Abstract: Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.
Abstract: People have the habitual pitch level which is used when people say something generally. However this pitch should be changed irregularly in the presence of noise. So it is useful to estimate SNR of speech signal by pitch. In this paper, we obtain the energy of input speech signal and then we detect a stationary region on voiced speech. And we get the pitch period by NAMDF for the stationary region that is not varied pitch rapidly. After getting pitch, each frame is divided by pitch period and the likelihood of closed pitch is estimated. In this paper, we proposed new parameter, NLF, to estimate the SNR of received speech signal. The NLF is derived from the correlation of near pitch periods. The NLF is obtained for each stationary region in voiced speech. Finally we confirmed good performance of the estimation of the SNR of received input speech in the presence of noise.
Abstract: This paper presents an innovative computer system
that contributes to sustainable development of the enterprise. The
research refers to a rethinking of traditional systems of collaboration
and risk assessment, present in any organization, leading to a
sustainable enterprise. This concept integrates emerging tools that
allow the implementation and exploitation of the collective
intelligence of the enterprise, allowing the exchange of contextual,
agile and simplified information, and collaboration with networks of
customers and partners in an environment where risks are controlled.
Risk assessment is done in a systemic way: the enterprise as the
system compared to the contained departments and the enterprise as a
subsystem compared to: families of international standards and
sustainability-s responsibilities. The enterprise, in this systemic
vision, responds to the requirements that any existing system to
operate continuously in an indefinite future without reaching key
resource depletion. The research is done by integrating collaborative
science, engineering, management, psychology, obtaining thus a
cornerstone of sustainable development of the enterprise.
Abstract: A welded structure must be inspected to guarantee that the weld quality meets the design requirements to assure safety and reliability. However, X-ray image analyses and defect recognition with the computer vision techniques are very complex. Most difficulties lie in finding the small, irregular defects in poor contrast images which requires pre processing to image, extract, and classify features from strong background noise. This paper addresses the issue of designing methodology to extract defect from noisy background radiograph with image processing. Based on the use of actives contours this methodology seems to give good results
Abstract: This paper presents the 20-GHz fractional PLL (Phase
Locked Loop) circuit for the next generation Wi-Fi by using 90 nm
TSMC process. The newly suggested millimeter wave 16/17
pre-scalar is designed and verified by measurement to make the
fractional PLL having a low quantization noise. The operational
bandwidth of the 60 GHz system is 15 % of the carrier frequency
which requires large value of Kv (VCO control gain) resulting in
degradation of phase noise. To solve this problem, this paper adopts
AFC (Automatic Frequency Controller) controlled 4-bit millimeter
wave VCO with small value of Kv. Also constant Kv is implemented
using 4-bit varactor bank. The measured operational bandwidth is 18.2
~ 23.2 GHz which is 25 % of the carrier frequency. The phase noise of
-58 and -96.2 dBc/Hz at 100 KHz and 1 MHz offset is measured
respectively. The total power consumption of the PLL is only 30 mW.
Abstract: In this paper we propose a novel method for human
face segmentation using the elliptical structure of the human head. It
makes use of the information present in the edge map of the image.
In this approach we use the fact that the eigenvalues of covariance
matrix represent the elliptical structure. The large and small
eigenvalues of covariance matrix are associated with major and
minor axial lengths of an ellipse. The other elliptical parameters are
used to identify the centre and orientation of the face. Since an
Elliptical Hough Transform requires 5D Hough Space, the Circular
Hough Transform (CHT) is used to evaluate the elliptical parameters.
Sparse matrix technique is used to perform CHT, as it squeeze zero
elements, and have only a small number of non-zero elements,
thereby having an advantage of less storage space and computational
time. Neighborhood suppression scheme is used to identify the valid
Hough peaks. The accurate position of the circumference pixels for
occluded and distorted ellipses is identified using Bresenham-s
Raster Scan Algorithm which uses the geometrical symmetry
properties. This method does not require the evaluation of tangents
for curvature contours, which are very sensitive to noise. The method
has been evaluated on several images with different face orientations.
