Peak-to-average power ratio of IEEE 802.11a PHY layer signals

In this chapter we propose clipping with amplitude and phase corrections to reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexed (OFDM) signals in high-speed wireless local area networks defined in IEEE 802.11a physical layer. The proposed techniques can be implemented with a small modification at the transmitter and the receiver remains standard compliant. PAR reduction as much as 4dB can be achieved by selecting a suitable clipping ratio and a correction factor depending on the constellation used. Out of band noise (OBN) is also reduced.

Interleaved PC-OFDM to reduce peak-to-average power ratio

Parallel combinatory orthogonal frequency division multiplexing (PC-OFDM yields lower maximum peak-to-average power ratio (PAR), high bandwidth efficiency and lower bit error rate (BER) on Gaussian channels compared to OFDM systems. However, PC-OFDM does not improve the statistics of PAR significantly. In this chapter, the use of a set of fixed permutations to improve the statistics of the PAR of a PC-OFDM signal is presented. For this technique, interleavers are used to produce K-1 permuted sequences from the same information sequence. The sequence with the lowest PAR, among K sequences is chosen for the transmission. The PAR of a PC-OFDM signal can be further reduced by 3-4 dB by this technique. Mathematical expressions for the complementary cumulative density function (CCDF)of PAR of PC-OFDM signal and interleaved PC-OFDM signal are also presented.

Biological monitoring and Biological Limit Values (BLV): the strategy of the European Union

Occupational standards concerning allowable concentrations of chemical compounds in the ambient air of workplaces have been established in several countries worldwide. With the integration of the European Union (EU), there has been a need of establishing harmonised Occupational Exposure Limits (OEL). The European Commission Directive 95/320/EC of 12 July 1995 has given the tasks to a Scientific Committee for Occupational Exposure Limits (SCOEL) to propose, based on scientific data and where appropriate, occupational limit values which may include the 8-h time-weighted average (TWA), short-term limits/excursion limits (STEL) and Biological Limit Values (BLVs). In 2000, the European Union issued a list of 62 chemical substances with Occupational Exposure Limits. Of these, 25 substances received a "skin" notation, indicating that toxicologically significant amounts may be taken up via the skin. For such substances, monitoring of concentrations in ambient air may not be sufficient, and biological monitoring strategies appear of potential importance in the medical surveillance of exposed workers. Recent progress has been made with respect to formulation of a strategy related to health-based BLVs.

Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy ofdiagno sis.

Dropout from internet-based treatment for psychological disorders

Purpose: The purpose of this review was to present an in-depth analysis of literature identifying the extent of dropout from Internet-based treatment programmes for psychological disorders, and literature exploring the variables associated with dropout from such programmes. ----- ----- Methods: A comprehensive literature search was conducted on PSYCHINFO and PUBMED with the keywords: dropouts, drop out, dropout, dropping out, attrition, premature termination, termination, non-compliance, treatment, intervention, and program, each in combination with the key words Internet and web. A total of 19 studies published between 1990 and April 2009 and focusing on dropout from Internet-based treatment programmes involving minimal therapist contact were identified and included in the review. ----- ----- Results: Dropout ranged from 2 to 83% and a weighted average of 31% of the participants dropped out of treatment. A range of variables have been examined for their association with dropout from Internet-based treatment programmes for psychological disorders. Despite the numerous variables explored, evidence on any specific variables that may make an individual more likely to drop out of Internet-based treatment is currently limited. ----- ----- Conclusions: This review highlights the need for more rigorous and theoretically guided research exploring the variables associated with dropping out of Internet-based treatment for psychological disorders.

Exposure to particles from laser printers operating within office workplaces

While recent research has provided valuable information as to the composition of laser printer particles, their formation mechanisms, and explained why some printers are emitters whilst others are low emitters, fundamental questions relating to the potential exposure of office workers remained unanswered. In particular, (i) what impact does the operation of laser printers have on the background particle number concentration (PNC) of an office environment over the duration of a typical working day?; (ii) what is the airborne particle exposure to office workers in the vicinity of laser printers; (iii) what influence does the office ventilation have upon the transport and concentration of particles?; (iv) is there a need to control the generation of, and/or transport of particles arising from the operation of laser printers within an office environment?; (v) what instrumentation and methodology is relevant for characterising such particles within an office location? We present experimental evidence on printer temporal and spatial PNC during the operation of 107 laser printers within open plan offices of five buildings. We show for the first time that the eight-hour time-weighted average printer particle exposure is significantly less than the eight-hour time-weighted local background particle exposure, but that peak printer particle exposure can be greater than two orders of magnitude higher than local background particle exposure. The particle size range is predominantly ultrafine (< 100nm diameter). In addition we have established that office workers are constantly exposed to non-printer derived particle concentrations, with up to an order of magnitude difference in such exposure amongst offices, and propose that such exposure be controlled along with exposure to printer derived particles. We also propose, for the first time, that peak particle reference values be calculated for each office area analogous to the criteria used in Australia and elsewhere for evaluating exposure excursion above occupational hazardous chemical exposure standards. A universal peak particle reference value of 2.0 x 104 particles cm-3 has been proposed.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

