An FPGA-based Remote Laboratory: Implementing Semi-Automatic Experiments in the Hybrid Cloud

An FPGA-based remote laboratory implemented in a hybrid cloud for semi-automatic FPGA-run experiments is presented. The design file and the FPGA test flow file including the FPGA-run parameters are sent to the hybrid cloud to trigger the sequence of the following tasks automatically: the logic-synthesis, and the FPGA-run, with the video recording. In this way, the amount of the users works can be reduced. The user only prepares the high-level Verilog HDL description and a few FPGA device setup parameters. The remote laboratory manager which provides semi-automatic experiment service handles the rest of the works, and the FPGA-run results together with the live recorded video are returned to the user for further inspection/debugging. This semi-automatic experiment service eliminates many cumbersome user works. Each user can have an access to the remote laboratory through a web browser and communicate with the experiment task manager via the Web Services and the WebSockets. As the manager acts like an agent for the user and realizes the semi-automatic experiment, the user can concentrate on the high-level designs. The user can get the feeling of the exclusive and interactive use of an FPGA-based experiment platform, while allowing efficient sharing of the platforms at the same time. In case of the device usage conflicts, the task manager resolves the contentions by reallocating the platforms in both space- and time-division fashions

Gbit/second lossless data compression hardware

This thesis investigates how to improve the performance of lossless data compression hardware as a tool to reduce the cost per bit stored in a computer system or transmitted over a communication network. Lossless data compression allows the exact reconstruction of the original data after decompression. Its deployment in some high-bandwidth applications has been hampered due to performance limitations in the compressing hardware that needs to match the performance of the original system to avoid becoming a bottleneck. Advancing the area of lossless data compression hardware, hence, offers a valid motivation with the potential of doubling the performance of the system that incorporates it with minimum investment. This work starts by presenting an analysis of current compression methods with the objective of identifying the factors that limit performance and also the factors that increase it. [Continues.

ICTERI 2020: ІКТ в освіті, дослідженнях та промислових застосуваннях. Інтеграція, гармонізація та передача знань 2020: Матеріали 16-ї Міжнародної конференції. Том II: Семінари. Харків, Україна, 06-10 жовтня 2020 р.

This volume represents the proceedings of the Workshops co-located with the 16th International Conference on ICT in Education, Research, and Industrial Applications, held in Kharkiv, Ukraine, in October 2020. It comprises 101 contributed papers that were carefully peer-reviewed and selected from 233 submissions for the five workshops: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. The volume is structured in six parts, each presenting the contributions for a particular workshop. The topical scope of the volume is aligned with the thematic tracks of ICTERI 2020: (I) Advances in ICT Research; (II) Information Systems: Technology and Applications; (III) Academia/Industry ICT Cooperation; and (IV) ICT in Education.Цей збірник представляє матеріали семінарів, які були проведені в рамках 16-ї Міжнародної конференції з ІКТ в освіті, наукових дослідженнях та промислових застосуваннях, що відбулася в Харкові, Україна, у жовтні 2020 року. Він містить 101 доповідь, які були ретельно рецензовані та відібрані з 233 заявок на участь у п'яти воркшопах: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. Збірник складається з шести частин, кожна з яких представляє матеріали для певного семінару. Тематична спрямованість збірника узгоджена з тематичними напрямками ICTERI 2020: (I) Досягнення в галузі досліджень ІКТ; (II) Інформаційні системи: Технології і застосування; (ІІІ) Співпраця в галузі ІКТ між академічними і промисловими колами; і (IV) ІКТ в освіті

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF