|
Paper Title |
Authors |
Conference |
Abstract |
|
|
Bit-width Optimization of CS-ACELP Speech Coder by SIMULINK: Core Layer of the New G.729.1 Standard |
Zohre Sharifi Mehrjardi
Neda Kazemian Amiri
Sied Mehdi Fakhraie
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
In this work, we present SIMUIINK bit-true
modeling of the conjugate structure-algebraic CELP
(CS-ACELP) speech coder which has been chosen as
the core layer of speech coder standard ITU-T
G.729.1. The optimum bit numbers ... more
In this work, we present SIMUIINK bit-true
modeling of the conjugate structure-algebraic CELP
(CS-ACELP) speech coder which has been chosen as
the core layer of speech coder standard ITU-T
G.729.1. The optimum bit numbers of the
computational blocks are defined as the minimum
word-widths that maintain the quality of the output
with minimum chip area and power. Such optimum bitwidth
of the coefficients and the internal computations
are extracted. As a result, a golden model of the codec
which best suits as a reference for its hardware
implementation is developed. The power and area
improvements are estimated in two blocks of CSACELP
speech coder. less
In this work, we present SIMUIINK bit-true
modeling of the conjugate structure-algebraic CELP
(CS-ACELP) speech coder which has been chosen as
the core layer of speech coder standard ITU-T
G.729.1. The optimum bit numbers ... more |
خرید مقاله
|
|
Tensor-Based Face Representation and Recognition Using Multi-Linear Subspace Analysis |
Hadis Mohseni
Shohreh Kasaei
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Discriminative subspace analysis is a popular approach
for a variety of applications. There is a growing
interest in subspace learning techniques for face
recognition. Principal component analysis (PCA) and
eigenfaces are two important subspace ... more
Discriminative subspace analysis is a popular approach
for a variety of applications. There is a growing
interest in subspace learning techniques for face
recognition. Principal component analysis (PCA) and
eigenfaces are two important subspace analysis methods
have been widely applied in a variety of areas.
However, the excessive dimension of data space often
causes the curse of dimensionality dilemma, expensive
computational cost, and sometimes the singularity
problem. In this paper, a new supervised discriminative
subspace analysis is presented by encoding face
image as a high order general tensor. As face space
can be considered as a nonlinear submanifold embedded
in the tensor space, a decomposition method called
Tucker tensor is used which can effectively decomposes
this sparse space. The performance of the proposed
method is compared with that of eigenface, Fisherface,
tensor LPP, and ORO4×2 on ORL and Weizermann
databases. Conducted experimental results show the
superiority of the proposed method. less
Discriminative subspace analysis is a popular approach
for a variety of applications. There is a growing
interest in subspace learning techniques for face
recognition. Principal component analysis (PCA) and
eigenfaces are two important subspace ... more |
خرید مقاله
|
|
Dynamic and memory efficient web page prediction model using LZ78 and LZW algorithms |
Alborz moghaddam
Ehsanollah kabir
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Web access prediction has attracted significant
attention in recent years. Web prefetching and some
personalization systems use prediction algorithms. Most
current applications that predict the next user web page
have an offline component that ... more
Web access prediction has attracted significant
attention in recent years. Web prefetching and some
personalization systems use prediction algorithms. Most
current applications that predict the next user web page
have an offline component that does the data preparation
task and an online section that provides personalized
content to the users based on their current navigational
activities. In this paper we present an online prediction
model that does not have an offline component and fit in the
memory with good prediction accuracy. Our algorithm is
based on LZ78 and LZW algorithms that are adapted for
modeling the user navigation in web. Our model decreases
computational complexities which is a serious problem in
developing online prediction systems. A performance
evaluation is presented using real web logs. This evaluation
shows that our model needs much less memory than PPM
family of algorithms with good prediction accuracy. less
Web access prediction has attracted significant
attention in recent years. Web prefetching and some
personalization systems use prediction algorithms. Most
current applications that predict the next user web page
have an offline component that ... more |
خرید مقاله
|
|
Facial Feature Detection and Extraction using Symmetry and Region-based Deformable Template Matching |
Hoda Bahonar
Nasrollah M. Charkari
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
In this paper, we propose a method for selecting
the symmetry axis of eyes region from two or more
candidates. We propose a region-based deformable
template matching from two new defined operations:
intensity-based 2-clustering ... more
In this paper, we propose a method for selecting
the symmetry axis of eyes region from two or more
candidates. We propose a region-based deformable
template matching from two new defined operations:
intensity-based 2-clustering and edge shadowing. The
results display the effectiveness of our method for
extraction of eye, eyebrow and nose templates. The
parameters of these templates can be used as feature
vectors in low bit rate transmission. Evaluation of the
proposed method on an Iranian database shows the
accuracy of 99% for feature region extraction and
86% in average for feature template extraction. less
In this paper, we propose a method for selecting
the symmetry axis of eyes region from two or more
candidates. We propose a region-based deformable
template matching from two new defined operations:
intensity-based 2-clustering ... more |
خرید مقاله
|
|
A Contrast Independent Algorithm for Adaptive Binarization of Degraded Document Images |
M. valizadeh
M. komeili
N. armanfard
E. kabir
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
This paper presents an efficient algorithm for
adaptive binarization of degraded document images.