Abstract: Business Process Management (BPM) helps in optimizing the business processes inside an enterprise. But BPM architecture does not provide any help for extending the enterprise. Modern business environments and rapidly changing technologies are asking for brisk changes in the business processes. Service Oriented Architecture (SOA) can help in enabling the success of enterprise-wide BPM. SOA supports agility in software development that is directly related to achieve loose coupling of interacting software agents. Agility is a premium concern of the current software designing architectures. Together, BPM and SOA provide a perfect combination for enterprise computing. SOA provides the capabilities for services to be combined together and to support and create an agile, flexible enterprise. But there are still many questions to answer; BPM is better or SOA? and what is the future track of BPM and SOA? This paper tries to answer some of these important questions.
Abstract: Polynomial maps offer analytical properties used to obtain better performances in the scope of chaos synchronization under noisy channels. This paper presents a new method to simplify equations of the Exact Polynomial Kalman Filter (ExPKF) given in [1]. This faster algorithm is compared to other estimators showing that performances of all considered observers vanish rapidly with the channel noise making application of chaos synchronization intractable. Simulation of ExPKF shows that saturation drawn on the emitter to keep it stable impacts badly performances for low channel noise. Then we propose a particle filter that outperforms all other Kalman structured observers in the case of noisy channels.
Abstract: The ideal sinc filter, ignoring the noise statistics, is often
applied for generating an arbitrary sample of a bandlimited signal by
using the uniformly sampled data. In this article, an optimal interpolator is proposed; it reaches a minimum mean square error (MMSE)
at its output in the presence of noise. The resulting interpolator is
thus a Wiener filter, and both the optimal infinite impulse response
(IIR) and finite impulse response (FIR) filters are presented. The
mean square errors (MSE-s) for the interpolator of different length
impulse responses are obtained by computer simulations; it shows that
the MSE-s of the proposed interpolators with a reasonable length are
improved about 0.4 dB under flat power spectra in noisy environment with signal-to-noise power ratio (SNR) equal 10 dB. As expected,
the results also demonstrate the improvements for the MSE-s with various fractional delays of the optimal interpolator against the ideal
sinc filter under a fixed length impulse response.
Abstract: Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.
Abstract: A new fast correlation algorithm for calibrating the
wavelength of Optical Spectrum Analyzers (OSAs) was introduced
in [1]. The minima of acetylene gas spectra were measured and
correlated with saved theoretical data [2]. So it is possible to find the
correct wavelength calibration data using a noisy reference spectrum.
First tests showed good algorithmic performance for gas line spectra
with high noise. In this article extensive performance tests were made
to validate the noise resistance of this algorithm. The filter and
correlation parameters of the algorithm were optimized for improved
noise performance. With these parameters the performance of this
wavelength calibration was simulated to predict the resulting
wavelength error in real OSA systems. Long term simulations were
made to evaluate the performance of the algorithm over the lifetime
of a real OSA.
Abstract: Speckled images arise when coherent microwave,
optical, and acoustic imaging techniques are used to image an object, surface or scene. Examples of coherent imaging systems include synthetic aperture radar, laser imaging systems, imaging sonar
systems, and medical ultrasound systems. Speckle noise is a form of object or target induced noise that results when the surface of the object is Rayleigh rough compared to the wavelength of the illuminating radiation. Detection and estimation in images corrupted
by speckle noise is complicated by the nature of the noise and is not
as straightforward as detection and estimation in additive noise. In
this work, we derive stochastic models for speckle noise, with an emphasis on speckle as it arises in medical ultrasound images. The
motivation for this work is the problem of segmentation and tissue classification using ultrasound imaging. Modeling of speckle in this
context involves partially developed speckle model where an underlying Poisson point process modulates a Gram-Charlier series
of Laguerre weighted exponential functions, resulting in a doubly
stochastic filtered Poisson point process. The statistical distribution of partially developed speckle is derived in a closed canonical form.
It is observed that as the mean number of scatterers in a resolution cell is increased, the probability density function approaches an
exponential distribution. This is consistent with fully developed speckle noise as demonstrated by the Central Limit theorem.
