Abstract: This paper presents a new method for estimating the nonstationary
noise power spectral density given a noisy signal. The
method is based on averaging the noisy speech power spectrum using
time and frequency dependent smoothing factors. These factors are
adjusted based on signal-presence probability in individual frequency
bins. Signal presence is determined by computing the ratio of the
noisy speech power spectrum to its local minimum, which is updated
continuously by averaging past values of the noisy speech power
spectra with a look-ahead factor. This method adapts very quickly to
highly non-stationary noise environments. The proposed method
achieves significant improvements over a system that uses voice
activity detector (VAD) in noise estimation.
Abstract: This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].
Abstract: Data gathering is an essential operation in wireless
sensor network applications. So it requires energy efficiency
techniques to increase the lifetime of the network. Similarly,
clustering is also an effective technique to improve the energy
efficiency and network lifetime of wireless sensor networks. In this
paper, an energy efficient cluster formation protocol is proposed with
the objective of achieving low energy dissipation and latency without
sacrificing application specific quality. The objective is achieved by
applying randomized, adaptive, self-configuring cluster formation
and localized control for data transfers. It involves application -
specific data processing, such as data aggregation or compression.
The cluster formation algorithm allows each node to make
independent decisions, so as to generate good clusters as the end.
Simulation results show that the proposed protocol utilizes minimum
energy and latency for cluster formation, there by reducing the
overhead of the protocol.
Abstract: Voltage stability has become an important issue to many power systems around the world due to the weak systems and long line on power system networks. In this paper, MATLAB load flow program is applied to obtain the weak points in the system combined with finding the voltage stability limit. The maximum permissible loading of a system, within the voltage stability limit, is usually determined. The methods for varying tap ratio (using tap changing transformer) and applying different values of shunt capacitor injection to improve the voltage stability within the limit are also provided.
Abstract: This article simulates the wind generator set which has
two fault bearing collar rail destruction and the gear box oil leak fault.
The electric current signal which produced by the generator, We use
Empirical Mode Decomposition (EMD) as well as Fast Fourier
Transform (FFT) obtains the frequency range-s signal figure and
characteristic value. The last step is use a kind of Artificial Neural
Network (ANN) classifies which determination fault signal's type and
reason. The ANN purpose of the automatic identification wind
generator set fault..
Abstract: To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 99.9% when SNR is not lower than 5dB).
Abstract: The demand for autonomous resource
management for distributed systems has increased in recent
years. Distributed systems require an efficient and powerful
communication mechanism between applications running on
different hosts and networks. The use of mobile agent
technology to distribute and delegate management tasks
promises to overcome the scalability and flexibility limitations
of the currently used centralized management approach. This
work proposes a multiagent system that adopts mobile agents
as a technology for tasks distribution, results collection, and
management of resources in large-scale distributed systems. A
new mobile agent-based approach for collecting results from
distributed system elements is presented. The technique of
artificial intelligence based on intelligent agents giving the
system a proactive behavior. The presented results are based
on a design example of an application operating in a mobile
environment.
Abstract: A set of Artificial Neural Network (ANN) based methods
for the design of an effective system of speech recognition of
numerals of Assamese language captured under varied recording
conditions and moods is presented here. The work is related to
the formulation of several ANN models configured to use Linear
Predictive Code (LPC), Principal Component Analysis (PCA) and
other features to tackle mood and gender variations uttering numbers
as part of an Automatic Speech Recognition (ASR) system in
Assamese. The ANN models are designed using a combination of
Self Organizing Map (SOM) and Multi Layer Perceptron (MLP)
constituting a Learning Vector Quantization (LVQ) block trained in a
cooperative environment to handle male and female speech samples
of numerals of Assamese- a language spoken by a sizable population
in the North-Eastern part of India. The work provides a comparative
evaluation of several such combinations while subjected to handle
speech samples with gender based differences captured by a microphone
in four different conditions viz. noiseless, noise mixed, stressed
and stress-free.
