Abstract: This paper deals with the design, development & implementation of a temperature sensor using zigbee. The main aim of the work undertaken in this paper is to sense the temperature and to display the result on the LCD using the zigbee technology. ZigBee operates in the industrial, scientific and medical (ISM) radio bands; 868 MHz in Europe, 915 MHz in the USA and 2.4 GHz in most jurisdictions worldwide. The technology is intended to be simpler and cheaper than other WPANs such as Bluetooth. The most capable ZigBee node type is said to require only about 10 % of the software of a typical Bluetooth or Wireless Internet node, while the simplest nodes are about 2 %. However, actual code sizes are much higher, more like 50 % of the Bluetooth code size. ZigBee chip vendors have announced 128-kilobyte devices. In this work undertaken in the design & development of the temperature sensor, it senses the temperature and after amplification is then fed to the micro controller, this is then connected to the zigbee module, which transmits the data and at the other end the zigbee reads the data and displays on to the LCD. The software developed is highly accurate and works at a very high speed. The method developed shows the effectiveness of the scheme employed.
Abstract: In this paper a new maximum power point tracking
algorithm for photovoltaic arrays is proposed. The algorithm detects
the maximum power point of the PV. The computed maximum
power is used as a reference value (set point) of the control system.
ON/OFF power controller with hysteresis band is used to control the
operation of a Buck chopper such that the PV module always
operates at its maximum power computed from the MPPT algorithm.
The major difference between the proposed algorithm and other
techniques is that the proposed algorithm is used to control directly
the power drawn from the PV.
The proposed MPPT has several advantages: simplicity, high
convergence speed, and independent on PV array characteristics. The
algorithm is tested under various operating conditions. The obtained
results have proven that the MPP is tracked even under sudden
change of irradiation level.
Abstract: Support Vector Machine (SVM) is a statistical
learning tool developed to a more complex concept of
structural risk minimization (SRM). In this paper, SVM is
applied to signal detection in communication systems in the
presence of channel noise in various environments in the form
of Rayleigh fading, additive white Gaussian background noise
(AWGN), and interference noise generalized as additive color
Gaussian noise (ACGN). The structure and performance of
SVM in terms of the bit error rate (BER) metric is derived and
simulated for these advanced stochastic noise models and the
computational complexity of the implementation, in terms of
average computational time per bit, is also presented. The
performance of SVM is then compared to conventional binary
signaling optimal model-based detector driven by binary
phase shift keying (BPSK) modulation. We show that the
SVM performance is superior to that of conventional matched
filter-, innovation filter-, and Wiener filter-driven detectors,
even in the presence of random Doppler carrier deviation,
especially for low SNR (signal-to-noise ratio) ranges. For
large SNR, the performance of the SVM was similar to that of
the classical detectors. However, the convergence between
SVM and maximum likelihood detection occurred at a higher
SNR as the noise environment became more hostile.
Abstract: This paper describes studies carried out to investigate
the viability of using wireless cameras as a tool in monitoring
changes in air quality. A camera is used to monitor the change in
colour of a chemically responsive polymer within view of the camera
as it is exposed to varying chemical species concentration levels. The
camera captures this image and the colour change is analyzed by
averaging the RGB values present. This novel chemical sensing
approach is compared with an established chemical sensing method
using the same chemically responsive polymer coated onto LEDs. In
this way, the concentration levels of acetic acid in the air can be
tracked using both approaches. These approaches to chemical plume
tracking have many applications for air quality monitoring.
Abstract: In this paper, a method based on Non-Dominated
Sorting Genetic Algorithm (NSGA) has been presented for the Volt /
Var control in power distribution systems with dispersed generation
(DG). Genetic algorithm approach is used due to its broad
applicability, ease of use and high accuracy. The proposed method is
better suited for volt/var control problems. A multi-objective
optimization problem has been formulated for the volt/var control of
the distribution system. The non-dominated sorting genetic algorithm
based method proposed in this paper, alleviates the problem of tuning
the weighting factors required in solving the multi-objective volt/var
control optimization problems. Based on the simulation studies
carried out on the distribution system, the proposed scheme has been
found to be simple, accurate and easy to apply to solve the multiobjective
volt/var control optimization problem of the distribution
system with dispersed generation.
Abstract: Single side band modulation is a widespread technique in communication with significant impact on communication technologies such as DSL modems and ATSC TV. Its widespread utilization is due to its bandwidth and power saving characteristics. In this paper, we present a new scheme for SSB signal generation which is cost efficient and enjoys superior characteristics in terms of frequency stability, selectivity, and robustness to noise. In the process, we develop novel Hilbert transform properties.
Abstract: Regenerative Thermal Oxidizer (RTO) is one of the
best solutions for removal of Volatile Organic Compounds (VOC)
from industrial processes. In the RTO, VOC in a raw gas are usually
decomposed at 950-1300 K and the combustion heat of VOC is
recovered by regenerative heat exchangers charged with ceramic
honeycombs. The optimization of the treatment of VOC leads to the
reduction of fuel addition to VOC decomposition, the minimization of
CO2 emission and operating cost as well.
