1)Ramp
% Repeating Ramp Function in MATLAB
% Define time vector
t = linspace(0, 20, 1000); % Time from 0 to 20, smooth resolution
% Define ramp period and slope
T = 5; % Period of the ramp (in seconds)
slope = 1; % Slope of the ramp
% Create repeating ramp waveform using modulus
ramp = slope * mod(t, T);
% Plot the repeating ramp function
figure;
plot(t, ramp, 'r', 'LineWidth', 2);
grid on;
xlabel('Time (t)');
ylabel('Ramp(t)');
title('Repeating (Periodic) Ramp Function');
legend(['Ramp with period T = ' num2str(T)], 'Location', 'northwest');
2) DSBSC
fs = 100000; % Higher sampling frequency for better resolution
t = 0:1/fs:0.05; % Shorter time window to see detail
fm = 100; % Message frequency (Hz)
fc = 2000; % Carrier frequency (Hz)
Am = 1; % Message amplitude
Ac = 1; % Carrier amplitude
% Message Signal
m = Am * sin(2*pi*fm*t);
% Carrier Signal
c = Ac * cos(2*pi*fc*t);
% DSB-SC Modulation
dsbsc = m .* c;
% Coherent Demodulation
demod = dsbsc .* c;
% Design Low-Pass Filter (cutoff just above fm)
cutoff = 2 * fm / fs;
[b, a] = butter(6, cutoff);
% Filter the demodulated signal
recovered = filter(b, a, demod);
% Normalize amplitude to match original message
recovered = recovered * (Am / max(abs(recovered)));
% Plotting
figure;
subplot(4,1,1);
plot(t, m);
title('Original Message Signal');
xlabel('Time (s)'); ylabel('Amplitude');
subplot(4,1,2);
plot(t, dsbsc);
title('DSB-SC Modulated Signal');
xlabel('Time (s)'); ylabel('Amplitude');
subplot(4,1,3);
plot(t, demod);
title('Demodulated Signal (Before LPF)');
xlabel('Time (s)'); ylabel('Amplitude');
subplot(4,1,4);
plot(t, recovered);
title('Recovered Signal (After LPF, Amplitude Normalized)');
xlabel('Time (s)'); ylabel('Amplitude');
2)SSB
% Parameters
fs = 100000; % High sampling frequency
t = 0:1/fs:0.05; % Short time window for clarity
fm = 100; % Message frequency (Hz)
fc = 2000; % Carrier frequency (Hz)
Am = 1; % Message amplitude
Ac = 1; % Carrier amplitude
% Message Signal
m = Am * sin(2*pi*fm*t);
% Generate analytic signal using Hilbert transform
m_analytic = hilbert(m);
% SSB Modulation (USB)
ssb = real(m_analytic .* exp(1j*2*pi*fc*t));
% Coherent Demodulation
carrier = cos(2*pi*fc*t); % Coherent carrier
demod = ssb .* 2 .* carrier; % Multiply by 2*cos(2*pi*fc*t)
% Low-pass filter
cutoff = 2 * fm / fs; % Normalized cutoff frequency
[b, a] = butter(6, cutoff); % 6th-order Butterworth filter
recovered = filter(b, a, demod);
% Normalize recovered signal to match amplitude
recovered = recovered * (Am / max(abs(recovered)));
% Plotting
figure;
subplot(4,1,1);
plot(t, m, 'b');
title('Original Message Signal');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
subplot(4,1,2);
plot(t, ssb, 'r');
title('SSB Modulated Signal (Upper Sideband)');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
subplot(4,1,3);
plot(t, demod, 'g');
title('Demodulated Signal (Before LPF)');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
subplot(4,1,4);
plot(t, recovered, 'k');
title('Recovered Signal (After LPF, Amplitude Normalized)');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
3)VSB
% VSB Modulation and Demodulation Example
fs = 10000; % Sampling frequency
t = 0:1/fs:0.5-1/fs; % Time vector
fm = 100; % Message frequency
fc = 1000; % Carrier frequency
% Message signal (message modulated at fm)
m = cos(2*pi*fm*t);
% Carrier signal (carrier modulated at fc)
