44 resultados para TO-NOISE RATIO


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Neuroprostheses interfaced with transected peripheral nerves are technological routes to control robotic limbs as well as convey sensory feedback to patients suffering from traumatic neural injuries or degenerative diseases. To maximize the wealth of data obtained in recordings, interfacing devices are required to have intrafascicular resolution and provide high signal-to-noise ratio (SNR) recordings. In this paper, we focus on a possible building block of a three-dimensional regenerative implant: a polydimethylsiloxane (PDMS) microchannel electrode capable of highly sensitive recordings in vivo. The PDMS 'micro-cuff' consists of a 3.5 mm long (100 µm × 70 µm cross section) microfluidic channel equipped with five evaporated Ti/Au/Ti electrodes of sub-100 nm thickness. Individual electrodes have average impedance of 640 ± 30 kΩ with a phase angle of −58 ± 1 degrees at 1 kHz and survive demanding mechanical handling such as twisting and bending. In proof-of-principle acute implantation experiments in rats, surgically teased afferent nerve strands from the L5 dorsal root were threaded through the microchannel. Tactile stimulation of the skin was reliably monitored with the three inner electrodes in the device, simultaneously recording signal amplitudes of up to 50 µV under saline immersion. The overall SNR was approximately 4. A small but consistent time lag between the signals arriving at the three electrodes was observed and yields a fibre conduction velocity of 30 m s−1. The fidelity of the recordings was verified by placing the same nerve strand in oil and recording activity with hook electrodes. Our results show that PDMS microchannel electrodes open a promising technological path to 3D regenerative interfaces.

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Increasing concentrations of greenhouse gases in the atmosphere are expected to modify the global water cycle with significant consequences for terrestrial hydrology. We assess the impact of climate change on hydrological droughts in a multimodel experiment including seven global impact models (GIMs) driven by bias-corrected climate from five global climate models under four representative concentration pathways (RCPs). Drought severity is defined as the fraction of land under drought conditions. Results show a likely increase in the global severity of hydrological drought at the end of the 21st century, with systematically greater increases for RCPs describing stronger radiative forcings. Under RCP8.5, droughts exceeding 40% of analyzed land area are projected by nearly half of the simulations. This increase in drought severity has a strong signal-to-noise ratio at the global scale, and Southern Europe, the Middle East, the Southeast United States, Chile, and South West Australia are identified as possible hotspots for future water security issues. The uncertainty due to GIMs is greater than that from global climate models, particularly if including a GIM that accounts for the dynamic response of plants to CO2 and climate, as this model simulates little or no increase in drought frequency. Our study demonstrates that different representations of terrestrial water-cycle processes in GIMs are responsible for a much larger uncertainty in the response of hydrological drought to climate change than previously thought. When assessing the impact of climate change on hydrology, it is therefore critical to consider a diverse range of GIMs to better capture the uncertainty.

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Observations of the amplitudes and Doppler shifts of received HF radio waves are compared with model predictions made using a two-dimensional ray-tracing program. The signals are propagated over a sub-auroral path, which is shown to lie along the latitudes of the mid-latitude trough at times of low geomagnetic activity. Generalizing the predictions to include a simple model of the trough in the density and height of the F2 peak enables the explanation of the anomalous observed diurnal variations. The behavior of received amplitude, Doppler shift, and signal-to-noise ratio as a function of the Kp index value, the time of day, and the season (in 17 months of continuous recording) is found to agree closely with that predicted using the statistical position of the trough as deduced from 8 years of Alouette satellite soundings. The variation in the times of the observation of large signal amplitudes with the Kp value and the complete absence of such amplitudes when it exceeds 2.75 are two features that implicate the trough in these effects.