In the present work, the thermal efficiency of the RTO was
investigated experimentally in a pilot-scale RTO unit using toluene as
a typical representative of VOC. As a result, it was recognized that the
radiative heat transfer was dominant in the preheating process of a raw
gas when the gas flow rate was relatively low. Further, it was found
that a minimum heat exchanger volume to achieve self combustion of
toluene without additional heating of the RTO by fuel combustion was
dependent on both the flow rate of a raw gas and the concentration of
toluene. The thermal efficiency calculated from fuel consumption and
the decomposed toluene ratio, was found to have a maximum value of
0.95 at a raw gas mass flow rate of 1810 kg·h-1 and honeycombs height
of 1.5m.
Abstract: In this paper we propose a new criterion for solving
the problem of channel shortening in multi-carrier systems. In a
discrete multitone receiver, a time-domain equalizer (TEQ) reduces
intersymbol interference (ISI) by shortening the effective duration of
the channel impulse response. Minimum mean square error (MMSE)
method for TEQ does not give satisfactory results. In [1] a new
criterion for partially equalizing severe ISI channels to reduce the
cyclic prefix overhead of the discrete multitone transceiver (DMT),
assuming a fixed transmission bandwidth, is introduced. Due to
specific constrained (unit morm constraint on the target impulse
response (TIR)) in their method, the freedom to choose optimum
vector (TIR) is reduced. Better results can be obtained by avoiding
the unit norm constraint on the target impulse response (TIR). In
this paper we change the cost function proposed in [1] to the cost
function of determining the maximum of a determinant subject to
linear matrix inequality (LMI) and quadratic constraint and solve the
resulting optimization problem. Usefulness of the proposed method
is shown with the help of simulations.
Abstract: There are two common methodologies to verify
signatures: the functional approach and the parametric approach. This
paper presents a new approach for dynamic handwritten signature
verification (HSV) using the Neural Network with verification by the
Conjugate Gradient Neural Network (NN). It is yet another avenue in
the approach to HSV that is found to produce excellent results when
compared with other methods of dynamic. Experimental results show
the system is insensitive to the order of base-classifiers and gets a
high verification ratio.
Abstract: This paper presents the convergence analysis
of a prediction based blind equalizer for IIR channels.
Predictor parameters are estimated by using the recursive
least squares algorithm. It is shown that the prediction
error converges almost surely (a.s.) toward a scalar
multiple of the unknown input symbol sequence. It is
also proved that the convergence rate of the parameter
estimation error is of the same order as that in the iterated
logarithm law.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.
Abstract: Freeways are originally designed to provide high
mobility to road users. However, the increase in population and
vehicle numbers has led to increasing congestions around the world.
Daily recurrent congestion substantially reduces the freeway capacity
when it is most needed. Building new highways and expanding the
existing ones is an expensive solution and impractical in many
situations. Intelligent and vision-based techniques can, however, be
efficient tools in monitoring highways and increasing the capacity of
the existing infrastructures. The crucial step for highway monitoring
is vehicle detection. In this paper, we propose one of such
techniques. The approach is based on artificial neural networks
(ANN) for vehicles detection and counting. The detection process
uses the freeway video images and starts by automatically extracting
the image background from the successive video frames. Once the
background is identified, subsequent frames are used to detect
moving objects through image subtraction. The result is segmented
using Sobel operator for edge detection. The ANN is, then, used in
the detection and counting phase. Applying this technique to the
busiest freeway in Riyadh (King Fahd Road) achieved higher than
98% detection accuracy despite the light intensity changes, the
occlusion situations, and shadows.
Abstract: Versatile dual-mode class-AB CMOS four-quadrant
analog multiplier circuit is presented. The dual translinear loops and
current mirrors are the basic building blocks in realization scheme.
This technique provides; wide dynamic range, wide-bandwidth response
and low power consumption. The major advantages of this
approach are; its has single ended inputs; since its input is dual translinear
loop operate in class-AB mode which make this multiplier
configuration interesting for low-power applications; current multiplying,
voltage multiplying, or current and voltage multiplying can
be obtainable with balanced input. The simulation results of versatile
analog multiplier demonstrate a linearity error of 1.2 %, a -3dB bandwidth
of about 19MHz, a maximum power consumption of 0.46mW,
and temperature compensated. Operation of versatile analog multiplier
was also confirmed through an experiment using CMOS transistor
array.
Abstract: In this paper a neural adaptive control method has
been developed and applied to robot control. Simulation results are
presented to verify the effectiveness of the controller. These results
show that the performance by using this controller is better than
those which just use either direct inverse control or predictive
control. In addition, they show that the resulting is a useful method
which combines the advantages of both direct inverse control and
predictive control.