Abstract: Factoring Boolean functions is one of the basic operations in algorithmic logic synthesis. A novel algebraic factorization heuristic for single-output combinatorial logic functions is presented in this paper and is developed based on the set theory paradigm. The impact of factoring is analyzed mainly from a low power design perspective for standard cell based digital designs in this paper. The physical implementation of a number of MCNC/IWLS combinational benchmark functions and sub-functions are compared before and after factoring, based on a simple technology mapping procedure utilizing only standard gate primitives (readily available as standard cells in a technology library) and not cells corresponding to optimized complex logic. The power results were obtained at the gate-level by means of an industry-standard power analysis tool from Synopsys, targeting a 130nm (0.13μm) UMC CMOS library, for the typical case. The wire-loads were inserted automatically and the simulations were performed with maximum input activity. The gate-level simulations demonstrate the advantage of the proposed factoring technique in comparison with other existing methods from a low power perspective, for arbitrary examples. Though the benchmarks experimentation reports mixed results, the mean savings in total power and dynamic power for the factored solution over a non-factored solution were 6.11% and 5.85% respectively. In terms of leakage power, the average savings for the factored forms was significant to the tune of 23.48%. The factored solution is expected to better its non-factored counterpart in terms of the power-delay product as it is well-known that factoring, in general, yields a delay-efficient multi-level solution.
Abstract: Continuous innovation is becoming a necessity if
firms want to stay competitive. Different factors influence the rate of
innovation in a firm, among which corporate culture has often been
recognized among the most important factors. In this paper we argue
that the development of corporate culture that will support and foster
innovation must be accompanied with an appropriate reward system.
A research conducted among Croatian firms showed that a
statistically significant relationship exists among corporate culture
that supports innovations and reward system features.
Abstract: This paper discusses the utilization of marine biomass as an energy resource in Japan. A marine biomass energy system in Japan was proposed consisting of seaweed cultivation (Laminaria japonica) at offshore marine farms, biogas production via methane fermentation of the seaweeds, and fuel cell power generation driven by the generated biogas. We estimated energy output, energy supply potential, and CO2 mitigation in Japan on the basis of the proposed system. As a result, annual energy production was estimated to be 1.02-109 kWh/yr at nine available sites. Total CO2 mitigation was estimated to be 1.04-106 tonnes per annum at the nine sites. However, the CO2 emission for the construction of relevant facilities is not taken into account in this paper. The estimated CO2 mitigation is equivalent to about 0.9% of the required CO2 mitigation for Japan per annum under the Kyoto Protocol framework.
Abstract: The two-dimensional gel electrophoresis method
(2-DE) is widely used in Proteomics to separate thousands of proteins
in a sample. By comparing the protein expression levels of proteins in
a normal sample with those in a diseased one, it is possible to identify
a meaningful set of marker proteins for the targeted disease. The major
shortcomings of this approach involve inherent noises and irregular
geometric distortions of spots observed in 2-DE images. Various
experimental conditions can be the major causes of these problems. In
the protein analysis of samples, these problems eventually lead to
incorrect conclusions. In order to minimize the influence of these
problems, this paper proposes a partition based pair extension method
that performs spot-matching on a set of gel images multiple times and
segregates more reliable mapping results which can improve the
accuracy of gel image analysis. The improved accuracy of the
proposed method is analyzed through various experiments on real
2-DE images of human liver tissues.
Abstract: Vector quantization is a powerful tool for speech
coding applications. This paper deals with LPC Coding of speech
signals which uses a new technique called Multi Switched Split
Vector Quantization, This is a hybrid of two product code vector
quantization techniques namely the Multi stage vector quantization
technique, and Switched split vector quantization technique,. Multi
Switched Split Vector Quantization technique quantizes the linear
predictive coefficients in terms of line spectral frequencies. From
results it is proved that Multi Switched Split Vector Quantization
provides better trade off between bitrate and spectral distortion
performance, computational complexity and memory requirements
when compared to Switched Split Vector Quantization, Multi stage
vector quantization, and Split Vector Quantization techniques. By
employing the switching technique at each stage of the vector
quantizer the spectral distortion, computational complexity and
memory requirements were greatly reduced. Spectral distortion was
measured in dB, Computational complexity was measured in
floating point operations (flops), and memory requirements was
measured in (floats).
