Scanning Device for Sampling the Spatial Distribution of the E-field
This paper presents a low cost automatic system for
sampling the electric field in a limited area. The scanning area is a
flat surface parallel to the ground at a selected height. We discuss
in detail the hardware, software and all the arrangements involved
in the system operation. In order to show the system performance
we include a campaign of narrow band measurements with 6017
sample points in the surroundings of a cellular base station. A
commercial isotropic antenna with three orthogonal axes was used
as sampling device. The results are analyzed in terms of its space
average, standard deviation and statistical distribution.
Modeling and Control of Two Manipulators Handling a Flexible Beam
This paper seeks to develop simple yet practical and
efficient control scheme that enables cooperating arms to handle a
flexible beam. Specifically the problem studied herein is that of two
arms rigidly grasping a flexible beam and such capable of generating
forces/moments in such away as to move a flexible beam along a
predefined trajectory. The paper develops a sliding mode control law
that provides robustness against model imperfection and uncertainty.
It also provides an implicit stability proof. Simulation results for two
three joint arms moving a flexible beam, are presented to validate the
Supervisory Fuzzy Learning Control for Underwater Target Tracking
This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.
Electric Load Forecasting Using Genetic Based Algorithm, Optimal Filter Estimator and Least Error Squares Technique: Comparative Study
This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.
New Enhanced Hexagon-Based Search Using Point-Oriented Inner Search for Fast Block Motion Estimation
Recently, an enhanced hexagon-based search (EHS)
algorithm was proposed to speedup the original hexagon-based search
(HS) by exploiting the group-distortion information of some evaluated
points. In this paper, a second version of the EHS is proposed with a
new point-oriented inner search technique which can further speedup
the HS in both large and small motion environments. Experimental
results show that the enhanced hexagon-based search version-2
(EHS2) is faster than the HS up to 34% with negligible PSNR
Performance Improvements of DSP Applications on a Generic Reconfigurable Platform
Speedups from mapping four real-life DSP
applications on an embedded system-on-chip that couples coarsegrained
reconfigurable logic with an instruction-set processor are
presented. The reconfigurable logic is realized by a 2-Dimensional
Array of Processing Elements. A design flow for improving
application-s performance is proposed. Critical software parts, called
kernels, are accelerated on the Coarse-Grained Reconfigurable
Array. The kernels are detected by profiling the source code. For
mapping the detected kernels on the reconfigurable logic a prioritybased
mapping algorithm has been developed. Two 4x4 array
architectures, which differ in their interconnection structure among
the Processing Elements, are considered. The experiments for eight
different instances of a generic system show that important overall
application speedups have been reported for the four applications.
The performance improvements range from 1.86 to 3.67, with an
average value of 2.53, compared with an all-software execution.
These speedups are quite close to the maximum theoretical speedups
imposed by Amdahl-s law.
Sensorless PM Motor with Multi Degree of Freedom Fuzzy Control
This paper introduces application of multi degree of freedom fuzzy(MDOFF) controller in permanent magnet (PM)drive system. The drive system model is developed for FO control. Simulation of the system is carried out to predict the performance at NL and under load,. The results indicate that application of MDOFF controller is effective for sensorless PM drive system.
High Dynamic Range Resampling for Software Radio
The classic problem of recovering arbitrary values of
a band-limited signal from its samples has an added complication
in software radio applications; namely, the resampling calculations
inevitably fold aliases of the analog signal back into the original
bandwidth. The phenomenon is quantified by the spur-free dynamic
range. We demonstrate how a novel application of the Remez (Parks-
McClellan) algorithm permits optimal signal recovery and SFDR, far
surpassing state-of-the-art resamplers.
Energy-Efficient Sensing Concept for a Micromachined Yaw Rate Sensor
The need for micromechanical inertial sensors is increasing
in future electronic stability control (ESC) and other positioning,
navigation and guidance systems. Due to the rising density of
sensors in automotive and consumer devices the goal is not only to get
high performance, robustness and smaller package sizes, but also to
optimize the energy management of the overall sensor system. This
paper presents an evaluation concept for a surface micromachined
yaw rate sensor. Within this evaluation concept an energy-efficient
operation of the drive mode of the yaw rate sensor is enabled. The
presented system concept can be realized within a power management
Blind Identification of MA Models Using Cumulants
In this paper, many techniques for blind identification of moving average (MA) process are presented. These methods utilize third- and fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed (i.i.d) non-Gaussian sequence that is not observed. Two nonlinear optimization algorithms, namely the Gradient Descent and the Gauss-Newton algorithms are exposed. An algorithm based on the joint-diagonalization of the fourth-order cumulant matrices (FOSI) is also considered, as well as an improved version of the classical C(q, 0, k) algorithm based on the choice of the Best 1-D Slice of fourth-order cumulants. To illustrate the effectiveness of our methods, various simulation examples are presented.