A hybrid smoothed finite element method (H-SFEM) to solid mechanics problems

In this paper, a hybrid smoothed finite element method (H-SFEM) is developed for solid mechanics problems by combining techniques of finite element method (FEM) and Node-based smoothed finite element method (NS-FEM) using a triangular mesh. A parameter is equipped into H-SFEM, and the strain field is further assumed to be the weighted average between compatible stains from FEM and smoothed strains from NS-FEM. We prove theoretically that the strain energy obtained from the H-SFEM solution lies in between those from the compatible FEM solution and the NS-FEM solution, which guarantees the convergence of H-SFEM. Intensive numerical studies are conducted to verify these theoretical results and show that (1) the upper and lower bound solutions can always be obtained by adjusting ; (2) there exists a preferable at which the H-SFEM can produce the ultrasonic accurate solution.

Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data

Data in germplasm collections contain a mixture of data types; binary, multistate and quantitative. Given the multivariate nature of these data, the pattern analysis methods of classification and ordination have been identified as suitable techniques for statistically evaluating the available diversity. The proximity (or resemblance) measure, which is in part the basis of the complementary nature of classification and ordination techniques, is often specific to particular data types. The use of a combined resemblance matrix has an advantage over data type specific proximity measures. This measure accommodates the different data types without manipulating them to be of a specific type. Descriptors are partitioned into their data types and an appropriate proximity measure is used on each. The separate proximity matrices, after range standardisation, are added as a weighted average and the combined resemblance matrix is then used for classification and ordination. Germplasm evaluation data for 831 accessions of groundnut (Arachis hypogaea L.) from the Australian Tropical Field Crops Genetic Resource Centre, Biloela, Queensland were examined. Data for four binary, five ordered multistate and seven quantitative descriptors have been documented. The interpretative value of different weightings - equal and unequal weighting of data types to obtain a combined resemblance matrix - was investigated by using principal co-ordinate analysis (ordination) and hierarchical cluster analysis. Equal weighting of data types was found to be more valuable for these data as the results provided a greater insight into the patterns of variability available in the Australian groundnut germplasm collection. The complementary nature of pattern analysis techniques enables plant breeders to identify relevant accessions in relation to the descriptors which distinguish amongst them. This additional information may provide plant breeders with a more defined entry point into the germplasm collection for identifying sources of variability for their plant improvement program, thus improving the utilisation of germplasm resources.

The effect of ß-alanine on buffering capacity and exercise performance during and following high-intensity exercise in healthy males

