Abstract: We present a method for fast volume rendering using
graphics hardware (GPU). To our knowledge, it is the first implementation
on the GPU. Based on the Shear-Warp algorithm, our
GPU-based method provides real-time frame rates and outperforms
the CPU-based implementation. When the number of slices is not
sufficient, we add in-between slices computed by interpolation. This
improves then the quality of the rendered images. We have also
implemented the ray marching algorithm on the GPU. The results
generated by the three algorithms (CPU-based and GPU-based Shear-
Warp, GPU-based Ray Marching) for two test models has proved that
the ray marching algorithm outperforms the shear-warp methods in
terms of speed up and image quality.
Abstract: Image mosaicing is a technique that permits to enlarge the field of view of a camera. For instance, it is employed to achieve panoramas with common cameras or even in scientific applications, to achieve the image of a whole culture in microscopical imaging. Usually, a mosaic of cell cultures is achieved through using automated microscopes. However, this is often performed in batch, through CPU intensive minimization algorithms. In addition, live stem cells are studied in phase contrast, showing a low contrast that cannot be improved further. We present a method to study the flat field from live stem cells images even in case of 100% confluence, this permitting to build accurate mosaics on-line using high performance algorithms.
Abstract: The dynamic spectrum allocation solutions such as
cognitive radio networks have been proposed as a key technology to
exploit the frequency segments that are spectrally underutilized.
Cognitive radio users work as secondary users who need to
constantly and rapidly sense the presence of primary users or
licensees to utilize their frequency bands if they are inactive. Short
sensing cycles should be run by the secondary users to achieve
higher throughput rates as well as to provide low level of interference
to the primary users by immediately vacating their channels once
they have been detected. In this paper, the throughput-sensing time
relationship in local and cooperative spectrum sensing has been
investigated under two distinct scenarios, namely, constant primary
user protection (CPUP) and constant secondary user spectrum
usability (CSUSU) scenarios. The simulation results show that the
design of sensing slot duration is very critical and depends on the
number of cooperating users under CPUP scenario whereas under
CSUSU, cooperating more users has no effect if the sensing time
used exceeds 5% of the total frame duration.
Abstract: Mapping between local and global coordinates is an
important issue in finite element method, as all calculations are
performed in local coordinates. The concern arises when subparametric
are used, in which the shape functions of the field variable
and the geometry of the element are not the same. This is particularly
the case for C* elements in which the extra degrees of freedoms
added to the nodes make the elements sub-parametric. In the present
work, transformation matrix for C1* (an 8-noded hexahedron
element with 12 degrees of freedom at each node) is obtained using
equivalent C0 elements (with the same number of degrees of
freedom). The convergence rate of 8-noded C1* element is nearly
equal to its equivalent C0 element, while it consumes less CPU time
with respect to the C0 element. The existence of derivative degrees
of freedom at the nodes of C1* element along with excellent
convergence makes it superior compared with it equivalent C0
element.
Abstract: For complete support of Quality of Service, it is better that environment itself predicts resource requirements of a job by using special methods in the Grid computing. The exact and correct prediction causes exact matching of required resources with available resources. After the execution of each job, the used resources will be saved in the active database named "History". At first some of the attributes will be exploit from the main job and according to a defined similarity algorithm the most similar executed job will be exploited from "History" using statistic terms such as linear regression or average, resource requirements will be predicted. The new idea in this research is based on active database and centralized history maintenance. Implementation and testing of the proposed architecture results in accuracy percentage of 96.68% to predict CPU usage of jobs and 91.29% of memory usage and 89.80% of the band width usage.
Abstract: In this paper processes including large deformations of a rubber with hyperelastic material behavior are simulated by the RKPM method. Due to the loss of kronecker delta properties in the mesh less shape functions, the imposition of essential boundary conditions consumes significant CPU time in mesh free computations. In this work transformation method is used for imposition of essential boundary conditions. A RKPM material shape function is used in this analysis. The support of the material shape functions covers the same set of particles during material deformation and hence the transformation matrix is formed only once at the initial stages. A computer program in MATLAB is developed for simulations.
