Abstract: Myoelectric control system is the fundamental
component of modern prostheses, which uses the myoelectric signals
from an individual’s muscles to control the prosthesis movements.
The surface electromyogram signal (sEMG) being noninvasive has
been used as an input to prostheses controllers for many years.
Recent technological advances has led to the development of
implantable myoelectric sensors which enable the internal
myoelectric signal (MES) to be used as input to these prostheses
controllers. The intramuscular measurement can provide focal
recordings from deep muscles of the forearm and independent signals
relatively free of crosstalk thus allowing for more independent
control sites. However, little work has been done to compare the two
inputs. In this paper we have compared the classification accuracy of
six pattern recognition based myoelectric controllers which use
surface myoelectric signals recorded using untargeted (symmetric)
surface electrode arrays to the same controllers with multichannel
intramuscular myolectric signals from targeted intramuscular
electrodes as inputs. There was no significant enhancement in the
classification accuracy as a result of using the intramuscular EMG
measurement technique when compared to the results acquired using
the surface EMG measurement technique. Impressive classification
accuracy (99%) could be achieved by optimally selecting only five
channels of surface EMG.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. 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 handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: The article describes the effect of the replacement of
the used reference coordinate system in the georeferencing of an old
map of Europe. The map was georeferenced into three types of
projection – the equal-area conic (original cartographic projection),
cylindrical Plate Carrée and cylindrical Mercator map projection. The
map was georeferenced by means of the affine and the second-order
polynomial transformation. The resulting georeferenced raster
datasets from the Plate Carrée and Mercator projection were
projected into the equal-area conic projection by means of projection
equations. The output is the comparison of drawn graphics, the
magnitude of standard deviations for individual projections and types
of transformation.
Abstract: Recent perceived climate variability raises concerns
with unprecedented hydrological phenomena and extremes.
Distribution and circulation of the waters of the Earth become
increasingly difficult to determine because of additional uncertainty
related to anthropogenic emissions. The world wide observed
changes in the large-scale hydrological cycle have been related to an
increase in the observed temperature over several decades. Although
the effect of change in climate on hydrology provides a general
picture of possible hydrological global change, new tools and
frameworks for modelling hydrological series with nonstationary
characteristics at finer scales, are required for assessing climate
change impacts. Of the downscaling techniques, dynamic
downscaling is usually based on the use of Regional Climate Models
(RCMs), which generate finer resolution output based on atmospheric
physics over a region using General Circulation Model (GCM) fields
as boundary conditions. However, RCMs are not expected to capture
the observed spatial precipitation extremes at a fine cell scale or at a
basin scale. Statistical downscaling derives a statistical or empirical
relationship between the variables simulated by the GCMs, called
predictors, and station-scale hydrologic variables, called predictands.
The main focus of the paper is on the need for using statistical
downscaling techniques for projection of local hydrometeorological
variables under climate change scenarios. The projections can be then
served as a means of input source to various hydrologic models to
obtain streamflow, evapotranspiration, soil moisture and other
hydrological variables of interest.
Abstract: The objective of this paper is finding the way of economic restructuring - that is, change in the shares of sectoral gross outputs - resulting in the maximum possible increase in the gross domestic product (GDP) combined with decreases in energy consumption and CO2 emissions. It uses an input-output model for the GDP and factorial models for the energy consumption and CO2 emissions to determine the projection of the gradient of GDP, and the antigradients of the energy consumption and CO2 emissions, respectively, on a subspace formed by the structure-related variables. Since the gradient (antigradient) provides a direction of the steepest increase (decrease) of the objective function, and their projections retain this property for the functions' limitation to the subspace, each of the three directional vectors solves a particular problem of optimal structural change. In the next step, a type of factor analysis is applied to find a convex combination of the projected gradient and antigradients having maximal possible positive correlation with each of the three. This convex combination provides the desired direction of the structural change. The national economy of the United States is used as an example of applications.
Abstract: This paper considers the NP-hard problem of reconstructing binary matrices satisfying exactly-1-4-adjacency constraint from its row and column projections. This problem is formulated into a maximization problem. The objective function gives a measure of adjacency constraint for the binary matrices. The maximization problem is solved by the simulated annealing algorithm and experimental results are presented.