Abstract: Dynamics of a vapour bubble generated due to a high local energy input near a circular thin bronze plate in the absence of the buoyancy forces is numerically investigated in this paper. The bubble is generated near a thin bronze plate and during the growth and collapse of the bubble, it deforms the nearby plate. The Boundary Integral Equation Method is employed for numerical simulation of the problem. The fluid is assumed to be incompressible, irrotational and inviscid and the surface tension on the bubble boundary is neglected. Therefore the fluid flow around the vapour bubble can be assumed as a potential flow. Furthermore, the thin bronze plate is assumed to have perfectly plastic behaviour. Results show that the displacement of the circular thin bronze plate has considerable effect on the dynamics of its nearby vapour bubble. It is found that by decreasing the thickness of the thin bronze plate, the growth and collapse rate of the bubble becomes higher and consequently the lifetime of the bubble becomes shorter.
Abstract: Droplet size distributions in the cold spray of a fuel
are important in observed combustion behavior. Specification of
droplet size and velocity distributions in the immediate downstream
of injectors is also essential as boundary conditions for advanced
computational fluid dynamics (CFD) and two-phase spray transport
calculations. This paper describes the development of a new model to
be incorporated into maximum entropy principle (MEP) formalism
for prediction of droplet size distribution in droplet formation region.
The MEP approach can predict the most likely droplet size and
velocity distributions under a set of constraints expressing the
available information related to the distribution.
In this article, by considering the mechanisms of turbulence
generation inside the nozzle and wave growth on jet surface, it is
attempted to provide a logical framework coupling the flow inside the
nozzle to the resulting atomization process. The purpose of this paper
is to describe the formulation of this new model and to incorporate it
into the maximum entropy principle (MEP) by coupling sub-models
together using source terms of momentum and energy. Comparison
between the model prediction and experimental data for a gas turbine
swirling nozzle and an annular spray indicate good agreement
between model and experiment.
Abstract: We report in this paper the model adopted by our
system of continuous speech recognition in Arab language SySRA
and the results obtained until now. This system uses the database
Arabdic-10 which is a corpus of word for the Arab language and
which was manually segmented. Phonetic decoding is represented
by an expert system where the knowledge base is translated in the
form of production rules. This expert system transforms a vocal
signal into a phonetic lattice. The higher level of the system takes
care of the recognition of the lattice thus obtained by deferring it in
the form of written sentences (orthographical Form). This level
contains initially the lexical analyzer which is not other than the
module of recognition. We subjected this analyzer to a set of
spectrograms obtained by dictating a score of sentences in Arab
language. The rate of recognition of these sentences is about 70%
which is, to our knowledge, the best result for the recognition of the
Arab language. The test set consists of twenty sentences from four
speakers not having taken part in the training.
Abstract: This study aims to segment objects using the K-means
algorithm for texture features. Firstly, the algorithm transforms color
images into gray images. This paper describes a novel technique for
the extraction of texture features in an image. Then, in a group of
similar features, objects and backgrounds are differentiated by using
the K-means algorithm. Finally, this paper proposes a new object
segmentation algorithm using the morphological technique. The
experiments described include the segmentation of single and multiple
objects featured in this paper. The region of an object can be
accurately segmented out. The results can help to perform image
retrieval and analyze features of an object, as are shown in this paper.
Abstract: Sensor networks are often deployed in unattended
environments, thus leaving these networks vulnerable to false data
injection attacks in which an adversary injects forged reports into the
network through compromised nodes, with the goal of deceiving the
base station or depleting the resources of forwarding nodes. Several
research solutions have been recently proposed to detect and drop such
forged reports during the forwarding process. Each design can provide
the equivalent resilience in terms of node compromising. However,
their energy consumption characteristics differ from each other. Thus,
employing only a single filtering scheme for a network is not a
recommendable strategy in terms of energy saving. It's very important
the threshold determination for message authentication to identify. We
propose the recursive contract net protocols which less energy level of
terminal node in wireless sensor network.
