ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers

This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. Thesignal is received by an antenna array in a scenario with interferenceand multipath propagation. These two effects are generallythe limiting error sources in most high-precision positioning applications.A new estimator of the code- and carrier-phases is derivedby using a simplified signal model and the maximum likelihood(ML) principle. The simplified model consists essentially ofgathering all signals, except for the direct one, in a component withunknown spatial correlation. The estimator exploits the knowledgeof the direction-of-arrival of the direct signal and is much simplerthan other estimators derived under more detailed signal models.Moreover, we present an iterative algorithm, that is adequate for apractical implementation and explores an interesting link betweenthe ML estimator and a hybrid beamformer. The mean squarederror and bias of the new estimator are computed for a numberof scenarios and compared with those of other methods. The presentedestimator and the hybrid beamforming outperform the existingtechniques of comparable complexity and attains, in manysituations, the Cramér–Rao lower bound of the problem at hand.

Evaluating the efficiency of fractional integration parameter estimators

This article deals with the efficiency of fractional integration parameter estimators. This study was based on Monte Carlo experiments involving simulated stochastic processes with integration orders in the range]-1,1[. The evaluated estimation methods were classified into two groups: heuristics and semiparametric/maximum likelihood (ML). The study revealed that the comparative efficiency of the estimators, measured by the lesser mean squared error, depends on the stationary/non-stationary and persistency/anti-persistency conditions of the series. The ML estimator was shown to be superior for stationary persistent processes; the wavelet spectrum-based estimators were better for non-stationary mean reversible and invertible anti-persistent processes; the weighted periodogram-based estimator was shown to be superior for non-invertible anti-persistent processes.

A Unitary Approach to Information and Estimation Theory in Digital Communication Systems

This thesis presents the outcomes of a Ph.D. course in telecommunications engineering. It is focused on the optimization of the physical layer of digital communication systems and it provides innovations for both multi- and single-carrier systems. For the former type we have first addressed the problem of the capacity in presence of several nuisances. Moreover, we have extended the concept of Single Frequency Network to the satellite scenario, and then we have introduced a novel concept in subcarrier data mapping, resulting in a very low PAPR of the OFDM signal. For single carrier systems we have proposed a method to optimize constellation design in presence of a strong distortion, such as the non linear distortion provided by satellites' on board high power amplifier, then we developed a method to calculate the bit/symbol error rate related to a given constellation, achieving an improved accuracy with respect to the traditional Union Bound with no additional complexity. Finally we have designed a low complexity SNR estimator, which saves one-half of multiplication with respect to the ML estimator, and it has similar estimation accuracy.

Estimation of Time-Limited Channel Spectra From Nonuniform Samples

This paper deals with the estimation of a time-invariant channel spectrum from its own nonuniform samples, assuming there is a bound on the channel’s delay spread. Except for this last assumption, this is the basic estimation problem in systems providing channel spectral samples. However, as shown in the paper, the delay spread bound leads us to view the spectrum as a band-limited signal, rather than the Fourier transform of a tapped delay line (TDL). Using this alternative model, a linear estimator is presented that approximately minimizes the expected root-mean-square (RMS) error for a deterministic channel. Its main advantage over the TDL is that it takes into account the spectrum’s smoothness (time width), thus providing a performance improvement. The proposed estimator is compared numerically with the maximum likelihood (ML) estimator based on a TDL model in pilot-assisted channel estimation (PACE) for OFDM.

Non-line-of-sight localization with frequency-selective metasurfaces

An emerging technology, that Smart Radio Environments rely on to improve wireless link quality, are Reconfigurable Intelligent Surfaces (RISs). A RIS, in general, can be understood as a thin layer of EM composite material, typically mounted on the walls or ceilings of buildings, which can be reconfigured even after its deployment in the network. RISs made by composing artificial materials in an engineered way, in order to obtain unconventional characteristics, are called metasurfaces. Through the programming of the RIS, it is possible to control and/or modify the radio waves that affect it, thus shaping the radio environment. To overcome the limitations of RISs, the metaprism represents an alternative: it is a passive and non-reconfigurable frequency-selective metasurface that acts as a metamirror to improve the efficiency of the wireless link. In particular, using an OFDM (Orthogonal Frequency-Division Multiplexing) signaling it is possible to control the reflection of the signal, suitably selecting the sub-carrier assigned to each user, without having to interact with the metaprism or having to estimate the CSI. This thesis investigates how OFDM signaling and metaprism can be used for localization purposes, especially to extend the coverage area at low cost, in a scenario where the user is in NLoS (Non-line-of-sight) conditions with respect to the base station, both single antenna. In particular, the paper concerns the design of the analytical model and the corresponding Matlab implementation of a Maximum Likelihood (ML) estimator able to estimate the unknown position, behind an obstacle, from which a generic user transmits to a base station, exploiting the metaprism.

