Abstract: Voltage level must be raised in order to deliver the
produced energy to the consumption zones with less loss and less
cost. Power transformers used to raise or lower voltage are important
parts of the energy transmission system. Power transformers used in
switchgear and power generation plants stay in human's intensive
habitat zones as a result of expanding cities. Accordingly, noise
levels produced by power transformers have begun more and more
important and they have established itself as one of the research field.
In this research, the noise cause on transformers has been
investigated, it's causes has been examined and noise measurement
techniques have been introduced. Examples of transformer noise test
results are submitted and precautions to be taken were discussed for
the purpose of decreasing of the noise which will occurred by
transformers.
Abstract: This paper investigates the characteristics of wall
pressure fluctuations in naturally developing boundary layer flows
on axisymmetric bodies experimentally. The axisymmetric body has
a modified ellipsoidal blunt nose. Flush-mounted microphones are
used to measure the wall pressure fluctuations in the boundary layer
flow over the body. The measurements are performed in a low noise
wind tunnel. It is found that the correlation between the flow regime
and the characteristics of the pressure fluctuations is distinct. The
process from small fluctuation in laminar flow to large fluctuation in
turbulent flow is investigated. Tollmien-Schlichting wave (T-S wave)
is found to generate and develop in transition. Because of the T-S
wave, the wall pressure fluctuations in the transition region are higher
than those in the turbulent boundary layer.
Abstract: Fluctuations of Schottky diode parameters in a
structure of the mixer are investigated. These fluctuations are
manifested in two ways. At the first, they lead to fluctuations in the
transfer factor that is lead to the amplitude fluctuations in the signal
of intermediate frequency. On the basis of the measurement data of
1/f noise of the diode at forward current, the estimation of a spectrum
of relative fluctuations in transfer factor of the mixer is executed.
Current dependence of the spectrum of relative fluctuations in
transfer factor of the mixer and dependence of the spectrum of
relative fluctuations in transfer factor of the mixer on the amplitude
of the heterodyne signal are investigated. At the second, fluctuations
in parameters of the diode lead to occurrence of 1/f noise in the
output signal of the mixer. This noise limits the sensitivity of the
mixer to the value of received signal.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: The Adaptive Line Enhancer (ALE) is widely used for
enhancing narrowband signals corrupted by broadband noise. In this
paper, we propose novel ALE methods to improve the enhancing
capability. The proposed methods are motivated by the fact that the
output of the ALE is a fine estimate of the desired narrowband signal
with the broadband noise component suppressed. The proposed
methods preprocess the input signal using ALE filter to regenerate a
finer input signal. Thus the proposed ALE is driven by the input signal
with higher signal-to-noise ratio (SNR). The analysis and simulation
results are presented to demonstrate that the proposed ALE has better
performance than conventional ALE’s.
Abstract: One of the most important challenging factors in
medical images is nominated as noise. Image denoising refers to the
improvement of a digital medical image that has been infected by
Additive White Gaussian Noise (AWGN). The digital medical image
or video can be affected by different types of noises. They are
impulse noise, Poisson noise and AWGN. Computed tomography
(CT) images are subjects to low quality due to the noise. Quality of
CT images is dependent on absorbed dose to patients directly in such
a way that increase in absorbed radiation, consequently absorbed
dose to patients (ADP), enhances the CT images quality. In this
manner, noise reduction techniques on purpose of images quality
enhancement exposing no excess radiation to patients is one the
challenging problems for CT images processing. In this work, noise
reduction in CT images was performed using two different
directional 2 dimensional (2D) transformations; i.e., Curvelet and
Contourlet and Discrete Wavelet Transform (DWT) thresholding
methods of BayesShrink and AdaptShrink, compared to each other
and we proposed a new threshold in wavelet domain for not only
noise reduction but also edge retaining, consequently the proposed
method retains the modified coefficients significantly that result good
visual quality. Data evaluations were accomplished by using two
criterions; namely, peak signal to noise ratio (PSNR) and Structure
similarity (Ssim).
Abstract: Structure-borne noise is an important aspect of
offshore platform sound field. It can be generated either directly by
vibrating machineries induced mechanical force, indirectly by the
excitation of structure or excitation by incident airborne noise.
