Violeta Holmes

Bio: Violeta Holmes is an academic researcher from University of Huddersfield. The author has contributed to research in topics: Photovoltaic system & Fault detection and isolation. The author has an hindex of 18, co-authored 71 publications receiving 1186 citations. Previous affiliations of Violeta Holmes include Durham University.

Measurement, simulation and optimization of wideband log-periodic antennas

Abstract: Log-periodic antenna is a special antenna type utilized with great success in many broadband applications due to its ability to achieve nearly constant gain over a wide frequency range. Such antennas are extensively used in electromagnetic compatibility measurements, spectrum monitoring and TV reception. In this study, a log-periodic dipole array is measured, simulated, and then optimized in the 470–860 MHz frequency band. Two simulations of the antenna are initially performed in time and frequency domain respectively. The comparison between these simulations is presented to ensure accurate modelling of the antenna. The practically measured net gain is in good agreement with the simulated net gain. The antenna is then optimized to concurrently improve voltage standing wave ration, net gain and front-to-back ratio. The optimization process has been implemented by using various algorithms included in CST Microwave Studio, such as Trusted Region Framework, Nelder Mead Simplex algorithm, Classic Powell and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The Trusted Region Framework seems to have the best performance in sufficiently optimizing all predefined goals specified for the antenna.

CDES: An Approach to HPC Workload Modelling

Abstract: Computational science and complex system administration relies on being able to model user interactions. When it comes to managing HPC, HTC and grid systems user workloads - their job submission behaviour, is an important metric when designing systems or scheduling algorithms. Most simulators are either inflexible or tied in to proprietary scheduling systems. For system administrators being able to model how a scheduling algorithm behaves or how modifying system configurations can affect the job completion rates is critical. Within computer science research many algorithms are presented with no real description or verification of behaviour. In this paper we are presenting the Cluster Discrete Event Simulator (CDES) as an strong candidate for HPC workload simulation. Built around an open framework, CDES can take system definitions, multi-platform real usage logs and can be interfaced with any scheduling algorithm through the use of an API. CDES has been tested against 3 years of usage logs from a production level HPC system and verified to a greater than 95% accuracy.

Fault detection algorithm for multiple GCPV array configurations

Abstract: In this paper, a fault detection algorithm for multiple grid-connected photovoltaic (GCPV) array configurations is introduced. For a given set of conditions such as solar irradiance and photovoltaic module temperature, a number of attributes such as power, voltage and current are calculated using a mathematical simulation model. Virtual instrumentation (VI) LabVIEW software is used to monitor the performance of the GCPV system and to simulate the theoretical I-V and P-V curves of the examined system. The fault detection algorithm is evaluated on multiple GCPV array configurations such as series, parallel and series-parallel array configuration. The fault detection algorithm has been validated using 1.98 kWp GCPV system installed at the University of Huddersfield. The results indicates that the algorithm is capable to detect multiple faults in the examined GCPV plant and can therefore be used in large GCPV installations.

Hybrid HPC – Establishing a Bi-Stable Dual Boot Cluster for Linux with OSCAR middleware and Windows HPC 2008 R2

Abstract: The advent of open source software leading to Beowulf clusters has enabled small to medium sized Higher and Further education institutions to remove the “computational power” factor from research ventures. In an effort to catch up with leading Universities in the realm of research, many Universities are investing in small departmental HPC clusters to help with simulations, renders and calculations. These small HE/FE institutions have in the past benefited from cheaper software and operating system licenses. This raises the question as to which platform Linux of Windows should be implemented on the cluster. As the smaller/medium Universities move into research, many Linux based applications and code better suit their research needs, but the teaching base still keeps the department tied to Windows based applications. In such institutions, where it is usually recycled machines that are linked to form the clusters, it is not often feasible to setup more than one cluster. This paper will propose a method to implement a Linux-Windows Hybrid HPC Cluster that seamlessly and automatically accepts and schedules jobs in both domains. Using Linux CentOS 5.4 with OSCAR 5.2 beta 2 middleware with Windows Server 2008 and Windows HPC 2008 R2 (beta) a bi-stable hybrid system has been deployed at the University of Huddersfield. This hybrid cluster is known as the Queensgate Cluster. We will also examine innovative solutions and practices that are currently being followed in the academic world as well as those that have been recommended by Microsoft® Corp.

GPU cluster for accelerated processing and visualisation of scientific and engineering data

Abstract: The ability to process, visualise, and work with large volumes of data in a way that is fast, meaningful, and accurate is an essential part of many fields of scientific research today. The success of video game industry has resulted in on-going developments in the complexity of Graphical Processing Units (GPU), as well as rapidly falling cost per core. Their characteristics make them excellently suited to any task exhibiting a high level of data parallelism. Recent development of GPU architectures is aimed at HPC systems and applications. In this paper we are presenting our experience in designing and deploying a small dedicated GPU based cluster for processing and visualising data generated by engineering and scientific application. This GPU cluster is helping our researchers to analyse complex data using visualisation, and to accelerate large data processing. We have shown that our GPU cluster solution can achieve five to ten times speed up compared to the CPU system. As a result of our work we can demonstrate that even a small GPU cluster can benefit Higher Education institutions. Keywords—GPU, CUDA, GPU Cluster, Visualisation

Flow The Psychology Of Optimal Experience

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