tested the model with real GW data from the Gravitational Wave Transient Catalog (GWTC-3) and successfully classified ~90% of signals. These results are an important step towards low-latency real-time GW detection.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.8
• /
• pp.725-730
• /
• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.05a
• /
• pp.185-191
• /
• 1999

Mathematical morphology based on set theory has been applied to various areas in image processing. Pitas proposed a object recognition algorithm using Morphological Shape Decomposition(MSD), and a new representation scheme called Morphological Shape Representation(MSR). The Pitas's algorithm is a simple and adequate approach to recognize objects that are rotated 45 degree-units with respect to the model object. However, this recognition scheme fails in case of random rotation. This disadvantage may be compensated by defining small angle increments. However, this solution may greatly increase computational complexity because the smaller the step makes more number of rotations to be necessary. In this paper, we propose a new method for object recognition based on MSD. The first step of our method decomposes a binary shape into a union of simple binary shapes, and then a new tree structure is constructed which ran represent the relations of binary shapes in an object. finally, we obtain the feature informations invariant to the rotation, translation, and scaling from the tree and calculate matching scores using efficient matching measure. Because our method does not need to rotate the object to be tested, it could be more efficient than Pitas's one. MSR has an intricate structure so that it might be difficult to calculate matching scores even for a little complex object. But our tree has simpler structure than MSR, and easier to calculated the matchng score. We experimented 20 test images scaled, rotated, and translated versions of five kinds of automobile images. The simulation result using octagonal structure elements shows 95% correct recognition rate. The experimental results using approximated circular structure elements are examined. Also, the effect of noise on MSR scheme is considered.