Abstract: Brain Computer Interface (BCI) has been recently
increased in research. Functional Near Infrared Spectroscope (fNIRs)
is one the latest technologies which utilize light in the near-infrared
range to determine brain activities. Because near infrared technology
allows design of safe, portable, wearable, non-invasive and wireless
qualities monitoring systems, fNIRs monitoring of brain
hemodynamics can be value in helping to understand brain tasks. In
this paper, we present results of fNIRs signal analysis indicating that
there exist distinct patterns of hemodynamic responses which
recognize brain tasks toward developing a BCI. We applied two
different mathematics tools separately, Wavelets analysis for
preprocessing as signal filters and feature extractions and Neural
networks for cognition brain tasks as a classification module. We
also discuss and compare with other methods while our proposals
perform better with an average accuracy of 99.9% for classification.
Abstract: Traditional wind tunnel models are meticulously machined from metal in a process that can take several months. While very precise, the manufacturing process is too slow to assess a new design's feasibility quickly. Rapid prototyping technology makes this concurrent study of air vehicle concepts via computer simulation and in the wind tunnel possible. This paper described the Affects layer thickness models product with rapid prototyping on Aerodynamic Coefficients for Constructed wind tunnel testing models. Three models were evaluated. The first model was a 0.05mm layer thickness and Horizontal plane 0.1μm (Ra) second model was a 0.125mm layer thickness and Horizontal plane 0.22μm (Ra) third model was a 0.15mm layer thickness and Horizontal plane 4.6μm (Ra). These models were fabricated from somos 18420 by a stereolithography (SLA). A wing-body-tail configuration was chosen for the actual study. Testing covered the Mach range of Mach 0.3 to Mach 0.9 at an angle-of-attack range of -2° to +12° at zero sideslip. Coefficients of normal force, axial force, pitching moment, and lift over drag are shown at each of these Mach numbers. Results from this study show that layer thickness does have an effect on the aerodynamic characteristics in general; the data differ between the three models by fewer than 5%. The layer thickness does have more effect on the aerodynamic characteristics when Mach number is decreased and had most effect on the aerodynamic characteristics of axial force and its derivative coefficients.
Abstract: ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.
Abstract: Terrorism represents an unexpected and unwanted change which challenges one-s social identity. We carried out a study to explore the demographic variables- role on the perception of personal and national threat, and to investigate the effects of perceived terrorist threat on people-s ways of life, moods, opinions and hopes. 313 residents of Palermo (Italy) were interviewed. The results pointed out that the fear of terrorism affects three areas: the cognitive, the emotional and the behavioural one.
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: This paper proposes method of diagnosing ball screw
preload loss through the Hilbert-Huang Transform （HHT） and
Multiscale entropy （MSE） process. The proposed method can
diagnose ball screw preload loss through vibration signals when the
machine tool is in operation. Maximum dynamic preload of 2 %, 4 %,
and 6 % ball screws were predesigned, manufactured, and tested
experimentally. Signal patterns are discussed and revealed using
Empirical Mode Decomposition（EMD）with the Hilbert Spectrum.
Different preload features are extracted and discriminated using HHT.
The irregularity development of a ball screw with preload loss is
determined and abstracted using MSE based on complexity
perception. Experiment results show that the proposed method can
predict the status of ball screw preload loss. Smart sensing for the
health of the ball screw is also possible based on a comparative
evaluation of MSE by the signal processing and pattern matching of
EMD/HHT. This diagnosis method realizes the purposes of prognostic
effectiveness on knowing the preload loss and utilizing convenience.
Abstract: The previous researches focused on the influence of
anthropogenic greenhouse gases exerting global warming, but not
consider whether desert sand may warm the planet, this could be
improved by accounting for sand's physical and geometric properties.
Here we show, sand particles (because of their geometry) at the desert
surface form an extended surface of up to 1 + π/4 times the planar area
of the desert that can contact sunlight, and at shallow depths of the
desert form another extended surface of at least 1 + π times the planar
area that can contact air. Based on this feature, an enhanced heat
exchange system between sunlight, desert sand, and air in the spaces
between sand particles could be built up automatically, which can
increase capture of solar energy, leading to rapid heating of the sand
particles, and then the heating of sand particles will dramatically heat
the air between sand particles. The thermodynamics of deserts may
thus have contributed to global warming, especially significant to
future global warming if the current desertification continues to
expand.
Abstract: This paper presents the automated methods employed
for extracting craniofacial landmarks in white light images as part of
a registration framework designed to support three neurosurgical
procedures. The intraoperative space is characterised by white light
stereo imaging while the preoperative plan is performed on CT scans.
The registration aims at aligning these two modalities to provide a
calibrated environment to enable image-guided solutions. The
neurosurgical procedures can then be carried out by mapping the
entry and target points from CT space onto the patient-s space. The
registration basis adopted consists of natural landmarks (eye corner
and ear tragus). A 5mm accuracy is deemed sufficient for these three
procedures and the validity of the selected registration basis in
achieving this accuracy has been assessed by simulation studies. The
registration protocol is briefly described, followed by a presentation
of the automated techniques developed for the extraction of the
craniofacial features and results obtained from tests on the AR and
FERET databases. Since the three targeted neurosurgical procedures
are routinely used for head injury management, the effect of
bruised/swollen faces on the automated algorithms is assessed. A
user-interactive method is proposed to deal with such unpredictable
circumstances.