ML approaches to channel estimation for pilot-aided multi-rate DS/CDMA systems

This paper analyzes the asymptotic performance of maximum likelihood (ML) channel estimation algorithms in wideband code division multiple access (WCDMA) scenarios. We concentrate on systems with periodic spreading sequences (period larger than or equal to the symbol span) where the transmitted signal contains a code division multiplexed pilot for channel estimation purposes. First, the asymptotic covariances of the training-only, semi-blind conditional maximum likelihood (CML) and semi-blind Gaussian maximum likelihood (GML) channelestimators are derived. Then, these formulas are further simplified assuming randomized spreading and training sequences under the approximation of high spreading factors and high number of codes. The results provide a useful tool to describe the performance of the channel estimators as a function of basicsystem parameters such as number of codes, spreading factors, or traffic to training power ratio.

Uso do teste ML-Flow como auxiliar na classificação e tratamento da hanseníase

FUNDAMENTOS: O tratamento da hanseníase é definido pela classificação de pacientes em paucibacilares (PB) e multibacilares (MB). A OMS (Organização Mundial de Saúde) classifica os doentes de acordo com o número de lesões, mas Ridley-Jopling (R&J) utiliza também exames complementares, porém é de difícil utilização fora dos serviços de referência. Em 2003 foi desenvolvido um teste denominado ML-Flow, uma alternativa à sorologia por ELISA para auxiliar na classificação de pacientes em PB e MB e auxiliar na decisão terapêutica. OBJETIVOS: Observar a concordância entre o teste de ML-Flow e baciloscopia de linfa, exame já consagrado para detecção de MB. Analisar a utilidade do teste de ML-Flow em campo. MATERIAL E MÉTODOS: Estudo retrospectivo avaliando prontuário de 55 pacientes virgens de tratamento, diagnosticados como PB ou MB por R&J. Submetidos à baciloscopia e ao teste de ML-Flow. RESULTADOS: Nos MB, a baciloscopia foi positiva em 80% dos casos, o ML-flow foi positivo em 82,5%. Entre os PB, o ML-Flow foi positivo em 37,5% e a baciloscopia do esfregaço foi negativa em 100% dos casos. A concordância entre os resultados da baciloscopia do esfregaço e ML-Flow foi de 87,5%, kappa=0,59, p<0,001. CONCLUSÃO: Nenhum teste laboratorial é 100% sensível e específico para a correta classificação de todas as formas de hanseníase. O ML-Flow é um teste rápido, de fácil manuseio em campo, menos invasivo que a baciloscopia podendo ser útil para auxiliar na decisão terapêutica em locais de difícil acesso a serviços de referência.

Suitability of relative humidity as an estimator of leaf wetness duration

Leaf wetness duration (LWD) is a key parameter in agricultural meteorology since it is related to epidemiology of many important crops, controlling pathogen infection and development rates. Because LWD is not widely measured, several methods have been developed to estimate it from weather data. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results, but their complexity is a disadvantage for operational use. Alternatively, empirical models have been used despite their limitations. The simplest empirical models use only relative humidity data. The objective of this study was to evaluate the performance of three RH-based empirical models to estimate LWD in four regions around the world that have different climate conditions. Hourly LWD, air temperature, and relative humidity data were obtained from Ames, IA (USA), Elora, Ontario (Canada), Florence, Toscany (Italy), and Piracicaba, Sao Paulo State (Brazil). These data were used to evaluate the performance of the following empirical LWD estimation models: constant RH threshold (RH >= 90%); dew point depression (DPD); and extended RH threshold (EXT_RH). Different performance of the models was observed in the four locations. In Ames, Elora and Piracicaba, the RH >= 90% and DPD models underestimated LWD, whereas in Florence these methods overestimated LWD, especially for shorter wet periods. When the EXT_RH model was used, LWD was overestimated for all locations, with a significant increase in the errors. In general, the RH >= 90% model performed best, presenting the highest general fraction of correct estimates (F(C)), between 0.87 and 0.92, and the lowest false alarm ratio (F(AR)), between 0.02 and 0.31. The use of specific thresholds for each location improved accuracy of the RH model substantially, even when independent data were used; MAE ranged from 1.23 to 1.89 h, which is very similar to errors obtained with published physical models for LWD estimation. Based on these results, we concluded that, if calibrated locally, LWD can be estimated with acceptable accuracy by RH above a specific threshold, and that the EXT_RH method was unsuitable for estimating LWD at the locations used in this study. (C) 2007 Elsevier B.V. All rights reserved.

