مجال
التميز
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تميز دراسي وبحثي
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البحوث المنشورة
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البحث (1):
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عنوان البحث:
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Distinguishing chaos from noise: A new
approach
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رابط إلى البحث:
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تاريخ النشر:
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27 May 2014
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موجز عن البحث:
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Literature suggests that distinguishing
chaos from noise continues to remain a highly contentious issue in the modern
age as it has been historically. This is because chaos and noise share a
common property which in turn makes it indistinguishable. In this paper, we
seek to provide a viable solution to this problem by presenting a novel
approach for the differentiating and identifying chaos from noise. The
proposed approach is one that is based on dynamical systems, embedding
theorem, matrix algebra and statistical theory. To achieve our objective, the
distribution, pattern and behaviour of eigenvalues are analysed in-depth.
This yields several important properties with broad application, enabling the
distinction between chaos and noise in time series analysis. The
applicability of the proposed approach is examined using WTI Spot Price time
series.
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البحث (2):
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عنوان البحث:
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A short note on the pattern of the ingular values of
a scaled random hankel matrix
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رابط إلى البحث:
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تاريخ النشر:
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May 2014
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موجز عن البحث:
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This note considers some of the properties and
studies the distribution of the eigenvalues of the matrix XXT divided by its
trace, where X is a Hankel random matrix. The results make a novel
contribution in the area of signal processing and noise reduction.
KeyWords: Hankel
matrix, eigenvalue, singular value, random process, noise.
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البحث (3):
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عنوان البحث:
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The
Empirical Distribution of the Singular Values of a Random Hankel Matrix
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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September 2015
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موجز عن البحث:
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The empirical distribution of the
eigenvalues of the matrix HHT divided by its trace is considered,
where H is a Hankel random matrix. The normal distribution with
different parameters are considered and the effect of scale and shape parameters
are evaluated. The correlation among eigenvalues are assessed using
parametric and non-parametric association criteria.
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البحث (4):
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عنوان البحث:
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A
study on the empirical distribution of the scaled Hankel matrix eigenvalues
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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November 2015
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موجز عن البحث:
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The empirical distribution of the
eigenvalues of the matrix XXT divided by its trace is evaluated, where X is a
random Hankel matrix. The distribution of eigenvalues for symmetric and
nonsymmetric distributions is assessed with various criteria. This yields
several important properties with broad application, particularly for noise
reduction and filtering in signal processing and time series analysis.
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البحث (5):
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عنوان البحث:
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A new approach for selecting the number of the
eigenvalues in singular spectrum analysis
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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11/11/2015
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موجز عن البحث:
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Singular spectrum analysis (SSA) is a reliable
technique for separating an arbitrary signal from a noisy time series
(signal+noise). The SSA technique is based upon two main selections: window
length, L, and the number of the eigenvalues, r. These values play
an important role for the reconstruction stage. In this paper, we introduce a
new approach for selecting the optimal value of r, which is based on the
distribution of the eigenvalues of a scaled Hankel matrix. The proposed
approach is applied to a number of simulated and real data with different
structures. The results indicate that the proposed approach has potential in
selecting the value of r for signal extraction.
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البحث (6):
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عنوان البحث:
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Noise correction in gene expression data: A new
approach based on subspace method
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رابط إلى البحث:
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NYA
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تاريخ النشر:
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2016
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موجز عن البحث:
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We
present a new approach for removing the nonspecific noise from Drosophila
segmentation genes. The algorithm used for filtering here is an enhanced
version of Singular Spectrum Analysis (SSA) method which decomposes a gene profile
into the sum of a signal and noise. Since the main issue in extracting signal
using SSA procedure lies in identifying the number of eigenvalues needed for
signal reconstruction, this paper seeks to explore the applicability of the
new proposed method for eigenvalues identification in four different gene
expression profiles. Our findings indicate that when extracting signal
from different genes, for optimised signal and noise separation, different
number of eigenvalues need to be chosen for each gene.
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البحث (7):
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عنوان البحث:
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Noise correction in gene expression data: a new
approach based on subspace method
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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08/03/2016
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موجز عن البحث:
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We present a new approach for removing the
nonspecific noise from Drosophila segmentation genes. The algorithm
used for filtering here is an enhanced version of singular spectrum analysis
method, which decomposes a gene profile into the sum of a signal and noise.
