Novel array geometries for free-space optical interconnects with improved signal-to-noise ratio

We investigate the effect of transmitter and receiver array configurations on the stray-light and diffraction-caused crosstalk in free-space optical interconnects. The optical system simulation software (Code V) is used to simulate both the stray-light and diffraction-caused crosstalk. Experimentally measured, spectrally-resolved, near-field images of VCSEL higher order modes were used as extended sources in our simulation model. Our results show that by changing the square lattice geometry to a hexagonal configuration, we obtain the reduction in the stray-light crosstalk of up to 9 dB and an overall signal-to-noise ratio improvement of 3 dB.

effects of weight and injected dose in the liver’s Signal-to-Noise Ratio in patients submitted to PET/CT scans

The quality of the image of 18F-FDG PET/CT scans in overweight patients is commonly degraded. This study evaluates, retrospectively, the relation between SNR, weight and dose injected in 65 patients, with a range of weights from 35 to 120 kg, with scans performed using the Biograph mCT using a standardized protocol in the Nuclear Medicine Department at Radboud University Medical Centre in Nijmegen, The Netherlands. Five ROI’s were made in the liver, assumed to be an organ of homogenous metabolism, at the same location, in five consecutive slices of the PET/CT scans to obtain the mean uptake (signal) values and its standard deviation (noise). The ratio of both gave us the Signal-to- Noise Ratio in the liver. With the help of a spreadsheet, weight, height, SNR and Body Mass Index were calculated and graphs were designed in order to obtain the relation between these factors. The graphs showed that SNR decreases as the body weight and/or BMI increased and also showed that, even though the dose injected increased, the SNR also decreased. This is due to the fact that heavier patients receive higher dose and, as reported, heavier patients have less SNR. These findings suggest that the quality of the images, measured by SNR, that were acquired in heavier patients are worst than thinner patients, even though higher FDG doses are given. With all this taken in consideration, it was necessary to make a new formula to calculate a new dose to give to patients and having a good and constant SNR in every patient. Through mathematic calculations, it was possible to reach to two new equations (power and exponential), which would lead to a SNR from a scan made with a specific reference weight (86 kg was the considered one) which was independent of body mass. The study implies that with these new formulas, patients heavier than the reference weight will receive higher doses and lighter patients will receive less doses. With the median being 86 kg, the new dose and new SNR was calculated and concluded that the quality of the image remains almost constant as the weight increases and the quantity of the necessary FDG remains almost the same, without increasing the costs for the total amount of FDG used in all these patients.

Calculation of mutual information for nonlinear communication channel at large signal-to-noise ratio

Using the path-integral technique we examine the mutual information for the communication channel modeled by the nonlinear Schrödinger equation with additive Gaussian noise. The nonlinear Schrödinger equation is one of the fundamental models in nonlinear physics, and it has a broad range of applications, including fiber optical communications - the backbone of the internet. At large signal-to-noise ratio we present the mutual information through the path-integral, which is convenient for the perturbative expansion in nonlinearity. In the limit of small noise and small nonlinearity we derive analytically the first nonzero nonlinear correction to the mutual information for the channel.

Dose modulated retrospective ECG-gated versus non-gated 64-row CT angiography of the aorta at the same radiation dose: Comparison of motion artifacts, diagnostic confidence and signal-to-noise-ratios

To compare ECG-gated and non-gated CT angiography of the aorta at the same radiation dose, with regard to motion artifacts (MA), diagnostic confidence (DC) and signal-to-noise-ratios (SNRs).

Inverse Modeling of GPS Multipath for Snow Depth Estimation-Part I: Formulation and Simulations

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Variational data assimilation for parameter estimation: application to a simple morphodynamic model

Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce state and parameter estimates that most accurately approximate the current and future states of the true system. The technique is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var, Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more complex morphodynamic models.

A hybrid data assimilation scheme for model parameter estimation: application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.

Data assimilation for state and parameter estimation: application to morphodynamic modelling

Data assimilation is predominantly used for state estimation; combining observational data with model predictions to produce an updated model state that most accurately approximates the true system state whilst keeping the model parameters fixed. This updated model state is then used to initiate the next model forecast. Even with perfect initial data, inaccurate model parameters will lead to the growth of prediction errors. To generate reliable forecasts we need good estimates of both the current system state and the model parameters. This paper presents research into data assimilation methods for morphodynamic model state and parameter estimation. First, we focus on state estimation and describe implementation of a three dimensional variational(3D-Var) data assimilation scheme in a simple 2D morphodynamic model of Morecambe Bay, UK. The assimilation of observations of bathymetry derived from SAR satellite imagery and a ship-borne survey is shown to significantly improve the predictive capability of the model over a 2 year run. Here, the model parameters are set by manual calibration; this is laborious and is found to produce different parameter values depending on the type and coverage of the validation dataset. The second part of this paper considers the problem of model parameter estimation in more detail. We explain how, by employing the technique of state augmentation, it is possible to use data assimilation to estimate uncertain model parameters concurrently with the model state. This approach removes inefficiencies associated with manual calibration and enables more effective use of observational data. We outline the development of a novel hybrid sequential 3D-Var data assimilation algorithm for joint state-parameter estimation and demonstrate its efficacy using an idealised 1D sediment transport model. The results of this study are extremely positive and suggest that there is great potential for the use of data assimilation-based state-parameter estimation in coastal morphodynamic modelling.

