An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Localization Algorithm Performance in Ultra Low Power Active RFID Based Patient Tracking

Indoor personnel localization research has generated a range of potential techniques and algorithms. However, these typically do not account for the influence of the user's body upon the radio channel. In this paper an active RFID based patient tracking system is demonstrated and three localization algorithms are used to estimate the location of a user within a modern office building. It is shown that disregarding body effects reduces the accuracy of the algorithms' location estimates and that body shadowing effects create a systematic position error that estimates the user's location as closer to the RFID reader that the active tag has line of sight to.

Ultra-Wideband Communications - An idea whose time has still yet to come? [Wireless Corner]

This paper presents a critical analysis of ultrawideband (UWB) and considers the turbulent journey it has had from the Federal Communications Commission's bandwidth allocation in 2002 to today. It analyzes the standards, the standoffs, and the stalemate in standardization activities and investigates the past and present research and commercial activities in realizing the UWB dream. In this paper, statistical evidence is presented to depict UWB's changing fortunes and is utilized as an indicator of future prominence. This paper reviews some of the opinions and remarks from commentators and analyzes predictions that were made. Finally, it presents possible ways forward to reignite the high-data-rate UWB standardization pursuit.

Ultra-Compact and Robust FPGA-Based PUF Identification Generator

Physically Unclonable Functions (PUFs), exploit inherent manufacturing variations and present a promising solution for hardware security. They can be used for key storage, authentication and ID generations. Low power cryptographic design is also very important for security applications. However, research to date on digital PUF designs, such as Arbiter PUFs and RO PUFs, is not very efficient. These PUF designs are difficult to implement on Field Programmable Gate Arrays (FPGAs) or consume many FPGA hardware resources. In previous work, a new and efficient PUF identification generator was presented for FPGA. The PUF identification generator is designed to fit in a single slice per response bit by using a 1-bit PUF identification generator cell formed as a hard-macro. In this work, we propose an ultra-compact PUF identification generator design. It is implemented on ten low-cost Xilinx Spartan-6 FPGA LX9 microboards. The resource utilization is only 2.23%, which, to the best of the authors' knowledge, is the most compact and robust FPGA-based PUF identification generator design reported to date. This PUF identification generator delivers a stable range of uniqueness of around 50% and good reliability between 85% and 100%.

Ultra Thin Resistively Loaded FSS Absorber For Polarisation Independent Operation at Large Incident Angles

In this paper we report on a resistively loaded Frequency Selective Surface (FSS) absorber design which is insensitive to the polarization of microwave signals incident at angles of 45o ± 5o. The metal backed periodic structure is composed of an array of conductive rectangular loops, each loaded with a resistor at the center of the four sides. The geometry of the absorber and the resistance value of the vertical and horizontal resistor pairs are carefully chosen so that the structure presents a real impedance of 377 Ω at the center operating frequency for both TE and TM polarized waves incident at 45o. Numerical predictions of the electromagnetic scattering from three different absorbers, designed to work at X-band, are used to investigate the effect of thickness and resistance value on the reflectivity bandwidth and angular sensitivity.

Optical measurement of the temporal delay between two ultra-short and focussed laser pluses

Temporal overlapping of ultra-short and focussed laser pulses is a particularly challenging task, as this timescale lies orders of magnitude below the typical range of fast electronic devices. Here we present an optical technique that allows for the measurement of the temporal delay between two focussed and ultra-short laser pulses. This method is virtually applicable to any focussing geometry and relative intensity of the two lasers. Experimental implementation of this technique provides excellent quantitative agreement with theoretical expectations. The proposed technique will prove highly beneficial for high-power multiple-beam laser experiments.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Ultra-high-resolution Observations of MHD Waves in Photospheric Magnetic Structures

