Background Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs). Objective This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology. Methods To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020. Results We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease. Conclusions Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

https://www.ukm.my/apjitm/view.php?id=195

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This paper presents a single LC tank base cell-to-cell active voltage balancing algorithm for Li-ion batteries in electric vehicle (EV) applications. EV batteries face challenges in accomplishing fast balancing and high balancing efficiency with low circuit and control complexity. It addresses that LC resonant tank uses an energy carrier to transfer the voltage from an excessive voltage cell to the lowest voltage cell. The method requires 2N - 4 bidirectional MOSFET switches and a single LC resonant circuit, where N is the number of cells in the battery strings. The balancing speed is improved by allowing a short balancing path for voltage transfer and guarantees a fast balancing speed between any two cells in the battery string, and power consumption is reduced by operating all switches in zero-current switching conditions. The circuit was tested for 4400 mAh Li-ion battery cells under static, cyclic, and dynamic charging/discharging conditions. Two battery cells at the voltage 3.93 V and 3.65 V were balanced after 76 min, and the balancing efficiency is 94.8%. The result of dynamic and cyclic charging/discharging conditions shows that the balancing circuit is applicable for the energy storage devices and Li-ion battery cells for EV.

Microservice is a new transformation of Service-Oriented Architecture (SOA) which is gaining momentum in both academic and industry. The success of microservice began when giant companies like Netflix used them as a service architecture for the purpose of serving customers. Monolithic architecture used by Netflix previously was no longer able to cope with business development and it is difficult to scale to meet user demands. Although Netflix has been successful with microservice architecture, there is no systematic method introduced to produce microservice. Academic studies related to microservice are still in the early stages and have not yet reached maturity. Microservice is seen to require a method that helps organizations to systematically design microservices and replicate the success achieved by Netflix. In forming a method for this systematic microservice then the methods for building an existing microservice are studied. Based on the Design Science Research method, two research artefacts have been produced. The first artefact is a systematic design of microservice that has four main steps. The second artefact is the instantiation by applying the proposed microservice design method to the case studies, namely, MyFlix. Next the evaluation is made on the new method produced by obtaining expert opinions through the process of demonstration and interviewing. The expert assessment results found that the proposed method was able to produce a systematic microservice design based on the six proposed principles and the four main steps. This method can also produce a complete feature microservice such as cohesive, loose coupling, distributed and decentralized that will contribute to the production of scalable system.