Abstract: Electric impedance imaging is a method of
reconstructing spatial distribution of electrical conductivity inside a
subject. In this paper, a new method of electrical impedance imaging
using eddy current is proposed. The eddy current distribution in the
body depends on the conductivity distribution and the magnetic field
pattern. By changing the position of magnetic core, a set of voltage
differences is measured with a pair of electrodes. This set of voltage
differences is used in image reconstruction of conductivity
distribution. The least square error minimization method is used as a
reconstruction algorithm. The back projection algorithm is used to
get two dimensional images. Based on this principle, a measurement
system is developed and some model experiments were performed
with a saline filled phantom. The shape of each model in the
reconstructed image is similar to the corresponding model,
respectively. From the results of these experiments, it is confirmed
that the proposed method is applicable in the realization of electrical
imaging.
Abstract: In this paper, zigbee communication based wireless energy surveillance system is presented. The proposed system consists of multiple energy surveillance devices and an energy surveillance monitor. Each different standby power-off value of electric device is set automatically by using learning function of energy surveillance device. Thus adaptive standby power-off function provides user convenience and it maximizes the energy savings. Also, power consumption monitoring function is helpful to reduce inefficient energy consumption in home. The zigbee throughput simulator is designed to evaluate minimum transmission power and maximum allowable information quantity in the proposed system. The test result of prototype has been satisfied all the requirements. The proposed system has confirmed that can be used as an intelligent energy surveillance system for energy savings in home or office.
Abstract: This paper presents the generation of bipolar square
wave pulses with characteristics that are suitable for liquid food
sterilization using a Cascaded H-bridge Multilevel Inverter (CHMI).
Bipolar square waves pulses have been reported as stable for a
longer time during the sterilization process with minimum heat
emission and increased efficiency. The CHMI allows the system to
produce bipolar square wave pulses and yielding high output voltage
without using a transformer while fulfilling the pulse requirements
for effective liquid food sterilization. This in turn can reduce power
consumption and cost of the overall liquid food sterilization system.
The simulation results have shown that pulses with peak output
voltage of 2.4 kV, pulse width of between 1 2s and 1 ms at
frequencies of 50 Hz and 100 Hz can be generated by a 7-level
CHMI. Results from the experimental set-up based on a 5-level
CHMI has indicated the potential of the proposed circuit in
producing bipolar square wave output pulses with peak values that
depends on the DC source level supplied to the CHMI modules,
pulse width of between 12.5 2s and 1 ms at frequencies of 50 Hz
and 100 Hz.
Abstract: In the hardening energy context, the transport sector
which constitutes a large worldwide energy demand has to be
improving for decrease energy demand and global warming impacts.
In a controversial situation where subsists an increasing demand for
long-distance and high-speed travels, high-speed trains offer many
advantages, as consuming significantly less energy than road or air
transports.
At the project phase of new rail infrastructures, it is nowadays
important to characterize accurately the energy that will be induced
by its operation phase, in addition to other more classical criteria as
construction costs and travel time.
Current literature consumption models used to estimate railways
operation phase are obsolete or not enough accurate for taking into
account the newest train or railways technologies.
In this paper, an updated model of consumption for high-speed is
proposed, based on experimental data obtained from full-scale tests
performed on a new high-speed line. The assessment of the model
is achieved by identifying train parameters and measured power
consumptions for more than one hundred train routes. Perspectives
are then discussed to use this updated model for accurately assess
the energy impact of future railway infrastructures.
Abstract: Recently, much research has been conducted for
security for wireless sensor networks and ubiquitous computing.
Security issues such as authentication and data integrity are major
requirements to construct sensor network systems. Advanced
Encryption Standard (AES) is considered as one of candidate
algorithms for data encryption in wireless sensor networks. In this
paper, we will present the hardware architecture to implement low
power AES crypto module. Our low power AES crypto module has
optimized architecture of data encryption unit and key schedule unit
which could be applicable to wireless sensor networks. We also details
low power design methods used to design our low power AES crypto
module.