Document binarization algorithms suffer from poor
and variable contrast in document images. We propose
a contrast independent binarization algorithm that
does not ... more
This paper presents an efficient algorithm for
adaptive binarization of degraded document images.
Document binarization algorithms suffer from poor
and variable contrast in document images. We propose
a contrast independent binarization algorithm that
does not require any parameter setting by user.
Therefore, it can handle various types of degraded
document images. The proposed algorithm involves
two consecutive stages. At the first stage, independent
of contrast between foreground and background, some
parts of each character are extracted and in the second
stage, the gray level of foreground and background are
locally estimated. For each pixel, the average of
estimated foreground and background gray levels is
defined as threshold. After extensive experiments, the
proposed binarization algorithm demonstrate superior
performance against four well-know binarization
algorithms on a set of degraded document images
captured with camera. less
This paper presents an efficient algorithm for
adaptive binarization of degraded document images.
Document binarization algorithms suffer from poor
and variable contrast in document images. We propose
a contrast independent binarization algorithm that
does not ... more |
خرید مقاله
|
|
Application of Neuro-Fuzzy models In Short Term Electricity Load Forecast |
A. R. Koushki
M. Nosrati Maralloo
C. Lucas
A. Kalhor
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
One of the important requirements for operational
planning of electrical utilities is the prediction of
hourly load up to several days, known as Short Term
Load Forecasting (STLF). Considering the effect of its
accuracy ... more
One of the important requirements for operational
planning of electrical utilities is the prediction of
hourly load up to several days, known as Short Term
Load Forecasting (STLF). Considering the effect of its
accuracy on system security and also economical
aspects, there is an on-going attention toward putting
new approaches to the task. Recently, Neuro Fuzzy
modeling has played a successful role in various
applications over nonlinear time series prediction.
This paper presents a neuro-fuzzy model for the
application of short-term load forecasting. This model
is identified through Locally Liner Model Tree
(LoLiMoT) learning algorithm. The model is compared
to a multilayer perceptron and Kohonen Classification
and Intervention Analysis. The models are trained and
assessed on load data extracted from EUNITE network
competition. less
One of the important requirements for operational
planning of electrical utilities is the prediction of
hourly load up to several days, known as Short Term
Load Forecasting (STLF). Considering the effect of its
accuracy ... more |
خرید مقاله
|
|
Quantitative Similarity-based Evaluation of Text Retrieval Algorithms |
Parastoo Didari
Behrad Babai
Azadeh Shakery
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Text retrieval engines, such as search engines,
always return a list of documents in response to a given
query. Existing evaluations of text retrieval algorithms
mostly use Precision and Recall of the returned ... more
Text retrieval engines, such as search engines,
always return a list of documents in response to a given
query. Existing evaluations of text retrieval algorithms
mostly use Precision and Recall of the returned list of
documents as main quality measures of a search engine. In
this paper, we propose a novel approach for comparing
different algorithms adopted by different search engines
and evaluate their performance. In our approach, the
results of each algorithm is treated as an inter-related set of
documents and the effectiveness of the algorithm is
evaluated based on the degree of relation in the set of
documents. After verifying the correctness of the evaluation
measure by examining the results of the two retrieval
algorithms, BM25 and pivoted normalization, and
comparing these results with an ideal ranking, we compare
the results of these algorithms and investigate the impact of
certain major factors like stemming on the results of the
suggested algorithm. The effectiveness of our proposed
method is justified through obtained experimental results. less
Text retrieval engines, such as search engines,
always return a list of documents in response to a given
query. Existing evaluations of text retrieval algorithms
mostly use Precision and Recall of the returned ... more |
خرید مقاله
|
|
Polynomial Kernel Function and its Application in Locally Polynomial Neurofuzzy Models |
A. Shirvani
H. Chegini
S. Setayeshi
C. Lucas
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Polynomials are one of the most powerful functions
that have been used in many fields of mathematics
such as curve fitting and regression. Low order
polynomials are desired for their smoothness1, good
local approximation ... more
Polynomials are one of the most powerful functions
that have been used in many fields of mathematics
such as curve fitting and regression. Low order
polynomials are desired for their smoothness1, good
local approximation and interpolation. Being smooth,
they can be used to locally approximate almost any
derivable function. This means that when linear
functions fail in approximation (e.g. where the first
order Taylor expansion equals zero) polynomial
functions can be used in local approximation, such
that one can achieve better estimations at extremums.