Perturbations of the EM-field Meters Reading Caused by Flat Roof Security Wall
The wide increase and diffusion on telecommunication
technologies have caused a huge spread of electromagnetic sources
in most European Countries. Since the public is continuously being
exposed to electromagnetic radiation the possible health effects have
become the focus of population concerns. As a result, electromagnetic
field monitoring stations which control field strength in commercial
frequency bands are being placed on the flat roof of many buildings.
However there is no guidance on where to place them. This paper
presents an analysis of frequency, polarization and angles of incidence
of a plane wave which impinges on a flat roof security wall and its
dependence on electromagnetic field strength meters placement.
Mobile Robot Navigation Using Local Model Networks
Developing techniques for mobile robot navigation constitutes one of the major trends in the current
research on mobile robotics. This paper develops a local
model network (LMN) for mobile robot navigation. The
LMN represents the mobile robot by a set of locally valid
submodels that are Multi-Layer Perceptrons (MLPs).
Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular
region. The submodels then are combined in a unified
structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This
proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the
proposed LMN reflect the soundness of the proposed
Chaos Synchronization Using Sliding Mode Technique
In this paper, an effective sliding mode design is
applied to chaos synchronization. The proposed controller can make
the states of two identical modified Chua-s circuits globally
asymptotically synchronized. Numerical results are provided to show
the effectiveness and robustness of the proposed method.
Design of an Stable GPC for Nonminimum Phase LTI Systems
The current methods of predictive controllers are
utilized for those processes in which the rate of output variations is
not high. For such processes, therefore, stability can be achieved by
implementing the constrained predictive controller or applying
infinite prediction horizon. When the rate of the output growth is
high (e.g. for unstable nonminimum phase process) the stabilization
seems to be problematic. In order to avoid this, it is suggested to
change the method in the way that: first, the prediction error growth
should be decreased at the early stage of the prediction horizon, and
second, the rate of the error variation should be penalized. The
growth of the error is decreased through adjusting its weighting
coefficients in the cost function. Reduction in the error variation is
possible by adding the first order derivate of the error into the cost
function. By studying different examples it is shown that using these
two remedies together, the closed-loop stability of unstable
nonminimum phase process can be achieved.
Anti-Synchronization of two Different Chaotic Systems via Active Control
This paper presents anti-synchronization of chaos
between two different chaotic systems using active control method.
The proposed technique is applied to achieve chaos antisynchronization
for the Lü and Rössler dynamical systems.
Numerical simulations are implemented to verify the results.
A New Stabilizing GPC for Nonminimum Phase LTI Systems Using Time Varying Weighting
In this paper, we show that the stability can not be
achieved with current stabilizing MPC methods for some unstable
processes. Hence we present a new method for stabilizing these
processes. The main idea is to use a new time varying weighted cost
function for traditional GPC. This stabilizes the closed loop system
without adding soft or hard constraint in optimization problem. By
studying different examples it is shown that using the proposed
method, the closed-loop stability of unstable nonminimum phase
process is achieved.
A Cost Function for Joint Blind Equalization and Phase Recovery
In this paper a new cost function for blind equalization
is proposed. The proposed cost function, referred to as the modified
maximum normalized cumulant criterion (MMNC), is an extension
of the previously proposed maximum normalized cumulant criterion
(MNC). While the MNC requires a separate phase recovery system
after blind equalization, the MMNC performs joint blind equalization
and phase recovery. To achieve this, the proposed algorithm
maximizes a cost function that considers both amplitude and phase of
the equalizer output. The simulation results show that the proposed
algorithm has an improved channel equalization effect than the MNC
algorithm and simultaneously can correct the phase error that the
MNC algorithm is unable to do. The simulation results also show that
the MMNC algorithm has lower complexity than the MNC algorithm.
Moreover, the MMNC algorithm outperforms the MNC algorithm
particularly when the symbols block size is small.
System Performance Comparison of Turbo and Trellis Coded Optical CDMA Systems
In this paper, we have compared the performance of a Turbo and Trellis coded optical code division multiple access (OCDMA) system. The comparison of the two codes has been accomplished by employing optical orthogonal codes (OOCs). The Bit Error Rate (BER) performances have been compared by varying the code weights of address codes employed by the system. We have considered the effects of optical multiple access interference (OMAI), thermal noise and avalanche photodiode (APD) detector noise. Analysis has been carried out for the system with and without double optical hard limiter (DHL). From the simulation results it is observed that a better and distinct comparison can be drawn between the performance of Trellis and Turbo coded systems, at lower code weights of optical orthogonal codes for a fixed number of users. The BER performance of the Turbo coded system is found to be better than the Trellis coded system for all code weights that have been considered for the simulation. Nevertheless, the Trellis coded OCDMA system is found to be better than the uncoded OCDMA system. Trellis coded OCDMA can be used in systems where decoding time has to be kept low, bandwidth is limited and high reliability is not a crucial factor as in local area networks. Also the system hardware is less complex in comparison to the Turbo coded system. Trellis coded OCDMA system can be used without significant modification of the existing chipsets. Turbo-coded OCDMA can however be employed in systems where high reliability is needed and bandwidth is not a limiting factor.
