Human Eye is one of the most important sensor organs of human being, thus diseases and aliments of human Eye have a most important on a patient's life and thus advanced detection and prevention techniques are required, Diabetes is a contributing factor in causing eye aliments and catalyzing them. Two of the major eye diseases that possess saver threat to vision are Glaucoma and Micro-aneurysm. Glaucoma causes damage to the eyes optic nerve and worsens with time. Glaucoma has no early symptoms as pain, if continues untreated can cause per moment loss of vision micro-aneurysm is a swelling that occurs in the wall of capillary blood vessels. These small swelling may reports and allow blood leak in near by tissue. People suffering from diabetes may get micro aneurysm in the retina and can progress into vision loss. As Glaucoma and Micro aneurysm are not can able, but preventable diseases there early detection is the key for good prognosis on this work. We have propose on advanced algorithm using combination of image processing and artificial intelligence for early detection of Glaucoma and Micro Aneurysm. The steps accomplished are retinal blood vessel detection, Optic Disc segmentation optic disc and Optic cup detection CPR (cup disc ratio) computation and use of artificial neural network for training prediction.
In this paper, we have optimize specificities with the use of massive MIMO in 5 G systems. Massive MIMO uses a large number, low cost and low power antennas at the base stations. These antennas provide benefit such as improved spectrum performance, which allows the base station to serve more users, reduced latency due to reduced fading power consumption and much more. By employing the lens antenna array, beam space MIMO can utilize beam selection to reduce the number of required RF chains in mm Wave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beam space channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. In this work we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beam space channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beam space channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beam space channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beam space channel can be exploited.
In this paper we have used an efficient algorithm for hiding secret image also called payload in different types of cover image using histogram shifting method of reversible data hiding technique. Image utilized is jpeg, bmp and tiff images. We have analyzed this algorithm in MATLAB simulation tool. In this analysis we have calculated some parameters by varying payload in different types of cover image.
This paper comparatively analyzes decagonal 4 cladding ring structure having circular & square hole within core and structure without hole in core. All simulations have been performed in COMSOL Multiphysics simulation tool. Effective refractive index is calculated for each design by varying pitch keeping cladding air hole diameter constant and by varying air hole diameter keeping pitch constant. Dispersion is calculated using finite element method. Optimized design is obtained by comparing all the designs.
In this paper we have simulated and analyzed histogram shifting method on different types of cover images. Secret image which is used to hide in cover image is called payload. We have analyzed this algorithm in MATLAB simulation tool. This analysis is performed to find out the performance of this method on different types of cover images. We have analyzed this to find out how much accuracy can we get when extracting payload from cover image. We have computed peak signal to noise ratio, mean square error.
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