Abstract: The method described in this paper deals with the problems of T-wave detection in an ECG. Determining the position of a T-wave is complicated due to the low amplitude, the ambiguous and changing form of the complex. A wavelet transform approach handles these complications therefore a method based on this concept was developed. In this way we developed a detection method that is able to detect T-waves with a sensitivity of 93% and a correct-detection ratio of 93% even with a serious amount of baseline drift and noise.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: In this study, noise characteristics of structure were analyzed in an effort to reduce noise passing through an opening of an
enclosure surrounding the structure that generates noise. Enclosures
are essential measure to protect noise propagation from operating machinery. Access openings of the enclosures are important path of noise leakage. First, noise characteristics of structure were analyzed
and feed-forward noise control was performed using simulation in
order to reduce noise passing through the opening of enclosure, which
surrounds a structure generating noise. We then implemented a
feed-forward controller to actively control the acoustic power through
the opening. Finally, we conducted optimization of placement of the
reference sensors for several cases of the number of sensors. Good
control performances were achieved using the minimum number of microphones arranged an optimal placement.
Abstract: In this work, we present a comparison between
different techniques of image compression. First, the image is
divided in blocks which are organized according to a certain scan.
Later, several compression techniques are applied, combined or
alone. Such techniques are: wavelets (Haar's basis), Karhunen-Loève
Transform, etc. Simulations show that the combined versions are the
best, with minor Mean Squared Error (MSE), and higher Peak Signal
to Noise Ratio (PSNR) and better image quality, even in the presence
of noise.
Abstract: Whit the increasing of traffic, noise emanated from
motor vehicles increases as well, which subsequently causes adding
to the stress of modern city. Thus, it is needed to look for most
critical areas in terms of environmental and social impact of noise.
There are several critical situations for noise emanated from motor
vehicles such as stop and go situation which usually occurs near
junctions or at-grade intersections. This study was conducted in two
locations, most common types of intersections, crossroads and Tjunctions.
The highest average noise levels are recorded during Go
phase for T-junction, 64.4 dB, and Drive phase for crossroad, 64 dB.
It implies that the existence of intersection caused the noise level to
increase. The vehicles starting to move produce more sound than
when they travel at a constant speed along the intersection. It is
suggested that special considerations and priority of allocating funds
should be given to these critical spots.
Abstract: Oil debris signal generated from the inductive oil
debris monitor (ODM) is useful information for machine condition
monitoring but is often spoiled by background noise. To improve the
reliability in machine condition monitoring, the high-fidelity signal
has to be recovered from the noisy raw data. Considering that the noise
components with large amplitude often have higher frequency than
that of the oil debris signal, the integral transform is proposed to
enhance the detectability of the oil debris signal. To cancel out the
baseline wander resulting from the integral transform, the empirical
mode decomposition (EMD) method is employed to identify the trend
components. An optimal reconstruction strategy including both
de-trending and de-noising is presented to detect the oil debris signal
with less distortion. The proposed approach is applied to detect the oil
debris signal in the raw data collected from an experimental setup. The
result demonstrates that this approach is able to detect the weak oil
debris signal with acceptable distortion from noisy raw data.
Abstract: Noise causes significant sensibility changes on a human. This study investigated the effect of five different noises on electroencephalogram (EEG) and subjective evaluation. Six human subjects were exposed to classic piano, ocean wave, alarm in army, ambulance, mosquito noise and EEG data were collected during the experimental session. Alpha band activity in the mosquito noise was smaller than that in the classic piano. Alpha band activity decreased 43.4 ± 8.2 % in the mosquito noise. On the other hand, Beta band activity in the mosquito noise was greater than that in the classic piano. Beta band activity increased 60.1 ± 10.7 % in the mosquito noise. The advances from this study may aid the product design process with human sensibility engineering. This result may provide useful information in designing a human-oriented product to avoid the stress.
Abstract: In Orthogonal Frequency Division Multiplexing (OFDM) systems, the peak to average power ratio (PAR) is much high. The clipping signal scheme is a useful method to reduce PAR. Clipping the OFDM signal, however, increases the overall noise level by introducing clipping noise. It is necessary to recover the figure of the original signal at receiver in order to reduce the clipping noise. Considering the continuity of the signal and the figure of the peak, we obtain a certain conic function curve to replace the clipped signal module within the clipping time. The results of simulation show that the proposed scheme can reduce the systems? BER (bit-error rate) 10 times when signal-to-interference-and noise-ratio (SINR) equals to 12dB. And the BER performance of the proposed scheme is superior to that of kim's scheme, too.