Abstract: This work aims to test the application of computational fluid dynamics (CFD) modeling to fixed bed catalytic cracking reactors. Studies of CFD with a fixed bed design commonly use a regular packing with N=2 to define bed geometry. CFD allows us to obtain a more accurate view of the fluid flow and heat transfer mechanisms present in fixed bed equipment. Naphtha was used as feedstock and the reactor length was 80cm. It is divided in three sections that catalyst bed packed in the middle section of the reactor. The reaction scheme was involved one primary reaction and 24 secondary reactions. Because of high CPU times in these simulations, parallel processing have been used. In this study the coke formation process in fixed bed and empty tube reactor was simulated and coke in these reactors are compared. In addition, the effect of steam ratio and feed flow rate on coke formation was investigated.
Abstract: Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.
Abstract: The development of many measurement and inspection systems of products based on real-time image processing can not be carried out totally in a laboratory due to the size or the temperature of the manufactured products. Those systems must be developed in successive phases. Firstly, the system is installed in the production line with only an operational service to acquire images of the products and other complementary signals. Next, a recording service of the image and signals must be developed and integrated in the system. Only after a large set of images of products is available, the development of the real-time image processing algorithms for measurement or inspection of the products can be accomplished under realistic conditions. Finally, the recording service is turned off or eliminated and the system operates only with the real-time services for the acquisition and processing of the images. This article presents a systematic performance evaluation of the image compression algorithms currently available to implement a real-time recording service. The results allow establishing a trade off between the reduction or compression of the image size and the CPU time required to get that compression level.
Abstract: In most of the popular implementation of Parallel GAs
the whole population is divided into a set of subpopulations, each
subpopulation executes GA independently and some individuals are
migrated at fixed intervals on a ring topology. In these studies,
the migrations usually occur 'synchronously' among subpopulations.
Therefore, CPUs are not used efficiently and the communication
do not occur efficiently either. A few studies tried asynchronous
migration but it is hard to implement and setting proper parameter
values is difficult.
The aim of our research is to develop a migration method which is
easy to implement, which is easy to set parameter values, and which
reduces communication traffic. In this paper, we propose a traffic
reduction method for the Asynchronous Parallel Distributed GA by
migration of elites only. This is a Server-Client model. Every client
executes GA on a subpopulation and sends an elite information to the
server. The server manages the elite information of each client and
the migrations occur according to the evolution of sub-population in
a client. This facilitates the reduction in communication traffic.
To evaluate our proposed model, we apply it to many function optimization
problems. We confirm that our proposed method performs
as well as current methods, the communication traffic is less, and
setting of the parameters are much easier.
Abstract: This paper considers the problem of scheduling maintenance actions for identical aircraft gas turbine engines. Each one of the turbines consists of parts which frequently require replacement. A finite inventory of spare parts is available and all parts are ready for replacement at any time. The inventory consists of both new and refurbished parts. Hence, these parts have different field lives. The goal is to find a replacement part sequencing that maximizes the time that the aircraft will keep functioning before the inventory is replenished. The problem is formulated as an identical parallel machine scheduling problem where the minimum completion time has to be maximized. Two models have been developed. The first one is an optimization model which is based on a 0-1 linear programming formulation, while the second one is an approximate procedure which consists in decomposing the problem into several two-machine subproblems. Each subproblem is optimally solved using the first model. Both models have been implemented using Lingo and have been tested on two sets of randomly generated data with up to 150 parts and 10 turbines. Experimental results show that the optimization model is able to solve only instances with no more than 4 turbines, while the decomposition procedure often provides near-optimal solutions within a maximum CPU time of 3 seconds.
Abstract: This article proposes an Ant Colony Optimization
(ACO) metaheuristic to minimize total makespan for scheduling a set
of jobs and assign workers for uniformly related parallel machines.
An algorithm based on ACO has been developed and coded on a
computer program Matlab®, to solve this problem. The paper
explains various steps to apply Ant Colony approach to the problem
of minimizing makespan for the worker assignment & jobs
scheduling problem in a parallel machine model and is aimed at
evaluating the strength of ACO as compared to other conventional
approaches. One data set containing 100 problems (12 Jobs, 03
machines and 10 workers) which is available on internet, has been
taken and solved through this ACO algorithm. The results of our
ACO based algorithm has shown drastically improved results,
especially, in terms of negligible computational effort of CPU, to
reach the optimal solution. In our case, the time taken to solve all 100
problems is even lesser than the average time taken to solve one
problem in the data set by other conventional approaches like GA
algorithm and SPT-A/LMC heuristics.