c = cos(2*pi*fc*t);
% DSB-SC Modulated signal (simple multiplication of message and carrier)
dsb_sc = m .* c;
% VSB filter (simulated as a low-pass filter — simplified model)
[b, a] = butter(6, 0.2); % Butterworth low-pass filter as vestigial sideband
filter
vsb_signal = filter(b, a, dsb_sc);
% Coherent Demodulation (multiply by the carrier signal again)
demod = vsb_signal .* c; % Multiply again with carrier
% Low-pass filter to recover the baseband signal (message)
recovered = filter(b, a, demod);
% Normalize recovered signal to match the original message amplitude
recovered = recovered * (max(abs(m)) / max(abs(recovered)));
% Plotting the results
figure;
subplot(4,1,1);
plot(t, m, 'b');
title('Original Message Signal');
xlabel('Time (s)');
ylabel('Amplitude');
grid on;
subplot(4,1,2);
plot(t, dsb_sc, 'k');
title('DSB-SC Modulated Signal');
xlabel('Time (s)');
ylabel('Amplitude');
grid on;
subplot(4,1,3);
plot(t, vsb_signal, 'r');
title('VSB Modulated Signal (Filtered DSB-SC)');
xlabel('Time (s)');
ylabel('Amplitude');
grid on;
subplot(4,1,4);
plot(t, recovered, 'g');
title('Recovered Message Signal (After Demodulation & LPF)');
xlabel('Time (s)');
ylabel('Amplitude');
grid on;
4)FM
% Narrowband and Wideband FM Spectrum Analysis in MATLAB
% Parameters
Fs = 10000; % Sampling frequency (Hz)
t = 0:1/Fs:0.1; % Time vector
fc = 1000; % Carrier frequency (Hz)
kf_narrow = 2*pi*10; % Frequency sensitivity for Narrowband FM (small deviation)
kf_wide = 2*pi*100; % Frequency sensitivity for Wideband FM (large deviation)
% Message signal (sine wave)
fm = 100; % Message frequency (Hz)
m = cos(2*pi*fm*t); % Message signal
% Narrowband FM Modulation (small frequency deviation)
int_m_narrow = cumsum(m) / Fs;
narrow_fm_signal = cos(2*pi*fc*t + kf_narrow * int_m_narrow);
% Wideband FM Modulation (large frequency deviation)
int_m_wide = cumsum(m) / Fs;
wide_fm_signal = cos(2*pi*fc*t + kf_wide * int_m_wide);
% Spectrum analysis using FFT for both NBFM and WBFM
N = length(narrow_fm_signal); % Length of the signal
f_axis = Fs*(0:N-1)/N; % Frequency axis for the spectrum
% Narrowband FM Spectrum
narrow_fm_fft = abs(fft(narrow_fm_signal));
% Wideband FM Spectrum
wide_fm_fft = abs(fft(wide_fm_signal));
% Plotting the spectra
figure;
% Narrowband FM Spectrum Plot
subplot(2,1,1);
plot(f_axis(1:N/2), narrow_fm_fft(1:N/2)); % Plot first half of the spectrum
title('Narrowband FM Spectrum');
xlabel('Frequency (Hz)'); ylabel('Magnitude');
xlim([0 Fs/2]);
% Wideband FM Spectrum Plot
subplot(2,1,2);
plot(f_axis(1:N/2), wide_fm_fft(1:N/2)); % Plot first half of the spectrum
title('Wideband FM Spectrum');
xlabel('Frequency (Hz)'); ylabel('Magnitude');
xlim([0 Fs/2]);
5) PM
% PM with Demodulation in MATLAB
% Parameters
Fs = 10000; % Sampling frequency (Hz)
t = 0:1/Fs:0.1; % Time vector (0 to 0.1s)
fc = 1000; % Carrier frequency (Hz)
kf = pi; % Phase sensitivity
% Message signal (simple sine wave)
fm = 100; % Message frequency (Hz)
m = cos(2*pi*fm*t); % Message signal
% Phase Modulation
pm = cos(2*pi*fc*t + kf * m); % PM signal
% PM Demodulation (Differentiation and Envelope Detection)
x_diff = diff([0 pm]) * Fs; % Differentiate the PM signal
x_env = abs(hilbert(x_diff)); % Envelope detection using Hilbert transform
% Low-pass filter to recover original signal
lpFilt = designfilt('lowpassfir', 'PassbandFrequency', 200, ...
