Abstract: The paper analyzes the response of buildings and industrially structures on seismic waves (low frequency mechanical vibration) generated by blasting operations. The principles of seismic analysis can be applied for different kinds of excitation such as: earthquakes, wind, explosions, random excitation from local transportation, periodic excitation from large rotating and/or machines with reciprocating motion, metal forming processes such as forging, shearing and stamping, chemical reactions, construction and earth moving work, and other strong deterministic and random energy sources caused by human activities. The article deals with the response of seismic, low frequency, mechanical vibrations generated by nearby blasting operations on a residential home. The goal was to determine the fundamental natural frequencies of the measured structure; therefore it is important to determine the resonant frequencies to design a suitable modal damping. The article also analyzes the package of seismic waves generated by blasting (Primary waves – P-waves and Secondary waves S-waves) and investigated the transfer regions. For the detection of seismic waves resulting from an explosion, the Fast Fourier Transform (FFT) and modal analysis, in the frequency domain, is used and the signal was acquired and analyzed also in the time domain. In the conclusions the measured results of seismic waves caused by blasting in a nearby quarry and its effect on a nearby structure (house) is analyzed. The response on the house, including the fundamental natural frequency and possible fatigue damage is also assessed.
Abstract: In many applications retransmissions of lost packets are not permitted. OFDM is a multi-carrier modulation scheme having excellent performance which allows overlapping in frequency domain. With OFDM there is a simple way of dealing with multipath relatively simple DSP algorithms.
In this paper, an image frame is compressed using DWT, and the compressed data is arranged in data vectors, each with equal number of coefficients. These vectors are quantized and binary coded to get the bit steams, which are then packetized and intelligently mapped to the OFDM system. Based on one-bit channel state information at the transmitter, the descriptions in order of descending priority are assigned to the currently good channels such that poorer sub-channels can only affect the lesser important data vectors. We consider only one-bit channel state information available at the transmitter, informing only about the sub-channels to be good or bad. For a good sub-channel, instantaneous received power should be greater than a threshold Pth. Otherwise, the sub-channel is in fading state and considered bad for that batch of coefficients. In order to reduce the system power consumption, the mapped descriptions onto the bad sub channels are dropped at the transmitter. The binary channel state information gives an opportunity to map the bit streams intelligently and to save a reasonable amount of power. By using MAT LAB simulation we can analysis the performance of our proposed scheme, in terms of system energy saving without compromising the received quality in terms of peak signal-noise ratio.
Abstract: Determination of attentional status is important because working performance and an unexpected accident is highly related with the attention. The autonomic nervous and the central nervous systems can reflect the changes in person’s attentional status. Reduced number of suitable pysiological parameters among autonomic and central nervous systems related signal parameters will be critical in optimum design of attentional devices. In this paper, we analyze the EEG (Electroencephalography) and HRV (Heart Rate Variability) signals to demonstrate the effective relation with brain signal and cardiovascular signal during event-related attention, which will be later used in selecting the minimum set of attentional parameters. Time and frequency domain parameters from HRV signal and frequency domain parameters from EEG signal are used as input to the optimum feature parameters selector.
Abstract: This paper describes the development of a fully
automated measurement software for antenna radiation pattern
measurements in a Compact Antenna Test Range (CATR). The
CATR has a frequency range from 2-40 GHz and the measurement
hardware includes a Network Analyzer for transmitting and
Receiving the microwave signal and a Positioner controller to control
the motion of the Styrofoam column. The measurement process
includes Calibration of CATR with a Standard Gain Horn (SGH)
antenna followed by Gain versus angle measurement of the Antenna
under test (AUT). The software is designed to control a variety of
microwave transmitter / receiver and two axis Positioner controllers
through the standard General Purpose interface bus (GPIB) interface.
Addition of new Network Analyzers is supported through a slight
modification of hardware control module. Time-domain gating is
implemented to remove the unwanted signals and get the isolated
response of AUT. The gated response of the AUT is compared with
the calibration data in the frequency domain to obtain the desired
results. The data acquisition and processing is implemented in
Agilent VEE and Matlab. A variety of experimental measurements
with SGH antennas were performed to validate the accuracy of
software. A comparison of results with existing commercial
softwares is presented and the measured results are found to be
within .2 dBm.
