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Efficient SNR Enhancement Schemes on Wireless Electromagnetic Signal Environment Proceedings of the 5th WSEAS International Conference on MATHEMATICAL BIOLOGY and ECOLOGY (MABE'09) Efficient SNR Enhancement Schemes on Wireless Electromagnetic Signal Environment HYUN-SO LEE, YUN-HO LEE, KYUNG-SEOK KIM Department of Radio Engineering Chungbuk National University 12 Gashin-dong Heungduk-gu Cheongju Chungbuk 361-763 SOUTH KOREA [email protected], [email protected], [email protected] Abstract: - Recently according to the growth of an information society, a lot of information instruments which a clock frequency has over giga herts have been developed. With using the characteristic of leakage electromagnetic waves to happen in such electrical appliance the research to use at a communication tapping is becoming recently the progress. In this paper, we analyzed leakage electromagnetic signals to happen in the monitors. And we proposed efficient recovery technique and to restore the screen of the monitor. First of all, we understand a screen characteristic of the monitor. And then we restored a monitor screen. For also better performance we tried to use a wavelet, binary-level and filters to remove the noise. In the result of the experiment, we used leakage electromagnetic signals and confirmed the possibility of a monitor screen of the recovery. And we improve the performance with binary-level and filters. Key-Words: - Leakage electromagnetic waves, Van Eck phreaking, eavesdropping, signal processing, wavelet 1 Introduction First of all, we understand the characteristic of leakage monitor electromagnetic signals and analyze. Also we apply SNR improvement scheme and study object signals Recently according to the development of an information (leakage monitor electromagnetic signals) tapping limit society the ITE(Information Technology Equipment) which uses Van Eck phreaking. And we receive an object which a clock frequency has the facility over a number signals and find out about a tapping possibility through a giga hertz have been developed much. And an unwanted signal recovery. In this paper, we proposed the efficient emission of the ITE is causing malfunction of electronic scheme that applied the noise reduction techniques at the equipment. An unwanted emission induces the obstacle at wireless monitor electromagnetic signals. The proposed short distance and low power communication service like scheme is composed of both the production wavelet, mobile communication and wireless LAN which is used Binary-level and noise reduction methods. The proposed recently much. And we have research which an unwanted scheme is confirmed at various simulation situations with emission can use at the communication security and antenna. tapping [1]~[4]. A communication tapping is achieved by phenomenon called Van Eck phreaking. Van Eck phreaking is a form of eavesdropping in which special equipment is used to 2 Recovery principle of wireless leakage pick up telecommunication signals or data within a electromagnetic signals computer device by monitoring and picking up the electromagnetic fields (EM fields) that are produced by 2.1 Measurement System Structure the signals or movement of the data. This electromagnetic radiation is present in, and with the proper equipment, can be captured from computer displays that use cathode ray tubes (CRT), from printers, and from other devices. In this paper we used leakage electromagnetic signals from a computer monitor and restored the image of the monitor. For this we applied the technique to improve SNR (Signal to Noise Ratio) for recovery performance improvement of monitor radiation signals which is leaked Fig.1 System Environment for wireless leakage out in a noise environment. electromagnetic signals measurement. ISSN: 1790-5125 78 ISBN: 978-960-474-038-3 Proceedings of the 5th WSEAS International Conference on MATHEMATICAL BIOLOGY and ECOLOGY (MABE'09) which line and pixel based on received data, find the start Fig.1 shows the wireless leakage electromagnetic signals location of monitor image, and distribute the sequence measurement environment. The distance of Monitor and data line by line, reassignment the screen to a monitor antenna is 60cm. Transmitter signal contents at the fig.2is signal which expresses software. composed of 1280 X 1024 resolution. Fig.4 shows wireless electromagnetic waves signal recovery using Matlab program. Fig.2 Transmitter signal contents Leakage electromagnetic signal are received through the Fig.4 Received signal contents at the receiver direction antenna and the digital receiver with the high resolution capability. The recovery result shows white signals and black signals applied threshold level, white signals mapped white, For monitor signal recovery, we need to know resolution black signals mapped black. Fig.4 shows same part of which is applied. Each monitor structure with resolution transmitter signal contents, but is not easy to read. is standardized by VESA [5]. After grasping the monitor characteristic, signal is recovered by resolution VESA 3 Proposed efficient noise reduction standard. schemes If