Abstract: Although the field of parametric Pattern Recognition (PR) has been thoroughly studied for over five decades, the use of the Order Statistics (OS) of the distributions to achieve this has not been reported. The pioneering work on using OS for classification was presented in [1] for the Uniform distribution, where it was shown that optimal PR can be achieved in a counter-intuitive manner, diametrically opposed to the Bayesian paradigm, i.e., by comparing the testing sample to a few samples distant from the mean. This must be contrasted with the Bayesian paradigm in which, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding central points, for example, the means. In [2], we showed that the results could be extended for a few symmetric distributions within the exponential family. In this paper, we attempt to extend these results significantly by considering asymmetric distributions within the exponential family, for some of which even the closed form expressions of the cumulative distribution functions are not available. These distributions include the Rayleigh, Gamma and certain Beta distributions. As in [1] and [2], the new scheme, referred to as Classification by Moments of Order Statistics (CMOS), attains an accuracy very close to the optimal Bayes’ bound, as has been shown both theoretically and by rigorous experimental testing.
Abstract: Quality of school meals is one of the major concerns of governments and international organizations worldwide. This study aims to evaluate nutritional compliance of meals served at a Portuguese primary school considering the portions stated by Portuguese Education Ministry. To evaluate adequacy of portions served, weighing of all meal components offered to students and leftovers was performed during ten consecutive days at two different moments. Plate waste (%) was calculated by the ratio of food discarded and food served to the children. Nutritional evaluation of menus was made using the Portuguese Food Composition Table. Meals evaluated showed a percent contribution to energetic daily intake higher than recommendations. Meals served to children were considered high energy and protein dense. No significant waste of soup was accounted and the main meal components wasted were fish and vegetables. It will be necessary to adjust portions indicated by Ministry of Education in order to comply with recommendations and reduce food waste.
Abstract: We investigate properties of convective solutions of the
Boussinesq thermal convection in a moderately rotating spherical
shell allowing the inner and outer sphere rotation due to the viscous
torque of the fluid. The ratio of the inner and outer radii of the
spheres, the Prandtl number and the Taylor number are fixed to 0.4,
1 and 5002, respectively. The inertial moments of the inner and outer
spheres are fixed to about 0.22 and 100, respectively. The Rayleigh
number is varied from 2.6 × 104 to 3.4 × 104. In this parameter
range, convective solutions transit from equatorially symmetric quasiperiodic
ones to equatorially asymmetric chaotic ones as the Rayleigh
number is increased. The transition route in the system allowing
rotation of both the spheres is different from that in the co-rotating
system, which means the inner and outer spheres rotate with the
same constant angular velocity: the convective solutions transit as
equatorially symmetric quasi-periodic solution → equatorially symmetric
chaotic solution → equatorially asymmetric chaotic solution
in the system allowing both the spheres rotation, while equatorially
symmetric quasi-periodic solution → equatorially asymmetric quasiperiodic
solution → equatorially asymmetric chaotic solution in the
co-rotating system.
Abstract: This article presents a computationally tractable probabilistic model for the relation between the complex wavelet coefficients of two images of the same scene. The two images are acquisitioned at distinct moments of times, or from distinct viewpoints, or by distinct sensors. By means of the introduced probabilistic model, we argue that the similarity between the two images is controlled not by the values of the wavelet coefficients, which can be altered by many factors, but by the nature of the wavelet coefficients, that we model with the help of hidden state variables. We integrate this probabilistic framework in the construction of a new image registration algorithm. This algorithm has sub-pixel accuracy and is robust to noise and to other variations like local illumination changes. We present the performance of our algorithm on various image types.
Abstract: High level and high velocity flood flows are
potentially harmful to bridge piers as evidenced in many toppled
piers, and among them the single-column piers were considered as
the most vulnerable. The flood flow characteristic parameters
including drag coefficient, scouring and vortex shedding are built into
a pier-flood interaction model to investigate structural safety against
flood hazards considering the effects of local scouring, hydrodynamic
forces, and vortex induced resonance vibrations. By extracting the
pier-flood simulation results embedded in a neural networks code,
two cases of pier toppling occurred in typhoon days were reexamined:
(1) a bridge overcome by flash flood near a mountain side;
(2) a bridge washed off in flood across a wide channel near the
estuary. The modeling procedures and simulations are capable of
identifying the probable causes for the tumbled bridge piers during
heavy floods, which include the excessive pier bending moments and
resonance in structural vibrations.