Abstract: Analysis of heart rate variability (HRV) has become a
popular non-invasive tool for assessing the activities of autonomic
nervous system. Most of the methods were hired from techniques
used for time series analysis. Currently used methods are time
domain, frequency domain, geometrical and fractal methods. A new
technique, which searches for pattern repeatability in a time series, is
proposed for quantifying heart rate (HR) time series. These set of
indices, which are termed as pattern repeatability measure and
pattern repeatability ratio are able to distinguish HR data clearly
from noise and electroencephalogram (EEG). The results of analysis
using these measures give an insight into the fundamental difference
between the composition of HR time series with respect to EEG and
noise.
Abstract: In this paper a new fast simplification method is
presented. Such method realizes Karnough map with large
number of variables. In order to accelerate the operation of the
proposed method, a new approach for fast detection of group
of ones is presented. Such approach implemented in the
frequency domain. The search operation relies on performing
cross correlation in the frequency domain rather than time one.
It is proved mathematically and practically that the number of
computation steps required for the presented method is less
than that needed by conventional cross correlation. Simulation
results using MATLAB confirm the theoretical computations.
Furthermore, a powerful solution for realization of complex
functions is given. The simplified functions are implemented
by using a new desigen for neural networks. Neural networks
are used because they are fault tolerance and as a result they
can recognize signals even with noise or distortion. This is
very useful for logic functions used in data and computer
communications. Moreover, the implemented functions are
realized with minimum amount of components. This is done
by using modular neural nets (MNNs) that divide the input
space into several homogenous regions. Such approach is
applied to implement XOR function, 16 logic functions on one
bit level, and 2-bit digital multiplier. Compared to previous
non- modular designs, a clear reduction in the order of
computations and hardware requirements is achieved.
Abstract: In this paper channel estimation techniques are
considered as the support methods for OFDM transmission systems
based on Non Binary LDPC (Low Density Parity Check) codes.
Standard frequency domain pilot aided LS (Least Squares) and
LMMSE (Linear Minimum Mean Square Error) estimators are
investigated. Furthermore, an iterative algorithm is proposed as a
solution exploiting the NB-LDPC channel decoder to improve the
performance of the LMMSE estimator. Simulation results of signals
transmitted through fading mobile channels are presented to compare
the performance of the proposed channel estimators.
Abstract: In this paper, an intelligent algorithm for optimal
document archiving is presented. It is kown that electronic archives
are very important for information system management. Minimizing
the size of the stored data in electronic archive is a main issue to
reduce the physical storage area. Here, the effect of different types of
Arabic fonts on electronic archives size is discussed. Simulation
results show that PDF is the best file format for storage of the Arabic
documents in electronic archive. Furthermore, fast information
detection in a given PDF file is introduced. Such approach uses fast
neural networks (FNNs) implemented in the frequency domain. The
operation of these networks relies on performing cross correlation in
the frequency domain rather than spatial one. It is proved
mathematically and practically that the number of computation steps
required for the presented FNNs is less than that needed by
conventional neural networks (CNNs). Simulation results using
MATLAB confirm the theoretical computations.
Abstract: In this paper, a new model order reduction
phenomenon is introduced at the design stage of linear phase digital
IIR filter. The complexity of a system can be reduced by adopting the
model order reduction method in their design. In this paper a mixed
method of model order reduction is proposed for linear IIR filter. The
proposed method employs the advantages of factor division technique
to derive the reduced order denominator polynomial and the reduced
order numerator is obtained based on the resultant denominator
polynomial. The order reduction technique is used to reduce the delay
units at the design stage of IIR filter. The validity of the proposed
method is illustrated with design example in frequency domain and
stability is also examined with help of nyquist plot.
Abstract: The main focus of this paper is on the human induced
forces. Almost all existing force models for this type of load (defined
either in the time or frequency domain) are developed from the
assumption of perfect periodicity of the force and are based on force
measurements conducted on rigid (i.e. high frequency) surfaces. To
verify the different authors conclusions the vertical pressure
measurements invoked during the walking was performed, using
pressure gauges in various configurations. The obtained forces are
analyzed using Fourier transformation. This load is often decisive in
the design of footbridges. Design criteria and load models proposed
by widely used standards and other researchers were introduced and a
comparison was made.
Abstract: In this paper, the application of the Mode Matching
(MM) method in the case of photonic crystal waveguide
discontinuities is presented. The structure under consideration is
divided into a number of cells, which supports a number of guided
and evanescent modes. These modes can be calculated numerically
by an alternative formulation of the plane wave expansion method
for each frequency. A matrix equation is then formed relating the
modal amplitudes at the beginning and at the end of the structure.