Abstract: With the advance of information technology in the
new era the applications of Internet to access data resources has
steadily increased and huge amount of data have become accessible
in various forms. Obviously, the network providers and agencies,
look after to prevent electronic attacks that may be harmful or may
be related to terrorist applications. Thus, these have facilitated the
authorities to under take a variety of methods to protect the special
regions from harmful data. One of the most important approaches is
to use firewall in the network facilities. The main objectives of
firewalls are to stop the transfer of suspicious packets in several
ways. However because of its blind packet stopping, high process
power requirements and expensive prices some of the providers are
reluctant to use the firewall. In this paper we proposed a method to
find a discriminate function to distinguish between usual packets and
harmful ones by the statistical processing on the network router logs.
By discriminating these data, an administrator may take an approach
action against the user. This method is very fast and can be used
simply in adjacent with the Internet routers.
Abstract: Stream Control Transmission Protocol (SCTP) has been
proposed to provide reliable transport of real-time communications.
Due to its attractive features, such as multi-streaming and multihoming,
the SCTP is often expected to be an alternative protocol
for TCP and UDP. In the original SCTP standard, the secondary path
is mainly regarded as a redundancy. Recently, most of researches
have focused on extending the SCTP to enable a host to send its
packets to a destination over multiple paths simultaneously. In order
to transfer packets concurrently over the multiple paths, the SCTP
should be well designed to avoid unnecessary fast retransmission
and the mis-estimation of congestion window size through the paths.
Therefore, we propose an Enhanced Cooperative ACK SCTP (ECASCTP)
to improve the path recovery efficiency of multi-homed host
which is under concurrent multiple transfer mode. We evaluated the
performance of our proposed scheme using ns-2 simulation in terms
of cwnd variation, path recovery time, and goodput. Our scheme
provides better performance in lossy and path asymmetric networks.
Abstract: This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.
Abstract: A large amount of valuable information is available in
plain text clinical reports. New techniques and technologies are
applied to extract information from these reports. In this study, we
developed a domain based software system to transform 600
Otorhinolaryngology discharge notes to a structured form for
extracting clinical data from the discharge notes. In order to decrease
the system process time discharge notes were transformed into a data
table after preprocessing. Several word lists were constituted to
identify common section in the discharge notes, including patient
history, age, problems, and diagnosis etc. N-gram method was used
for discovering terms co-Occurrences within each section. Using this
method a dataset of concept candidates has been generated for the
validation step, and then Predictive Apriori algorithm for Association
Rule Mining (ARM) was applied to validate candidate concepts.
Abstract: Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
Abstract: This paper presents a hybrid association control
scheme that can maintain load balancing among access points in the
wireless LANs and can satisfy the quality of service requirements of
the multimedia traffic applications. The proposed model is
mathematically described as a linear programming model. Simulation
study and analysis were conducted in order to demonstrate the
performance of the proposed hybrid load balancing and association
control scheme. Simulation results shows that the proposed scheme
outperforms the other schemes in term of the percentage of blocking
and the quality of the data transfer rate providing to the multimedia
and real-time applications.
Abstract: The curriculum of the primary school science course was redesigned on the basis of constructivism in 2005-2006 academic years, in Turkey. In this context, the name of this course has been changed as “Science and Technology"; and both content and course books, students workbooks for this course have been redesigned in light of constructivism. The aim of this study is to determine whether the Science and Technology course books and student work books for primary school 5th grade are appropriate for the constructivism by evaluating them in terms of the fundamental principles of constructivism. In this study, out of qualitative research methods, documentation technique (i.e. document analysis) is applied; while selecting samples, criterion-sampling is used out of purposeful sampling techniques. When the Science and Technology course book and workbook for the 5th grade in primary education are examined, it is seen that both books complete each other in certain areas. Consequently, it can be claimed that in spite of some inadequate and missing points in the course book and workbook of the primary school Science and Technology course for the 5th grade students, these books are attempted to be designed in terms of the principles of constructivism. To overcome the inadequacies in the books, it can be suggested to redesign them. In addition to them, not to ignore the technology dimension of the course, the activities that encourage the students to prepare projects using technology cycle should be included.
Abstract: Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.
Abstract: In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.
Abstract: We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.