Abstract: Space exploration is a highly visible endeavour of
humankind to seek profound answers to questions about the origins
of our solar system, whether life exists beyond Earth, and how we
could live on other worlds. Different platforms have been utilized in
planetary exploration missions, such as orbiters, landers, rovers, and
penetrators.
Having low mass, good mechanical contact with the surface,
ability to acquire high quality scientific subsurface data, and ability to
be deployed in areas that may not be conducive to landers or rovers,
Penetrators provide an alternative and complimentary solution that
makes possible scientific exploration of hardly accessible sites (icy
areas, gully sites, highlands etc.).
The Canadian Space Agency (CSA) has put space exploration as
one of the pillars of its space program, and established ExCo program
to prepare Canada for future international planetary exploration.
ExCo sets surface mobility as its focus and priority, and invests
mainly in the development of rovers because of Canada's niche space
robotics technology. Meanwhile, CSA is also investigating how
micro-penetrators can help Canada to fulfill its scientific objectives
for planetary exploration.
This paper presents a review of the micro-penetrator technologies,
past missions, and lessons learned. It gives a detailed analysis of the
technical challenges of micro-penetrators, such as high impact
survivability, high precision guidance navigation and control, thermal
protection, communications, and etc. Then, a Canadian perspective of
a possible micro-penetrator mission is given, including Canadian
scientific objectives and priorities, potential instruments, and flight
opportunities.
Abstract: This paper describes new computer vision algorithms
that have been developed to track moving objects as part of a
long-term study into the design of (semi-)autonomous vehicles. We
present the results of a study to exploit variable kernels for tracking in
video sequences. The basis of our work is the mean shift
object-tracking algorithm; for a moving target, it is usual to define a
rectangular target window in an initial frame, and then process the data
within that window to separate the tracked object from the background
by the mean shift segmentation algorithm. Rather than use the
standard, Epanechnikov kernel, we have used a kernel weighted by the
Chamfer distance transform to improve the accuracy of target
representation and localization, minimising the distance between the
two distributions in RGB color space using the Bhattacharyya
coefficient. Experimental results show the improved tracking
capability and versatility of the algorithm in comparison with results
using the standard kernel. These algorithms are incorporated as part of
a robot test-bed architecture which has been used to demonstrate their
effectiveness.
Abstract: Model-based approaches have been applied successfully
to a wide range of tasks such as specification, simulation, testing, and
diagnosis. But one bottleneck often prevents the introduction of these
ideas: Manual modeling is a non-trivial, time-consuming task.
Automatically deriving models by observing and analyzing running
systems is one possible way to amend this bottleneck. To
derive a model automatically, some a-priori knowledge about the
model structure–i.e. about the system–must exist. Such a model
formalism would be used as follows: (i) By observing the network
traffic, a model of the long-term system behavior could be generated
automatically, (ii) Test vectors can be generated from the model,
(iii) While the system is running, the model could be used to diagnose
non-normal system behavior.
The main contribution of this paper is the introduction of a model
formalism called 'probabilistic regression automaton' suitable for the
tasks mentioned above.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: This paper explores the knowledge and attitude of
women and men in decision making on pap smear screening. This
qualitative study recruited 52 respondents with 44 women and 8 men,
using the purposive sampling with snowballing technique through indepth
interviews. This study demonstrates several key findings:
Female respondents have better knowledge compared to male. Most
of the women perceived that pap smear screening is beneficial and
important, but to proceed with the test is still doubtful. Male
respondents were supportive in terms of sending their spouses to the
health facilities or give more freedom to their wives to choose and
making decision on their own health due to prominent reason that
women know best on their own health. It is expected that the results
from this study will provide useful guideline for healthcare providers
to prepare any action/intervention to provide an extensive education
to improve people-s knowledge and attitude towards pap smear.
Abstract: Support vector machines (SVMs) have shown
superior performance compared to other machine learning techniques,
especially in classification problems. Yet one limitation of SVMs is
the lack of an explanation capability which is crucial in some
applications, e.g. in the medical and security domains. In this paper, a
novel approach for eclectic rule-extraction from support vector
machines is presented. This approach utilizes the knowledge acquired
by the SVM and represented in its support vectors as well as the
parameters associated with them. The approach includes three stages;
training, propositional rule-extraction and rule quality evaluation.
Results from four different experiments have demonstrated the value
of the approach for extracting comprehensible rules of high accuracy
and fidelity.