Abstract: This article proposes modeling, simulation and
kinematic and workspace analysis of a spatial cable suspended robot
as incompletely Restrained Positioning Mechanism (IRPM). These
types of robots have six cables equal to the number of degrees of
freedom. After modeling, the kinds of workspace are defined then an
statically reachable combined workspace for different geometric
structures of fixed and moving platform is obtained. This workspace
is defined as the situations of reference point of the moving platform
(center of mass) which under external forces such as weight and with
ignorance of inertial effects, the moving platform should be in static
equilibrium under conditions that length of all cables must not be
exceeded from the maximum value and all of cables must be at
tension (they must have non-negative tension forces). Then the effect
of various parameters such as the size of moving platform, the size of
fixed platform, geometric configuration of robots, magnitude of
applied forces and moments to moving platform on workspace of
these robots with different geometric configuration are investigated.
Obtained results should be effective in employing these robots under
different conditions of applied wrench for increasing the workspace
volume.
Abstract: Masonry cavity walls are loaded by wind pressure and vertical load from upper floors. These loads results in bending moments and compression forces in the ties connecting the outer and the inner wall in a cavity wall. Large cavity walls are furthermore loaded by differential movements from the temperature gradient between the outer and the inner wall, which results in critical increase of the bending moments in the ties. Since the ties are loaded by combined compression and moment forces, the loadbearing capacity is derived from instability equilibrium equations. Most of them are iterative, since exact instability solutions are complex to derive, not to mention the extra complexity introducing dimensional instability from the temperature gradients. Using an inverse variable substitution and comparing an exact theory with an analytical instability solution a method to design tie-connectors in cavity walls was developed. The method takes into account constraint conditions limiting the free length of the wall tie, and the instability in case of pure compression which gives an optimal load bearing capacity. The model is illustrated with examples from praxis.
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: In this paper we present an off line system for the
recognition of the handwritten numeric chains. Our work is divided
in two big parts. The first part is the realization of a recognition
system of the isolated handwritten digits. In this case the study is
based mainly on the evaluation of neural network performances,
trained with the gradient back propagation algorithm. The used
parameters to form the input vector of the neural network are
extracted on the binary images of the digits by several methods: the
distribution sequence, the Barr features and the centred moments of
the different projections and profiles. The second part is the
extension of our system for the reading of the handwritten numeric
chains constituted of a variable number of digits. The vertical
projection is used to segment the numeric chain at isolated digits and
every digit (or segment) will be presented separately to the entry of
the system achieved in the first part (recognition system of the
isolated handwritten digits). The result of the recognition of the
numeric chain will be displayed at the exit of the global system.
Abstract: The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.
Abstract: In this paper we present a system for classifying videos
by frequency spectra. Many videos contain activities with repeating
movements. Sports videos, home improvement videos, or videos
showing mechanical motion are some example areas. Motion of these
areas usually repeats with a certain main frequency and several side
frequencies. Transforming repeating motion to its frequency domain
via FFT reveals these frequencies. Average amplitudes of frequency
intervals can be seen as features of cyclic motion. Hence determining
these features can help to classify videos with repeating movements.
In this paper we explain how to compute frequency spectra for video
clips and how to use them for classifying. Our approach utilizes series
of image moments as a function. This function again is transformed
into its frequency domain.
Abstract: Multimedia security is an incredibly significant area of concern. The paper aims to discuss a robust image watermarking scheme, which can withstand geometric attacks. The source image is initially moment normalized in order to make it withstand geometric attacks. The moment normalized image is wavelet transformed. The first level wavelet transformed image is segmented into blocks if size 8x8. The product of mean and standard and standard deviation of each block is computed. The second level wavelet transformed image is divided into 8x8 blocks. The product of block mean and the standard deviation are computed. The difference between products in the two levels forms the watermark. The watermark is inserted by modulating the coefficients of the mid frequencies. The modulated image is inverse wavelet transformed and inverse moment normalized to generate the watermarked image. The watermarked image is now ready for transmission. The proposed scheme can be used to validate identification cards and financial instruments. The performance of this scheme has been evaluated using a set of parameters. Experimental results show the effectiveness of this scheme.
Abstract: Order reduction of linear-time invariant systems employing two methods; one using the advantages of Routh approximation and other by an evolutionary technique is presented in this paper. In Routh approximation method the denominator of the reduced order model is obtained using Routh approximation while the numerator of the reduced order model is determined using the indirect approach of retaining the time moments and/or Markov parameters of original system. By this method the reduced order model guarantees stability if the original high order model is stable. In the second method Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical examples.
Abstract: Medical applications are among the most impactful
areas of microrobotics. The ultimate goal of medical microrobots is
to reach currently inaccessible areas of the human body and carry out
a host of complex operations such as minimally invasive surgery
(MIS), highly localized drug delivery, and screening for diseases at
their very early stages. Miniature, safe and efficient propulsion
systems hold the key to maturing this technology but they pose
significant challenges. A new type of propulsion developed recently,
uses multi-flagella architecture inspired by the motility mechanism of
prokaryotic microorganisms. There is a lack of efficient methods for
designing this type of propulsion system. The goal of this paper is to
overcome the lack and this way, a numerical strategy is proposed to
design multi-flagella propulsion systems. The strategy is based on the
implementation of the regularized stokeslet and rotlet theory, RFT
theory and new approach of “local corrected velocity". The effects of
shape parameters and angular velocities of each flagellum on overall
flow field and on the robot net forces and moments are considered.
