Abstract: Electrocardiogram (ECG) is considered to be the
backbone of cardiology. ECG is composed of P, QRS & T waves and
information related to cardiac diseases can be extracted from the
intervals and amplitudes of these waves. The first step in extracting
ECG features starts from the accurate detection of R peaks in the
QRS complex. We have developed a robust R wave detector using
wavelets. The wavelets used for detection are Daubechies and
Symmetric. The method does not require any preprocessing therefore,
only needs the ECG correct recordings while implementing the
detection. The database has been collected from MIT-BIH arrhythmia
database and the signals from Lead-II have been analyzed. MatLab
7.0 has been used to develop the algorithm. The ECG signal under
test has been decomposed to the required level using the selected
wavelet and the selection of detail coefficient d4 has been done based
on energy, frequency and cross-correlation analysis of decomposition
structure of ECG signal. The robustness of the method is apparent
from the obtained results.
Abstract: This paper presents an algorithm for the recognition
and tracking of moving objects, 1/10 scale model car is used to verify
performance of the algorithm. Presented algorithm for the recognition
and tracking of moving objects in the paper is as follows. SURF
algorithm is merged with Lucas-Kanade algorithm. SURF algorithm
has strong performance on contrast, size, rotation changes and it
recognizes objects but it is slow due to many computational
complexities. Processing speed of Lucas-Kanade algorithm is fast but
the recognition of objects is impossible. Its optical flow compares the
previous and current frames so that can track the movement of a pixel.
The fusion algorithm is created in order to solve problems which
occurred using the Kalman Filter to estimate the position and the
accumulated error compensation algorithm was implemented. Kalman
filter is used to create presented algorithm to complement problems
that is occurred when fusion two algorithms. Kalman filter is used to
estimate next location, compensate for the accumulated error. The
resolution of the camera (Vision Sensor) is fixed to be 640x480. To
verify the performance of the fusion algorithm, test is compared to
SURF algorithm under three situations, driving straight, curve, and
recognizing cars behind the obstacles. Situation similar to the actual is
possible using a model vehicle. Proposed fusion algorithm showed
superior performance and accuracy than the existing object
recognition and tracking algorithms. We will improve the performance
of the algorithm, so that you can experiment with the images of the
actual road environment.
Abstract: This paper presents a new method of analog fault diagnosis based on back-propagation neural networks (BPNNs) using wavelet decomposition and fractal dimension as preprocessors. The proposed method has the capability to detect and identify faulty components in an analog electronic circuit with tolerance by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically de-noises the inputs to the neural network. The second preprocessing by fractal dimension can extract unique features, which are the fed to a neural network as inputs for further classification. A comparison of our work with [1] and [6], which also employs back-propagation (BP) neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.
Abstract: Information on weed distribution within the field is necessary to implement spatially variable herbicide application. Since hand labor is costly, an automated weed control system could be feasible. This paper deals with the development of an algorithm for real time specific weed recognition system based on Histogram Maxima with threshold of an image that is used for the weed classification. This algorithm is specifically developed to classify images into broad and narrow class for real-time selective herbicide application. The developed system has been tested on weeds in the lab, which have shown that the system to be very effectiveness in weed identification. Further the results show a very reliable performance on images of weeds taken under varying field conditions. The analysis of the results shows over 95 percent classification accuracy over 140 sample images (broad and narrow) with 70 samples from each category of weeds.
Abstract: The importance of ensuring safe meat handling and
processing practices has been demonstrated in global reports on food
safety scares and related illness and deaths. This necessitated stricter
meat safety control strategies. Today, many countries have regulated
towards preventative and systematic control over safe meat
processing at abattoirs utilizing the Hazard Analysis Critical Control
Point (HACCP) principles. HACCP systems have been reported as
effective in managing food safety risks, if correctly implemented.
South Africa has regulated the Hygiene Management System (HMS)
based on HACCP principles applicable to abattoirs. Regulators utilise
the Hygiene Assessment System (HAS) to audit compliance at
abattoirs. These systems were benchmarked from the United
Kingdom (UK). Little research has been done them since inception as
of 2004. This paper presents a review of the two systems, its
implementation and comparison with HACCP. Recommendations are
made for future research to demonstrate the utility of the HMS and
HAS in assuring safe meat to consumers.
Abstract: In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection by ignorance of the background music as much as possible. Finally, we apply dual dynamic programming algorithm for feature similarity matching. Experiments will show us its online performance in precision and efficiency.
Abstract: This paper presents the design related to the
electronic system design of the respiratory signal, including phases
for processing, followed by the transmission and reception of this
signal and finally display. The processing of this signal is added to
the ECG and temperature sign, put up last year. Under this scheme is
proposed that in future also be conditioned blood pressure signal
under the same final printed circuit and worked.
Abstract: This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.
Abstract: Deformable active contours are widely used in
computer vision and image processing applications for image
segmentation, especially in biomedical image analysis. The active
contour or “snake" deforms towards a target object by controlling the
internal, image and constraint forces. However, if the contour
initialized with a lesser number of control points, there is a high
probability of surpassing the sharp corners of the object during
deformation of the contour. In this paper, a new technique is
proposed to construct the initial contour by incorporating prior
knowledge of significant corners of the object detected using the
Harris operator. This new reconstructed contour begins to deform, by
attracting the snake towards the targeted object, without missing the
corners. Experimental results with several synthetic images show the
ability of the new technique to deal with sharp corners with a high
accuracy than traditional methods.