Abstract: A generalized Digital Modulation Identification algorithm for adaptive demodulator has been developed and presented in this paper. The algorithm developed is verified using wavelet Transform and histogram computation to identify QPSK and QAM with GMSK and M–ary FSK modulations. It has been found that the histogram peaks simplifies the procedure for identification. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB and 12 dB for GMSK and QPSK respectively. When SNR is above 5 dB the throughput of the proposed algorithm is more than 97.8%. The receiver operating characteristics (ROC) has been computed to measure the performance of the proposed algorithm and the analysis shows that the probability of detection (Pd) drops rapidly when SNR is 5 dB and probability of false alarm (Pf) is smaller than 0.3. The performance of the proposed algorithm has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.
Abstract: An integrated Artificial Neural Network- Particle Swarm Optimization (PSO) is presented for analyzing global electricity consumption. To aim this purpose, following steps are done: STEP 1: in the first step, PSO is applied in order to determine world-s oil, natural gas, coal and primary energy demand equations based on socio-economic indicators. World-s population, Gross domestic product (GDP), oil trade movement and natural gas trade movement are used as socio-economic indicators in this study. For each socio-economic indicator, a feed-forward back propagation artificial neural network is trained and projected for future time domain. STEP 2: in the second step, global electricity consumption is projected based on the oil, natural gas, coal and primary energy consumption using PSO. global electricity consumption is forecasted up to year 2040.
Abstract: Isobaric vapor-liquid equilibrium measurements are
reported for the binary mixture of Methyl acetate and
Isopropylbenzene at 97.3 kPa. The measurements have been
performed using a vapor recirculating type (modified Othmer's)
equilibrium still. The mixture shows positive deviation from ideality
and does not form an azeotrope. The activity coefficients have been
calculated taking into consideration the vapor phase nonideality. The
data satisfy the thermodynamic consistency tests of Herington and
Black. The activity coefficients have been satisfactorily correlated by
means of the Margules, NRTL, and Black equations. A comparison
of the values of activity coefficients obtained by experimental data
with the UNIFAC model has been made.
Abstract: In this paper we illuminate a frequency domain based
classification method for video scenes. Videos from certain topical
areas often contain activities with repeating movements. Sports
videos, home improvement videos, or videos showing mechanical
motion are some example areas. Assessing main and side frequencies
of each repeating movement gives rise to the motion type. We
obtain the frequency domain by transforming spatio-temporal motion
trajectories. Further on we explain how to compute frequency features
for video clips and how to use them for classifying. The focus of
the experimental phase is on transforms utilized for our system.
By comparing various transforms, experiments show the optimal
transform for a motion frequency based approach.
Abstract: Ethanol has been known for a long time, being
perhaps the oldest product obtained through traditional biotechnology
fermentation. Agriculture waste as substrate in fermentation is vastly
discussed as alternative to replace edible food and utilization of
organic material. Pineapple peel, highly potential source as substrate
is a by-product of the pineapple processing industry. Bio-ethanol
from pineapple (Ananas comosus) peel extract was carried out by
controlling fermentation without any treatment. Saccharomyces
ellipsoides was used as inoculum in this fermentation process as it is
naturally found at the pineapple skin. In this study, the capability of
Response Surface Methodology (RSM) for optimization of ethanol
production from pineapple peel extract using Saccharomyces
ellipsoideus in batch fermentation process was investigated. Effect of
five test variables in a defined range of inoculum concentration 6-
14% (v/v), pH (4.0-6.0), sugar concentration (14-22°Brix),
temperature (24-32°C) and time of incubation (30-54 hrs) on the
ethanol production were evaluated. Data obtained from experiment
were analyzed with RSM of MINITAB Software (Version 15)
whereby optimum ethanol concentration of 8.637% (v/v) was
determined. The optimum condition of 14% (v/v) inoculum
concentration, pH 6, 22°Brix, 26°C and 30hours of incubation. The
significant regression equation or model at the 5% level with
correlation value of 99.96% was also obtained.
Abstract: In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces
Abstract: The paper proposes the novel design of a 3T XOR gate combining complementary CMOS with pass transistor logic. The design has been compared with earlier proposed 4T and 6T XOR gates and a significant improvement in silicon area and power-delay product has been obtained. An eight transistor full adder has been designed using the proposed three-transistor XOR gate and its performance has been investigated using 0.15um and 0.35um technologies. Compared to the earlier designed 10 transistor full adder, the proposed adder shows a significant improvement in silicon area and power delay product. The whole simulation has been carried out using HSPICE.
Abstract: Positron emission particle tracking (PEPT) is a
technique in which a single radioactive tracer particle can be
accurately tracked as it moves. A limitation of PET is that in order to
reconstruct a tomographic image it is necessary to acquire a large
volume of data (millions of events), so it is difficult to study rapidly
changing systems. By considering this fact, PEPT is a very fast
process compared with PET.
In PEPT detecting both photons defines a line and the annihilation
is assumed to have occurred somewhere along this line. The location
of the tracer can be determined to within a few mm from coincident
detection of a small number of pairs of back-to-back gamma rays and
using triangulation. This can be achieved many times per second and
the track of a moving particle can be reliably followed. This
technique was invented at the University of Birmingham [1].