Introduction Intense exercise induced acidosis occurs from the accumulation of hydrogen ions as by-products of anaerobic metabolism. Oral ingestion of ß-alanine, a limiting precursor of the intracellular physiochemical buffer carnosine in skeletal muscle, may counteract any detrimental effect of acidosis and benefit performance. The aim of this study was to investigate the effect of ß-alanine as an ergogenic aid during high intensity exercise performance in healthy males. Methods Five males ingested either ß-alanine (BAl) (4.8 g.d-1 for 4wk, then 6.4 g.d-1 for 2wk) or placebo (Pl) (CaCO3) in a crossover design with 6 wk washout between. Following supplementation, participants performed two different intense exercise protocols over consecutive days. On the first day a repeated sprint ability (RSA) test of 5 x 6s, with 24s rest periods, was performed. On the second day a cycling capacity test measuring the time to exhaustion (TTE) was performed at 110% of their max workload achieved in a pre supplementation max test (CCT110%). Non-invasive quantification of carnosine, prior to, and following each supplementation, with magnetic resonance spectrometry was performed in the soleus and gastrocnemius. Time to fatigue (CCT110%), peak and mean power (RSA), blood pH, and plasma lactate were measured. Results Muscle carnosine concentration was not different prior to ß-alanine supplementation and increased 18% in the soleus and 26% in the gastrocnemius, respectively with 6 wk supplementation. There was no difference in the measured performance variables during the RSA test (peak and average power output). TTE during the CCT110% was significantly enhanced following the ingestion of BAl (155s ± 19.03) compared to Pl (134s ± 26.16). No changes were observed in blood pH during either exercise protocol and during the recovery from exercise. Plasma lactate in the BAl condition was significantly higher than Pl only from the 15th minute following exercise during the CCT110%. FIG. 1: Changes in carnosine concentration in the gastrocnemius prior and post 6 week chronic supplementation of placebo and β-alanine. Values expressed as mean.* p<0.05 from Pl at 6 weeks, # p<0.05 from pre supplementation. Conclusion/Discussion Greater muscle carnosine content following 6wk supplementation of ß-alanine enhanced the potential for intracellular buffering capacity. However, this only translated into enhanced performance during the CCT110% high intensity cycling exercise protocol, with no change observed during the RSA test. No differences in post exercise and recovery plasma lactates and blood pH, indicates that 6wks ß-alanine supplementation has no effect on anaerobic metabolism during multiple bout high intensity exercise. Changes in plasma lactate during recovery supports that ß-alanine supplementation may affect anaerobic metabolism however during single bout high intensity.

The effect of ß-alanine on buffering capacity and exercise performance during and following high-intensity exercise in healthy males

Intense exercise induced acidosis occurs after accumulation of hydrogen ions as by-products of anaerobic metabolism. Oral ingestion of ß-alanine, a limiting precursor of the intracellular physiochemical buffer carnosine in skeletal muscle, may counteract detrimental effects of acidosis and benefit performance. This study aimed to investigate the effect of ß-alanine as an ergogenic aid during high intensity exercise performance. Five healthy males ingested either ß-alanine or placebo (Pl) (CaCO3) in a crossover design with 6 wk washout between. Participants performed two different intense exercise protocols over consecutive days. On the first day a repeated sprint ability (RSA) test was performed. On the second day a cycling capacity test measuring the time to exhaustion (TTE) was performed at 110% of maximum workload achieved in a pre supplementation max test (CCT110%). Non-invasive quantification of carnosine, prior to, and following each supplementation, with in vivo magnetic resonance spectrometry was performed in the soleus and gastrocnemius muscle. Time to fatigue (CCT110%), peak and mean power (RSA), blood pH, and plasma lactate were measured. Muscle carnosine concentration was not different prior to ß-alanine supplementation and increased 18% in the soleus and 26% in the gastrocnemius, respectively after supplementation. There was no difference in the measured performance variables during the RSA test (peak and average power output). TTE during the CCT110% was significantly enhanced following the ingestion of BAl (155s ± 19.03) compared to Pl (134s ± 26.16). No changes were observed in blood pH during either exercise protocol and during the recovery from exercise. Plasma lactate after BAI was significantly higher than Pl only from the 15th minute following exercise during the CCT110%. Greater muscle carnosine content following 6wk supplementation of ß-alanine enhanced the potential for intracellular buffering capacity. This translated into enhanced performance during the CCT110% high intensity cycling exercise protocol but not during the RSA test. The lack of change in plasma lactate or blood pH indicates that 6wks ß-alanine supplementation has no effect on anaerobic metabolism during multiple-bout high-intensity exercise. Changes measured in plasma lactate during recovery support the hypothesis that ß-alanine supplementation may affect anaerobic metabolism particularly during single bout high intensity.

A normal-distribution based reputation model

Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.

A rating aggregation method for generating product reputations

Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items. Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms state-of-the-art methods over different sparsity datasets.

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

Meta-analysis of prevalence

Meta-analysis is a method to obtain a weighted average of results from various studies. In addition to pooling effect sizes, meta-analysis can also be used to estimate disease frequencies, such as incidence and prevalence. In this article we present methods for the meta-analysis of prevalence. We discuss the logit and double arcsine transformations to stabilise the variance. We note the special situation of multiple category prevalence, and propose solutions to the problems that arise. We describe the implementation of these methods in the MetaXL software, and present a simulation study and the example of multiple sclerosis from the Global Burden of Disease 2010 project. We conclude that the double arcsine transformation is preferred over the logit, and that the MetaXL implementation of multiple category prevalence is an improvement in the methodology of the meta-analysis of prevalence.