Abstract: Gradual patterns have been studied for many years as
they contain precious information. They have been integrated in
many expert systems and rule-based systems, for instance to reason
on knowledge such as “the greater the number of turns, the greater
the number of car crashes”. In many cases, this knowledge has been
considered as a rule “the greater the number of turns → the greater
the number of car crashes” Historically, works have thus been
focused on the representation of such rules, studying how implication
could be defined, especially fuzzy implication. These rules were
defined by experts who were in charge to describe the systems they
were working on in order to turn them to operate automatically. More
recently, approaches have been proposed in order to mine databases
for automatically discovering such knowledge. Several approaches
have been studied, the main scientific topics being: how to determine
what is an relevant gradual pattern, and how to discover them as
efficiently as possible (in terms of both memory and CPU usage).
However, in some cases, end-users are not interested in raw level
knowledge, and are rather interested in trends. Moreover, it may be
the case that no relevant pattern can be discovered at a low level of
granularity (e.g. city), whereas some can be discovered at a higher
level (e.g. county). In this paper, we thus extend gradual pattern
approaches in order to consider multiple level gradual patterns. For
this purpose, we consider two aggregation policies, namely
horizontal and vertical.
Abstract: System is using multiple processors for computing and information processing, is increasing rapidly speed operation of these systems compared with single processor systems, very significant impact on system performance is increased .important differences to yield a single multi-processor cpu, the scheduling policies, to reduce the implementation time of all processes. Notwithstanding the famous algorithms such as SPT, LPT, LSPT and RLPT for scheduling and there, but none led to the answer are not optimal.In this paper scheduling using genetic algorithms and innovative way to finish the whole process faster that we do and the result compared with three algorithms we mentioned.
Abstract: Animation is simply defined as the sequencing of a
series of static images to generate the illusion of movement. Most
people believe that actual drawings or creation of the individual
images is the animation, when in actuality it is the arrangement of
those static images that conveys the motion. To become an animator,
it is often assumed that needed the ability to quickly design
masterpiece after masterpiece. Although some semblance of artistic
skill is a necessity for the job, the real key to becoming a great
animator is in the comprehension of timing. This paper will use a
combination of sprite animation, frame animation, and some other
techniques to cause a group of multi-colored static images to slither
around in the bounded area. In addition to slithering, the images
will also change the color of different parts of their body, much like
the real world creatures that have this amazing ability to change the
colors on their bodies do. This paper was implemented by using
Java 2 Standard Edition (J2SE).
It is both time-consuming and expensive to create animations,
regardless if they are created by hand or by using motion-capture
equipment. If the animators could reuse old animations and even
blend different animations together, a lot of work would be saved in
the process. The main objective of this paper is to examine a method
for blending several animations together in real time. This paper
presents and analyses a solution using Weighted Skeleton
Animation (WSA) resulting in limited CPU time and memory waste
as well as saving time for the animators. The idea presented is
described in detail and implemented. In this paper, text animation,
vertex animation, sprite part animation and whole sprite animation
were tested.
In this research paper, the resolution, smoothness and movement
of animated images will be carried out from the parameters, which
will be obtained from the experimental research of implementing
this paper.
Abstract: This paper presents an effective traffic lights
recognition method at the daytime. First, Potential Traffic Lights
Detector (PTLD) use whole color source of YCbCr channel image and
make each binary image of green and red traffic lights. After PTLD
step, Shape Filter (SF) use to remove noise such as traffic sign, street
tree, vehicle, and building. At this time, noise removal properties
consist of information of blobs of binary image; length, area, area of
boundary box, etc. Finally, after an intermediate association step witch
goal is to define relevant candidates region from the previously
detected traffic lights, Adaptive Multi-class Classifier (AMC) is
executed. The classification method uses Haar-like feature and
Adaboost algorithm. For simulation, we are implemented through Intel
Core CPU with 2.80 GHz and 4 GB RAM and tested in the urban and
rural roads. Through the test, we are compared with our method and
standard object-recognition learning processes and proved that it
reached up to 94 % of detection rate which is better than the results
achieved with cascade classifiers. Computation time of our proposed
method is 15 ms.
