NON-INVASIVE IMAGE DENOISING AND CONTRAST ENHANCEMENT TECHNIQUES FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases. However, digital colour fundus images suffer from low and varied contrast, and are also affected by noise, requiring the use of fundus angiogram modality. The Fundus Fluorescein Angiogram (FFA) modality gives 5 to 6 time’s higher contrast. However, FFA is an invasive method that requires contrast agents to be injected and this can lead other physiological problems. A reported digital image enhancement technique named RETICA that combines Retinex and ICA (Independent Component Analysis) techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images

Future of Microgrids with Distributed Generation and Electric Vehicles

This chapter examines the current energy scenario for microgrids over the world and discusses the challenges and opportunities due to the increasing penetration of distributed power generation systems and electric vehicles (EVs) into the microgrids. Wind power and solar power can be generated by wind turbines and photovoltaics, respectively, while these are intermittent in nature. EVs and hybrid EVs use a battery energy storage system and charging facilities while the latter also include an Internal Combustion Engine (ICE) to provide an extra energy source. The features of these systems in the context of microgrids are studied in detail, in terms of their components, efficiency, reliability, charging and discharging arrangements, active and reactive power control. The chapter provides a reference to the development of microgrid systems especially for developing countries

Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation

This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods

Design of a high speed 18/12 switched reluctance motor drive with an asymmetrical bridge converter for electric vehicles

The application of permanent magnet free motors have gained a huge attention for pure electric and hybrid electric vehicles. This paper proposed the design of 20-kW switched reluctance motor having 18 stator poles and 12 rotor poles by using finite element analysis machine design software Infolytica magnet and the main focus is to achieve the high speed, torque with adequate performance for electric vehicles. The asymmetric bridge converter has been used and the series of varying the excitation voltage, slot fill factor with respect to the number of turns and stranded area of the conductor has been analysed. Additionally, in order to the electromagnetic force vector, the switching sequence is examined. The simulation results show the great potential of the suggested motor and can provide a good starting torque with high speed and can be suitable to achieve the freedom Car 2020 electric vehicle target

Dual-Branch U-Net Architecture for Retinal Lesions Segmentation on Fundus Image

Deep learning has found widespread application in diabetic retinopathy (DR) screening, primarily for lesion detection. However, this approach encounters challenges such as information loss due to convolutional operations, shape uncertainty, and the high similarity between different lesions types. These factors collectively hinder the accurate segmentation of lesions. In this research paper, we introduce a novel dual-branch U-Net architecture, referred to as Dual-Branch (DB)-U-Net, tailored to address the intricacies of small-scale lesion segmentation. Our approach involves two branches: one employs a U-Net to capture the shared characteristics of lesions, while the other utilizes a modified U-Net, known as U2Net, equipped with two decoders that share a common encoder. U2Net is responsible for generating probability maps for lesion segmentation as well as corresponding boundary segmentation. DB U-Net combines the outputs of U2Net and U-Net as a dual branch, concatenating their segmentation maps to produce the final result. To mitigate the challenge of imbalanced data, we employ the Dice loss as a loss function. We evaluate the effectiveness of our approach on publicly available datasets, including DDR, IDRiD, and E-Ophtha. Our results demonstrate that DB U-Net achieves AUPR values of 0.5254 and 0.7297 for Microaneurysms and soft exudates segmentation, respectively, on the IDRiD dataset. These results outperform other models, highlighting the potential clinical utility of our method in identifying retinal lesions from retinal fundus images

NON INVASIVE IMAGE CONTRAST ENHANCEMENT TECHNIQUE FOR RETINAL FUNDUS IMAGES

The analysis of retinal vasculature in digital fundus images is important for diagnosing eye related diseases in particular, diabetic retinopathy (DR). However, digital colour fundus images suffer from low and varied contrast. With added presence of noise it becomes difficult to analyse retinal vasculature digitally requiring the use of fundus angiogram modality. The fundus fluorescein angiogram (FFA) modality gives 5 to 6 time's higher contrast for the retinal vasculature but it is an invasive method (injection of contrasting dyes) that can lead to other physiological problems.—A reported digital image enhancement technique named RETICA thai combines Retinex and ICA techniques, reduces varied contrast, and enhances the low contrast blood vessels of model fundus images

Pixel2point: 3D object reconstruction from a single image using CNN and initial sphere

3D reconstruction from a single image has many useful applications. However, it is a challenging and ill-posed problem as various candidates can be a solution for the reconstruction. In this paper, we propose a simple, yet powerful, CNN model that generates a point cloud of an object from a single image. 3D data can be represented in different ways. Point clouds have proven to be a common and simple representation. The proposed model was trained end-to-end on synthetic data with 3D supervision. It takes a single image of an object and generates a point cloud with a fixed number of points. An initial point cloud of a sphere shape is used to improve the generated point cloud. The proposed model was tested on synthetic and real data. Qualitative evaluations demonstrate that the proposed model is able to generate point clouds that are very close to the ground-truth. Also, the initial point cloud has improved the final results as it distributes the points on the object surface evenly. Furthermore, the proposed method outperforms the state-of-the-art in solving this problem quantitatively and qualitatively on synthetic and real images. The proposed model illustrates an outstanding generalization to the new and unseen images and scenes.TU Berlin, Open-Access-Mittel – 202