Abstract: Since MEMS gyro sensors measure not angle of rotation but angular rate, an estimator is designed to estimate the angles in many applications. Gyro and accelerometer are used to improve estimating accuracy of the angle. This paper presents a method of finding filter coefficients of the well-known estimator which is to get rotation angles from gyro and accelerometer data. In order to verify the performance of our method, the estimated angle is compared with the encoder output in a rotary pendulum system.
Abstract: This paper presents a new configurable decimation
filter for sigma-delta modulators. The filter employs the Pascal-s
triangle-s theorem for building the coefficients of non-recursive
decimation filters. The filter can be connected to the back-end of
various modulators with different output accuracy. In this work two
methods are shown and then compared from area occupation
viewpoint. First method uses the memory and the second one
employs Pascal-s triangle-s method, aiming to reduce required gates.
XILINX ISE v10 is used for implementation and confirmation the
filter.
Abstract: The author present PID controller design for
following control of hard disk drive by characteristic ratio assignment
method. The study in this paper concerns design of a PID controller
which sufficiently robust to the disturbances and plant perturbations
on following control of hard disk drive. Characteristic Ratio
Assignment (CRA) is shown to be an efficient control technique to
serve this requirement. The controller design by CRA is based on the
choice of the coefficients of the characteristic polynomial of the
closed loop system according to the convenient performance criteria
such as equivalent time constant and ration of characteristic
coefficient. Hence, in this study, CRA method is applied in PID
controller design for following control of hard disk drive. Matlab
simulation results shown that CRA design is fairly stable and robust
whilst giving the convenience in controller-s parameters adjustment.
Abstract: In this study a two dimensional axisymmetric, steady state and incompressible laminar flow in a rotating single disk is numerically investigated. The finite volume method is used for solving the momentum equations. The numerical model and results
are validated by comparing it to previously reported experimental data for velocities, angles and moment coefficients. It is
demonstrated that increasing the axial distance increases the value of axial velocity and vice versa for tangential and total velocities. However, the maximum value of nondimensional radial velocity
occurs near the disk wall. It is also found that with increase rotational Reynolds number, moment coefficient decreases.
Abstract: This paper introduces and studies new indexing techniques for content-based queries in images databases. Indexing is the key to providing sophisticated, accurate and fast searches for queries in image data. This research describes a new indexing approach, which depends on linear modeling of signals, using bases for modeling. A basis is a set of chosen images, and modeling an image is a least-squares approximation of the image as a linear combination of the basis images. The coefficients of the basis images are taken together to serve as index for that image. The paper describes the implementation of the indexing scheme, and presents the findings of our extensive evaluation that was conducted to optimize (1) the choice of the basis matrix (B), and (2) the size of the index A (N). Furthermore, we compare the performance of our indexing scheme with other schemes. Our results show that our scheme has significantly higher performance.
Abstract: In this paper we consider the issue of distributed adaptive estimation over sensor networks. To deal with more realistic scenario, different variance for observation noise is assumed for sensors in the network. To solve the problem of different variance of observation noise, the proposed method is divided into two phases: I) Estimating each sensor-s observation noise variance and II) using the estimated variances to obtain the desired parameter. Our proposed algorithm is based on a diffusion least mean square (LMS) implementation with linear combiner model. In the proposed algorithm, the step-size parameter the coefficients of linear combiner are adjusted according to estimated observation noise variances. As the simulation results show, the proposed algorithm considerably improves the diffusion LMS algorithm given in literature.
Abstract: The speech signal conveys information about the
identity of the speaker. The area of speaker identification is
concerned with extracting the identity of the person speaking the
utterance. As speech interaction with computers becomes more
pervasive in activities such as the telephone, financial transactions
and information retrieval from speech databases, the utility of
automatically identifying a speaker is based solely on vocal
characteristic. This paper emphasizes on text dependent speaker
identification, which deals with detecting a particular speaker from a
known population. The system prompts the user to provide speech
utterance. System identifies the user by comparing the codebook of
speech utterance with those of the stored in the database and lists,
which contain the most likely speakers, could have given that speech
utterance. The speech signal is recorded for N speakers further the
features are extracted. Feature extraction is done by means of LPC
coefficients, calculating AMDF, and DFT. The neural network is
trained by applying these features as input parameters. The features
are stored in templates for further comparison. The features for the
speaker who has to be identified are extracted and compared with the
stored templates using Back Propogation Algorithm. Here, the
trained network corresponds to the output; the input is the extracted
features of the speaker to be identified. The network does the weight
adjustment and the best match is found to identify the speaker. The
number of epochs required to get the target decides the network
performance.