The theory is highly efficient and accurate and can be applied to
study the transmission sensitivity of photonic crystal devices due to
fabrication tolerances. The accuracy of the MM method is compared
to the Finite Difference Frequency Domain (FDFD) and the Adjoint
Variable Method (AVM) and good agreement is observed.
Abstract: In this paper, applying frequency domain approach, a delayed predator-prey fishery model with prey reserve is investigated. By choosing the delay τ as a bifurcation parameter, It is found that Hopf bifurcation occurs as the bifurcation parameter τ passes a sequence of critical values. That is, a family of periodic solutions bifurcate from the equilibrium when the bifurcation parameter exceeds a critical value. The length of delay which preserves the stability of the positive equilibrium is calculated. Some numerical simulations are included to justify the theoretical analysis results. Finally, main conclusions are given.
Abstract: In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique called “modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.
Abstract: Frequency domain independent component analysis has
a scaling indeterminacy and a permutation problem. The scaling
indeterminacy can be solved by use of a decomposed spectrum. For
the permutation problem, we have proposed the rules in terms of gain
ratio and phase difference derived from the decomposed spectra and
the source-s coarse directions.
The present paper experimentally clarifies that the gain ratio and
the phase difference work effectively in a real environment but their
performance depends on frequency bands, a microphone-space and
a source-microphone distance. From these facts it is seen that it is
difficult to attain a perfect solution for the permutation problem in a
real environment only by either the gain ratio or the phase difference.
For the perfect solution, this paper gives a solution to the problems
in a real environment. The proposed method is simple, the amount of
calculation is small. And the method has high correction performance
without depending on the frequency bands and distances from source
signals to microphones. Furthermore, it can be applied under the real
environment. From several experiments in a real room, it clarifies
that the proposed method has been verified.
Abstract: This paper presents the development of an active
vibration control using direct adaptive controller to suppress the
vibration of a flexible beam system. The controller is realized based
on linear parametric form. Differential evolution optimisation
algorithm is used to optimize the controller using single objective
function by minimizing the mean square error of the observed
vibration signal. Furthermore, an alternative approach is developed to
systematically search for the best controller model structure together
with it parameter values. The performance of the control scheme is
presented and analysed in both time and frequency domain.
Simulation results demonstrate that the proposed scheme is able to
suppress the unwanted vibration effectively.
Abstract: The flexible follower response of a translating cam with
four different profiles for rise-dwell-fall-dwell (RDFD) motion is
investigated. The cycloidal displacement motion, the modified
sinusoidal acceleration motion, the modified trapezoidal acceleration
motion, and the 3-4-5 polynomial motion are employed to describe the
rise and the fall motions of the follower and the associated four kinds of
cam profiles are studied. Since the follower flexibility is considered,
the contact point of the roller and the cam is an unknown. Two
geometric constraints formulated to restrain the unknown position are
substituted into Hamilton-s principle with Lagrange multipliers.
Applying the assumed mode method, one can obtain the governing
equations of motion as non-linear differential-algebraic equations. The
equations are solved using Runge-Kutta method. Then, the responses of
the flexible follower undergoing the four different motions are
investigated in time domain and in frequency domain.
Abstract: In this paper we present a system for classifying videos
by frequency spectra. Many videos contain activities with repeating
movements. Sports videos, home improvement videos, or videos
showing mechanical motion are some example areas. Motion of these
areas usually repeats with a certain main frequency and several side
frequencies. Transforming repeating motion to its frequency domain
via FFT reveals these frequencies. Average amplitudes of frequency
intervals can be seen as features of cyclic motion. Hence determining
these features can help to classify videos with repeating movements.
In this paper we explain how to compute frequency spectra for video
clips and how to use them for classifying. Our approach utilizes series
of image moments as a function. This function again is transformed
into its frequency domain.
Abstract: Hydrodynamic pressures acting on upstream of concrete dams during an earthquake are an important factor in designing and assessing the safety of these structures in Earthquake regions. Due to inherent complexities, assessing exact hydrodynamic pressure is only feasible for problems with simple geometry. In this research, the governing equation of concrete gravity dam reservoirs with effect of fluid viscosity in frequency domain is solved and then compared with that in which viscosity is assumed zero. The results show that viscosity influences the reservoir-s natural frequency. In excitation frequencies near the reservoir's natural frequencies, hydrodynamic pressure has a considerable difference in compare to the results of non-viscose fluid.
Abstract: In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.
Abstract: In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.