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The Finnish Meteorological Institute, in collaboration with the University of Helsinki, has established a new ground-based remote-sensing network in Finland. The network consists of five topographically, ecologically and climatically different sites distributed from southern to northern Finland. The main goal of the network is to monitor air pollution and boundary layer properties in near real time, with a Doppler lidar and ceilometer at each site. In addition to these operational tasks, two sites are members of the Aerosols, Clouds and Trace gases Research InfraStructure Network (ACTRIS); a Ka band cloud radar at Sodankylä will provide cloud retrievals within CloudNet, and a multi-wavelength Raman lidar, PollyXT (POrtabLe Lidar sYstem eXTended), in Kuopio provides optical and microphysical aerosol properties through EARLINET (the European Aerosol Research Lidar Network). Three C-band weather radars are located in the Helsinki metropolitan area and are deployed for operational and research applications. We performed two inter-comparison campaigns to investigate the Doppler lidar performance, compare the backscatter signal and wind profiles, and to optimize the lidar sensitivity through adjusting the telescope focus length and data-integration time to ensure sufficient signal-to-noise ratio (SNR) in low-aerosol-content environments. In terms of statistical characterization, the wind-profile comparison showed good agreement between different lidars. Initially, there was a discrepancy in the SNR and attenuated backscatter coefficient profiles which arose from an incorrectly reported telescope focus setting from one instrument, together with the need to calibrate. After diagnosing the true telescope focus length, calculating a new attenuated backscatter coefficient profile with the new telescope function and taking into account calibration, the resulting attenuated backscatter profiles all showed good agreement with each other. It was thought that harsh Finnish winters could pose problems, but, due to the built-in heating systems, low ambient temperatures had no, or only a minor, impact on the lidar operation – including scanning-head motion. However, accumulation of snow and ice on the lens has been observed, which can lead to the formation of a water/ice layer thus attenuating the signal inconsistently. Thus, care must be taken to ensure continuous snow removal.

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The detection of anthropogenic climate change can be improved by recognising the seasonality in the climate change response. This is demonstrated for the North Atlantic jet (zonal wind at 850 hPa, U850) and European precipitation responses projected by the CMIP5 climate models. The U850 future response is characterised by a marked seasonality: an eastward extension of the North Atlantic jet into Europe in November-April, and a poleward shift in May-October. Under the RCP8.5 scenario, the multi-model mean response in U850 in these two extended seasonal means emerges by 2035-2040 for the lower--latitude features and by 2050-2070 for the higher--latitude features, relative to the 1960-1990 climate. This is 5-15 years earlier than when evaluated in the traditional meteorological seasons (December--February, June--August), and it results from an increase in the signal to noise ratio associated with the spatial coherence of the response within the extended seasons. The annual mean response lacks important information on the seasonality of the response without improving the signal to noise ratio. The same two extended seasons are demonstrated to capture the seasonality of the European precipitation response to climate change and to anticipate its emergence by 10-20 years. Furthermore, some of the regional responses, such as the Mediterranean precipitation decline and the U850 response in North Africa in the extended winter, are projected to emerge by 2020-2025, according to the models with a strong response. Therefore, observations might soon be useful to test aspects of the atmospheric circulation response predicted by some of the CMIP5 models.

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Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

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In this paper, we investigate half-duplex two-way dual-hop channel state information (CSI)-assisted amplify-and-forward (AF) relaying in the presence of high-power amplifier (HPA) nonlinearity at relays. The expression for the end-to-end signal-to-noise ratio (SNR) is derived as per the modified system model by taking into account the interference caused by relaying scheme and HPA nonlinearity. The system performance of the considered relaying network is evaluated in terms of average symbol error probability (SEP) in Nakagami-$m$ fading channels, by making use of the moment-generating function (MGF) approach. Numerical results are provided and show the effects of several parameters, such as quadrature amplitude modulation (QAM) order, number of relays, HPA parameters, and Nakagami parameter, on performance.

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Sixteen monthly air–sea heat flux products from global ocean/coupled reanalyses are compared over 1993–2009 as part of the Ocean Reanalysis Intercomparison Project (ORA-IP). Objectives include assessing the global heat closure, the consistency of temporal variability, comparison with other flux products, and documenting errors against in situ flux measurements at a number of OceanSITES moorings. The ensemble of 16 ORA-IP flux estimates has a global positive bias over 1993–2009 of 4.2 ± 1.1 W m−2. Residual heat gain (i.e., surface flux + assimilation increments) is reduced to a small positive imbalance (typically, +1–2 W m−2). This compensation between surface fluxes and assimilation increments is concentrated in the upper 100 m. Implied steady meridional heat transports also improve by including assimilation sources, except near the equator. The ensemble spread in surface heat fluxes is dominated by turbulent fluxes (>40 W m−2 over the western boundary currents). The mean seasonal cycle is highly consistent, with variability between products mostly <10 W m−2. The interannual variability has consistent signal-to-noise ratio (~2) throughout the equatorial Pacific, reflecting ENSO variability. Comparisons at tropical buoy sites (10°S–15°N) over 2007–2009 showed too little ocean heat gain (i.e., flux into the ocean) in ORA-IP (up to 1/3 smaller than buoy measurements) primarily due to latent heat flux errors in ORA-IP. Comparisons with the Stratus buoy (20°S, 85°W) over a longer period, 2001–2009, also show the ORA-IP ensemble has 16 W m−2 smaller net heat gain, nearly all of which is due to too much latent cooling caused by differences in surface winds imposed in ORA-IP.

