• Title/Summary/Keyword: Noise Identification

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Measurements of Dark Area in Sensing RFID Transponders

  • Kang, J.H.;Kim, J.Y.
    • Journal of Sensor Science and Technology
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    • v.21 no.2
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    • pp.103-108
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    • 2012
  • Radiofrequency(RF) signal is a key medium to the most of the present wireless communication devices including RF identification devices(RFID) and smart sensors. However, the most critical barrier to overcome in RFID application is in the failure rate in detection. The most notable improvement in the detection was from the introduction of EPC Class1 Gen2 protocol, but the fundamental problems in the physical properties of the RF signal drew less attention. In this work, we focused on the physical properties of the RF signal in order to understand the failure rate by noting the existence of the ground planes and noise sources in the real environment. By using the mathematical computation software, Maple, we simulated the distribution of the electromagnetic field from a dipole antenna when ground planes exist. Calculations showed that the dark area can be formed by interference. We also constructed a test system to measure the failure rate in the detection of a RFID transponder. The test system was composed of a fixed RFID reader and an EPC Class1 Gen2 transponder which was attached to a scanner to sweep in the x-y plane. Labview software was used to control the x-y scanner and to acquire data. Tests in the laboratory environment showed that the dark area can be as much as 43 %. One who wants to use RFID and smart sensors should carefully consider the extent of the dark area.

A study for implementation of wireless sensor network to optimize building environment (건물 환경 최적화를 위한 무선 센서 네트워크 구현에 대한 연구)

  • Chung, Sung-Boo;Kim, Joo-Woong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.10
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    • pp.2235-2241
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    • 2009
  • RFID and USN are major technology in Ubiquitous. RFID is an automatic identification method, relying on storing and remotely retrieving data using devices called RFID tags or transponders through RFID reader. USN is wireless sense network and monitoring environment conditions that is temperature, noise, pressure, oscillation. In this paper, we propose wireless sensor network system that is monitoring to optimize environment conditions.

Separation of Text and Non-text in Document Layout Analysis using a Recursive Filter

  • Tran, Tuan-Anh;Na, In-Seop;Kim, Soo-Hyung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.10
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    • pp.4072-4091
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    • 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.

Hilbert transform based approach to improve extraction of "drive-by" bridge frequency

  • Tan, Chengjun;Uddin, Nasim
    • Smart Structures and Systems
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    • v.25 no.3
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    • pp.265-277
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    • 2020
  • Recently, the concept of "drive-by" bridge monitoring system using indirect measurements from a passing vehicle to extract key parameters of a bridge has been rapidly developed. As one of the most key parameters of a bridge, the natural frequency has been successfully extracted theoretically and in practice using indirect measurements. The frequency of bridge is generally calculated applying Fast Fourier Transform (FFT) directly. However, it has been demonstrated that with the increase in vehicle velocity, the estimated frequency resolution of FFT will be very low causing a great extracted error. Moreover, because of the low frequency resolution, it is hard to detect the frequency drop caused by any damages or degradation of the bridge structural integrity. This paper will introduce a new technique of bridge frequency extraction based on Hilbert Transform (HT) that is not restricted to frequency resolution and can, therefore, improve identification accuracy. In this paper, deriving from the vehicle response, the closed-form solution associated with bridge frequency removing the effect of vehicle velocity is discussed in the analytical study. Then a numerical Vehicle-Bridge Interaction (VBI) model with a quarter car model is adopted to demonstrate the proposed approach. Finally, factors that affect the proposed approach are studied, including vehicle velocity, signal noise, and road roughness profile.

Fast Remote Detection Algorithms for Chemical Gases Using Pre-Detection with a Passive FTIR Spectrometer (수동형 FTIR 분광계에서 초동 탐지 기법을 이용한 고속 원거리 화학 가스 탐지 알고리즘)

  • Yu, Hyeonggeun;Park, Dongjo;Nam, Hyunwoo;Park, Byeonghwang
    • Journal of the Korea Institute of Military Science and Technology
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    • v.21 no.6
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    • pp.744-751
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    • 2018
  • In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.