Application of Neuro-Fuzzy Dynamic Programming to Improve the Reactive Power and Voltage Profile of a Distribution Substation
Improving the reactive power and voltage profile of a
distribution substation is investigated in this paper. The purpose is to
properly determination of the shunt capacitors on/off status and
suitable tap changer (TC) position of a substation transformer. In
addition, the limitation of secondary bus voltage, the maximum
allowable number of switching operation in a day for on load tap
changer and on/off status of capacitors are taken into account. To
achieve these goals, an artificial neural network (ANN) is designed to
provide preliminary scheduling. Input of ANN is active and reactive
powers of transformer and its primary and secondary bus voltages.
The output of ANN is capacitors on/off status and TC position. The
preliminary schedule is further refined by fuzzy dynamic
programming in order to reach the final schedule. The operation of
proposed method in Q/V improving is compared with the results
obtained by operator operation in a distribution substation.
Nonlinear Torque Control for PMSM: A Lyapunov Technique Approach
This study presents a novel means of designing a simple and effective torque controller for Permanent Magnet Synchronous Motor (PMSM). The overall stability of the system is shown using Lyapunov technique. The Lyapunov functions used contain a term penalizing the integral of the tracking error, enhancing the stability. The tracking error is shown to be globally uniformly bounded. Simulation results are presented to show the effectiveness of the approach.
A Finite Precision Block Floating Point Treatment to Direct Form, Cascaded and Parallel FIR Digital Filters
This paper proposes an efficient finite precision block floating point (BFP) treatment to the fixed coefficient finite impulse response (FIR) digital filter. The treatment includes effective implementation of all the three forms of the conventional FIR filters, namely, direct form, cascaded and par- allel, and a roundoff error analysis of them in the BFP format. An effective block formatting algorithm together with an adaptive scaling factor is pro- posed to make the realizations more simple from hardware view point. To this end, a generic relation between the tap weight vector length and the input block length is deduced. The implementation scheme also emphasises on a simple block exponent update technique to prevent overflow even during the block to block transition phase. The roundoff noise is also investigated along the analogous lines, taking into consideration these implementational issues. The simulation results show that the BFP roundoff errors depend on the sig- nal level almost in the same way as floating point roundoff noise, resulting in approximately constant signal to noise ratio over a relatively large dynamic range.
Hardware Implementations for the ISO/IEC 18033-4:2005 Standard for Stream Ciphers
In this paper the FPGA implementations for four
stream ciphers are presented. The two stream ciphers, MUGI and
SNOW 2.0 are recently adopted by the International Organization for
Standardization ISO/IEC 18033-4:2005 standard. The other two
stream ciphers, MICKEY 128 and TRIVIUM have been submitted
and are under consideration for the eSTREAM, the ECRYPT
(European Network of Excellence for Cryptology) Stream Cipher
project. All ciphers were coded using VHDL language. For the
hardware implementation, an FPGA device was used. The proposed
implementations achieve throughputs range from 166 Mbps for
MICKEY 128 to 6080 Mbps for MUGI.
Cross Layer Optimization for Fairness Balancing Based on Adaptively Weighted Utility Functions in OFDMA Systems
Cross layer optimization based on utility functions has
been recently studied extensively, meanwhile, numerous types of
utility functions have been examined in the corresponding literature.
However, a major drawback is that most utility functions take a fixed
mathematical form or are based on simple combining, which can
not fully exploit available information. In this paper, we formulate a
framework of cross layer optimization based on Adaptively Weighted
Utility Functions (AWUF) for fairness balancing in OFDMA networks.
Under this framework, a two-step allocation algorithm is
provided as a sub-optimal solution, whose control parameters can be
updated in real-time to accommodate instantaneous QoS constrains.
The simulation results show that the proposed algorithm achieves
high throughput while balancing the fairness among multiple users.
