Simultaneous Wound Border Segmentation and Tissue Classification Using a Conditional Generative Adversarial Network

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images as well as the number of epochs using GAN for wound border segmentation and tissue classification. The results show that the proposed GAN model performs efficiently for wound border segmentation and tissue classification tasks with a set of 2000 images at 200 epochs

Performance Evaluation of Communication Technologies and Network Structure for Smart Grid Applications

The design of an effective and reliable communication network supporting smart grid applications requires the selection of appropriate communication technologies and protocols. The objective of this study is to study and quantify the capabilities of an advanced metring infrastructure (AMI) to support the simultaneous operation of major smart grid functions. These include smart metring, price-induced controls, distribution automation, demand response, and electric vehicle charging/discharging applications in terms of throughput and latency. OPNET is used to simulate the performance of selected communication technologies and protocols. Research findings indicate that smart grid applications can operate simultaneously by piggybacking on an existing AMI infrastructure and still achieve their latency requirements

Digitalization of Power Markets and Systems Using Energy Informatics

The objective of this textbook is to introduce students and professionals to fundamental principles and techniques and emerging technologies in energy informatics and the digitalization of power markets and systems. The book covers such areas as smart grids and artificial intelligence (AI) and distributed ledger technology (DLT), with a focus on information and communication technologies (ICT) deployed to modernize the electric energy infrastructure. It also provides an overview of the smart grid and its main components: smart grid applications at transmission, distribution, and customer level, network requirements with communications technologies, and standards and protocols. In addition, the book addresses emerging technologies and trends in next-generation power systems, i.e., energy informatics, such as digital green shift, energy cyber-physical-social systems (E-CPSS), energy IoT, energy blockchain, and advanced optimization. Future aspects of digitalized power markets and systems will be discussed with real-world energy informatics projects. The book is designed to be a core text in upper-undergraduate and graduate courses such as Introduction to Smart Grids, Digitalization of Power Systems, and Advanced Power System Topics in Energy Informatics. [Amazon.com]https://digitalcommons.odu.edu/engtech_books/1003/thumbnail.jp