Guide Digital Circuit Analysis and Design with SIMULINK Modeling: And Introduction to CPLDs and FPGAs

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Digital Circuit Analysis and Design with Simulink Modeling and Introduction to CPLDs and FPGAs Combinational Logic Circuits, Sequential Logic Circuits, Memory Devices, Advanced Arithmetic and Logic Operations, Introduction to Field.
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Lecture 36 FPGA Interconnection, Design Methodology by IISC

Chapter 8: Sequential Logic Circuits. Chapter 9: Memory Devices. Chapter Advanced Arithmetic and Logic Operations. Chapter Introduction to Field Programmable Devices.

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Appendix B: Introduction to Simulink. Appendix E: Introduction to Verilog. Chapter 1: Common Number Systems and Conversions This chapter is an introduction to the decimal, binary, octal, and hexadecimal numbers, their representation, and conversion from one base to another. Copyright Orchard Publications under license agreement with Books24x7. Featured Products. The information request powers paid. Your online Pensieri e altri scritti di e su Pascal ends detected the first finality of files. Prelinger Archives download digital circuit analysis and design with simulink modeling and introduction to cplds and usually!

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In Figure 4 , the main signal is indicated along with signal after elimination of the artifact. In Figure 6 , the main signal can be observed simultaneously along with the signal after elimination of artifact. As seen below, based on recorded EOG, the exact part which is related to EOG is estimated and its effect is eliminated. This network has also diagnosed the effect of EOG artifact efficiently and has eliminated it. In Figure 7 , the amplitude of the signal power spectrum PSM is used for comparing.

As you can see, the effect of parts with low frequency related to EOG artifact has been reduced after eliminating artifact in the signal.

This method has also been applied to eliminate the effect of EOG artifact whose result is indicated in Figure 8. In the same way, ECG artifact has been applied as an input for this network. Moreover in this case, two inputs of ECG signals and its delayed forms are used. In this regard, the correlation between signals along with artifact and ECG artifact were compared with the correlation of signals after elimination of artifact and ECG artifact as observed in Table 1.

Figure 10 also indicates EEG out of methods after elimination of noise. Compared with the form of the main signal, it can be observed that the artifact has been efficiently diagnosed and eliminated. One of the ways to compare the results of implemented methods is to use MSE criteria. The following equation is used to compute these criteria:.

The results of MSE for methods can be observed in Table 2. As mentioned before, one advantage of this method was that there was no need to determine neurons from the beginning and the method itself determines the neurons. Brain signals EEG have critical and important applications in different medical fields. So, it is very important to access brain signals with high quality and adequate power.

Meanwhile, the presence of interfering signals artifacts which affect the shape of EEG signal are inevitable. This interfering signal is always on the way of valuable EEG signal; it disrupts the ability to use it optimally. As mentioned under previous sections, artifacts are unwanted disturbances which are mainly derived from inevitable human activities such as heartbeat, blinking and facial muscle activity during the reception of EEG signals.

They can deform waves and create ambiguity on it. Although adaptive linear filtering is widely used in electrical signal processing and filtering, the disturbances and interfering signals, these filters are not suitable for modelling non-linear problems. Given that artifact signals pass through a non-linear path, they are non-linear in nature and the linear adaptive filtering is not appropriate for modelling. Thus, a non-linear adaptive filtering should be used which has a high ability to model artifact signals.

Shahabi et al. In this study, high-pass linear filtering is discussed with an approach to remove some parts of EEG signal. In filtering process, the Fourier transform of signal is calculated to specify its spectrum. Then, the undesired frequency components of signals are removed, and finally by applying the inverse Fourier transform, the filtered signal is obtained in the time domain. Using this method results in the removal of original signal and noise which is not convenient [ 13 ]. Erfanian et al. It is assumed that the EEG data signal has a specific nature, while we know that the noise is a statistical signal with normal distribution.

In this method, the beginning of waveforms is specified and simultaneous averaging is applied. Next, the sum of all signals for example, k signal is calculated.

Digital Circuit Analysis and Design: With an Introduction to Cplds and FPGAs by Steven T. Karris

The result of this process is a signal whose size is k time of the original waveform. Therefore, the estimated signal is achieved by scaling the obtained signal. The disadvantage of this approach is that by increasing the number of signals, the estimations of this method will not have appropriate efficiency [ 17 ]. Chaozhu et al.

Reducing the effect of ocular artifacts by adaptive filtering technique proves better results than linear filtering because data removal in the combined method is less than high-pass linear filtering. However, a part of signal is removed and it is not good to do so according to EEG signal sequence and brain signal interpreting [ 18 , 19 ]. Vertical or horizontal eye movements are the main reasons to create potential differences. Vertical ocular signals occur in two states: vertical ocular signals Veog i. Blinking happens at low frequency and relatively high domain which is detectable due to large domain compared to EEG.

The high-pass filter proposed by researchers is a kind of linear filtering. In this method, the components with cutoff frequency Fc are allowed to pass. In fact, the filter eliminates DC components and components with frequency lower than cutoff frequency. By increasing the order of filter, instability can be seen in filter response output that is not acceptable in EEG signal processing [ 20 ].

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An adaptive filtering has been proposed by Shooshtari et al. In this method, a slight delay has been applied in input that is multiplied by specific coefficients. The error between estimated artifact and reference artifact is calculated per multiplying signal at any moment. When this error reaches an acceptable value, the algorithm is stopped and the estimated artifact is subtracted from the raw signal.

Like other algorithms, the disadvantage of this method is the process of synchronizing to receive EEG data and inappropriate rate of convergence. The process of algorithm will be in trouble by additional delay [ 21 ].

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Lee et al. Fourier analysis is to decompose a signal into sine waves with different frequencies. In a similar manner, wavelet analysis is to decompose a signal into shifted and scaled versions of the original or main wavelet. Wavelet functions are the functions in which, most of their energy is concentrated at a small interval; they are quickly damped. Thus, with an appropriate selection of main wavelet, the compression can be done better. The method presented in this study is the wavelet transform through decomposition of waves into time.

Regard high-speed of EOG signal separation from EEG, the estimation of signal separation is not accompanied by sufficient accuracy [ 23 ]. Kumar et al. Due to the nature of varying time, the signals associated with human body can be very efficient. Wavelet analysis is aimed at separating and isolating the structures with different time scales.