Optimal frame length for keeping normalized goodput with lowest requirement on BER

This paper presents an adaptive frame length mechanism based on a cross-layer analysis of intrinsic relations between the MAC frame length, bit error rate (BER) of the wireless link and normalized goodput. The proposed mechanism selects the optimal frame length that keeps the service normalized goodput at required levels while satisfying the lowest requirement on the BER, thus increasing the transmission reliability. Numerical results are provided and show that an optimal frame length satisfying the lowest BER requirement does indeed exist. The performance of BER requirement as a function of the MAC frame length is evaluated and compared for transmission scenarios with and without automatic repeat request (ARQ). Furthermore, issues related to the MAC overhead length are also discussed to illuminate the functionality and performance of the proposed mechanism.

An analytical method for capacity dimensioning of WCDMA with high speed wireless link

Capacity dimensioning is one of the key problems in wireless network planning. Analytical and simulation methods are usually used to pursue the accurate capacity dimensioning of wireless network. In this paper, an analytical capacity dimensioning method for WCDMA with high speed wireless link is proposed based on the analysis on relations among system performance and high speed wireless transmission technologies, such as H-ARQ, AMC and fast scheduling. It evaluates system capacity in closed-form expressions from link level and system level. Numerical results show that the proposed method can calculate link level and system level capacity for WCDMA system with HSDPA and HSUPA.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7 μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0 μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST. In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39 K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Surface water temperature observations of large lakes by optimal estimation

Optimal estimation (OE) and probabilistic cloud screening were developed to provide lake surface water temperature (LSWT) estimates from the series of (advanced) along-track scanning radiometers (ATSRs). Variations in physical properties such as elevation, salinity, and atmospheric conditions are accounted for through the forward modelling of observed radiances. Therefore, the OE retrieval scheme developed is generic (i.e., applicable to all lakes). LSWTs were obtained for 258 of Earth's largest lakes from ATSR-2 and AATSR imagery from 1995 to 2009. Comparison to in situ observations from several lakes yields satellite in situ differences of −0.2 ± 0.7 K for daytime and −0.1 ± 0.5 K for nighttime observations (mean ± standard deviation). This compares with −0.05 ± 0.8 K for daytime and −0.1 ± 0.9 K for nighttime observations for previous methods based on operational sea surface temperature algorithms. The new approach also increases coverage (reducing misclassification of clear sky as cloud) and exhibits greater consistency between retrievals using different channel–view combinations. Empirical orthogonal function (EOF) techniques were applied to the LSWT retrievals (which contain gaps due to cloud cover) to reconstruct spatially and temporally complete time series of LSWT. The new LSWT observations and the EOF-based reconstructions offer benefits to numerical weather prediction, lake model validation, and improve our knowledge of the climatology of lakes globally. Both observations and reconstructions are publically available from http://hdl.handle.net/10283/88.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K.

Observations of wind speed profiles over Greater London, UK, using a Doppler lidar

To calculate the potential wind loading on a tall building in an urban area, an accurate representation of the wind speed profile is required. However, due to a lack of observations, wind engineers typically estimate the characteristics of the urban boundary layer by translating the measurements from a nearby reference rural site. This study presents wind speed profile data obtained from a Doppler lidar in central London, UK, during an 8 month observation period. Used in conjunction with wind speed data measured at a nearby airport, the data have been used to assess the accuracy of the predictions made by the wind engineering tools currently available. When applied to multiple changes in surface roughness identified from morphological parameters, the non-equilibrium wind speed profile model developed by Deaves (1981) provides a good representation of the urban wind speed profile. For heights below 500 m, the predicted wind speed remains within the 95% confidence interval of the measured data. However, when the surface roughness is estimated using land use as a proxy, the model tends to overestimate the wind speed, particularly for very high wind speed periods. These results highlight the importance of a detailed assessment of the nature of the surface when estimating the wind speed above an urban surface.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

High-speed Keck spectroscopy of flares and oscillations in AE Aquarii

Using high-time-resolution (72 ms) spectroscopy of AE Aqr obtained with LRIS on Keck II we have determined the spectrum and spectral evolution of a small flare. Continuum and integrated line fluxes in the flare spectrum are measured, and the evolution of the flare is parametrized for future comparison with detailed models of the flares. We find that the velocities of the flaring components are consistent with those previously reported for AE Aqr by Welsh, Horne & Gomer and Horne. The characteristics of the 33-s oscillations are investigated: we derive the oscillation amplitude spectrum, and from that determine the spectrum of the heated regions on the rotating white dwarf. Blackbody fits to the major and minor pulse spectra and an analysis of the emission-line oscillation properties highlight the shortfalls in the simple hotspot model for the oscillations.