Simulation Study for Feature Identification of Dynamic Medical Image Reconstruction Technique Based on Singular Value Decomposition (특이값분해 기반 동적의료영상 재구성기법의 특징 파악을 위한 시뮬레이션 연구)

  • Kim, Do-Hui;Jung, YoungJin
    • Journal of radiological science and technology
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    • v.42 no.2
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    • pp.119-130
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    • 2019
  • Positron emission tomography (PET) is widely used imaging modality for effective and accurate functional testing and medical diagnosis using radioactive isotopes. However, PET has difficulties in acquiring images with high image quality due to constraints such as the amount of radioactive isotopes injected into the patient, the detection time, the characteristics of the detector, and the patient's motion. In order to overcome this problem, we have succeeded to improve the image quality by using the dynamic image reconstruction method based on singular value decomposition. However, there is still some question about the characteristics of the proposed technique. In this study, the characteristics of reconstruction method based on singular value decomposition was estimated over computational simulation. As a result, we confirmed that the singular value decomposition based reconstruction technique distinguishes the images well when the signal - to - noise ratio of the input image is more than 20 decibels and the feature vector angle is more than 60 degrees. In addition, the proposed methode to estimate the characteristics of reconstruction technique can be applied to other spatio-temporal feature based dynamic image reconstruction techniques. The deduced conclusion of this study can be useful guideline to apply medical image into SVD based dynamic image reconstruction technique to improve the accuracy of medical diagnosis.

Bayesian forecasting approach for structure response prediction and load effect separation of a revolving auditorium

  • Ma, Zhi;Yun, Chung-Bang;Shen, Yan-Bin;Yu, Feng;Wan, Hua-Ping;Luo, Yao-Zhi
    • Smart Structures and Systems
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    • v.24 no.4
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    • pp.507-524
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    • 2019
  • A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

Zero-Watermarking Algorithm in Transform Domain Based on RGB Channel and Voting Strategy

  • Zheng, Qiumei;Liu, Nan;Cao, Baoqin;Wang, Fenghua;Yang, Yanan
    • Journal of Information Processing Systems
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    • v.16 no.6
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    • pp.1391-1406
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    • 2020
  • A zero-watermarking algorithm in transform domain based on RGB channel and voting strategy is proposed. The registration and identification of ownership have achieved copyright protection for color images. In the ownership registration, discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) are used comprehensively because they have the characteristics of multi-resolution, energy concentration and stability, which is conducive to improving the robustness of the proposed algorithm. In order to take full advantage of the characteristics of the image, we use three channels of R, G, and B of a color image to construct three master shares, instead of using data from only one channel. Then, in order to improve security, the master share is superimposed with the copyright watermark encrypted by the owner's key to generate an ownership share. When the ownership is authenticated, copyright watermarks are extracted from the three channels of the disputed image. Then using voting decisions, the final copyright information is determined by comparing the extracted three watermarks bit by bit. Experimental results show that the proposed zero watermarking scheme is robust to conventional attacks such as JPEG compression, noise addition, filtering and tampering, and has higher stability in various common color images.

Electrooculography Filtering Model Based on Machine Learning (머신러닝 기반의 안전도 데이터 필터링 모델)

  • Hong, Ki Hyeon;Lee, Byung Mun
    • Journal of Korea Multimedia Society
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    • v.24 no.2
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    • pp.274-284
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    • 2021
  • Customized services to a sleep induction for better sleepcare are more effective because of different satisfaction levels to users. The EOG data measured at the frontal lobe when a person blinks his eyes can be used as biometric data because it has different values for each person. The accuracy of measurement is degraded by a noise source, such as toss and turn. Therefore, it is necessary to analyze the noisy data and remove them from normal EOG by filtering. There are low-pass filtering and high-pass filtering as filtering using a frequency band. However, since filtering within a frequency band range is also required for more effective performance, we propose a machine learning model for the filtering of EOG data in this paper as the second filtering method. In addition, optimal values of parameters such as the depth of the hidden layer, the number of nodes of the hidden layer, the activation function, and the dropout were found through experiments, to improve the performance of the machine learning filtering model, and the filtering performance of 95.7% was obtained. Eventually, it is expected that it can be used for effective user identification services by using filtering model for EOG data.

Ship Monitoring around the Ieodo Ocean Research Station Using FMCW Radar and AIS: November 23-30, 2013

  • Kim, Tae-Ho;Yang, Chan-Su
    • Korean Journal of Remote Sensing
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    • v.38 no.1
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    • pp.45-56
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    • 2022
  • The Ieodo Ocean Research Station (IORS) lies between the exclusive economic zone (EEZ) boundaries of Korea, Japan, and China. The geographical positioning of the IORS makes it ideal for monitoring ships in the area. In this study, we introduce ship monitoring results by Automatic Identification System (AIS) and the Broadband 3GTM radar, which has been developed for use in small ships using the Frequency Modulated Continuous Wave (FMCW) technique. AIS and FMCW radar data were collected at IORS from November 23th to 30th, 2013. The acquired FMCW radar data was converted to 2-D binary image format over pre-processing, including the internal and external noise filtering. The ship positions detected by FMCW radar images were passed into a tracking algorithm. We then compared the detection and tracking results from FMCW radar with AIS information and found that they were relatively well matched. Tracking performance is especially good when ships are across from each other. The results also show good monitoring capability for small fishing ships, even those not equipped with AIS or with a dysfunctional AIS.