A Novel Nonparametric Density Estimator

We present a novel nonparametric density estimator and a new data-driven bandwidth selection method with excellent properties. The approach is in- spired by the principles of the generalized cross entropy method. The pro- posed density estimation procedure has numerous advantages over the tra- ditional kernel density estimator methods. Firstly, for the first time in the nonparametric literature, the proposed estimator allows for a genuine incor- poration of prior information in the density estimation procedure. Secondly, the approach provides the first data-driven bandwidth selection method that is guaranteed to provide a unique bandwidth for any data. Lastly, simulation examples suggest the proposed approach outperforms the current state of the art in nonparametric density estimation in terms of accuracy and reliability.

Chronic exposure to ambient levels of urban particles affects mouse lung development

Rationale- Chronic exposure to air pollution has been associated with adverse effects on children`s lung growth. Objectives: We analyzed the effects of chronic exposure to urban levels of particulate matter (PM) on selected phases of mouse lung development. Methods: The exposure occurred in two open-top chambers (filtered and nonfiltered) placed 20 m from a street with heavy traffic in Sao Paulo, 24 hours/day for 8 months. There was a significant reduction of the levels of PM(2.5) inside the filtered chamber (filtered = 2.9 +/- 3.0 mu g/m(3), nonfiltered = 16.8 +/- 8.3 mu g/m(3); P = 0.001). At this exposure site, vehicular sources are the major components of PM(2.5) (PM <= 2.5 mu m). Exposure of the parental generation in the two chambers occurred from the 10th to the 120th days of life. After mating and birth of offspring, a crossover of mothers and pups occurred within the chambers, resulting in four groups of pups: nonexposed, prenatal, postnatal, and pre+postnatal. Offspring were killed at the age of 15 (n = 42) and 90 (n = 35) days; lungs were analyzed by morphometry for surface to volume ratio (as an estimator of alveolization). Pressure-volume curves were performed in the older groups, using a 20-ml plethysmograph. Measurements and Main Results: Mice exposed to PM(2.5) pre+postnatally presented a smaller surface to volume ratio when compared with nonexposed animals (P = 0.036). The pre+postnatal group presented reduced inspiratory and expiratory volumes at higher levels of transpulmonary pressure (P = 0.001). There were no differences among prenatal and postnatal exposure and nonexposed animals. Conclusions: Our data provide anatomical and functional support to the concept that chronic exposure to urban PM affects lung growth.

Crotamine toxicity and efficacy in mouse models of melanoma

Objectives: Selective anticancer cell activity for both cell-penetrating and cationic antimicrobial peptides has previously been reported. As crotamine possesses activities similar to both of these, this study investigates crotamine`s anticancer toxicity in vitro and in vivo. Research design and methods: In vitro cancer cell viability was evaluated after treatment with 1 and 5 mu g/ml of crotamine. In vivo crotamine cytotoxic effects in C57Bl/6J mice bearing B16-F10 primary cutaneous melanoma were tested, with two groups each containing 35 mice. The crotamine-treated group received 1 mu g/day of crotamine per animal, subcutaneously which was well tolerated; the untreated group received a placebo. Results: Crotamine at 5 mu g/ml was lethal to B16-F10, Mia PaCa-2 and SK-Mel-28 cells and inoffensive to normal cells. In vivo crotamine treatment over 21 days significantly delayed tumor implantation, inhibited tumor growth and prolonged the lifespan of the mice. Mice in the crotamine-treated group survived at significantly higher rates (n = 30/35) than those in the untreated group (n = 7/35) (significance calculated with the Kaplan-Meier estimator). The average tumor weight in the untreated group was 4.60 g but was only about 0.27 g in the crotamine-treated mice, if detectable. Conclusions: These data warrant further exploration of crotamine as a tumor inhibition compound.

Rock fracture mean trace length estimation and confidence interval calculation using maximum likelihood methods

There has been a resurgence of interest in the mean trace length estimator of Pahl for window sampling of traces. The estimator has been dealt with by Mauldon and Zhang and Einstein in recent publications. The estimator is a very useful one in that it is non-parametric. However, despite some discussion regarding the statistical distribution of the estimator, none of the recent works or the original work by Pahl provide a rigorous basis for the determination a confidence interval for the estimator or a confidence region for the estimator and the corresponding estimator of trace spatial intensity in the sampling window. This paper shows, by consideration of a simplified version of the problem but without loss of generality, that the estimator is in fact the maximum likelihood estimator (MLE) and that it can be considered essentially unbiased. As the MLE, it possesses the least variance of all estimators and confidence intervals or regions should therefore be available through application of classical ML theory. It is shown that valid confidence intervals can in fact be determined. The results of the work and the calculations of the confidence intervals are illustrated by example. (C) 2003 Elsevier Science Ltd. All rights reserved.

F-LQE: a Fuzzy Link Quality Estimator for wireless sensor networks

Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.