7 research outputs found
Spectrum Sensing and Signal Identification with Deep Learning based on Spectral Correlation Function
Spectrum sensing is one of the means of utilizing the scarce source of
wireless spectrum efficiently. In this paper, a convolutional neural network
(CNN) model employing spectral correlation function which is an effective
characterization of cyclostationarity property, is proposed for wireless
spectrum sensing and signal identification. The proposed method classifies
wireless signals without a priori information and it is implemented in two
different settings entitled CASE1 and CASE2. In CASE1, signals are jointly
sensed and classified. In CASE2, sensing and classification are conducted in a
sequential manner. In contrary to the classical spectrum sensing techniques,
the proposed CNN method does not require a statistical decision process and
does not need to know the distinct features of signals beforehand.
Implementation of the method on the measured overthe-air real-world signals in
cellular bands indicates important performance gains when compared to the
signal classifying deep learning networks available in the literature and
against classical sensing methods. Even though the implementation herein is
over cellular signals, the proposed approach can be extended to the detection
and classification of any signal that exhibits cyclostationary features.
Finally, the measurement-based dataset which is utilized to validate the method
is shared for the purposes of reproduction of the results and further research
and development
On the Investigation of Wireless Signal Identification Using Spectral Correlation Function and SVMs
Measurement Based LOS/NLOS Signal Detection for Terahertz Communication: Energy Detector Approach
Determination of probabilities of vacancy transfer from K to L shell using K X-ray intensity ratios
K to L shell vacancy transfer probabilities (ηKL) for
26 elements in the atomic region 23≤Z≤57 were determined by measuring
the IKβ/IKα intensity ratios. The targets were irradiated
with γ-photons at 59.543 keV from 241Am annular source. The K
X-rays from different targets were detected with a high resolution Si(Li)
detector. Theoretical values were calculated using the radiative and
radiationless transition rates of these elements. The measured values of
ηKL are compared with the theoretical values and data of others. The
measurement vacancy transfer probabilities are least-square fitted to
third-order polynomials to obtain analytical relations that represent these
probabilities as a function of atomic number. The measured values of ηKL
for V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se and Br are being reported here for
the first time