Dominant set approach to ECG biometrics

Electrocardiographic (ECG) signals are emerging as a recent trend in the field of biometrics. In this paper, we propose a novel ECG biometric system that combines clustering and classification methodologies. Our approach is based on dominant-set clustering, and provides a framework for outlier removal and template selection. It enhances the typical workflows, by making them better suited to new ECG acquisition paradigms that use fingers or hand palms, which lead to signals with lower signal to noise ratio, and more prone to noise artifacts. Preliminary results show the potential of the approach, helping to further validate the highly usable setups and ECG signals as a complementary biometric modality.

Multipath reduction from GPS double differences using wavelets: How far can we go?

Wavelets are being extensively used in Geodetic applications. In this paper, the Multi-Resolution Analysis (MRA) using wavelets is applied to pseudorange and carrier phase GPS double differences (DDs) in order to reduce multipath effects. The wavelets were already applied to GPS carrier phase DDs, but some questions remain: How good can be the results, and are all multipath effects reduced? The answers to these questions are discussed in this paper. Thus, the wavelet transform is used to decompose the DD signals, splitting them in lower resolution components. After the decomposition process, the wavelet shrinkage is performed by thresholding to eliminate the components relative to multipath effects. Then, the DD observation can be reconstructed. This new DD signal is used to perform the baseline processing. The daily multipath repeatability was verified. With the application of the proposed approach, the results showed that the reliability of the ambiguity resolution and accuracy of the results improved when compared with the standard procedure. Furthermore, the method showed to be very efficient computationally, because, it is not noticed, at practical level, difference in the time span between the processing with and without application of the proposed method. However, only the high frequency multipath was eliminated.

Distortion-free enhancement of terahertz signals measured by electro-optic sampling. II. Experiment

Three methods for distortion-free enhancement of electro-optic sampling measurements of terahertz signals are tested. In the first part of this two-paper series [J. Opt. Soc. Am B 31, 904–910 (2014)], the theoretical framework for describing the signal enhancement was presented and discussed. As the applied optical bias is decreased, individual signal traces become enhanced but distorted. Here we experimentally show that nonlinear signal components that distort the terahertz electric field measurement can be removed by subtracting traces recorded with opposite optical bias values. In all three methods tested, we observe up to an order of magnitude increase in distortion-free signal enhancement, in agreement with the theory, making possible measurements of small terahertz-induced transient birefringence signals with increased signal-to-noise ratio.

ECG Biometrics: A Template Selection Approach

Electrocardiography (ECG) biometrics is emerging as a viable biometric trait. Recent developments at the sensor level have shown the feasibility of performing signal acquisition at the fingers and hand palms, using one-lead sensor technology and dry electrodes. These new locations lead to ECG signals with lower signal to noise ratio and more prone to noise artifacts; the heart rate variability is another of the major challenges of this biometric trait. In this paper we propose a novel approach to ECG biometrics, with the purpose of reducing the computational complexity and increasing the robustness of the recognition process enabling the fusion of information across sessions. Our approach is based on clustering, grouping individual heartbeats based on their morphology. We study several methods to perform automatic template selection and account for variations observed in a person's biometric data. This approach allows the identification of different template groupings, taking into account the heart rate variability, and the removal of outliers due to noise artifacts. Experimental evaluation on real world data demonstrates the advantages of our approach.

Nuclear magnetic resonance spectroscopy and ultrasound

The work described in this thesis is directed to the examination of the hypothesis that ultrasound may be used to perturb molecular motion in the liquid phase. These changes can then be detected by nuclear magnetic resonance (NMR) in spin-lattice and spin-spin relaxation times. The objective being to develop a method capable of reducing the pulsed NMR acquisition times of slowly relaxing nuclei. The thesis describes the theoretical principles underlying both NMR spectroscopy and ultrasonics with particular attention being paid to factors that impinge on testing the above hypothesis. Apparatus has been constructed to enable ultrasound at frequencies between 1 and 10 mega-hertz with a variable power up to 100W/cm-2 to be introduced in the NMR sample. A broadband high frequency generator is used to drive PZT piezo-electric transducer via various transducer to liquid coupling arrangements. A commercial instrument of 20 kilo-hertz has also been employed to test the above hypothesis and also to demonstrate the usefulness of ultrasound in sonochemistry. The latter objective being, detection of radical formation in monomer and polymer ultrasonic degradation. The principle features of the results obtained are: Ultrasonic perturbation of T1 is far smaller for pure liquids than is for mixtures. The effects appear to be greater on protons (1H) than on carbon-13 nuclei (13C) relaxation times. The observed effect of ultrasonics is not due to temperature changes in the sample. As the power applied to the transducer is progressively increased T1 decreases to a minimum and then increases. The T1's of the same nuclei in different functional groups are influenced to different extents by ultrasound. Studies of the 14N resonances from an equimolar mixture of N, N-dimethylformamide and deuterated chloroform with ultrasonic frequencies at 1.115, 6, 6.42 and 10 MHz show that as the frequency is increased the NMR signal to noise ratio decreases to zero at the Larmor frequency of 6.42 MHz and then again rises. This reveals the surprising indication that an effect corresponding to nuclear acoustic saturation in the liquid may be observable. Ultrasonic irradiation of acidified ammonium chloride solution at and around 6.42 MHz appears to cause distinctive changes in the proton-nitrogen J coupling resonance at 89.56 MHz. Ultrasonic irradiation of N, N-dimethylacetamide at 2 KHz using the lowest stable power revealed the onset of coalescence in the proton spectrum. The corresponding effect achieved by direct heating required a temperature rise of approximately 30oC. The effects of low frequency (20 KHz) on relaxation times appear to be nil. Detection of radical formation proved difficult but is still regarded as the principle route for monomer and polymer degradation. The initial hypothesis is considered proven with the results showing significant changes in the mega-hertz region and none at 20 KHz.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.