Abstract: Recognizing human action from videos is an active
field of research in computer vision and pattern recognition. Human
activity recognition has many potential applications such as video
surveillance, human machine interaction, sport videos retrieval and
robot navigation. Actually, local descriptors and bag of visuals words
models achieve state-of-the-art performance for human action
recognition. The main challenge in features description is how to
represent efficiently the local motion information. Most of the
previous works focus on the extension of 2D local descriptors on 3D
ones to describe local information around every interest point. In this
paper, we propose a new spatio-temporal descriptor based on a spacetime
description of moving points. Our description is focused on an
Accordion representation of video which is well-suited to recognize
human action from 2D local descriptors without the need to 3D
extensions. We use the bag of words approach to represent videos.
We quantify 2D local descriptor describing both temporal and spatial
features with a good compromise between computational complexity
and action recognition rates. We have reached impressive results on
publicly available action data set
Abstract: This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.
Abstract: Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
Abstract: Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.
Abstract: Classification of Persian printed numeral characters
has been considered and a proposed system has been introduced. In
representation stage, for the first time in Persian optical character
recognition, extended moment invariants has been utilized as
characters image descriptor. In classification stage, four different
classifiers namely minimum mean distance, nearest neighbor rule,
multi layer perceptron, and fuzzy min-max neural network has been
used, which first and second are traditional nonparametric statistical
classifier. Third is a well-known neural network and forth is a kind of
fuzzy neural network that is based on utilizing hyperbox fuzzy sets.
Set of different experiments has been done and variety of results has
been presented. The results showed that extended moment invariants
are qualified as features to classify Persian printed numeral
characters.
Abstract: It has been shown that a load discontinuity at the end of
an impulse will result in an extra impulse and hence an extra amplitude
distortion if a step-by-step integration method is employed to yield the
shock response. In order to overcome this difficulty, three remedies
are proposed to reduce the extra amplitude distortion. The first remedy
is to solve the momentum equation of motion instead of the force
equation of motion in the step-by-step solution of the shock response,
where an external momentum is used in the solution of the momentum
equation of motion. Since the external momentum is a resultant of the
time integration of external force, the problem of load discontinuity
will automatically disappear. The second remedy is to perform a single
small time step immediately upon termination of the applied impulse
while the other time steps can still be conducted by using the time step
determined from general considerations. This is because that the extra
impulse caused by a load discontinuity at the end of an impulse is
almost linearly proportional to the step size. Finally, the third remedy
is to use the average value of the two different values at the integration
point of the load discontinuity to replace the use of one of them for
loading input. The basic motivation of this remedy originates from the
concept of no loading input error associated with the integration point
of load discontinuity. The feasibility of the three remedies are
analytically explained and numerically illustrated.
Abstract: Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.
Abstract: CO2 miscible displacement is not feasible in many oil fields due to high reservoir temperature as higher pressure is required to achieve miscibility. The miscibility pressure is far higher than the formation fracture pressure making it impossible to have CO2 miscible displacement. However, by using oleophilic chemicals, minimum miscibility pressure (MMP) could be lowered. The main objective of this research is to find the best oleophilic chemical in MMP reduction using slim-tube test and Vanishing Interfacial Tension (VIT) The chemicals are selected based on the characteristics that it must be oil soluble, low water solubility, have 4 – 8 carbons, semi polar, economical, and safe for human operation. The families of chemicals chosen are carboxylic acid, alcohol, and ketone. The whole experiment would be conducted at 100°C and the best chemical is said to be effective when it is able to lower CO2-crude oil MMP the most. Findings of this research would have great impact to the oil and gas industry in reduction of operation cost for CO2EOR which is applicable to both onshore and offshore operation.
Abstract: In this paper, a new face recognition method based on
PCA (principal Component Analysis), LDA (Linear Discriminant
Analysis) and neural networks is proposed. This method consists of
four steps: i) Preprocessing, ii) Dimension reduction using PCA, iii)
feature extraction using LDA and iv) classification using neural
network. Combination of PCA and LDA is used for improving the
capability of LDA when a few samples of images are available and
neural classifier is used to reduce number misclassification caused by
not-linearly separable classes. The proposed method was tested on
Yale face database. Experimental results on this database
demonstrated the effectiveness of the proposed method for face
recognition with less misclassification in comparison with previous
methods.
Abstract: Camera calibration is an important step in 3D
reconstruction. Camera calibration may be classified into two major types: traditional calibration and self-calibration. However, a calibration method in using a checkerboard is intermediate between traditional calibration and self-calibration. A self
is proposed based on a square in this paper. Only a square in the planar
template, the camera self-calibration can be completed through the single view. The proposed algorithm is that the virtual circle and straight line are established by a square on planar template, and
circular points, vanishing points in straight lines and the relation
between them are be used, in order to obtain the image of the absolute
conic (IAC) and establish the camera intrinsic parameters. To make
the calibration template is simpler, as compared with the Zhang Zhengyou-s method. Through real experiments and experiments, the experimental results show that this algorithm is
feasible and available, and has a certain precision and robustness.
Abstract: Massive use of places with strong tourist attraction
with the consequent possibility of losing place-identity produces
harmful effects on cities and their users. In order to mitigate this risk,
areas close to such places can be identified so as to widen the
visitor-s range of action and offer alternative activities integrated
with the main site. The cultural places and appropriate activities can
be identified using a method of analysis and design able to trace the
identity of the places, their characteristics and potential, and to
provide a sustainable improvement. The aim of this work is to
propose PlaceMaker as a method of urban analysis and design which
both detects elements that do not feature in traditional mapping and
which constitute the contemporary identity of the places, and
identifies appropriate project interventions. Two final complex maps
– the first of analysis and the second of design – respectively
represent the identity of places and project interventions. In order to
illustrate the method-s potential; the results of the experimentation
carried out in the Trevi-Pantheon route in Rome and the appropriate
interventions to decongest the area are illustrated.
Abstract: Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.