the data which noise mixed take same format, then the Average method improves performance easy to read. But the Average method needs sync of the data. If signal is non-correct sync, the signal comes to be weak. And it is too difficult to set the sync. So we can’t expect the performance of recovery result. In this paper, we proposed efficient method using wavelet and filter for improved SNR (Signal to Noise Ratio). Fig.3. A monitor signal recovery flow chart 3.1 Noise reduction Scheme using both Wavelet transform and Gaussian smoothing filter Fig.3 shows the monitor signal recovery. Theoretical part, data analysis of monitor resolution must 3.1.1 Wavelet transform by the de-noising method proceed according to VESA standardization. We correctly know each frame of composition and compare A wavelet is a kind of mathematical function used to with received data signal. Also, we must know divide a given function or continuous-time signal into measurement equipment performed. For simulation different frequency components and study each partially analysis, compared analyzed data with received component with a resolution that matches its scale. A data, begin data recovery. First, set the synchronization of wavelet transform is the representation of a function by the monitor about the frame start location. After finding wavelets. The wavelets are scaled and translated copies ISSN: 1790-5125 79 ISBN: 978-960-474-038-3 Proceedings of the 5th WSEAS International Conference on MATHEMATICAL BIOLOGY and ECOLOGY (MABE'09) of a finite-length or fast-decaying oscillating waveform. and convolution with it is the image processing Wavelet transforms have advantages over traditional equivalent of low pass filtering. Where sigma of (3) is the Fourier transforms for representing functions that have standard deviation of the distribution. The distribution is discontinuities and sharp peaks, and for accurately illustrated in Fig.5. deconstructing and reconstructing finite, non-periodic and non-stationary signals [6]. Characteristics of wavelet transform are building block to construct or represent a signal or function, Time-frequency localization of the signal and efficient calculation of the coefficients from the signal. And Wavelet expansion set is not unique. Wavelet transforms are classified into discrete wavelet transforms (DWTs) and continuous wavelet transforms (CWTs). Note that both DWT and CWT are of continuous-time (analog) transforms. They can be used to represent continuous-time signals. CWTs operate over every possible scale and translation whereas DWTs use a specific subset of scale and translation values or representation grid. Wavelet systems are generated from a single scaling function or wavelet by simply scaling and shifting. Fig.5 Gaussian distribution with various σ values j The higher the sigma more low pass the filter. The effect 2 j Ψ=Ψ−jk, ()tt 2 (2k ) (1) of Gaussian smoothing is to blur an image, in a similar fashion to the Mean filter. The gaussian outputs a f ()ta=Ψ ()t (2) ∑ jk,, jk ‘weighted average’ of each pixel’s neighborhood, with jk, the average weighted more towards the value of the central pixels. Ψ (t) is basis function of DWT. Arbitrary expression f(t) j,k Fig.6 shows principle of the Gaussian smoothing filter. is expressed composite function by basis function Ψj,k(t) and wavelet coefficient аj,k. In this paper, we used wden() (Automatic wavelet de-noising) of one-dimensional Discrete Wavelet Analysis of Matlab tool. Wden() is a one-dimensional de-noising function. Wden() performs an automatic de-noising process of a one-dimensional signal using wavelets. Fig.6 Principle of the Gaussian smoothing filter 3.1.2 Gaussian smoothing filter First, we selected some pixels of total signals. This Gaussian smoothing filter is typically used to reduce selected pixels called Window. After selecting pixels, we image noise and reduce detail levels. It uses a convolution calculated result from convolution mask of Gaussian mask. And it is accomplished through convolution. The smoothing filter with the pixels. The result is multiplied bigger mask, the greater the blurring effect. The visual each pixel by each value of convolution mask and add effect of this filtering technique is a smooth blur each multipled result. And the result is devided into resembling that of viewing the image through a number of pixels. It is new value of the central pixel. translucent screen. Gaussian smoothing filter can remove After all calculation, the Window slides right side one Gaussian noise. But, it has problem of blurring pixel. This is in contrast to the mean filter’s uniformly phenomenon [7]. weighted average. Because of this, a Gaussian provides The gaussians smoothing filter is a function of the form gentler smoothing and preserves edges better than a similary sized Mean filter. −+()x22y 1 2 Gxy[, ]= exp 2σ 2 3.2 Noise reduction Scheme using both Wavelet 2πσ (3) transform and Mean filter 3.2.1 Mean filter ISSN: 1790-5125 80 ISBN: 978-960-474-038-3 Proceedings of the 5th WSEAS International Conference on MATHEMATICAL BIOLOGY and ECOLOGY (MABE'09) various levels 2bits data.
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