To find out a simple and efficient equation to estimate maize ear grain weight on farm (in situ), twenty three maize crosses along with two synthetics were grown in the field. On the experimental farm of the Dept. of Field Crop Sci., College of Agric., Univ. of Baghdad, seeds of twenty five maize genotypes were grown in the fall season of 2013 with three replicates. At dough stage of the kernels, five naked ears of each experimental units were measured for length and maximum diameter. This will sum up 125 ears of the trial. The volumes of ears were calculated as cylinder (length× r2× 3.1416). Grain weight of all ears were determined after harvesting and drying to 15% grain moisture. A constant was calculated by dividing ear grain weight by each ear volume. Estimated ear grain weights were tested against observed by applying correlation coefficient and it was found to be positive and highly significant (r= 0.998**). The observed and estimated values of ear grain weights were tested by t-test. The two means of observed and estimated ear grain weights were fit to 0.89 probability of t-value. The final equation to estimate ear grain weight in situ is= r2× L× 0.94, where r is radius of ear and L is ear length. However, in case of super hybrids of high ear fertility and kernel filling, estimated ear grain weight will be= r2× L.
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Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
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The morphological description of inner ear in Barbus luteus have been investigated.
The results of the present study revealed that the fish under investigation has a pair of
inner ears which are embedded in two otic capsules of the skull. The inner ear contains two
main structures, the first is the Osseous Labyrinth (OL), and the second is the Membranous
Labyrinth (ML).
Both of (OL) and (ML) consist of three semicircular canal (SCC). These are anterior,
posterior and horizontal semicircular canals (ASCC, PSCC and HSCC).
The (OL) contains three chambers while the (ML) contains saccular structures which
are called otoliths organs represented by utriculus (U), sacculus (S) and lagena (L). Each of
the saccu
The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
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Identifying people by their ear has recently received import attention in the literature. The accurate segmentation of the ear region is vital in order to make successful person identification decisions. This paper presents an effective approach for ear region segmentation from color ear images. Firstly, the RGB color model was converted to the HSV color model. Secondly, thresholding was utilized to segment the ear region. Finally, the morphological operations were applied to remove small islands and fill the gaps. The proposed method was tested on a database which consisted of 105 ear images taken from the right sides of 105 subjects. The experimental results of the proposed approach on a variety of ear images revealed that this approac
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Background: The present study was planned to show the accuracy of clinical examination and tympanometry in diagnosis of middle ear effusion.
Patients and Methods: The study involved 80 patients (160 ears )suspected to have otitis media with effusion (OME) from different age groups ; 56 were males and 24 were females .
Clinical assessment for all patients included otoscopy , pneumatic otoscopy and audiological assessment by using pure tone audiometry and tympanometry then comparing the results to
findings at myringotomy as the gold standard for presence or absence of fluid in the middle ear.
Results : Fluid whether serous or glue was found in 100 ears ( 62.5 %) where as sixty ears were dry, sensitivity , sp
Background: Staphylococcus aureus is a Gram-positive, spherical, grape like clusters arrangment
bacterium, non-spore forming. Is a genus that causes many hard diseases such as food poisoning,
gastroenteritis with severe symptoms. S. aureus is commonly found in the wide environment (soil, air and
water) and is importantly found in the nose and skin in the humans. And can causes ear infection by
entering the ear. The diagnosis of Otitis externa is usually made clinically and bacterial tests.
Objective: To detection and isolation of the bacteria Staphylococcus aureus from pus specimens of ear
swab, among Iraqi children with Otitis externa.
Patients and methods: Eighty ear swab specimens from suspected cases of Otitis ex
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