Abstract: The fundamental defect inherent to the thermoforming
technology is wall-thickness variation of the products due to
inadequate thermal processing during production of polymer. A
nonlinear viscoelastic rheological model is implemented for
developing the process model. This model describes deformation
process of a sheet in thermoforming process. Because of relaxation
pause after plug-assist stage and also implementation of two stage
thermoforming process have minor wall-thickness variation and
consequently better mechanical properties of polymeric articles. For
model validation, a comparative analysis of the theoretical and
experimental data is presented.
Abstract: In this paper, a new technique for fast painting with
different colors is presented. The idea of painting relies on applying
masks with different colors to the background. Fast painting is
achieved by applying these masks in the frequency domain instead of
spatial (time) domain. New colors can be generated automatically as a
result from the cross correlation operation. This idea was applied
successfully for faster specific data (face, object, pattern, and code)
detection using neural algorithms. Here, instead of performing cross
correlation between the input input data (e.g., image, or a stream of
sequential data) and the weights of neural networks, the cross
correlation is performed between the colored masks and the
background. Furthermore, this approach is developed to reduce the
computation steps required by the painting operation. The principle of
divide and conquer strategy is applied through background
decomposition. Each background is divided into small in size subbackgrounds
and then each sub-background is processed separately by
using a single faster painting algorithm. Moreover, the fastest painting
is achieved by using parallel processing techniques to paint the
resulting sub-backgrounds using the same number of faster painting
algorithms. In contrast to using only faster painting algorithm, the
speed up ratio is increased with the size of the background when using
faster painting algorithm and background decomposition. Simulation
results show that painting in the frequency domain is faster than that in
the spatial domain.
Abstract: A finite element analysis (FEA) computer software HyperWorks is utilized in re-designing an automotive component to reduce its mass. Reduction of components mass contributes towards environmental sustainability by saving world-s valuable metal resources and by reducing carbon emission through improved overall vehicle fuel efficiency. A shape optimization analysis was performed on a rear spindle component. Pre-processing and solving procedures were performed using HyperMesh and RADIOSS respectively. Shape variables were defined using HyperMorph. Then optimization solver OptiStruct was utilized with fatigue life set as a design constraint. Since Stress-Number of Cycle (S-N) theory deals with uni-axial stress, the Signed von Misses stress on the component was used for looking up damage on S-N curve, and Gerber criterion for mean stress corrections. The optimization analysis resulted in mass reduction of 24% of the original mass. The study proved that the adopted approach has high potential use for environmental sustainability.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear particle
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear particle. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: Integration of system process information obtained
through an image processing system with an evolving knowledge
database to improve the accuracy and predictability of wear debris
analysis is the main focus of the paper. The objective is to automate
intelligently the analysis process of wear particle using classification
via self-organizing maps. This is achieved using relationship
measurements among corresponding attributes of various
measurements for wear debris. Finally, visualization technique is
proposed that helps the viewer in understanding and utilizing these
relationships that enable accurate diagnostics.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.
Abstract: In the last few years, three multivariate spectral
analysis techniques namely, Principal Component Analysis (PCA),
Independent Component Analysis (ICA) and Non-negative Matrix
Factorization (NMF) have emerged as effective tools for oscillation
detection and isolation. While the first method is used in determining
the number of oscillatory sources, the latter two methods
are used to identify source signatures by formulating the detection
problem as a source identification problem in the spectral domain.
In this paper, we present a critical drawback of the underlying linear
(mixing) model which strongly limits the ability of the associated
source separation methods to determine the number of sources
and/or identify the physical source signatures. It is shown that the
assumed mixing model is only valid if each unit of the process gives
equal weighting (all-pass filter) to all oscillatory components in its
inputs. This is in contrast to the fact that each unit, in general, acts
as a filter with non-uniform frequency response. Thus, the model
can only facilitate correct identification of a source with a single
frequency component, which is again unrealistic. To overcome
this deficiency, an iterative post-processing algorithm that correctly
identifies the physical source(s) is developed. An additional issue
with the existing methods is that they lack a procedure to pre-screen
non-oscillatory/noisy measurements which obscure the identification
of oscillatory sources. In this regard, a pre-screening procedure
is prescribed based on the notion of sparseness index to eliminate
the noisy and non-oscillatory measurements from the data set used
for analysis.
Abstract: In this paper, the experimental design of using the
Taguchi method is employed to optimize the processing parameters in
the plasma arc surface hardening process. The processing parameters
evaluated are arc current, scanning velocity and carbon content of
steel. In addition, other significant effects such as the relation between
processing parameters are also investigated. An orthogonal array,
signal-to-noise (S/N) ratio and analysis of variance (ANOVA) are
employed to investigate the effects of these processing parameters.
Through this study, not only the hardened depth increased and surface
roughness improved, but also the parameters that significantly affect
the hardening performance are identified. Experimental results are
provided to verify the effectiveness of this approach.
Abstract: The NGN (Next Generation Network), which can
provide advanced multimedia services over an all-IP based network, has been the subject of much attention for years. While there have
been tremendous efforts to develop its architecture and protocols, especially for IMS, which is a key technology of the NGN, it is far
from being widely deployed. However, efforts to create an advanced
signaling infrastructure realizing many requirements have resulted in a
large number of functional components and interactions between those
components. Thus, the carriers are trying to explore effective ways to
deploy IMS while offering value-added services. As one such
approach, we have proposed a self-organizing IMS. A self-organizing
IMS enables IMS functional components and corresponding physical
nodes to adapt dynamically and automatically based on situation such
as network load and available system resources while continuing IMS
operation. To realize this, service continuity for users is an important
requirement when a reconfiguration occurs during operation. In this
paper, we propose a mechanism that will provide service continuity to
users and focus on the implementation and describe performance
evaluation in terms of number of control signaling and processing time
during reconfiguration