Polyphase sampling rate converter
This example shows the application of the FIR Rate Conversion block.
This block performs an efficient polyphase
sampling rate conversion using
using a rational coefficient
L/M along the first dimension.
The block treats each column of the input
each column of the input signal as a separate channel and oversamples the
the data in them independently of each other.
Now let's look at the model itself, which was developed
for this example. It generates a complex signal
to which a polyphase sampling frequency transform is applied.
Auxiliary functions
# Подключение вспомогательной функции запуска модели.
function run_model( name_model)
Path = (@__DIR__) * "/" * name_model * ".engee"
if name_model in [m.name for m in engee.get_all_models()] # Проверка условия загрузки модели в ядро
model = engee.open( name_model ) # Открыть модель
model_output = engee.run( model, verbose=true ); # Запустить модель
else
model = engee.load( Path, force=true ) # Загрузить модель
model_output = engee.run( model, verbose=true ); # Запустить модель
engee.close( name_model, force=true ); # Закрыть модель
end
sleep(5)
return model_output
end
using FFTW
# Расчёт спектра сигнала
function compute_spectrum(signal, fs)
n = length(signal)
spectrum = abs.(fft(signal)) / n
freqs = (0:n-1) .* (fs / n)
spectrum[1:Int(n/2)], freqs[1:Int(n/2)] # Вернуть половину спектра (для удобства)
end
Running the model and analysing the calculation
In this example, the filter coefficients will be taken from the MAT file pre-recorded for this model.
using MAT
# Чтение данных из .mat файла
file = matopen("$(@__DIR__)/Hm.mat")
var_names = names(file)
print("$var_names")
for var_name in var_names
value = read(file, var_name)# Получаем значение переменной из файла
@eval $(Symbol(var_name)) = $value # Динамическое создание переменной с именем var_name
end
# Закрытие файла
close(file)
run_model("Rate_Conversion") # Запуск модели.
Now let's compare input and output data.
inp = collect(simout["dat2CDfix/inp"])
sim_time = vcat([m[] for m in inp.time]...) # Извлекаем значения из матриц
inp = vcat([m[] for m in inp.value]...) # Извлекаем значения из матриц
out = collect(simout["dat2CDfix/out"])
out = vcat([vec(m2) for m2 in out.value]...) # Преобразуем каждую матрицу в вектор
println("Кол-во входных залогированных данных: $(length(inp))")
print("Кол-во выходных залогированных данных: $(length(out))")
As we can see, the output has twice as many logged values
than the input. This indicates that interpolation, the process of increasing the sampling rate of the signal.
interpolation - the process of increasing the sampling rate of a signal
by adding new samples between the existing ones.
gr()
A = plot(real(inp[1:1000]), imag(inp[1:1000]), seriestype=:scatter, legend=false,
xlabel="Re", ylabel="Im", title="Вход")
B = plot(real(out[1:1000]), imag(out[1:1000]), seriestype=:scatter, legend=false,
xlabel="Re", ylabel="Im", title="Выход")
plot(A,B)
From the results of the data visualisation, it can be seen
that the amplitude of the distribution of values at the output is significantly
different from the values at the input.
Now let's see the results of spectral comparison of input and output.
spectrum_inp, freqs_inp = compute_spectrum(inp[1:4000], 1000)
spectrum_out, freqs_out = compute_spectrum(out[1:4000], 1000)
plot(
plot(freqs_inp, spectrum_inp, xlabel="Frequency (Hz)", ylabel="Amplitude", title="Вход", label=""),
plot(freqs_out, spectrum_out, xlabel="Frequency (Hz)", ylabel="Amplitude", title="Выход", label="")
)
This comparison shows that the spectrum of the signal is significantly
distorted, and the useful signal after conversion, based on the
the results of the spectral analysis, is lost.
Conclusion
In this demonstration we have looked at the polyphase converter
sampling rate converter and its potential applications for changing the sampling rate of a signal.
sampling rate of a signal. This option will be very useful for your projects.