The attempt in PEPT is not to form an image of the tracer particle
but simply to determine its location with time. If this tracer is
followed for a long enough period within a closed, circulating system
it explores all possible types of motion.
The application of PEPT to industrial process systems carried out
at the University of Birmingham is categorized in two subjects: the
behaviour of granular materials and viscous fluids. Granular
materials are processed in industry for example in the manufacture of
pharmaceuticals, ceramics, food, polymers and PEPT has been used
in a number of ways to study the behaviour of these systems [2].
PEPT allows the possibility of tracking a single particle within the
bed [3]. Also PEPT has been used for studying systems such as: fluid
flow, viscous fluids in mixers [4], using a neutrally-buoyant tracer
particle [5].
Abstract: Automatic detection of bleeding is of practical
importance since capsule endoscopy produces an extremely large
number of images. Algorithm development of bleeding detection in
the digestive tract is difficult due to different contrasts among the
images, food dregs, secretion and others. In this study, were assigned
weighting factors derived from the independent features of the
contrast and brightness between bleeding and normality. Spectral
analysis based on weighting factors was fast and accurate. Results
were a sensitivity of 87% and a specificity of 90% when the accuracy
was determined for each pixel out of 42 endoscope images.
Abstract: Social, culture and artistic status of a society in
various historical eras is affected by numerous, and sometimes
imposed, factors that better understanding requires analysis of such
conditions. Throughout history Iran has been involved with
determining and significant events that examining each of these
events can improve the understanding of social conditions of this
country in the intended time. Mongolian conquest of Iran is one of
most significant events in the history of Iran with consequences that
never left Iranian societies. During this tragic invasion and
subsequent devastating wars, which led to establishment of Ilkhanate
dynasty, numerous cultural and artistic changes occurred both in
Mongolian conquerors and Iranian society. This study examines these
changes with a glimpse towards art and architecture as important part
of cultural aspects and social communication.
Abstract: Feature and model selection are in the center of
attention of many researches because of their impact on classifiers-
performance. Both selections are usually performed separately but
recent developments suggest using a combined GA-SVM approach to
perform them simultaneously. This approach improves the
performance of the classifier identifying the best subset of variables
and the optimal parameters- values. Although GA-SVM is an
effective method it is computationally expensive, thus a rough
method can be considered. The paper investigates a joined approach
of Genetic Algorithm and kernel matrix criteria to perform
simultaneously feature and model selection for SVM classification
problem. The purpose of this research is to improve the classification
performance of SVM through an efficient approach, the Kernel
Matrix Genetic Algorithm method (KMGA).
Abstract: Motion estimation is a key problem in video
processing and computer vision. Optical flow motion estimation can
achieve high estimation accuracy when motion vector is small.
Three-step search algorithm can handle large motion vector but not
very accurate. A joint algorithm was proposed in this paper to
achieve high estimation accuracy disregarding whether the motion
vector is small or large, and keep the computation cost much lower
than full search.
Abstract: Vapour recompression system has been used to
enhance reduction in energy consumption and improvement in
energy effectiveness of distillation columns. However, the effects of
certain parameters have not been taken into consideration. One of
such parameters is the column heat loss which has either been
assumed to be a certain percent of reboiler heat transfer or negligible.
The purpose of this study was to evaluate the heat loss from an
ethanol-water vapour recompression distillation column with
pressure increase across the compressor (VRCAS) and compare the
results obtained and its effect on some parameters in similar system
(VRCCS) where the column heat loss has been assumed or neglected.
Results show that the heat loss evaluated was higher when compared
with that obtained for the column VRCCS. The results also showed
that increase in heat loss could have significant effect on the total
energy consumption, reboiler heat transfer, the number of trays and
energy effectiveness of the column.
Abstract: Most paddy rice fields in East Asia are small parcels,
and the weather conditions during the growing season are usually
cloudy. FORMOSAT-2 multi-spectral images have an 8-meter
resolution and one-day recurrence, ideal for mapping paddy rice fields
in East Asia. To map rice fields, this study first determined the
transplanting and the most active tillering stages of paddy rice and
then used multi-temporal images to distinguish different growing
characteristics between paddy rice and other ground covers. The
unsupervised ISODATA (iterative self-organizing data analysis
techniques) and supervised maximum likelihood were both used to
discriminate paddy rice fields, with training areas automatically
derived from ten-year cultivation parcels in Taiwan. Besides original
bands in multi-spectral images, we also generated normalized
difference vegetation index and experimented with object-based
pre-classification and post-classification. This paper discusses results
of different image classification methods in an attempt to find a
precise and automatic solution to mapping paddy rice in Taiwan.