Microwave sensor for liquid mixture identification based on composite right left hand-zero-order resonator for sensitivity improvement

This work aims to present an improved version of the liquid mixture identification sensor, the proposed sensor is tested experimentaly on mixture of water ethanol, the identification of liquid is based on the measurement of frequency displacement, and comparison with reference values of water ethanol. This device is based on metamaterial structure which is a CRLH (composite right left hand) resonator with ZOR (Zero Order Resonator). The CRLH in addition to its property of miniaturization effect, when combined with ZOR, the resonant frequency of various volume fraction are extended, which make the sensitivity higher. The high sensitivity of the sensor is obtained by an optimum choice of the CRLH components. The geometrical size of the sensor is 20 mm by 11 mm. It was printed on a RT/Duroid 5880 substrate with a very short testing surface area of 4 mm by 8 mm, the liquid is placed on the top side of the sensor, exactly on the CRLH structure. Three prototypes of sensors operating from 1 GHz to 3 GHz are proposed, designed and simulated using the commercial software HFSS (high-frequency structural simulator). The main advantages of this work is first miaturization effect, second high sensitivity and finaly a wide range of liquid can be tested with this sensor. To prove the working principle, ethanol with different volume fractions was adopted as a liquid under test, the obtained results present very good agreement with the literature and suggested that it is a miniaturised and high sensitive candidate (better than 1.38%) for liquid mixture identification

© 2007 Science Publications Segmentation and Recognition of Handwritten Numeric Chains

Abstract: Automatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, study was based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. Used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: distribution sequence, Barr features and centred moments of different projections and profiles. Secondly, study was extented for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The performances of the proposed system for the used database attain a recognition rate equal to 91.3%

A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments

International audienceIn the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods. The Distribution parameters, the centered moments of the different projections of the different segments, the centered moments of the word image coding according to the directions of Freeman, and the Barr features applied binary image of the word and on its different segments. The classification is achieved by a multi layers perceptron. A detailed experiment is carried and satisfactory recognition results are reported

A Neural Network for the Recognition of Brain Tumors Trough the Magnetic Resonance Images of the Brain

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A Neural Network and Central MOMENTS of Zones Histograms Obtained by Sliding a Window on the MRI Brain Image for the Classification of Brain Tumors

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Brain Tumors Classification From MR images Using a Neural Network and the Central Moments

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Brain Tumors Classification From MR images Using a Neural Network and the Central Moments

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