Therefore, limiting of the transmission of vibration energy
throughout the offshore platform is the key to control the structureborne
noise. This is usually done by introducing damping treatment
to the steel structures. Two types of damping treatment using onboard
are presented. By conducting a Statistical Energy Analysis
(SEA) simulation on a jack-up rig, the noise level in the source room,
the neighboring rooms, and remote living quarter cabins are
compared before and after the damping treatments been applied. The
results demonstrated that, in the source neighboring room and living
quarter area, there is a significant noise reduction with the damping
treatment applied, whereas in the source room where air-borne sound
predominates that of structure-borne sound, the impact is not
obvious. The conclusion on effective damping treatment in the
offshore platform is made which enable acoustic professionals to
implement noise control during the design stage for offshore crews’
hearing protection and habitant comfortability.
Abstract: Multiple User Interference (MUI) considers the
primary problem in Optical Code-Division Multiple Access
(OCDMA), which resulting from the overlapping among the users. In
this article we aim to mitigate this problem by studying an
interference cancellation scheme called successive interference
cancellation (SIC) scheme. This scheme will be tested on two
different detection schemes, spectral amplitude coding (SAC) and
direct detection systems (DS), using partial modified prime (PMP) as
the signature codes. It was found that SIC scheme based on both SAC
and DS methods had a potential to suppress the intensity noise, that is
to say it can mitigate MUI noise. Furthermore, SIC/DS scheme
showed much lower bit error rate (BER) performance relative to
SIC/SAC scheme for different magnitude of effective power. Hence,
many more users can be supported by SIC/DS receiver system.
Abstract: STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to real-world data
Abstract: The Orthogonal Frequency Division Multiplexing
(OFDM) with high data rate, high spectral efficiency and its ability to
mitigate the effects of multipath makes them most suitable in wireless
application. Impulsive noise distorts the OFDM transmission and
therefore methods must be investigated to suppress this noise. In this
paper, a State Space Recursive Least Square (SSRLS) algorithm
based adaptive impulsive noise suppressor for OFDM
communication system is proposed. And a comparison with another
adaptive algorithm is conducted. The state space model-dependent
recursive parameters of proposed scheme enables to achieve steady
state mean squared error (MSE), low bit error rate (BER), and faster
convergence than that of some of existing algorithm.
Abstract: In more complex systems, such as automotive
gearbox, a rigorous treatment of the data is necessary because there
are several moving parts (gears, bearings, shafts, etc.), and in this
way, there are several possible sources of errors and also noise. The
basic objective of this work is the detection of damage in automotive
gearbox. The detection methods used are the wavelet method, the
bispectrum; advanced filtering techniques (selective filtering) of
vibrational signals and mathematical morphology. Gearbox vibration
tests were performed (gearboxes in good condition and with defects)
of a production line of a large vehicle assembler. The vibration
signals are obtained using five accelerometers in different positions
of the sample. The results obtained using the kurtosis, bispectrum,
wavelet and mathematical morphology showed that it is possible to
identify the existence of defects in automotive gearboxes.
Abstract: A cyclostationary Gaussian linearization method is
formulated for investigating the time average response of nonlinear
system under sinusoidal signal and white noise excitation. The
quantitative measure of cyclostationary mean, variance, spectrum of
mean amplitude, and mean power spectral density of noise are
analyzed. The qualitative response behavior of stochastic jump and
bifurcation are investigated. The validity of the present approach in
predicting the quantitative and qualitative statistical responses is
supported by utilizing Monte Carlo simulations. The present analysis
without imposing restrictive analytical conditions can be directly
derived by solving non-linear algebraic equations. The analytical
solution gives reliable quantitative and qualitative prediction of mean
and noise response for the Duffing system subjected to both sinusoidal
signal and white noise excitation.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Mechanical stress has a strong effect on the magnitude
of the Barkhausen-noise in structural steels. Because the
measurements are performed at the surface of the material, for a
sample sheet, the full effect can be described by a biaxial stress field.
The measured Barkhausen-noise is dependent on the orientation of
the exciting magnetic field relative to the axis of the stress tensor.
The sample inhomogenities including the residual stress also
modifies the angular dependence of the measured Barkhausen-noise.
We have developed a laboratory device with a cross like specimen
for bi-axial bending. The measuring head allowed performing
excitations in two orthogonal directions. We could excite the two
directions independently or simultaneously with different amplitudes.
The simultaneous excitation of the two coils could be performed in
phase or with a 90 degree phase shift. In principle this allows to
measure the Barkhausen-noise at an arbitrary direction without
moving the head, or to measure the Barkhausen-noise induced by a
rotating magnetic field if a linear superposition of the two fields can
be assumed.
