An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation

In this work we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin

Comparison of results from different imputation techniques for missing data from an anti-obesity drug trial

BackgroundIn randomised trials of medical interventions, the most reliable analysis follows the intention-to-treat (ITT) principle. However, the ITT analysis requires that missing outcome data have to be imputed. Different imputation techniques may give different results and some may lead to bias. In anti-obesity drug trials, many data are usually missing, and the most used imputation method is last observation carried forward (LOCF). LOCF is generally considered conservative, but there are more reliable methods such as multiple imputation (MI).ObjectivesTo compare four different methods of handling missing data in a 60-week placebo controlled anti-obesity drug trial on topiramate.MethodsWe compared an analysis of complete cases with datasets where missing body weight measurements had been replaced using three different imputation methods: LOCF, baseline carried forward (BOCF) and MI.Results561 participants were randomised. Compared to placebo, there was a significantly greater weight loss with topiramate in all analyses: 9.5 kg (SE 1.17) in the complete case analysis (N = 86), 6.8 kg (SE 0.66) using LOCF (N = 561), 6.4 kg (SE 0.90) using MI (N = 561) and 1.5 kg (SE 0.28) using BOCF (N = 561).ConclusionsThe different imputation methods gave very different results. Contrary to widely stated claims, LOCF did not produce a conservative (i.e., lower) efficacy estimate compared to MI. Also, LOCF had a lower SE than MI

Investigation and implications of spatial and temporal patterns in sex ratio data from West Greenland minke whale catches.

The sub-group based its deliberations on the computations set out below, which were carried out by Givens following input from sub-group members

Accuracy of the Aspartic Acid Racemization Technique in Age Estimation of Mammals and the Influence of Body Temperature

The aspartic acid racemization (AAR) technique has been applied for age estimation of humans and other mammals for more than four decades. In this study, eye lenses from 124 animals representing 25 mammalian species were collected and D/L ratios obtained using the AAR technique. The animals were either of known age or had the age estimated by other methods. The purpose of the study was to: a) estimate the accuracy of the AAR technique, and b) examine the effect of body temperature on racemization rates. Samples from four of the 25 species covered the range of ages that is needed to estimate species-specific racemization rates. The sample size from a single species of known age, the pygmy goat (Capra hircus, n = 35), was also large enough to investigate the accuracy of ages obtained using the AAR technique. The 35 goats were divided into three datasets: all goats (n = 35), goats >0.5 yrs old (n = 26) and goats >2 yrs old (n = 19). Leave-one-out analyses were performed on the three sets of data. Normalized root mean squared errors for the group of goats >0.5 yrs old were found to be the smallest. The higher variation in D/L measurements found for young goats 0.5 yrs old was for three age groups of the goats: 0.934 yrs for young goats 8 yrs (n = 4). Thus, the age of an adult or an old animal can be predicted with approximately 10% accuracy, whereas the age of a young animal is difficult to predict. A goat specific racemization rate, as a 2kAsp value, was estimated to 0.0107 ± 3.8 x 10-4 SE (n = 26). The 2kAsp values from 12 species, four estimated in this study and another eight published, were used to examine the effect of core body temperature on the rate of racemization. A positive relationship between AAR and temperature was found (r2 = 0.321) but results also suggest that other factors besides temperature are involved in the racemization process in living animals. Based on our results we emphasize that non-species-specific racemization rates should be used with care in AAR age estimation studies and that the period of postnatal growth of the eye lens be considered when estimating species-specific D/L0 values and ages of young individuals

Self-assembly of ordered graphene nanodot arrays (vol 8, 47, 2017)

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