This paper presents a design and fabrication of an intelligent fiber-optic sensor used for examining and monitoring heart rate activity. It is found in the literature that the use of fiber sensors as heart rate sensor is widely studied. However, the use of smart sensors based on Hopfield neural networks is very low. In this work, the sensor is a three fibers without cladding of about 1 cm, fed by laser light of 1550 nm of wavelength. The sensing portions are mounted with a micro sensitive diaphragm to transfer the pulse pressure on the left radial wrist. The influenced light intensity will be detected by a three photodetectors as inputs into the Hopfield neural network algorithm. The latter is a singlelayer auto-associative memory structure with a same input and output layers. The prior training weights are stored in the net memory for the standard recorded normal heart rate signals. The sensors’ heads work on the reflection intensity basis. The novelty here is that the sensor uses a pulse pressure and Hopfield neural network in an integrity approach. The results showed a significant output measurements of heart rate and counting with a plausible error rate.
This work is presenting an analysis study for using optical fiber array as turbidity meter and topographical distribution.
Although many studies have been figure out of utilizing optical fibers as sensors for turbidity measurements, still the
topographical map of suspended particles in water as rare as expected among all of works in literatures in this scope. The
effect of suspended particles are highly affect the water quality which varies according to the source of these particles. A
two dimensional array of optical fibers in a 1 litter rectangular plastic container with 2 cm cladding off sensing portion
prepared to point out 632.8 nm laser power at each fiber location at the container center. The overall output map of the
optical power were found in an inhomogeneous distribution such that the top to down layers of a present water sample
show different magnitudes. Each sample prepared by mixing a distilled water with large grains sand, small grains sand,
glucose and salt. All with different amount of concentration which measured by refractometer and turbidity meter. The
measurements were done in different times i.e. from 10 min to 60 min. This is to let the heavy particles to move down
and accumulate at the bottom of the container. The results were as expected which had a gradually topographical map
from low power at top layers into high power at bottom layers. There are many applications can be implemented of this
study such as transport vehicles fuel meter, to measure the purity of tanks, and monitoring the fluids quality in pipes.
A numerical analysis of a refractive index sensor based on multimode interference (MMI) waveguide has been
performed in this paper. The nonlinear refractive index of graphene in the proposed sensor was investigated by applying
external electric field on the graphene cladding layer. The designed waveguide was constructed using silicon oxide
(SiO2) as substrate and silicon as a core while graphene is coated on top of the waveguide slab. The response of the
sensor in the output power was examined and validated by changing liquid samples with different refractive index. The
guided modes of the 1550 nm input plane source at the absence of external electric field were used as the initial
reference point. It is found that there was a threshold magnitude of the field which makes graphene sensitive to the
relative change in the refractive index of the solution. The output results showed a promising indication that this design
is appropriate for environmental monitoring.
A brain tumour is an abnormal growth of tissue in the brain. Most tumour volume measurement processes are carried out manually by the radiographer and radiologist without relying on any auto program. This manual method is a timeconsuming task and may give inaccurate results. Treatment, diagnosis, signs and symptoms of the brain tumours mainly depend on the tumour volume and its location. In this paper, an approach is proposed to improve volume measurement of brain tumors as well as using a new method to determine the brain tumour location. The current study presents a hybrid method that includes two methods. One method is hidden Markov random field - expectation maximization (HMRFEM), which employs a positive initial classification of the image. The other method employs the threshold, which enables the final segmentation. In this method, the tumour volume is calculated using voxel dimension measurements. The brain tumour location was determined accurately in T2- weighted MRI image using a new algorithm. According to the results, this process was proven to be more useful compared to the manual method. Thus, it provides the possibility of calculating the volume and determining location of a brain tumour.
In this paper, the simulation and design of a waveguide for water turbidity sensing are presented. The structure of the proposed sensor uses a 2x2 array of multimode interference (MMI) coupler based on micro graphene waveguide for high sensitivity. The beam propagation method (BPM) are used to efficiently design the sensor structure. The structure is consist of an array of two by two elements of sensors. Each element has three sections of single mode for field input tapered to MMI as the main core sensor without cladding which is graphene based material, and then a single mode fiber as an output. In this configuration MMI responses to any change in the environment. We validate and present the results by implementing the design on a set of sucrose solution and showing how these samples lead to a sensitivity change in the sensor based on the MMI structures. Overall results, the 3D design has a feasible and effective sensing by drawing topographical distribution of suspended particles in the water.
Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed
and memory capacity in different approaches. However, there is still a significant scope of developing HNN using
Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color
image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each
unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in
the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single
iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in
bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values
of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image
for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison
with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in
the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for
neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The
primary operation and simulation of the proposal HNN is demonstrated.
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