Abstract: The goal of this project is to design a system to
recognition voice commands. Most of voice recognition systems
contain two main modules as follow “feature extraction" and “feature
matching". In this project, MFCC algorithm is used to simulate
feature extraction module. Using this algorithm, the cepstral
coefficients are calculated on mel frequency scale. VQ (vector
quantization) method will be used for reduction of amount of data to
decrease computation time. In the feature matching stage Euclidean
distance is applied as similarity criterion. Because of high accuracy
of used algorithms, the accuracy of this voice command system is
high. Using these algorithms, by at least 5 times repetition for each
command, in a single training session, and then twice in each testing
session zero error rate in recognition of commands is achieved.
Abstract: This research was conducted for the first time at the
southeastern coasts of the Caspian Sea in order to evaluate the
performance of osteichthyes cooperatives through production (catch)
function. Using one of the indirect valuation methods in this research,
contributory factors in catch were identified and were inserted into
the function as independent variables. In order to carry out this
research, the performance of 25 Osteichthyes catching cooperatives
in the utilization year of 2009 which were involved in fishing in
Miankale wildlife refuge region. The contributory factors in catch
were divided into groups of economic, ecological and biological
factors. In the mentioned function, catch rate of the cooperative were
inserted into as the dependant variable and fourteen partial variables
in terms of nine general variables as independent variables. Finally,
after function estimation, seven variables were rendered significant at
99 percent reliably level. The results of the function estimation
indicated that human resource (fisherman quantity) had the greatest
positive effect on catch rate with an influence coefficient of 1.7 while
weather conditions had the greatest negative effect on the catch rate
of cooperatives with an influence coefficient of -2.07. Moreover,
factors like member's share, experience and fisherman training and
fishing effort played the main roles in the catch rate of cooperative
with influence coefficients of 0.81, 0.5 and 0.21, respectively.
Abstract: In this paper, we propose a Perceptually Optimized Foveation based Embedded ZeroTree Image Coder (POEFIC) that introduces a perceptual weighting to wavelet coefficients prior to control SPIHT encoding algorithm in order to reach a targeted bit rate with a perceptual quality improvement with respect to a given bit rate a fixation point which determines the region of interest ROI. The paper also, introduces a new objective quality metric based on a Psychovisual model that integrates the properties of the HVS that plays an important role in our POEFIC quality assessment. Our POEFIC coder is based on a vision model that incorporates various masking effects of human visual system HVS perception. Thus, our coder weights the wavelet coefficients based on that model and attempts to increase the perceptual quality for a given bit rate and observation distance. The perceptual weights for all wavelet subbands are computed based on 1) foveation masking to remove or reduce considerable high frequencies from peripheral regions 2) luminance and Contrast masking, 3) the contrast sensitivity function CSF to achieve the perceptual decomposition weighting. The new perceptually optimized codec has the same complexity as the original SPIHT techniques. However, the experiments results show that our coder demonstrates very good performance in terms of quality measurement.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: Finite impulse response (FIR) filters have the advantage of linear phase, guaranteed stability, fewer finite precision errors, and efficient implementation. In contrast, they have a major disadvantage of high order need (more coefficients) than IIR counterpart with comparable performance. The high order demand imposes more hardware requirements, arithmetic operations, area usage, and power consumption when designing and fabricating the filter. Therefore, minimizing or reducing these parameters, is a major goal or target in digital filter design task. This paper presents an algorithm proposed for modifying values and the number of non-zero coefficients used to represent the FIR digital pulse shaping filter response. With this algorithm, the FIR filter frequency and phase response can be represented with a minimum number of non-zero coefficients. Therefore, reducing the arithmetic complexity needed to get the filter output. Consequently, the system characteristic i.e. power consumption, area usage, and processing time are also reduced. The proposed algorithm is more powerful when integrated with multiplierless algorithms such as distributed arithmetic (DA) in designing high order digital FIR filters. Here the DA usage eliminates the need for multipliers when implementing the multiply and accumulate unit (MAC) and the proposed algorithm will reduce the number of adders and addition operations needed through the minimization of the non-zero values coefficients to get the filter output.
Abstract: In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively
Abstract: This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
Abstract: The authors present a mixed method for reducing the order of the large-scale dynamic systems. In this method, the denominator polynomial of the reduced order model is obtained by using the modified pole clustering technique while the coefficients of the numerator are obtained by Pade approximations. This method is conceptually simple and always generates stable reduced models if the original high-order system is stable. The proposed method is illustrated with the help of the numerical examples taken from the literature.