Abstract: The air transport impact on environment is more than
ever a limitative obstacle to the aeronautical industry continuous
growth. Over the last decades, considerable effort has been carried
out in order to obtain quieter aircraft solutions, whether by changing
the original design or investigating more silent maneuvers. The
noise propagated by rotating surfaces is one of the most important
sources of annoyance, being present in most aerial vehicles. Bearing
this is mind, CEIIA developed a new computational chain for
noise prediction with in-house software tools to obtain solutions in
relatively short time without using excessive computer resources. This
work is based on the new acoustic tool, which aims to predict the
rotor noise generated during steady and maneuvering flight, making
use of the flexibility of the C language and the advantages of GPU
programming in terms of velocity. The acoustic tool is based in the
Formulation 1A of Farassat, capable of predicting two important
types of noise: the loading and thickness noise. The present work
describes the most important features of the acoustic tool, presenting
its most relevant results and framework analyses for helicopters and
UAV quadrotors.
Abstract: Near-infrared spectroscopy (NIRS) has been widely
used as a non-invasive method to measure brain activity, but it is
corrupted by baseline drift noise. Here we present a method to measure
regional cerebral blood flow as a derivative of NIRS output. We
investigate whether, when listening to languages, blood flow can
reasonably localize and represent regional brain activity or not. The
prefrontal blood flow distribution pattern when advanced
second-language listeners listened to a second language (L2) was most
similar to that when listening to their first language (L1) among the
patterns of mean and standard deviation. In experiments with 25
healthy subjects, the maximum blood flow was localized to the left
BA46 of advanced listeners. The blood flow presented is robust to
baseline drift and stably localizes regional brain activity.
Abstract: We present an analytical model for the calculation of
the sensitivity, the spectral current noise and the detective parameter
for an optically illuminated In0.53Ga0.47As n+nn+ diode. The
photocurrent due to the excess carrier is obtained by solving the
continuity equation. Moreover, the current noise level is evaluated at
room temperature and under a constant voltage applied between the
diode terminals. The analytical calculation of the current noise in the
n+nn+ structure is developed by considering the free carries
fluctuations. The responsivity and the detection parameter are
discussed as functions of the doping concentrations and the emitter
layer thickness in one-dimensional homogeneous n+nn+ structure.
Abstract: In this paper, Least Mean Square (LMS) adaptive
noise reduction algorithm is proposed to enhance the speech signal
from the noisy speech. In this, the speech signal is enhanced by
varying the step size as the function of the input signal. Objective and
subjective measures are made under various noises for the proposed
and existing algorithms. From the experimental results, it is seen that
the proposed LMS adaptive noise reduction algorithm reduces Mean
square Error (MSE) and Log Spectral Distance (LSD) as compared to
that of the earlier methods under various noise conditions with
different input SNR levels. In addition, the proposed algorithm
increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR
improvement (ΔSNRseg) values; improves the Mean Opinion Score
(MOS) as compared to that of the various existing LMS adaptive
noise reduction algorithms. From these experimental results, it is
observed that the proposed LMS adaptive noise reduction algorithm
reduces the speech distortion and residual noise as compared to that
of the existing methods.
Abstract: Images are important source of information used as
evidence during any investigation process. Their clarity and accuracy
is essential and of the utmost importance for any investigation.
Images are vulnerable to losing blocks and having noise added to
them either after alteration or when the image was taken initially,
therefore, having a high performance image processing system and it
is implementation is very important in a forensic point of view. This
paper focuses on improving the quality of the forensic images.
For different reasons packets that store data can be affected,
harmed or even lost because of noise. For example, sending the
image through a wireless channel can cause loss of bits. These types
of errors might give difficulties generally for the visual display
quality of the forensic images.
Two of the images problems: noise and losing blocks are covered.
However, information which gets transmitted through any way of
communication may suffer alteration from its original state or even
lose important data due to the channel noise. Therefore, a developed
system is introduced to improve the quality and clarity of the forensic
images.
Abstract: The 1/f noise investigation in nanoscale light-emitting
diodes and lasers, based on GaAs and alloys, is presented here.
Leakage and additional (to recombination through quantum wells
and/or dots) nonlinear currents were detected and it was shown that
these currents are the main source of the 1/f noise in devices studied.