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.     

Nonisothermal melt-crystallization kinetics of isotactic polypropylene synthesized with a metallocene catalyst and compounded with different quantities of an α nucleator

The nonisothermal crystallization kinetics of a metallocene-made isotactic polypropylene (m-iPP) and its compounds with 0.1 wt % and 0.3 wt % of a sorbitol derivative [1,3:2,4-bis(3,4-dimethylbenzylidene)sorbitol (DMDBS); an α nucleator] were investigated by differential scanning calorimetry at different cooling rates from the melt. The nucleation efficiency was proved by a significant increase in the crystallization temperatures (accompanied by a slight augmentation of the degree of crystallinity and a decrease in the crystal sizes). This increase in the crystallization temperatures led to higher amounts of fractional content in the γ polymorph, even though DMDBS was supposed to be a nucleator for the α form. The Avrami and Ozawa methods effectively described only the early stage of crystallization, whereas a combined Avrami–Ozawa method was valid for the whole crystallization process. The values of the exponent for this method decreased for nucleated samples in the later stage of crystallization, especially in the case of m-iPP with 0.3 wt % DMDBS added (m-iPP03). The activation energy of the process and the surface free energy were also estimated. The production of considerable proportions of the γ polymorph in m-iPP03 corresponded to higher values of the activation energy and lower values of the surface free energy. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

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     

The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets

Drought diagnosis and forecasting are fundamental issues regarding hydrological management in Spain, where recurrent water scarcity periods are normal. Land-surface models (LSMs) could provide relevant information for water managers on how drought conditions evolve. Here, we explore the usefulness of LSMs driven by atmospheric analyses with different resolutions and accuracies in simulating drought and its propagation to precipitation, soil moisture and streamflow through the system. We perform simulations for the 1980-2014 period with SASER (5 km resolution) and LEAFHYDRO (2.5 km resolution), which are forced by the Spanish SAFRAN dataset (at 5km and 30km resolutions), and the global eartH2Observe datasets at 0.25 degrees (including the MSWEP precipitation dataset). We produce standardized indices for precipitation (SPI), soil moisture (SSMI) and streamflow (SSI). The results show that the model structure uncertainty remains an important issue in current generation large-scale hydrological simulations based on LSMs. This is true for both the SSMI and SSI. The differences between the simulated SSMI and SSI are large, and the propagation scales for drought regarding both soil moisture and streamflow are overly dependent on the model structure. Forcing datasets have an impact on the uncertainty of the results but, in general, this impact is not as large as the uncertainty due to model formulation. Concerning the global products, the precipitation product that includes satellite observations (MSWEP) represents a large improvement compared with the product that does not.

     

Interactive visualization of medical volume models in mobile devices

Interactive visualization of volume models in standard mobile devices is a challenging present problem with increasing interest from new application fields like telemedicine. The complexity of present volume models in medical applications is continuously increasing, therefore increasing the gap between the available models and the rendering capabilities in low-end mobile clients. New and efficient rendering algorithms and interaction paradigms are required for these small platforms. In this paper, we propose a transfer function-aware compression and interaction scheme, for client-server architectures with visualization on standard mobile devices. The scheme is block-based, supporting adaptive ray-casting in the client. Our two-level ray-casting allows focusing on small details on targeted regions while keeping bounded memory requirements in the GPU of the client. Our approach includes a transfer function-aware compression scheme based on a local wavelet transformation, together with a bricking scheme that supports interactive inspection and levels of detail in the mobile device client. We also use a quantization technique that takes into account a perceptive metrics of the visual error. Our results show that we can have full interaction with high compression rates and with transmitted model sizes that can be of the order of a single photographic image.     

Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.     

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.