'StopbandFrequency', 300, 'SampleRate', Fs);
demod = filter(lpFilt, x_env);
% Normalize demodulated signal to range -1 to 1
demod_norm = 2 * (demod - min(demod)) / (max(demod) - min(demod)) - 1;
% Plot results
figure;
% Plot original message signal
subplot(4,1,1);
plot(t, m);
title('Message Signal');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
% Plot PM modulated signal
subplot(4,1,2);
plot(t, pm);
title('PM Signal');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
% Plot demodulated signal (before low-pass filter)
subplot(4,1,3);
plot(t(2:end), x_env(2:end)); % Skip first element due to diff
title('Demodulated Signal (Envelope Detection)');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
% Plot demodulated signal (after low-pass filter)
subplot(4,1,4);
plot(t, demod_norm);
title('Recovered Message Signal (After LPF and Normalization)');
xlabel('Time (s)'); ylabel('Amplitude');
grid on;
6) ADC
clc; clear;
% Signal and Sampling
fs = 1000; % Sampling frequency
t = 0:1/fs:0.02; % Time vector
x = 2 * sin(2*pi*50*t); % Original analog signal
% Quantization setup
n_bits = 3; % Number of bits
L = 2^n_bits; % Levels
xmax = max(abs(x)); % Max amplitude
delta = 2 * xmax / L; % Step size
% ---- Mid-Tread Quantization ----
q_midtread = delta * round(x / delta); % Round to nearest step
% Binary Encoding for Mid-Tread
index_midtread = round((q_midtread + xmax) / delta);
binary_midtread = dec2bin(index_midtread, n_bits);
% ---- Mid-Rise Quantization ----
q_midrise = delta * (floor(x / delta) + 0.5); % Mid-rise
% Binary Encoding for Mid-Rise
index_midrise = floor((q_midrise + xmax) / delta);
binary_midrise = dec2bin(index_midrise, n_bits);
% ---- Demodulation / Reconstruction (Zero-order hold) ----
% Demodulation (Reconstruction of the quantized signal)
demod_midtread = q_midtread; % Reconstructed Mid-Tread
demod_midrise = q_midrise; % Reconstructed Mid-Rise
% ---- Normalize the Demodulated Signal to Match the Original Signal ----
demod_midtread = (demod_midtread / max(abs(demod_midtread))) * max(abs(x)); %
Normalize to original amplitude
demod_midrise = (demod_midrise / max(abs(demod_midrise))) * max(abs(x)); %
Normalize to original amplitude
% ---- Plot Results ----
figure;
% Original Signal Plot
subplot(4,1,1);
plot(t, x, 'k');
title('Original Analog Signal');
xlabel('Time');
ylabel('Amplitude');
% Mid-Tread Quantized Signal Plot
subplot(4,1,2);
stairs(t, q_midtread, 'b');
title('Mid-Tread Quantized Signal');
xlabel('Time');
ylabel('Amplitude');
% Mid-Rise Quantized Signal Plot
subplot(4,1,3);
stairs(t, q_midrise, 'r');
title('Mid-Rise Quantized Signal');
xlabel('Time');
ylabel('Amplitude');
% Mid-Tread Demodulated (Reconstructed) Signal Plot
subplot(4,1,4);
stairs(t, demod_midtread, 'g');
title('Mid-Tread Demodulated (Reconstructed) Signal');
xlabel('Time');
ylabel('Amplitude');
% ---- Show Sample Binary Codes ----
disp('First 5 binary codes (Mid-Tread):');
disp(binary_midtread(1:5,:));
disp('First 5 binary codes (Mid-Rise):');
disp(binary_midrise(1:5,:));
7) line code
clc; clear;
% --- USER INPUT ---
binary_str = input('Enter binary sequence (e.g. 1011001): ', 's');
data = binary_str - '0'; % Convert string to numeric array
% --- Parameters ---
bit_duration = 1;
fs = 100; % Samples per bit
t = 0:1/fs:bit_duration - 1/fs;
total_time = [];
% Time vector for full signal
for i = 1:length(data)
total_time = [total_time, t + (i - 1)*bit_duration];
end
% Initialize signals
unipolar_nrz = [];
bipolar_nrz = [];
bipolar_rz = [];
manchester = [];
on_off = [];
last_polarity = -1;
% --- Encoding ---
for i = 1:length(data)
bit = data(i);
% Unipolar NRZ
