Kerbala Journal for Engineering Sciences

This study looks into the use of an optical phase conjugation (OPC) technique in conjunction
with a Raman amplifier as a means of reducing the impact of fiber nonlinearities in dense wave division
multiplexing (DWDM) transmission systems. The OPC method mutes the input signal by use of a
polarization diversity loop arrangement. Since no spectral inefficiencies are introduced by the
wavelength changes, phase conjugated idlers can be created throughout a broad frequency range. In
nonlinear fibers, idle waves are generated by combining four waves with pump waves that are both out
of band and orthogonally polarized. In order to mitigate the negative consequences of fiber nonlinearity
due to mid-link spectrum inversion or multiple links, the OPC subsystem is used in transmission
experiments spanning 760 km over dispersion-controlled fiber spans with lumped amplification by
Improved Raman amplifiers. Using a 50 GHz channel spacing and eight 232 Gbps polarization division
multiplexed (PDM) sixteen ary quadrature amplitude modulation (DP-16QAM) subchannels, the
simulated results show a Q-factor improvement of up to 3.07 dB in mid OPC compared to 3.39 dB in
multiple OPC without a backward Raman amplifier. Furthermore, compared to the reference scenario
(conventional), BER would improve by about a factor of 10-6 if the OPC module were used. In addition,
(hybrid OPC with backward Raman amplifier) would allow for an average error vector magnitude
(EVM) improvement for the DWDM situation of more than 16%.

The goal of biometrics is to reliably and robustly identify people based on their unique personal
characteristics, primarily for security and authentication needs, but also to identify and track the users of
more intelligent applications. Fingerprints, iris, palm prints, faces, and voices are common biometric
modalities, but there are countless additional biometrics, such as stride, ear image, retina, DNA, and
even behaviour. An automatic way to identify a person depends on just one (single-modal biometrics)
or a mix of (multi-modal biometrics). A fusion of two or more images can create multi-modal biometrics,
and the resulting fused image will be more secure. Various fusion methods are now available and may
be categorised by the degree of information they combine. This paper discusses different fusion
approaches implemented in multi-modal biometrics to identify human biometrics by extracting features
and classifying images. It also describes the datasets that were used and the results and conclusions that
were obtained.