4 research outputs found
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Microwave sensor for liquid mixture identification based on composite right left hand-zero-order resonator for sensitivity improvement
YesThis 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
Investigating deep CNNs models applied in kinship verification through facial images
Abstract
The kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. The approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. The experimental results show that our approach is comparable with existed approaches
Kinship verification using mixed descriptors and multi block face representation
Abstract
Kinship verification is a challenging problem that recently attracted much interest in computer vision, this system has a number of applications such as organizing large collections of images and recognizing resemblances among humans and search for lost people. In this work, we propose a new method based on different descriptors mixed such as (LBP, LPQ, BSIF), and the Multi-Block (MB) representation. and we investigate the effect of different features representation for kinship verification, Moreover, the use of TTest to reduce the number of features and the support vector machine (SVM) for the kinship classification. Our approach consists of five stages: (1) features extraction, (2) face representation (3) features representation, (4) features selection and (5) classification. Our approach is tested on five datasets (Cornell, UB Kin Face, Familly 101, KinFac W-I and W-II). Our results are good comparable with other approaches