Abstract: Cognitive Infocommunications (CogInfoCom) is a new
research direction which has emerged as the synergic convergence
of infocommunications and the cognitive sciences. In this paper,
we provide the definition of CogInfoCom, and propose an architectural
framework for the interaction-oriented design of CogInfoCom
systems. We provide the outlines of an application example of
the interaction-oriented architecture, and briefly discuss its main
characteristics.
Abstract: In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Abstract: A combined three-microphone voice activity detector (VAD) and noise-canceling system is studied to enhance speech recognition in an automobile environment. A previous experiment clearly shows the ability of the composite system to cancel a single noise source outside of a defined zone. This paper investigates the performance of the composite system when there are frequently moving noise sources (noise sources are coming from different locations but are not always presented at the same time) e.g. there is other passenger speech or speech from a radio when a desired speech is presented. To work in a frequently moving noise sources environment, whilst a three-microphone voice activity detector (VAD) detects voice from a “VAD valid zone", the 3-microphone noise canceller uses a “noise canceller valid zone" defined in freespace around the users head. Therefore, a desired voice should be in the intersection of the noise canceller valid zone and VAD valid zone. Thus all noise is suppressed outside this intersection of area. Experiments are shown for a real environment e.g. all results were recorded in a car by omni-directional electret condenser microphones.
Abstract: In this paper, we propose an easily computable proximity index for predicting voltage collapse of a load bus using only measured values of the bus voltage and power; Using these measurements a polynomial of fourth order is obtained by using LES estimation algorithms. The sum of the absolute values of the polynomial coefficient gives an idea of the critical bus. We demonstrate the applicability of our proposed method on 6 bus test system. The results obtained verify its applicability, as well as its accuracy and the simplicity. From this indicator, it is allowed to predict the voltage instability or the proximity of a collapse. Results obtained by the PV curve are compared with corresponding values by QV curves and are observed to be in close agreement.
Abstract: To improve the characterization of blood flows, we propose a method which makes it possible to use the spectral analysis
of the Doppler signals. Our calculation induces a reasonable approximation, the error made on estimated speed reflects the fact
that speed depends on the flow conditions as well as on measurement parameters like the bore and the volume flow rate. The estimate of the Doppler signal frequency enables us to determine the maximum Doppler frequencie Fd max as well as the maximum flow speed. The
results show that the difference between the estimated frequencies
( Fde ) and the Doppler frequencies ( Fd ) is small, this variation tends to zero for important θ angles and it is proportional to the diameter D. The description of the speed of friction and the
coefficient of friction justify the error rate obtained.
Abstract: Traditional optical networks are gradually evolving towards intelligent optical networks due to the need for faster bandwidth provisioning, protection and restoration of the network that can be accomplished with devices like optical switch, add drop multiplexer and cross connects. Since dense wavelength multiplexing forms the physical layer for intelligent optical networking, the roll of high speed all optical switch is important. This paper analyzes such an ultra-high speed polymer electro-optic switch. The performances of the 2x2 optical waveguide switch with rectangular, triangular and trapezoidal grating profiles on various device parameters are analyzed. The simulation result shows that trapezoidal grating is the optimized structure which has the coupling length of 81μm and switching voltage of 11V for the operating wavelength of 1550nm. The switching time for this proposed switch is 0.47 picosecond. This makes the proposed switch to be an important element in the intelligent optical network.
Abstract: One of the major problems in liberalized power
markets is loss allocation. In this paper, a different method for
allocating transmission losses to pool market participants is
proposed. The proposed method is fundamentally based on
decomposition of loss function and current projection concept. The
method has been implemented and tested on several networks and
one sample summarized in the paper. The results show that the
method is comprehensive and fair to allocating the energy losses of a
power market to its participants.