High speed keck spectroscopy of flickering in AM Her

We present fast (72 ms) spectroscopy of AM Her obtained at an intermediate brightness state just before a rise to high state. Interesting features in the line behaviour of AM Her are noted and the variability spectrum is presented and compared to that of SS Cyg.

Optimal planning of cocoa clonal selection programmes

Data from three cocoa (Theobroma cacao) clonal selection trials are used to investigate the genetic and environmental components of variation in yield and the percentage of total pods affected by black pod disease (Phytophtora pod rot). Simulations based on these estimated components of variation are then used to discuss the best choice in future of numbers of clones, replicates and years of harvest to maximise selection advances in the traits measured. The three main conclusions are the need to increase the number of clones at the expense of the number of replicates of each clone, the diminishing returns from additional years of harvesting and the importance of widening the genetic base of the clones chosen to be tested.

Reconstruction of geomagnetic activity and near-Earth interplanetary conditions over the past 167 yr - Part 4: Near-Earth solar wind speed, IMF, and open solar flux

In the concluding paper of this tetralogy, we here use the different geomagnetic activity indices to reconstruct the near-Earth interplanetary magnetic field (IMF) and solar wind flow speed, as well as the open solar flux (OSF) from 1845 to the present day. The differences in how the various indices vary with near-Earth interplanetary parameters, which are here exploited to separate the effects of the IMF and solar wind speed, are shown to be statistically significant at the 93% level or above. Reconstructions are made using four combinations of different indices, compiled using different data and different algorithms, and the results are almost identical for all parameters. The correction to the aa index required is discussed by comparison with the Ap index from a more extensive network of mid-latitude stations. Data from the Helsinki magnetometer station is used to extend the aa index back to 1845 and the results confirmed by comparison with the nearby St Petersburg observatory. The optimum variations, using all available long-term geomagnetic indices, of the near-Earth IMF and solar wind speed, and of the open solar flux, are presented; all with ±2sigma� uncertainties computed using the Monte Carlo technique outlined in the earlier papers. The open solar flux variation derived is shown to be very similar indeed to that obtained using the method of Lockwood et al. (1999).

Optimal steady motions for oriented vehicles

Motivated by the motion planning problem for oriented vehicles travelling in a 3-Dimensional space; Euclidean space E3, the sphere S3 and Hyperboloid H3. For such problems the orientation of the vehicle is naturally represented by an orthonormal frame over a point in the underlying manifold. The orthonormal frame bundles of the space forms R3,S3 and H3 correspond with their isometry groups and are the Euclidean group of motion SE(3), the rotation group SO(4) and the Lorentzian group SO(1; 3) respectively. Orthonormal frame bundles of space forms coincide with their isometry groups and therefore the focus shifts to left-invariant control systems defined on Lie groups. In this paper a method for integrating these systems is given where the controls are time-independent. For constant twist motions or helical motions, the corresponding curves g(t) 2 SE(3) are given in closed form by using the well known Rodrigues’ formula. However, this formula is only applicable to the Euclidean case. This paper gives a method for computing the non-Euclidean screw/helical motions in closed form. This involves decoupling the system into two lower dimensional systems using the double cover properties of Lie groups, then the lower dimensional systems are solved explicitly in closed form.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Verification of European subseasonal wind speed forecasts

Analysis of the forecasts and hindcasts from the ECMWF 32-day forecast model reveals that there is statistically significant skill in predicting weekly mean wind speeds over areas of Europe at lead times of at least 14–20 days. Previous research on wind speed predictability has focused on the short- to medium-range time scales, typically finding that forecasts lose all skill by the later part of the medium-range forecast. To the authors’ knowledge, this research is the first to look beyond the medium-range time scale by taking weekly mean wind speeds, instead of averages at hourly or daily resolution, for the ECMWF monthly forecasting system. It is shown that the operational forecasts have high levels of correlation (~0.6) between the forecasts and observations over the winters of 2008–12 for some areas of Europe. Hindcasts covering 20 winters show a more modest level of correlation but are still skillful. Additional analysis examines the probabilistic skill for the United Kingdom with the application of wind power forecasting in mind. It is also shown that there is forecast “value” for end users (operating in a simple cost/loss ratio decision-making framework). End users that are sensitive to winter wind speed variability over the United Kingdom, Germany, and some other areas of Europe should therefore consider forecasts beyond the medium-range time scale as it is clear there is useful information contained within the forecast.