Abstract: In this paper in consideration of each available
techniques deficiencies for speech recognition, an advanced method
is presented that-s able to classify speech signals with the high
accuracy (98%) at the minimum time. In the presented method, first,
the recorded signal is preprocessed that this section includes
denoising with Mels Frequency Cepstral Analysis and feature
extraction using discrete wavelet transform (DWT) coefficients; Then
these features are fed to Multilayer Perceptron (MLP) network for
classification. Finally, after training of neural network effective
features are selected with UTA algorithm.
Abstract: Isobaric vapor-liquid equilibrium measurements are reported for the binary mixtures of Mesitylene + 1-Heptanol and Mesitylene + 1-Octanol at 97.3 kPa. The measurements have been performed using a vapor recirculating type (modified Othmer's) equilibrium still. Both the mixtures show positive deviation from ideality. The Mesitylene + 1-Heptanol mixture forms an azeotrope whereas Mesitylene + 1- Octanol form a non – azeotropic mixture. The activity coefficients have been calculated taking into consideration the vapor phase nonideality. The data satisfy the thermodynamic consistency tests of Herington, and Hirata. The activity coefficients have been satisfactorily correlated by means of the Margules, Redlich-Kister, Wilson, Black, and NRTL equations. The activity coefficient values have also been obtained by UNIFAC method.
Abstract: Among all geo-hydrological relationships, rainfallrunoff
relationship is of utmost importance in any hydrological
investigation and water resource planning. Spatial variation, lag time
involved in obtaining areal estimates for the basin as a whole can
affect the parameterization in design stage as well as in planning
stage. In conventional hydrological processing of data, spatial aspect
is either ignored or interpolated at sub-basin level. Temporal
variation when analysed for different stages can provide clues for its
spatial effectiveness. The interplay of space-time variation at pixel
level can provide better understanding of basin parameters.
Sustenance of design structures for different return periods and their
spatial auto-correlations should be studied at different geographical
scales for better management and planning of water resources.
In order to understand the relative effect of spatio-temporal
variation in hydrological data network, a detailed geo-hydrological
analysis of Betwa river catchment falling in Lower Yamuna Basin is
presented in this paper. Moreover, the exact estimates about the
availability of water in the Betwa river catchment, especially in the
wake of recent Betwa-Ken linkage project, need thorough scientific
investigation for better planning. Therefore, an attempt in this
direction is made here to analyse the existing hydrological and
meteorological data with the help of SPSS, GIS and MS-EXCEL
software. A comparison of spatial and temporal correlations at subcatchment
level in case of upper Betwa reaches has been made to
demonstrate the representativeness of rain gauges. First, flows at
different locations are used to derive correlation and regression
coefficients. Then, long-term normal water yield estimates based on
pixel-wise regression coefficients of rainfall-runoff relationship have
been mapped. The areal values obtained from these maps can
definitely improve upon estimates based on point-based
extrapolations or areal interpolations.
Abstract: The purpose of this study was primarily assessing how important economic factors namely: The Thai export price of white rice, the exchange rate, and the world rice consumption affect the overall Thai white rice export, using historical data during the period 1989-2013 from the Thai Rice Exporters Association, and Food and Agricultural Organization of the United Nations. The co-integration method, regression analysis, and error correction model were applied to investigate the econometric model. The findings indicated that in the long-run, the world rice consumption, the exchange rate, and the Thai export price of white rice were the important factors affecting the export quantity of Thai white rice respectively, as indicated by their significant coefficients. Meanwhile, the rice export price was an important factor affecting the export quantity of Thai white rice in the short-run. This information is useful in the business, export opportunities, price competitiveness, and policymaker in Thailand.
Abstract: Salinity is a measure of the amount of salts in the
water. Total Dissolved Solids (TDS) as salinity parameter are often
determined using laborious and time consuming laboratory tests, but
it may be more appropriate and economical to develop a method
which uses a more simple soil salinity index. Because dissolved ions
increase salinity as well as conductivity, the two measures are
related. The aim of this research was determine of constant
coefficients for predicting of Total Dissolved Solids (TDS) based on
Electrical Conductivity (EC) with Statistics of Correlation
coefficient, Root mean square error, Maximum error, Mean Bias
error, Mean absolute error, Relative error and Coefficient of residual
mass. For this purpose, two experimental areas (S1, S2) of Khuzestan
province-IRAN were selected and four treatments with three
replications by series of double rings were applied. The treatments
were included 25cm, 50cm, 75cm and 100cm water application. The
results showed the values 16.3 & 12.4 were the best constant
coefficients for predicting of Total Dissolved Solids (TDS) based on
EC in Pilot S1 and S2 with correlation coefficient 0.977 & 0.997 and
191.1 & 106.1 Root mean square errors (RMSE) respectively.