FASTER: Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration

The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) EU FP7 project, aims to ease the design and implementation of dynamically changing hardware systems. Our motivation stems from the promise reconfigurable systems hold for achieving high performance and extending product functionality and lifetime via the addition of new features that operate at hardware speed. However, designing a changing hardware system is both challenging and time-consuming. FASTER facilitates the use of reconfigurable technology by providing a complete methodology enabling designers to easily specify, analyze, implement and verify applications on platforms with general-purpose processors and acceleration modules implemented in the latest reconfigurable technology. Our tool-chain supports both coarse- and fine-grain FPGA reconfiguration, while during execution a flexible run-time system manages the reconfigurable resources. We target three applications from different domains. We explore the way each application benefits from reconfiguration, and then we asses them and the FASTER tools, in terms of performance, area consumption and accuracy of analysis

Advancements in Real-Time Simulation of Power and Energy Systems

Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics

Operating System Concepts for Reconfigurable Computing: Review and Survey

One of the key future challenges for reconfigurable computing is to enable higher design productivity and a more easy way to use reconfigurable computing systems for users that are unfamiliar with the underlying concepts. One way of doing this is to provide standardization and abstraction, usually supported and enforced by an operating system. This article gives historical review and a summary on ideas and key concepts to include reconfigurable computing aspects in operating systems. The article also presents an overview on published and available operating systems targeting the area of reconfigurable computing. The purpose of this article is to identify and summarize common patterns among those systems that can be seen as de facto standard. Furthermore, open problems, not covered by these already available systems, are identified

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

A brief review: Multimedia authoring modeling

Multimedia Authoring is a way to develop a multimedia presentation. Multimedia content includes images, sounds, videos, texts, and animations. The Kernel Mechanism of Multimedia Authoring consists of the Multimedia Authoring Programming Language and the Multimedia Authoring Model. Multimedia Authoring modeling is designed to enable the Multimedia Authoring function appropriately. Since the beginning of designing multimedia authoring tools, various studies were conducted to create a multimedia authoring model. Multimedia Authoring models that have been studied in existing research are Petri Nets, Hoare Logic, and LOTOS. The three models use different approaches. Petri Net uses a model based on graph calculations, Hoare logic uses mathematical logic, and LOTOS uses a formal specification language. Each of these models has been developed and modified to have higher capabilities. This model modification has advantages over the original model. This review article discusses the development and modifications of these models