16,672 research outputs found
Pattern recognition algorithm using temporal data
The value of a previously classified image is discussed with the use of spectral and temporal information. A probability theory is presented of a signal X, belonging to class pi sub i
Simulation and performance of an artificial retina for 40 MHz track reconstruction
We present the results of a detailed simulation of the artificial retina
pattern-recognition algorithm, designed to reconstruct events with hundreds of
charged-particle tracks in pixel and silicon detectors at LHCb with LHC
crossing frequency of . Performances of the artificial retina
algorithm are assessed using the official Monte Carlo samples of the LHCb
experiment. We found performances for the retina pattern-recognition algorithm
comparable with the full LHCb reconstruction algorithm.Comment: Final draft of WIT proceedings modified according to JINST referee's
comment
Automatic face alignment by maximizing similarity score
Accurate face registration is of vital importance to the performance of a face recognition algorithm. We propose a face registration method which searches for the optimal alignment by maximizing the score of a face recognition algorithm. In this paper we investigate the practical usability of our face registration method. Experiments show that our registration method achieves better results in face verification than the landmark based registration method. We even obtain face verification results which are similar to results obtained using landmark based registration with manually located eyes, nose and mouth as landmarks. The performance of the method is tested on the FRGCv1 database using images taken under both controlled and uncontrolled conditions
An M-QAM Signal Modulation Recognition Algorithm in AWGN Channel
Computing the distinct features from input data, before the classification,
is a part of complexity to the methods of Automatic Modulation Classification
(AMC) which deals with modulation classification was a pattern recognition
problem. Although the algorithms that focus on MultiLevel Quadrature Amplitude
Modulation (M-QAM) which underneath different channel scenarios was well
detailed. A search of the literature revealed indicates that few studies were
done on the classification of high order M-QAM modulation schemes like128-QAM,
256-QAM, 512-QAM and1024-QAM. This work is focusing on the investigation of the
powerful capability of the natural logarithmic properties and the possibility
of extracting Higher-Order Cumulant's (HOC) features from input data received
raw. The HOC signals were extracted under Additive White Gaussian Noise (AWGN)
channel with four effective parameters which were defined to distinguished the
types of modulation from the set; 4-QAM~1024-QAM. This approach makes the
recognizer more intelligent and improves the success rate of classification.
From simulation results, which was achieved under statistical models for noisy
channels, manifest that recognized algorithm executes was recognizing in M-QAM,
furthermore, most results were promising and showed that the logarithmic
classifier works well over both AWGN and different fading channels, as well as
it can achieve a reliable recognition rate even at a lower signal-to-noise
ratio (less than zero), it can be considered as an Integrated Automatic
Modulation Classification (AMC) system in order to identify high order of M-QAM
signals that applied a unique logarithmic classifier, to represents higher
versatility, hence it has a superior performance via all previous works in
automatic modulation identification systemComment: 18 page
"AVO-Polynom" Recognition Algorithm
Estimates Calculating Algorithms have a long story of application to recognition problems. Furthermore
they have formed a basis for algebraic recognition theory. Yet use of ECA polynomials was limited to theoretical
reasoning because of complexity of their construction and optimization. The new recognition method “AVO-
polynom” based upon ECA polynomial of simple structure is described
- …