Introduction There are the two main approaches used in optical multiplexing. One is optical wavelength division (frequency division) other is optical time division Multiplexing. This paper deals with optical time division Multiplexing. In optical time division Multiplexing (OTDM), a high bit rate streams constructed directly by time multiplexing several lower bit rate. At the receiver end of the system very high bit rate data streams demultiplexed into the lower bit streams before detection and conversion to the electrical signals.
they are two different and independent signals. In OFDM, sinc -shaped pulses are used as subcarrier spectra. According to the properties of sinc-pulses, zero crossings are located at the multiples of 1/T. The use of sinc-pulses and subcarrier center frequency fi selection with equation (3) ensures that the subcarrier orthogonality is maintained. f_i= f_c + i/T i= (-N)/2……N/2 (3) Where fc is the channel center frequency and N is the number of sub-channels.
The reflected wave received at the sensor’s receiver is a scaled and shifted version of the original wave. R_x (τ)=AT_x (τ-τ_0) Now assume this function R_x (τ) is also a function of another variable say η, this results in a function R_x (τ,η). In SAR η is defined as the azimuth time or slow time and is controlled by the motion of the platform. The azimuth direction ... ... middle of paper ... ... used in the range direction, the azimuth resolution is the inverse of the bandwidth multiplied by 0.866, p_a=0.866/(BW_a ) where the azimuth bandwidth is represented by BWa. In distance units this is multiplied by the speed with which the beam moves on the ground and the squint angle at the beam centre.
Now, here we first discuss about the two types of signal system which are given below. Stationary signal system Non-stationary signal system Here we discuss about how both type of signals will react in frequency domain and time domain by Fourier transformation of the input signal like below. x (t)= cos〖(2π*10t)〗+ cos〖(2π*25t)〗+cos〖(2π*50t)〗+cos〖(2π*100t)〗 First we show the response of the stationary signal system in both time and frequ... ... middle of paper ... ...ration of convolution of the signal with the impulse response of the filter. The convolution operation in discrete time is defined as follows: x[n]*h[n]=∑_(k= -∞)^∞▒〖x[k]∙h[n-k] 〗 (2.6) There is also one other method to decompose the signal into high pass and low pass signal by the lifting based scheme which is defined in the chapter 3. By the Discrete Wavelet Transform (DWT), we can separate the high and low-frequency portions of one dimensional signal through the use of filters [7, 8].
Quantization Quantization refers to the process of approximating the continuous set of values in the image data with a finite (preferably small) set of values. The input to a quantizer is the original data, and the output is always one among a finite number of levels. The quantizer is a function whose set of output values are discrete, and usually finite. Obviously, this is a process of approximation, and a good quantizer is one which represents the original signal with minimum loss or distortion. A quantizer simply reduces the number of bits needed to store the transformed coefficients by reducing the precision of those values.
Telecommunication Systems 1) If the noise pulse signal cross the decision level of the detector then an error will occur in the bit value. If the noise is Gaussian there is equal probability for the noise voltage to increase the sample value and to decrease the sample value. Sampling at mid-interval T could result in an error if the noise value is happen to be large. Backward error control is a scheme in which the data is sent coded so that the receiver can detect errors, the receiver requests retransmission of a data block (frame) contain an error. Examples of backward error control code are ARQ (Automatic Request for repeat), BI-QUINARY, GREY CODE.
It divides continuous time function in to wavelet. The CWT is defined as follows, X(a,b;x(t),φ(t)=1/√a ∫_(-∞)^∞▒〖x(t) φ^* 〗 ((t-b)/a)dt Where, * denote the complex conjugate Ѱ(t) continuous function in both time and frequency domain called as mother wavelet The wavelet type may also affect the value of the coefficients. By continuously varying the values of the scale parameter a, and the position parameter b, the CWT coefficients X (a, b) can be obtained. By multiplying each coefficient with the scale and shifted wavelet yields the constituents wavelet of the original signal. Normally the output X(a, b) is a real valued function when the mother wavelet is complex, the complex mother wavelet convert the CWT to a complex valued function.
Hence, we use the orthogonality parameter to measure the wavelet's deviation from orthogonality. It is given by: (4.2) Filter length: in shorter synthesis basis functions are desired for minimize deformation that affects the subjective quality of the image. The longer filters are responsible for ringing noise in the reconstructed image at low bit rates. The Vanishing order is a measure of the compaction property of the wavelets. The synthesis wavelet is said to have p vanishing moments.
Figure 2.1 Flow chart of DOT forward model Figure 2.2 : Reconstructed model Flowchart of DOT This forms the inner iteration to find the solution of system of linear equation for proper update. After getting optical property update it is added to current optical property and fed back to forward model and Jacobian routine. This forms the outer iteration and is carried out till the error become less than threshold or if maximum number of iteration is reached.
For combining the profit of PCA and wavelets, the capacity for each variable are decomposed to its wavelet coefficients by the same wavelet for each variable. This transformation of the data matrix brings X into a matrix, WX, while W is an n * n orthonormal matrix showing the orthonormal wavelet transformation operator that contains the filter coefficients. h_(L,1) h_(L,2) . . .