Example #1
0
    def __init__(self,  fft_size):
        """
        CTOR.
        @param fft_size FFT Size. WARNING: must be of the size of the pattern used by the WaveformDecision algorithm.
        @param algorithm A ThresholdAlgorithm. 
        """

        self._detector = WaveformDetectorGambi(fft_size=fft_size)

        # ::TRICKY:: Calls abstract _build method
        UHDSSArch.__init__(self,
                           name="WaveformSSArch",
                           input_signature=gr.io_signature(1, 1, gr.sizeof_gr_complex),
                           output_signature=gr.io_signature(1, 1, gr.sizeof_float*fft_size),
                           )
    def __init__(self, Np, P, L):
        """
        CTOR
        @param Np
        @param P
        @param L
        """

        self.Np = Np
        self.P  = P
        self.L  = L

        UHDSSArch.__init__(self,
                           name="CycloSSArch",
                           input_signature=gr.io_signature(1, 1, gr.sizeof_gr_complex),
                           output_signature=gr.io_signature(1, 1, gr.sizeof_gr_complex * Np * P)
                           )
Example #3
0
    def __init__(self, fft_size, mavg_size):
        """
        CTOR
        @param fft_size FFT Size.
        @param mavg_size Moving average array size.
        @param algorithm A ThresholdAlgorithm instance.
        """

        self._detector = EnergyDetectorGambi(
                fft_size = fft_size,
                mavg_size = mavg_size,
        )

        # ::TRICKY:: Calls abstract _build method
        UHDSSArch.__init__(self,
                           name="EnergySSArch",
                           input_signature=gr.io_signature(1, 1, gr.sizeof_gr_complex),
                           output_signature=gr.io_signature(1, 1, gr.sizeof_float * 1024),
                           )
Example #4
0
    def __init__(self, max_items_group):
        """
        CTOR.
        @param device     RadioDevice instance.
        @param detector   A SS detector. 
        @param max_items_group Number of ouputs to group.
        @return A tuple with 3 elements: (final decision, energy, avg energy)
        """

        #
        self._detector = SimpleRankingDetector(512, 5, 0.3)

        # Group items
        self._grouper = GroupInN(max_items_group=max_items_group, callback=None, n_inputs=2)

        UHDSSArch.__init__(
            self,
            name="ranking_arch",
            input_signature=gr.io_signature(1, 1, gr.sizeof_gr_complex),
            output_signature=gr.io_signature(0, 0, 0),
        )