Phase local approximation (PhaseLa) technique for phase unwrap from noisy data.

The local polynomial approximation (LPA) is a nonparametric regression technique with pointwise estimation in a sliding window. We apply the LPA of the argument of cos and sin in order to estimate the absolute phase from noisy wrapped phase data. Using the intersection of confidence interval (HCI) algorithm, the window size is selected as adaptive pointwise varying. This adaptation gives the phase estimate with the accuracy close to optimal in the mean squared sense. For calculations, we use a Gauss-Newton recursive procedure initiated by the phase estimates obtained for the neighboring points. It enables tracking properties of the algorithm and its ability to go beyond the principal interval [-pi, pi] and to reconstruct the absolute phase from wrapped phase observations even when the magnitude of the phase difference takes quite large values. The algorithm demonstrates a very good accuracy of the phase reconstruction which on many occasion overcomes the accuracy of the state-of-the-art algorithms developed for noisy phase unwrap. The theoretical analysis produced for the accuracy of the pointwise estimates is used for justification of the HCI adaptation algorithm.     

The Lambda Library: unnamed functions in C++

The Lambda Library (LL) adds a form of lambda functions to C++, which are common in functional programming languages. The LL is implemented as a template library using standard C++; thus no language extensions or preprocessing is required. The LL consists of a rich set of tools for defining unnamed functions. In particular these unnamed functions work seamlessly with the generic algorithms in the C++ Standard Library. The LL offers significant improvements, in terms of generality and ease of use, compared to the current tools in the C++ Standard Library. Copyright © 2003 John Wiley & Sons, Ltd.     

Potential of microbial growth control in water hydraulic systems by UV‐irradiation and filtration

Water hydraulic systems use water as a pressure medium and, thus, do not pose such adverse environmental impacts as oil hydraulics. Microbial deterioration of the pressure medium and biofouling of the surfaces restrict the applicability of the water‐based technology. The potential of microbial growth control by UV‐irradiation and filtration was studied in a pilot‐scale water hydraulic system. The UV‐irradiation (25 m Ws cm^?2) of the pressure medium reduced the total viable counts of bacteria by 1–2 log₁₀ cfu cm^?3, whereas the total microbial cell numbers and the numbers of surface‐attached microorganisms remained unaffected. Prefiltration (1.2 µm, absolute) of the pressure medium decreased the total microbial cell number in the water phase and retarded the attachment of bacteria. The filtration during the operation (2 µm, absolute) decreased the total numbers of microbial cells and the total viable counts in the pressure medium, and microbial attachment on the surfaces. Microbial attachment was not prevented by filtration. The microbial water quality obtained by pre‐ and on‐line filtration of the pressure medium was sufficient to ensure the long‐term operation of the water hydraulic system assuming that clean work practices are complied with in assembly and during the operation. © 2002 Society of Chemical Industry     

Do cardiovascular risk factors explain the relation between socioeconomic status, risk of all-cause mortality, cardiovascular mortality, and acute myocardial infarction?

Much remains to be understood about how low socioeconomic status (SES) increases cardiovascular disease and mortality risk. Data from the Kuopio Ischemic Heart Disease Risk Factor Study (1984-1993) were used to estimate the associations between acute myocardial infarction and income, all-cause mortality, and cardiovascular mortality in a population-based sample of 2,272 Finnish men, with adjustment for 23 biologic, behavioral, psychologic, and social risk factors. Compared with the highest income quintile, those in the bottom quintile had age-adjusted relative hazards of 3.14 (95% confidence interval (CI) 1.77-5.56), 2.66 (95% CI 1.25-5.66), and 4.34 (95% CI 1.95-9.66) for all-cause mortality, cardiovascular mortality, and AMI, respectively. After adjustment for risk factors, the relative hazards for the same comparisons were 1.32 (95% CI 0.70-2.49), 0.70 (95% CI 0.29-1.69), and 2.83 (95% CI 1.14-7.00). In the lowest income quintile, adjustment for risk factors reduced the excess relative risk of all-cause mortality by 85%, that of cardiovascular mortality by 118%, and that of acute myocardial infarction by 45%. These data show how the association between SES and cardiovascular mortality and all-cause mortality is mediated by known risk factor pathways, but full "explanations" for these associations will need to encompass why these biologic, behavioral, psychologic, and social risk factors are differentially distributed by SES.     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

The North Karelia Project: a programme for community control of cardiovascular diseases

Patients with chronic glomerulonephritis and mild hypertension show a consistent behaviour in their renin-aldosterone-system. There is a close correlation between the elevation of mean blood pressure and destruction of glomeruli. No correlation has been found between renin values and the degree of hypertension. Thus the cuase of mild hypertension occurring in the early stages of chronic GN remains to be elucidated. Normal PRA values in spite of hypertension and expansion of ECFV accompaning progression of chronic glomerulonephritis could be a sign of "relative hyperreninemia". Apparently various mechanisms are involved in the pathogenesis of renal hypertension. These include sodium retention, increased cardiac output. anemia, renin, aldosterone, prostaglandins, expanded plasma volume and peripheral vasoconstriction. These factors are more or less active in the different stages of hypertension and renal failure.     

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.