Abstract: The modern technologies and developments in computer and Global Positioning System (GPS) as well as Geographic Information System (GIS) and total station TS. This paper presents a new proposal for coordinates system by a harmonic equations “United projections”, which have five projections (Mercator, Lambert, Russell, Lagrange, and compound of projection) in one zone coordinate system width 14 degrees, also it has one degree for overlap between zones, as well as two standards parallels for zone from 10 S to 45 S. Also this paper presents two cases; first case is to compare distances between a new coordinate system and UTM, second case creating local coordinate system for the city of Sydney to measure the distances directly from rectangular coordinates using projection of Mercator, Lambert and UTM.
Abstract: Uniqueness and distinctiveness of localities (referred to as genius loci or sense of place) are important to ensure people-s identification with their locality. Existing frameworks reveals that the affective dimension of environments is rarely mentioned or explored and limited public participation was used in constructing the frameworks. This research argues that the complexity of sense of place would be recognised and appropriate planning guidelines formulated by exploring and integrating the affective dimension of a site. Aims of the research therefore are to (i) explore relational dimensions between people and a natural rural landscape, (ii) to implement a participatory approach to obtain insight into different relational dimensions, and (ii) to concretise socio-affective relational dimensions into site planning guidelines. A qualitative, interdisciplinary research approach was followed and conducted on the farm Kromdraai, Vredefort Dome World Heritage Site. In essence the first phase of the study reveals various affective responses and projections of personal meanings. The findings in phase 1 informed the second phase, to involve people from various disciplines and different involvement with the area to make visual presentations of appropriate planning and design of the site in order to capture meanings of the interactions between people and their environment. Final site planning and design guidelines were formulated, based on these. This research contributed to provide planners with new possibilities of exploring the dimensions between people and places as well as to develop appropriate methods for participation to obtain insight into the underlying meanings of sites.
Abstract: We explore entanglement in composite quantum systems
and how its peculiar properties are exploited in quantum
information and communication protocols by means of Diagrams
of States, a novel method to graphically represent and analyze how
quantum information is elaborated during computations performed
by quantum circuits.
We present quantum diagrams of states for Bell states generation,
measurements and projections, for dense coding and quantum teleportation,
for probabilistic quantum machines designed to perform
approximate quantum cloning and universal NOT and, finally, for
quantum privacy amplification based on entanglement purification.
Diagrams of states prove to be a useful approach to analyze quantum
computations, by offering an intuitive graphic representation of the
processing of quantum information. They also help in conceiving
novel quantum computations, from describing the desired information
processing to deriving the final implementation by quantum gate
arrays.
Abstract: In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.
Abstract: The purpose of this work is to present a method for
rigid registration of medical images using 1D binary projections
when a part of one of the two images is missing. We use 1D binary
projections and we adjust the projection limits according to the
reduced image in order to perform accurate registration. We use the
variance of the weighted ratio as a registration function which we
have shown is able to register 2D and 3D images more accurately and
robustly than mutual information methods. The function is computed
explicitly for n=5 Chebyshev points in a [-9,+9] interval and it is
approximated using Chebyshev polynomials for all other points. The
images used are MR scans of the head. We find that the method is
able to register the two images with average accuracy 0.3degrees for
rotations and 0.2 pixels for translations for a y dimension of 156 with
initial dimension 256. For y dimension 128/256 the accuracy
decreases to 0.7 degrees for rotations and 0.6 pixels for translations.
Abstract: DNA microarray technology is widely used by
geneticists to diagnose or treat diseases through gene expression.
This technology is based on the hybridization of a tissue-s DNA
sequence into a substrate and the further analysis of the image
formed by the thousands of genes in the DNA as green, red or yellow
spots. The process of DNA microarray image analysis involves
finding the location of the spots and the quantification of the
expression level of these. In this paper, a tool to perform DNA
microarray image analysis is presented, including a spot addressing
method based on the image projections, the spot segmentation
through contour based segmentation and the extraction of relevant
information due to gene expression.
Abstract: We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.
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: In illumination variant face recognition, existing
methods extracting face albedo as light normalized image may lead to
loss of extensive facial details, with light template discarded. To
improve that, a novel approach for realistic facial texture
reconstruction by combining original image and albedo image is
proposed. First, light subspaces of different identities are established
from the given reference face images; then by projecting the original
and albedo image into each light subspace respectively, texture
reference images with corresponding lighting are reconstructed and
two texture subspaces are formed. According to the projections in
texture subspaces, facial texture with normal light can be synthesized.
Due to the combination of original image, facial details can be
preserved with face albedo. In addition, image partition is applied to
improve the synthesization performance. Experiments on Yale B and
CMUPIE databases demonstrate that this algorithm outperforms the
others both in image representation and in face recognition.
