5.6 Band Pass FIR Digital Filter Design using kaiser Window The band pass FIR digital filter has been analysed with hanning window by using FDA tool in the MATLAB. The cut off frequency has been estimated by using nn tool. 5.6.1 Training using Feed Forward Back Propagation (FFBP) artificial Neural Network Algorithm for kaiser Window Figure 5.11: trained network for kaiser window with FFBP Figure 5.12: performance plot for kaiser window with FFBP In above figure 5.12, it shows the performance plot of a FFBP neural network. This plot is result of MLP training algorithm. Here after 7 epochs the mean square error is almost 0. That means the network is trained. Figure 5.13: regression plot for kaiser window with FFBP Here figure 5.13 …show more content…
Figure 5.16 shows an error graph between hamming window output obtained from the FDA tool of MATLAB and feed forward back propagation algorithm output obtained from nn tool of MATLAB. Figure 5.16: Error graph between desired cut-off frequencies and obtained cut-off frequencies for hamming window with FFBP Figure 5.17 shows an error graph between hamming window output obtained from the
The first simulation is loss location (9, 9). Compare with last group loss location (9, 9), this test is more inaccuracy. Firstly, this test is showing very lower value that is 6dB. The inaccuracy values are appear in normal values. In last test, 25dB is approximately 80% and 26dB is approximately 60% (see Fig 12). But 25dB is above 80% and less than 40% in 26dB in this test. the lower persentage in 26dB could casue extension dB values (see Fig 18, top left). The difference between this test and last test is negative SITA results. This test could show negative results because our setting
The third simulation is loss location (-9, -9). First f all, this test has error in one location. It is showing 28dB, this value is impossible. The reason is we haven't set program can detect value more than 27dB. This mistake may program error or test error. Compare with last two tests, this test is inaccuracy because many abnormal values are showing in SITA results. The normal values are higher frequency implements. The lowest has showing very abnormal value that is 14dB. This value should not possible shows in this test. The reason cause those errors may program error or test error. The reason is program using the computer vision technology, but the problem is image noise. The technology cannot guarantee all of flashlight can be detected.
either +1 or –1; where +1 represents a bit 1 and –1 represents a bit 0); and it is possible that one or both of c+ and c– may not exist due to the fact that the Chase algorithm operates using a subset of the 2n possible codewords. In the event that there are not two competing codewords, the Pyndiah algorithm uses a scaling factor, βm, which approximates the value of the extrinsic information for the mth decoding. These values, which are a function of m, are specified in [47]. The values for βm in [44] are approximated using computer simulation of a (64,57)2 BTC (the superscript “x” indicates x dimensional BTC). A simple, yet effective alternative for approximating βm, which does not require computer simulation, is presented in [53]. A soft-input value, λi,j, is computed by the decoder for each entry in C. These soft-values are used to select the codewords in each subset, the competing codewords, as well as, in computing the extrinsic information. The soft-input is computed using:
The DF measurement is based on amplitude comparison technique. The intercepted pulse amplitude is compared among the adjacent DF channels after the channel mismatch is computed and corrected and the DOA is computed. The technique essentially consists of finding the first and second maximums over the channels and the ratio of these two provides the required DF value. The pattern difference between the two adjacent channels is computed at the bore-sight and the crossover angles from which the DOA values are computed. The antenna pattern and channel matching is key parameters for calculating the final DOA. The DF accuracy depends on the channel mismatch and antenna patterns.
In many methods, skin and proximity effects are very important. Another important parameter is alternative current (a.c.) resistance, rac. This parameter is frequency dependence. It can be shown that rac increased with the increased of frequency (Du, & Burnett, (2000), (Demoulias, Labridis, Dokopoulos & Gouramanis, 2007), (Desmet, Vanalme, Belmans, & Van Dommelen, 2008). Some research papers used value from published graph such as by The Okonite Company and Anixter Inc. (The Okonite Company, 2001), (Anixter Inc., n.d.). However upon inspection, it was found that the graph is produced on the basis of fundamental frequency of 60 Hz and using imperial unit. Some other sources, although on 50 Hz as fundamental basis, give limited data such as (Moore, 1997), (Coates, n.d). As an alternative, formula from IEC 287-1-1 was used to calculate the rac (IEC, 1994).
MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations,
The detector then chooses one of the BM possible transmitted symbol vectors based on the available data. An optimal detector should return as x ̂ = x*.
comparison of mean square error using feed forward back propagation (FFBP) and radial basis function(RBF) neural network algorithms are given in table 5.2 for analysis of band pass FIR filter with hanning window.
Each sample was measured under two conditions: no-chopper and chopper. The spectral data of a set of 64 samples of no-chopper was processed by overlapping and averaging. The spectral data of the chopper was demodulated by the method mentioned in Section 3.2, and different primary frequency components were selected from 1 to 5, and spectral data for the set of 64 samples were calculated according to Eq.
In the proposed design sweep iterations has been used in the optical signal to noise ratio (OSNR) component and the BER TEST SET component. On using the feature of the simulator in order to make nested parameters, and thus obtaining the required average BER. With the higher order 16-QAM it is not easy to decide the filter to be used having minimum noise so graph have drawn between Average BER versus OSNR.
The comparison of simulated and measured results of 3rd iteration has been observed. Four frequency bands are observed at 3.1, 2.4, 1.81, and 0.82 GHz. Return loss at 3.1 GHz is 14.56 dB, at 2.4 GHz is 20.66 dB, at 1.81 is 16.96 dB and at 0.82 is 17.78 dB.
2Assistant Professor, Dept. of Electronics & Communication, Sri Mittapalli College Of Engineering, Guntur Email: bapannadora@gmail.com
First, knowing the signal definition, and the binning for the truth and reconstructed variable distribution, we process the microtree file to build the truth and reconstructed signal histograms, as well as the smearing matrix.
Van Nee and Wild suggested peak windowing method. In this method it is possible to remove large peaks. Peak windowing reduces PAPRs as with respect to increasing the BER and out-of-band radiation. Clipping introduces PAPR reduction technique which is self interference
Firstly I will explain what is signal ,signal processing ,analogue viruses digital signal types of signal processing their advantages and disadvantages and their comparison .I-e which one is better …….why analog signal processing (ASP) is replaced with digital signal processing (DSP).