The R2 values of the obtained ANN in validation mode were 0.918, 0.854 and 0.912 for the air cell, the thick albumen height and the yolk index, respectively. The absorbance spectra in the range thus identified (from 10.5 to 11.5GHz) were used to predict the quality indices by means of an artificial neural network (ANN). Simple linear regression models were therefore set up and the coefficient of determination was calculated. A first analysis was carried out in the range from 3 to 20GHz with a span of 1GHz to investigate which 1GHz frequency range contains most information for predicting the main quality indices of eggs during 15days of storage. The input of a waveguide probe for shell eggs was connected to a sinewave sweeper oscillator and the signal at the output was captured by a spectrum analyser. The obtained results indicate that the proposed system is highly reliable and applicable in the incubation industry. Comparisons with existing approaches show that the proposed method achieves a superior performance. The fertility detection accuracy of the system on the provided dataset reaches 47.13% at day 1 of incubation, 81.41% at day 2, 93.08% at day 3, 97.73% at day 4, and 98.25% at day 5. In order to evaluate the system, a large egg image dataset is provided using 240 incubated eggs (including 190 fertile and 50 infertile eggs). Finally, a robust machine vision algorithm is developed to process the captured images and distinguish fertile eggs from infertile ones. An appropriate and cheap light source is also introduced for illuminating the eggs, which potentially enables a CCD camera to obtain good quality and informative images from inner side of the eggs. To this end, a mechatronic machine is fabricated for acquiring accurate digital images of eggs without harming them. In this research, a fertility detection machine vision system is developed and evaluated. Read our work experience blog post for more information about this.One of the important factors in increasing the productivity of the incubation industry is to be sure that the eggs placed in the incubators are fertile. If you have worked part-time, the minimum requirement will have been accumulated over a longer period. Chevening Scholarships – two years’ work experience = 2,800 hours.The minimum number of hours an applicant must have before applying for a Chevening Scholarship is as follows: Anything that exceeds the upper limit for full-time employment will be deemed ineligible. For this calculation, a working week comprises 35-60 hours and a working year comprises 40-50 weeks. Your entries will be calculated automatically by multiplying the number of weeks worked by the number of hours worked per week. The British embassy/high commission or the Chevening Secretariat cannot intervene in this. You can refer them to the employers page on our website. You are advised to discuss your application with your employer and it is your responsibility to ensure that they will release you if you are selected for a Chevening Scholarship. If you are currently employed, it is not a requirement of Chevening that you resign from your position. If you have held more than fifteen positions then please enter the periods which make up the greatest number of hours worked. Applicants can submit up to fifteen different employment periods in order to meet the requirement. You do not need to meet the work experience requirement in one period of employment. Work experience can be completed before, during, or after graduating from your undergraduate studies, however, any mandatory employment that counted towards your undergraduate or postgraduate course would not be eligible. The types of work experience that are eligible for Chevening can include: If you do not already have the required level of work experience, you will be unable to submit your application. Chevening Scholarships require that applicants have at least two years of work experience. You must ensure that you meet the minimum work-experience requirement for the scholarship before submitting your Chevening application. Chevening Western Balkans Cyber Security Fellowship.Chevening OCIS Fellowship and OCIS Abdullah Gül Fellowship.Chevening Africa Media Freedom Fellows (CAMFF).Chevening Research, Science, and Innovation Leadership Fellowship (CRISP).Chevening India Cyber Security Fellowship.Meet our current fellows Expand dropdown.Online application system Expand dropdown.
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