Abstract: The increasing importance of data stream arising in a
wide range of advanced applications has led to the extensive study of
mining frequent patterns. Mining data streams poses many new
challenges amongst which are the one-scan nature, the unbounded
memory requirement and the high arrival rate of data streams. In this
paper, we propose a new approach for mining itemsets on data
stream. Our approach SFIDS has been developed based on FIDS
algorithm. The main attempts were to keep some advantages of the
previous approach and resolve some of its drawbacks, and
consequently to improve run time and memory consumption. Our
approach has the following advantages: using a data structure similar
to lattice for keeping frequent itemsets, separating regions from each
other with deleting common nodes that results in a decrease in search
space, memory consumption and run time; and Finally, considering
CPU constraint, with increasing arrival rate of data that result in
overloading system, SFIDS automatically detect this situation and
discard some of unprocessing data. We guarantee that error of results
is bounded to user pre-specified threshold, based on a probability
technique. Final results show that SFIDS algorithm could attain
about 50% run time improvement than FIDS approach.
Abstract: In this paper, we consider the problem of logic simplification for a special class of logic functions, namely complementary Boolean functions (CBF), targeting low power implementation using static CMOS logic style. The functions are uniquely characterized by the presence of terms, where for a canonical binary 2-tuple, D(mj) ∪ D(mk) = { } and therefore, we have | D(mj) ∪ D(mk) | = 0 [19]. Similarly, D(Mj) ∪ D(Mk) = { } and hence | D(Mj) ∪ D(Mk) | = 0. Here, 'mk' and 'Mk' represent a minterm and maxterm respectively. We compare the circuits minimized with our proposed method with those corresponding to factored Reed-Muller (f-RM) form, factored Pseudo Kronecker Reed-Muller (f-PKRM) form, and factored Generalized Reed-Muller (f-GRM) form. We have opted for algebraic factorization of the Reed-Muller (RM) form and its different variants, using the factorization rules of [1], as it is simple and requires much less CPU execution time compared to Boolean factorization operations. This technique has enabled us to greatly reduce the literal count as well as the gate count needed for such RM realizations, which are generally prone to consuming more cells and subsequently more power consumption. However, this leads to a drawback in terms of the design-for-test attribute associated with the various RM forms. Though we still preserve the definition of those forms viz. realizing such functionality with only select types of logic gates (AND gate and XOR gate), the structural integrity of the logic levels is not preserved. This would consequently alter the testability properties of such circuits i.e. it may increase/decrease/maintain the same number of test input vectors needed for their exhaustive testability, subsequently affecting their generalized test vector computation. We do not consider the issue of design-for-testability here, but, instead focus on the power consumption of the final logic implementation, after realization with a conventional CMOS process technology (0.35 micron TSMC process). The quality of the resulting circuits evaluated on the basis of an established cost metric viz., power consumption, demonstrate average savings by 26.79% for the samples considered in this work, besides reduction in number of gates and input literals by 39.66% and 12.98% respectively, in comparison with other factored RM forms.
Abstract: The ultimate goal of this article is to develop a robust and accurate numerical method for solving hyperbolic conservation laws in one and two dimensions. A hybrid numerical method, coupling a cheap fourth order total variation diminishing (TVD) scheme [1] for smooth region and a Robust seventh-order weighted non-oscillatory (WENO) scheme [2] near discontinuities, is considered. High order multi-resolution analysis is used to detect the high gradients regions of the numerical solution in order to capture the shocks with the WENO scheme, while the smooth regions are computed with fourth order total variation diminishing (TVD). For time integration, we use the third order TVD Runge-Kutta scheme. The accuracy of the resulting hybrid high order scheme is comparable with these of WENO, but with significant decrease of the CPU cost. Numerical demonstrates that the proposed scheme is comparable to the high order WENO scheme and superior to the fourth order TVD scheme. Our scheme has the added advantage of simplicity and computational efficiency. Numerical tests are presented which show the robustness and effectiveness of the proposed scheme.