Then a multi-layer perceptron artificial neural network is designed
and employed to adjust the angular velocities of the motors for
propulsion control. The proposed method applied successfully on a
sample configuration and useful demonstrative results is obtained.
Abstract: This paper presents unified theory for local (Savitzky-
Golay) and global polynomial smoothing. The algebraic framework
can represent any polynomial approximation and is seamless from
low degree local, to high degree global approximations. The representation
of the smoothing operator as a projection onto orthonormal
basis functions enables the computation of: the covariance matrix
for noise propagation through the filter; the noise gain and; the
frequency response of the polynomial filters. A virtually perfect Gram
polynomial basis is synthesized, whereby polynomials of degree
d = 1000 can be synthesized without significant errors. The perfect
basis ensures that the filters are strictly polynomial preserving. Given
n points and a support length ls = 2m + 1 then the smoothing
operator is strictly linear phase for the points xi, i = m+1. . . n-m.
The method is demonstrated on geometric surfaces data lying on an
invariant 2D lattice.
Abstract: The aim of this contribution is to present a new
approach in modeling the electrical activity of the human heart. A
recurrent artificial neural network is being used in order to exhibit a
subset of the dynamics of the electrical behavior of the human heart.
The proposed model can also be used, when integrated, as a
diagnostic tool of the human heart system.
What makes this approach unique is the fact that every model is
being developed from physiological measurements of an individual.
This kind of approach is very difficult to apply successfully in many
modeling problems, because of the complexity and entropy of the
free variables describing the complex system. Differences between
the modeled variables and the variables of an individual, measured at
specific moments, can be used for diagnostic purposes. The sensor
fusion used in order to optimize the utilization of biomedical sensors
is another point that this paper focuses on. Sensor fusion has been
known for its advantages in applications such as control and
diagnostics of mechanical and chemical processes.
Abstract: We propose our genuine research of geometric
moments which detects the mineral inadequacy in the frail groundnut
plant. This plant is prone to many deficiencies as a result of the
variance in the soil nutrients. By analyzing the leaves of the plant, we
detect the visual symptoms that are not recognizable to the naked eyes.
We have collected about 160 samples of leaves from the nearby fields.
The images have been taken by keeping every leaf into a black box to
avoid the external interference. For the first time, it has been possible
to provide the farmer with the stages of deficiencies. This paper has
applied the algorithms successfully to many other plants like Lady-s
finger, Green Bean, Lablab Bean, Chilli and Tomato. But we submit
the results of the groundnut predominantly. The accuracy of our
algorithm and method is almost 93%. This will again pioneer a kind of
green revolution in the field of agriculture and will be a boon to that
field.
Abstract: Counting people from a video stream in a noisy environment is a challenging task. This project aims at developing a counting system for transport vehicles, integrated in a video surveillance product. This article presents a method for the detection and tracking of multiple faces in a video by using a model of first and second order local moments. An iterative process is used to estimate the position and shape of multiple faces in images, and to track them. the trajectories are then processed to count people entering and leaving the vehicle.
Abstract: We present here the results for a comparative study of
some techniques, available in the literature, related to the relevance
feedback mechanism in the case of a short-term learning. Only one
method among those considered here is belonging to the data mining
field which is the K-nearest neighbors algorithm (KNN) while the
rest of the methods is related purely to the information retrieval field
and they fall under the purview of the following three major axes:
Shifting query, Feature Weighting and the optimization of the
parameters of similarity metric. As a contribution, and in addition to
the comparative purpose, we propose a new version of the KNN
algorithm referred to as an incremental KNN which is distinct from
the original version in the sense that besides the influence of the
seeds, the rate of the actual target image is influenced also by the
images already rated. The results presented here have been obtained
after experiments conducted on the Wang database for one iteration
and utilizing color moments on the RGB space. This compact
descriptor, Color Moments, is adequate for the efficiency purposes
needed in the case of interactive systems. The results obtained allow
us to claim that the proposed algorithm proves good results; it even
outperforms a wide range of techniques available in the literature.
Abstract: In this paper, we propose a supervised method for
color image classification based on a multilevel sigmoidal neural
network (MSNN) model. In this method, images are classified into
five categories, i.e., “Car", “Building", “Mountain", “Farm" and
“Coast". This classification is performed without any segmentation
processes. To verify the learning capabilities of the proposed method,
we compare our MSNN model with the traditional Sigmoidal Neural
Network (SNN) model. Results of comparison have shown that the
MSNN model performs better than the traditional SNN model in the
context of training run time and classification rate. Both color
moments and multi-level wavelets decomposition technique are used
to extract features from images. The proposed method has been
tested on a variety of real and synthetic images.