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A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993–2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean parameter to another, especially in global indices. However, despite several caveats in the design of the multi-system ensemble, the main conclusion from this study is that an eddy-permitting multi-system ensemble approach has become mature and our results provide a first step towards a systematic comparison of eddy-permitting global ocean reanalyses aimed at providing robust conclusions on the recent evolution of the oceanic state.

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Many institutions worldwide have developed ocean reanalyses systems (ORAs) utilizing a variety of ocean models and assimilation techniques. However, the quality of salinity reanalyses arising from the various ORAs has not yet been comprehensively assessed. In this study, we assess the upper ocean salinity content (depth-averaged over 0–700 m) from 14 ORAs and 3 objective ocean analysis systems (OOAs) as part of the Ocean Reanalyses Intercomparison Project. Our results show that the best agreement between estimates of salinity from different ORAs is obtained in the tropical Pacific, likely due to relatively abundant atmospheric and oceanic observations in this region. The largest disagreement in salinity reanalyses is in the Southern Ocean along the Antarctic circumpolar current as a consequence of the sparseness of both atmospheric and oceanic observations in this region. The West Pacific warm pool is the largest region where the signal to noise ratio of reanalysed salinity anomalies is >1. Therefore, the current salinity reanalyses in the tropical Pacific Ocean may be more reliable than those in the Southern Ocean and regions along the western boundary currents. Moreover, we found that the assimilation of salinity in ocean regions with relatively strong ocean fronts is still a common problem as seen in most ORAs. The impact of the Argo data on the salinity reanalyses is visible, especially within the upper 500m, where the interannual variability is large. The increasing trend in global-averaged salinity anomalies can only be found within the top 0–300m layer, but with quite large diversity among different ORAs. Beneath the 300m depth, the global-averaged salinity anomalies from most ORAs switch their trends from a slightly growing trend before 2002 to a decreasing trend after 2002. The rapid switch in the trend is most likely an artefact of the dramatic change in the observing system due to the implementation of Argo.

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Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.

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The combination of radar and lidar in space offers the unique potential to retrieve vertical profiles of ice water content and particle size globally, and two algorithms developed recently claim to have overcome the principal difficulty with this approach-that of correcting the lidar signal for extinction. In this paper "blind tests" of these algorithms are carried out, using realistic 94-GHz radar and 355-nm lidar backscatter profiles simulated from aircraft-measured size spectra, and including the effects of molecular scattering, multiple scattering, and instrument noise. Radiation calculations are performed on the true and retrieved microphysical profiles to estimate the accuracy with which radiative flux profiles could be inferred remotely. It is found that the visible extinction profile can be retrieved independent of assumptions on the nature of the size distribution, the habit of the particles, the mean extinction-to-backscatter ratio, or errors in instrument calibration. Local errors in retrieved extinction can occur in proportion to local fluctuations in the extinction-to-backscatter ratio, but down to 400 m above the height of the lowest lidar return, optical depth is typically retrieved to better than 0.2. Retrieval uncertainties are greater at the far end of the profile, and errors in total optical depth can exceed 1, which changes the shortwave radiative effect of the cloud by around 20%. Longwave fluxes are much less sensitive to errors in total optical depth, and may generally be calculated to better than 2 W m(-2) throughout the profile. It is important for retrieval algorithms to account for the effects of lidar multiple scattering, because if this is neglected, then optical depth is underestimated by approximately 35%, resulting in cloud radiative effects being underestimated by around 30% in the shortwave and 15% in the longwave. Unlike the extinction coefficient, the inferred ice water content and particle size can vary by 30%, depending on the assumed mass-size relationship (a problem common to all remote retrieval algorithms). However, radiative fluxes are almost completely determined by the extinction profile, and if this is correct, then errors in these other parameters have only a small effect in the shortwave (around 6%, compared to that of clear sky) and a negligible effect in the longwave.

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An experimental method is described which enables the inelastically scattered X-ray component to be removed from diffractometer data prior to radial density function analysis. At each scattering angle an energy spectrum is generated from a Si(Li) detector combined with a multi-channel analyser from which the coherently scattered component is separated. The data obtained from organic polymers has an improved signal/noise ratio at high values of scattering angle, and a commensurate enhancement of resolution of the RDF at low r is demonstrated for the case of PMMA (ICI `Perspex'). The method obviates the need for the complicated correction for multiple scattering.

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Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.