IMM based Kalman Filter for Channel Estimation in MB OFDM Systems
Ultra-wide band (UWB) communication is one of
the most promising technologies for high data rate wireless networks
for short range applications. This paper proposes a blind channel
estimation method namely IMM (Interactive Multiple Model) Based
Kalman algorithm for UWB OFDM systems. IMM based Kalman
filter is proposed to estimate frequency selective time varying
channel. In the proposed method, two Kalman filters are concurrently
estimate the channel parameters. The first Kalman filter namely
Static Model Filter (SMF) gives accurate result when the user is static
while the second Kalman filter namely the Dynamic Model Filter
(DMF) gives accurate result when the receiver is in moving state. The
static transition matrix in SMF is assumed as an Identity matrix
where as in DMF, it is computed using Yule-Walker equations. The
resultant filter estimate is computed as a weighted sum of individual
filter estimates. The proposed method is compared with other existing
channel estimation methods.
Application of Neural Networks in Power Systems; A Review
The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, this paper introduce a brief overview in fault diagnosis, security assessment, load forecasting, economic dispatch and harmonic analyzing. Advantages and disadvantages of using NNs in above mentioned subjects and the main challenges in these fields have been explained, too.
An Adaptive Approach to Synchronization of Two Chua's Circuits
This paper introduces an adaptive control scheme to synchronize two identical Chua's systems. Introductory part of the paper is presented in the first part of the paper and then in the second part, a new theorem is proposed based on which an adaptive control scheme is developed to synchronize two identical modified Chua's circuit. Finally, numerical simulations are included to verify the effectiveness of the proposed control method.
Wiener Filter as an Optimal MMSE Interpolator
The ideal sinc filter, ignoring the noise statistics, is often
applied for generating an arbitrary sample of a bandlimited signal by
using the uniformly sampled data. In this article, an optimal interpolator is proposed; it reaches a minimum mean square error (MMSE)
at its output in the presence of noise. The resulting interpolator is
thus a Wiener filter, and both the optimal infinite impulse response
(IIR) and finite impulse response (FIR) filters are presented. The
mean square errors (MSE-s) for the interpolator of different length
impulse responses are obtained by computer simulations; it shows that
the MSE-s of the proposed interpolators with a reasonable length are
improved about 0.4 dB under flat power spectra in noisy environment with signal-to-noise power ratio (SNR) equal 10 dB. As expected,
the results also demonstrate the improvements for the MSE-s with various fractional delays of the optimal interpolator against the ideal
sinc filter under a fixed length impulse response.
Using Neural Network for Execution of Programmed Pulse Width Modulation (PPWM) Method
Application of neural networks in execution of
programmed pulse width modulation (PPWM) of a voltage source
inverter (VSI) is studied in this paper. Using the proposed method it is
possible to cancel out the desired harmonics in output of VSI in
addition to control the magnitude of fundamental harmonic,
contineously. By checking the non-trained values and a performance
index, the most appropriate neural network is proposed. It is shown
that neural networks may solve the custom difficulties of practical
utilization of PPWM such as large size of memory, complex digital
circuits and controlling the magnitude of output voltage in a discrete
A New Method for Estimation of the Source Coherency Structure of Wideband Sources
Based on the sources- smoothed rank profile (SRP) and modified minimum description length (MMDL) principle, a method for estimation of the source coherency structure (SCS) and the number of wideband sources is proposed in this paper. Instead of focusing, we first use a spatial smoothing technique to pre-process the array covariance matrix of each frequency for de-correlating the sources and then use smoothed rank profile to determine the SCS and the number of wideband sources. We demonstrate the availability of the method by numerical simulations.
Comparison of the Existing Methods in Determination of the Characteristic Polynomial
This paper presents comparison among methods of
determination of the characteristic polynomial coefficients. First, the
resultant systems from the methods are compared based on frequency
criteria such as the closed loop bandwidth, gain and phase margins.
Then the step responses of the resultant systems are compared on the
basis of the transient behavior criteria including overshoot, rise time,
settling time and error (via IAE, ITAE, ISE and ITSE integral
indices). Also relative stability of the systems is compared together.
Finally the best choices in regards to the above diverse criteria are
Sonic Localization Cues for Classrooms: A Structural Model Proposal
We investigate sonic cues for binaural sound localization within classrooms and present a structural model for the same. Two of the primary cues for localization, interaural time difference (ITD) and interaural level difference (ILD) created between the two ears by sounds from a particular point in space, are used. Although these cues do not lend any information about the elevation of a sound source, the torso, head, and outer ear carry out elevation dependent spectral filtering of sounds before they reach the inner ear. This effect is commonly captured in head related transfer function (HRTF) which aids in resolving the ambiguity from the ITDs and ILDs alone and helps localize sounds in free space. The proposed structural model of HRTF produces well controlled horizontal as well as vertical effects. The implemented HRTF is a signal processing model which tries to mimic the physical effects of the sounds interacting with different parts of the body. The effectiveness of the method is tested by synthesizing spatial audio, in MATLAB, for use in listening tests with human subjects and is found to yield satisfactory results in comparison with existing models.