Iraqi Journal for Electrical And Electronic Engineering

In this paper, optical scalable parallel and
high-speed 2D data array adder for trinary signeddigit
(TSD) number is proposed. The digitdecomposition-
plane (DDP) coding method is used to
represent the 2D TSD data arrays. The algorithm
performs parallel TSD addition in constant time
independent of the size of the TSD data arrays. The
design describes methodology to involve two-step
TSD adder. The TSD addition is expressed with
several combination logic formulas that are newly
derived. Optical implementation with classical optical
elements is suggested for proposed TSD adder.
Preliminary demonstration example is also described.

An optical parallel quaternary signed digit (QSD) two-dimensional array multiplier based
on digit-decomposition (DDP) representation and duplication-shifting-superimposing
algorithm is proposed in this paper. The multiplication operation is done in three steps; one
for partial products generation and the other two steps perform accumulation to find the DDP
planes of the final result array. QSD multiplication and addition rules are used to obtain
a newly derived equations which are suitable for easy optical implementation using basic
optical tools. Finally, simulation results are presented to validate the successful of the
multiplication operation.

Recently, there is increasing interest in using joint transform correlation (JTC)
technique for optical pattern recognition. In this technique, the target and reference images are
jointed together in the input plane and no matched filter is required. In this paper, the JTC is
investigated using simulation technique. A new discrimination decision algorithm is proposed
to recognize the correlation output for different object shapes (dissimilar shapes). Also, new
architectures are proposed to overcome the main problems of the conventional JTC.

Although solar cell parameters are generally measured at 20-30 Co, flat plate
modules normally operate at 40-50 Co under terrestrial conditions and even higher
temperatures are used for some concentrator cell applications. Therefore it is
interesting to calculate the dependence of cell parameters on temperature. In this paper
a simple formulation has been derived for obtaining the temperature dependence of
open circuit voltage Voc, short circuit current density Jsc, fill factor FF, and
conversation efficiency η, for c-Si and a-Si solar cells.

Bin picking robots require vision sensors capable of recognizing objects in the bin
irrespective of the orientation and pose of the objects inside the bin. Bin picking systems are
still a challenge to the robot vision research community due to the complexity of segmenting of
occluded industrial objects as well as recognizing the segmented objects which have irregular
shapes. The problem becomes more complex when these objects look like entirely different
objects in various orientations. In this paper a simple object recognition method is presented
using singular value decomposition of the object image matrix and a functional link neural
network for a bin picking vision system. The results of the functional link net are compared
with that of a simple feed forward net. The network is trained using the error back propagation
procedure. The proposed method is robust for recognition of objects.

A model reference adaptive control of condenser and deaerator of steam power plant is
presented. A fuzzy-neural identification is constructed as an integral part of the fuzzy-neural
controller. Both forward and inverse identification is presented.
In the controller implementation, the indirect controller with propagating the error through the
fuzzy-neural identifier based on Back Propagating Through Time (BPTT) learning algorithm
as well as inverse control structure are proposed.
Simulation results are achieved using Multi Input-Multi output (MIMO) type of fuzzy-neural
network. Robustness of the plant is detected by including several tests and observations

In this paper, a hierarchical Arabic phoneme recognition system is proposed in which Mel Frequency
Cepstrum Coefficients (MFCC) features is used to train the hierarchical neural networks architecture.
Here, separate neural networks (subnetworks) are to be recursively trained to recognize subsets of
phonemes. The overall recognition process is a combination of the outputs of these subnetworks.
Experiments that explore the performance of the proposed hierarchical system in comparison to nonhierarchical
(flat) baseline systems are also presented in this paper.