Because the main issue in extracting signal using singular spectrum analysis
procedure lies in identifying the number of eigenvalues needed for signal
reconstruction, this paper seeks to explore the applicability of the new
proposed method for eigenvalues identification in four different gene
expression profiles. Our findings indicate that when extracting signal from
different genes, for optimised signal and noise separation, different number
of eigenvalues need to be chosen for each gene.
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البحث (8):
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عنوان البحث:
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A Novel Approach For Noise Removal And Distinction Of EEG Recordings
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رابط إلى البحث:
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Click
here
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تاريخ النشر:
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05/08/2017
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موجز عن البحث:
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This paper presents a novel approach for the analysis of electroencephalography
(EEG) signals. It is based on the distribution of the eigenvalues of a scaled
Hankel matrix, which can enable us to determine the number of eigenvalues
required for noise removal and signal extraction in singular spectrum
analysis. This paper examines the applicability of the approach to
discriminate between epileptic seizure and normal EEG signals, the extraction
of attractive patterns, the filtering of EEG signals and the elimination of
the noise included in the signals. Various criteria are used as features to
distinguish between epileptic and normal EEG segments. The results indicate
the capability of the approach for noise removal in both EEG signals, and for
discrimination between them.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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The 34 International Symposium on
Forecasting Economic Forecasting Past, Present and Future
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تاريخ الإنعقاد:
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Jun 2014
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مكان
الإنعقاد:
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Rotterdam, The Netherlands
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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A New Signal Extraction Method for Singular
Spectrum Analysis
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ملخص المشاركة:
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Structure matrices play an important role
in signal processing and time series analysis. A common occurring structure
is the Hankel form which transfers one dimensional series to
multi-dimensional series. The empirical distribution of the eigenvalues of a scaled
Hankel matrix is considered. This yields several important properties with
broad application, particularly for selecting the optimal value of the number
of eigenvalues with respect to the concept of separability between signal and
noise components in singular spectrum analysis. The distribution of the
eigenvalues and its related forms is proposed as a new approach for
extracting the signal component from a noisy time series. The output from
this research is of importance to the field of time series analysis where
noise reduction and filtering play a pivotal role in determining the accuracy
of forecasts.
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المؤتمر (2):
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عنوان المؤتمر:
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The 33rd International Symposium on
Forecasting: Forecasting with Big Data. Forecasting Past, Present and Future
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تاريخ الإنعقاد:
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Jun 2013
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مكان
الإنعقاد:
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Seoul, Korea
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Analysis and forecasting chaotic time
series with singular spectrum analysis
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ملخص المشاركة:
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The optimal value of the number of
eigenvalues in ingular spectrum analysis is considered for analysing and
forecasting chaotic time series. The concept of eparability between signal
and noise component and the pattern of eigenvalues are used to find the best
approximation of the signal component. The findings are assessed using
several chaotic series namely Hénon, logistic and Tent maps.
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المؤتمر (3):
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عنوان المؤتمر:
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The 6th Annual Postgraduate Research
Conference 2014 Past,
Present and Future
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تاريخ الإنعقاد:
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01/01/2014
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مكان
الإنعقاد:
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Bournemouth
University, UK
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طبيعة المشاركة:
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Poster
Presentation
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عنوان المشاركة:
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A New
Noise Reduction Method for Chaotic Time Series
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ملخص المشاركة:
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It is
well known that noise can seriously reduce the accuracy of time series
analysis and forecasting. There are several methods for filtering noisy
chaotic series; however, it is currently accepted that SVD-based methods are
more effective than the other ones for the reduction of noise. Here, a new
approach is proposed for signal extraction from a noisy series. The proposed
approach is based on eigenvalue pattern extracted from SVD and statistical
technique. The findings are assessed using several simulated and real time
series.
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المؤتمر (4):
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عنوان المؤتمر:
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The 5th Annual Postgraduate Research Conference
2013
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تاريخ الإنعقاد:
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29/04/2013
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مكان
الإنعقاد:
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Bournemouth
University, UK
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طبيعة المشاركة:
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Poster
Presentation
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عنوان المشاركة:
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Chaos and Noise: A new View
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ملخص المشاركة:
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Chaos
and noise have similar properties that make them indistinguishable. Here, we introduce
a viable solution to this problem by presenting a new approach for
distinguishing chaos from noise. The approach is based on embedding theorem,
matrix algebra and statistical theory. To achieve our objective, the
distribution, pattern and behaviour of eigenvalues are analysed
in-depth. This yields several
important properties with broad application, enabling the distinction between
chaos and noise in time series analysis.
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