Abstract: As the Computed Tomography(CT) requires normally
hundreds of projections to reconstruct the image, patients are exposed
to more X-ray energy, which may cause side effects such as cancer.
Even when the variability of the particles in the object is very less,
Computed Tomography requires many projections for good quality
reconstruction. In this paper, less variability of the particles in an
object has been exploited to obtain good quality reconstruction.
Though the reconstructed image and the original image have same
projections, in general, they need not be the same. In addition
to projections, if a priori information about the image is known,
it is possible to obtain good quality reconstructed image. In this
paper, it has been shown by experimental results why conventional
algorithms fail to reconstruct from a few projections, and an efficient
polynomial time algorithm has been given to reconstruct a bi-level
image from its projections along row and column, and a known sub
image of unknown image with smoothness constraints by reducing the
reconstruction problem to integral max flow problem. This paper also
discusses the necessary and sufficient conditions for uniqueness and
extension of 2D-bi-level image reconstruction to 3D-bi-level image
reconstruction.
Abstract: We study the problem of reconstructing a three dimensional binary matrices whose interiors are only accessible through few projections. Such question is prominently motivated by the demand in material science for developing tool for reconstruction of crystalline structures from their images obtained by high-resolution transmission electron microscopy. Various approaches have been suggested to reconstruct 3D-object (crystalline structure) by reconstructing slice of the 3D-object. To handle the ill-posedness of the problem, a priori information such as convexity, connectivity and periodicity are used to limit the number of possible solutions. Formally, 3Dobject (crystalline structure) having a priory information is modeled by a class of 3D-binary matrices satisfying a priori information. We consider 3D-binary matrices with periodicity constraints, and we propose a polynomial time algorithm to reconstruct 3D-binary matrices with periodicity constraints from two orthogonal projections.
Abstract: In this paper three different approaches for person
verification and identification, i.e. by means of fingerprints, face and
voice recognition, are studied. Face recognition uses parts-based
representation methods and a manifold learning approach. The
assessment criterion is recognition accuracy. The techniques under
investigation are: a) Local Non-negative Matrix Factorization
(LNMF); b) Independent Components Analysis (ICA); c) NMF with
sparse constraints (NMFsc); d) Locality Preserving Projections
(Laplacianfaces). Fingerprint detection was approached by classical
minutiae (small graphical patterns) matching through image
segmentation by using a structural approach and a neural network as
decision block. As to voice / speaker recognition, melodic cepstral
and delta delta mel cepstral analysis were used as main methods, in
order to construct a supervised speaker-dependent voice recognition
system. The final decision (e.g. “accept-reject" for a verification
task) is taken by using a majority voting technique applied to the
three biometrics. The preliminary results, obtained for medium
databases of fingerprints, faces and voice recordings, indicate the
feasibility of our study and an overall recognition precision (about
92%) permitting the utilization of our system for a future complex
biometric card.
Abstract: This paper presents the application of a signal
intensity independent registration criterion for 2D rigid body
registration of medical images using 1D binary projections. The
criterion is defined as the weighted ratio of two projections. The ratio
is computed on a pixel per pixel basis and weighting is performed by
setting the ratios between one and zero pixels to a standard high
value. The mean squared value of the weighted ratio is computed
over the union of the one areas of the two projections and it is
minimized using the Chebyshev polynomial approximation using
n=5 points. The sum of x and y projections is used for translational
adjustment and a 45deg projection for rotational adjustment. 20 T1-
T2 registration experiments were performed and gave mean errors
1.19deg and 1.78 pixels. The method is suitable for contour/surface
matching. Further research is necessary to determine the robustness
of the method with regards to threshold, shape and missing data.
Abstract: The design problem of Infinite Impulse Response (IIR)
digital filters is usually expressed as the minimization problem of
the complex magnitude error that includes both the magnitude and
phase information. However, the group delay of the filter obtained
by solving such design problem may be far from the desired group
delay. In this paper, we propose a design method of stable IIR digital
filters with prespecified maximum group delay errors. In the proposed
method, the approximation problems of the magnitude-phase and
group delay are separately defined, and these two approximation
problems are alternately solved using successive projections. As a
result, the proposed method can design the IIR filters that satisfy the
prespecified allowable errors for not only the complex magnitude but
also the group delay by alternately executing the coefficient update
for the magnitude-phase and the group delay approximation. The
usefulness of the proposed method is verified through some examples.