In this paper, application of polynomial kernel
functions in locally linear neurofuzzy models is shown.
Using polynomial kernels in local models, better local
approximations in prediction of chaotic time series
such as Mackey-Glass is achieved, and the capability
of the neurofuzzy network is enhanced. less
Polynomials are one of the most powerful functions
that have been used in many fields of mathematics
such as curve fitting and regression. Low order
polynomials are desired for their smoothness1, good
local approximation ... more |
خرید مقاله
|
|
A New Classification Approach Based on Cooperative Game |
Atefeh Torkaman
Nasrollah Moghaddam Charkari
Mahnaz Aghaeipour
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Classification is a well known task in data mining
and machine learning that aims to predict the class of
items as accurately as possible. A well planned data
classification system makes essential data ... more
Classification is a well known task in data mining
and machine learning that aims to predict the class of
items as accurately as possible. A well planned data
classification system makes essential data easy to find.
An object is classified into one of the categories called
classes according to the features that well separated
the classes. Actually, classification maps an object to
its classification label. Many researches used different
learning algorithms to classify data; neural networks,
decision trees, etc.
In this paper, a new classification approach based
on cooperative game is proposed. Cooperative game is
a branch of game theory consists of a set of players
and a characteristic function which specifies the value
created by different subsets of the players in the game.
In order to find classes in classification process,
objects can be imagine as the players in a game and
according to the values which obtained by these
players, classes will be separated. This approach can
be used to classify a population according to their
contributions. In the other words, it applies equally to
different types of data. Through out this paper, a
special case in medical diagnosis was studied. 304
samples taken from human leukemia tissue consists of
17 attributes which determine different CD markers
related to leukemia were analyzed. These samples
collected from different types of leukemia at Iran Blood
Transfusion Organization (IBTO). Obtained results
demonstrate that cooperative game is very promising
to use directly for classification. less
Classification is a well known task in data mining
and machine learning that aims to predict the class of
items as accurately as possible. A well planned data
classification system makes essential data ... more |
خرید مقاله
|
|
Feature integration for adaptive visual tracking in a particle filtering framework |
M. Komeili
N. Armanfard
M. Valizadeh
E. Kabir
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
In this paper we propose a new integration method for
multi-feature object tracking in a particle filter
framework. We divide particles into separate clusters.
All particles within a cluster measure a specific
feature. The ... more
In this paper we propose a new integration method for
multi-feature object tracking in a particle filter
framework. We divide particles into separate clusters.
All particles within a cluster measure a specific
feature. The number of particles within a cluster is in
proportion to the reliability of associated feature. We
do a compensation stage which neutralizes the effect of
particles weights mean within a cluster. Compensation
stage balances the concentration of particles around
local maximal. So, particles are distributed more
effectively in the scene. Proposed method provides
both effective hypothesis generation and effective
evaluation of hypothesis. Experimental results over a
set of real-world sequences demonstrate better
performance of our method compared to the common
methods of feature integration. less
In this paper we propose a new integration method for
multi-feature object tracking in a particle filter
framework. We divide particles into separate clusters.
All particles within a cluster measure a specific
feature. The ... more |
خرید مقاله
|
.gif){kind=icon}