unipolar_nrz = [unipolar_nrz, bit * ones(1, fs)];
% Bipolar NRZ
if bit == 1
last_polarity = -last_polarity;
bipolar_nrz = [bipolar_nrz, last_polarity * ones(1, fs)];
else
bipolar_nrz = [bipolar_nrz, zeros(1, fs)];
end
% Bipolar RZ
if bit == 1
last_polarity = +last_polarity;
bipolar_rz = [bipolar_rz, last_polarity * ones(1, fs/2), zeros(1, fs/2)];
else
bipolar_rz = [bipolar_rz, zeros(1, fs)];
end
% Manchester
if bit == 1
manchester = [manchester, ones(1, fs/2), -1*ones(1, fs/2)];
else
manchester = [manchester, -1*ones(1, fs/2), ones(1, fs/2)];
end
% On-Off Keying
on_off = [on_off, bit * ones(1, fs)];
end
% --- Plotting ---
figure('Name', 'Line Coding Schemes', 'NumberTitle', 'off');
subplot(5,1,1);
plot(total_time, unipolar_nrz, 'LineWidth', 1.5);
title('Unipolar NRZ'); ylim([-1.5 1.5]); grid on;
subplot(5,1,2);
plot(total_time, bipolar_nrz, 'LineWidth', 1.5);
title('Bipolar NRZ'); ylim([-1.5 1.5]); grid on;
subplot(5,1,3);
plot(total_time, bipolar_rz, 'LineWidth', 1.5);
title('Bipolar RZ'); ylim([-1.5 1.5]); grid on;
subplot(5,1,4);
plot(total_time, manchester, 'LineWidth', 1.5);
title('Manchester'); ylim([-1.5 1.5]); grid on;
subplot(5,1,5);
plot(total_time, on_off, 'LineWidth', 1.5);
title('On-Off Keying'); ylim([-0.5 1.5]); grid on;
xlabel('Time');
% --- Display Input ---
disp(['Binary Input: ', binary_str]);
8) audio signal
clc;
clear;
% Parameters for real-time spectrum analysis
fs = 8000; % Sampling frequency
n_fft = 512; % FFT length (number of frequency bins)
window_size = 256; % Window size (number of samples per frame)
overlap = 128; % Overlap between adjacent frames
n_frames = 100; % Number of frames to process (adjust based on real-time requirement)
% Initialize audio input (microphone)
audio_device = audioread('your_audio_file.wav'); % Replace this with microphone recording function
% Compute Short-Time Fourier Transform (STFT)
figure;
subplot(2,1,1);
specgram(audio_device, n_fft, fs, hamming(window_size), overlap);
title('Real-Time Speech Spectrum');
xlabel('Time (s)');
ylabel('Frequency (Hz)');
% Real-time plotting setup
subplot(2,1,2);
hold on;
xlabel('Time (s)');
ylabel('Amplitude');
axis([0, 100, -1, 1]);
title('Real-Time Speech Signal');
% Main real-time processing loop
for i = 1:n_frames
% Capture audio frame (here you can replace this with live microphone input)
audio_frame = audio_device(i : i + window_size - 1);
% Compute FFT of the frame
fft_frame = fft(audio_frame, n_fft);
freq = (0:n_fft-1) * (fs / n_fft); % Frequency axis
% Plot the spectrum (magnitude)
subplot(2,1,1);
plot(freq, abs(fft_frame(1:n_fft/2))); % Plot the magnitude spectrum
xlim([0, 4000]); % Focus on lower frequencies (for speech)
xlabel('Frequency (Hz)');
ylabel('Magnitude');
title('Real-Time Spectrum Analysis');
% Update time-domain signal plot
subplot(2,1,2);
plot(i * (1 / fs) + (0:window_size-1) / fs, audio_frame, 'k');
drawnow; % Update plots
end
9)Sinc
% Repeating Sinc Function in MATLAB
% Parameters
T = 5; % Period of repetition
numPeriods = 4; % Number of periods
t_single = linspace(-2.5, 2.5, 250); % Time for one sinc pulse
% Create one sinc pulse
sinc_pulse = sinc(t_single); % Built-in MATLAB sinc = sin(pi*x)/(pi*x)
% Generate full time vector for repeated sinc
t = [];
sinc_repeat = [];
% Repeat sinc at intervals of T
for k = 0:numPeriods-1
t = [t, t_single + k*T]; % Shift time for each period
sinc_repeat = [sinc_repeat, sinc_pulse]; % Append pulse
end
% Plot the repeated sinc waveform
figure;
plot(t, sinc_repeat, 'm', 'LineWidth', 2);
grid on;
xlabel('Time (t)');
ylabel('Amplitude');
title('Repeated Sinc Waveform');
legend('Repeated sinc(t)', 'Location', 'northeast');