Here we review the recent progress made in the detection, examination, characterisation and interpretation of oscillations manifesting in small-scale magnetic elements in the solar photosphere. This region of the Sun's atmosphere is especially dynamic, and importantly, permeated with an abundance of magnetic field concentrations. Such magnetic features can span diameters of hundreds to many tens of thousands of km, and are thus commonly referred to as the `building blocks' of the magnetic solar atmosphere. However, it is the smallest magnetic elements that have risen to the forefront of solar physics research in recent years. Structures, which include magnetic bright points, are often at the diffraction limit of even the largest of solar telescopes. Importantly, it is the improvements in facilities, instrumentation, imaging techniques and processing algorithms during recent years that have allowed researchers to examine the motions, dynamics and evolution of such features on the smallest spatial and temporal scales to date. It is clear that while these structures may demonstrate significant magnetic field strengths, their small sizes make them prone to the buffeting supplied by the ubiquitous surrounding convective plasma motions. Here, it is believed that magnetohydrodynamic waves can be induced, which propagate along the field lines, carrying energy upwards to the outermost extremities of the solar corona. Such wave phenomena can exist in a variety of guises, including fast and slow magneto-acoustic modes, in addition to Alfven waves. Coupled with rapid advancements in magnetohydrodynamic wave theory, we are now in an ideal position to thoroughly investigate how wave motion is generated in the solar photosphere, which oscillatory modes are most prevalent, and the role that these waves play in supplying energy to various layers of the solar atmosphere.

Numerical modelling of Compton Scattering in ultra-intense laser pulses

We examine current methods of numerically implementing Compton scattering in the context of intense laser-matter interactions. In a recent publication [1] it has been shown that a commonly used approach generates the correct spectra in nearly all cases, except those when the harmonic structure is important. Here we provide an explanation for this using an alternative, classical argument.

Control of ionization and dissociation of H2+ by elliptically polarized ultra-short VUV laser pulses

Resonance-enhanced multiphoton ionization of H₂ ⁺ exposed to elliptically polarized VUV laser pulses is investigated. Differential cross sections for nuclei and electron are obtained using numerical solutions of the time-dependent Schrödinger equation. In this work in progress, we explore the dependence of the dissociative ionization observables with the polarization of the light.

DocLite: A Docker-Based Lightweight Cloud Benchmarking Tool

Existing benchmarking methods are time consuming processes as they typically benchmark the entire Virtual Machine (VM) in order to generate accurate performance data, making them less suitable for real-time analytics. The research in this paper is aimed to surmount the above challenge by presenting DocLite - Docker Container-based Lightweight benchmarking tool. DocLite explores lightweight cloud benchmarking methods for rapidly executing benchmarks in near real-time. DocLite is built on the Docker container technology, which allows a user-defined memory size and number of CPU cores of the VM to be benchmarked. The tool incorporates two benchmarking methods - the first referred to as the native method employs containers to benchmark a small portion of the VM and generate performance ranks, and the second uses historic benchmark data along with the native method as a hybrid to generate VM ranks. The proposed methods are evaluated on three use-cases and are observed to be up to 91 times faster than benchmarking the entire VM. In both methods, small containers provide the same quality of rankings as a large container. The native method generates ranks with over 90% and 86% accuracy for sequential and parallel execution of an application compared against benchmarking the whole VM. The hybrid method did not improve the quality of the rankings significantly.

Ion acceleration and plasma jet formation in ultra-thin foils undergoing expansion and relativistic transparency

At sufficiently high laser intensities, the rapid heating to relativistic velocities and resulting decompression of plasma electrons in an ultra-thin target foil can result in the target becoming relativistically transparent to the laser light during the interaction. Ion acceleration in this regime is strongly affected by the transition from an opaque to a relativistically transparent plasma. By spatially resolving the laser-accelerated proton beam at near-normal laser incidence and at an incidence angle of 30°, we identify characteristic features both experimentally and in particle-in-cell simulations which are consistent with the onset of three distinct ion acceleration mechanisms: sheath acceleration; radiation pressure acceleration; and transparency-enhanced acceleration. The latter mechanism occurs late in the interaction and is mediated by the formation of a plasma jet extending into the expanding ion population. The effect of laser incident angle on the plasma jet is explored.