Part A: Laboratory coursework The main purpose of the assignment is to demonstrate understanding and critical evaluation of a practical digital signal processing (DSP) system. The assignment evaluates the critical understanding of analogue-to-digital conversion (ADC) and digital-to-analogue conversion (DAC) processes, distortion in a real system and techniques to reduce the noise. The aim of the course is to simulate a real-time digital signal processing system in Matlab environment, demonstrate understanding of a digital signal processing and critically evaluate the different blocks.ADC Ix[n] y[n] DSPFigure 1: A real-time digital signal processing system The assignment consists of following components:Party(t) DACAI)ii)Assi nment Details Consider a DSP system that requires enhancing a signal that is corrupted by noise. The block diagram of the DSP system is represented by Figure 1, where x(t) is the analogue input signal and y(t) is the analogue output signal. Assuming the analogue input signal has a frequency range of 0-2 kHz and the maximum peak-to-peak voltage of ±0.5V, perform the following task.Write a Matlab program to represent the discrete time signal x[n] using a sampling frequency of 40 kHz.Assuming 4-bit, 8-bit and 16-bit ADC, demonstrate the quantisation errors due to bit resolutions. Display histogram of quantisation noises and calculate quantisation signal-to-noise ratio. Based on the quantisation noises and the system complexity, comment on the ADC you would practically implement.Demonstrate the aliasing effect by sampling the input signal at the sampling rates of 3, 20 and 40 kHz and displaying results in appropriate time or frequency domain.Marks5%10%10%ii)iv)v)Add specified noises to the discrete time signal and analyse the combined signal in time and frequency domain. Assume the sampling frequency of 40 kHz ad 8-bit ADC. (Noise file will be provided and individually assigned based on student number.)Based on the analysis in (B, i), create a filter specification in order to recover the original signal from the corrupted signal. Justify your selection. Design a suitable linear phase finite impulse response (FIR) filter to that meets the specification in (B, ii). Use filter design and analysis (FDA) toolbox in Matlab and export the filter coefficients to workspace. Analyse the frequency response of the filter (created in B, iii) by creating a m-file. Plot amplitude and phase responses. Verify the amplitude response of the filter by inputting signals with varying frequency sinusoidal signals to the filter and measuring the amplitude responses. Does the amplitude response match with the response obtained using FDATOOI in (B, iii)?Apply the filter you have designed in B(iv) to the signal corrupted by noise. Investigate the effect of noise to the original signal and effectiveness of the10%5%5%10%15%Scanned by CamScanner

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