Lossless audio coding using adaptive linear prediction

Master'sMASTER OF ENGINEERIN

Analysis and implementation of active noise control strategies using Piezo and EAP actuators

Currently noise cancellation, which affects the lives of people and in the workplace is achieved through the active noise reduction. This measure is not expensive as passive or semi active measures also permits adequate air conduction in duct ventilation systems. The system control is achieved through a suitable location of the phase in the cancelling noise signal relative to the signal primary noise. Algorithms have been developed and strategies for active noise reduction and its implementation and experimental testing on duct ventilation. The actives elements used are Piezo Actuators and EAP as speakers; Individual and collective operation of the aforementioned actuators is examined. The work was evaluated as follows: Analysis of previous research on existing algorithms for active noise reduction. Study the strategies of simulation and implementation for active noise control algorithms designed.Tesi

Adaptive polynomial filters

Journal ArticleWhile linear filter are useful in a large number of applications and relatively simple from conceptual and implementational view points. there are many practical situations that require nonlinear processing of the signals involved. This article explains adaptive nonlinear filters equipped with polynomial models of nonlinearity. The polynomial systems considered are those nonlinear systems whose output signals can be related to the input signals through a truncated Volterra series expansion, or a recursive nonlinear difference equation. The Volterra series expansion can model a large class of nonlinear systems and is attractive in filtering applications because the expansion is a linear combination of nonlinear functions of the input signal. The basic ideas behind the development of gradient and recursive least-squares adaptive Volterra filters are first discussed. followed by adaptive algorithms using system models involving recursive nonlinear difference equations. Such systems are attractive because they may be able to approximate many nonlinear systems with great parsimony in the use pf coefficients. Also discussed are current research trends and new results and problem areas associated with these nonlinear filters. A lattice structure for polynomial models is also described

Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low “Differentiation Scores,” or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Serum lipid mediator profiles in COVID-19 patients and lung disease severity: a pilot study

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection is highly heterogeneous, ranging from asymptomatic to severe and fatal cases. COVID-19 has been characterized by an increase of serum pro-inflammatory cytokine levels which seems to be associated with fatal cases. By contrast, the role of pro-resolving lipid mediators (SPMs), involved in the attenuation of inflammatory responses, has been scarcely investigated, so further studies are needed to understand SPMs metabolism in COVID-19 and other infectious diseases. Our aim was to analyse the lipid mediator metabolome, quantifying pro- and anti-inflammatory serum bioactive lipids by LC–MS/MS in 7 non-infected subjects and 24 COVID-19 patients divided into mild, moderate, and severe groups according to the pulmonary involvement, to better understand the disease outcome and the severity of the pulmonary manifestations. Statistical analysis was performed with the R programming language (R Foundation for Statistical Computing, Vienna, Austria). All COVID-19 patients had increased levels of Prostaglandin E2. Severe patients showed a significant increase versus controls, mild- and moderate-affected patients, expressed as median (interquartile range), in resolvin E1 [112.6 (502.7) vs 0.0 (0.0) pg/ml in the other groups], as well as in maresin 2 [14.5 (7.0) vs 8.1 (4.2), 5.5 (4.3), and 3.0 (4.0) pg/ml, respectively]. Moreover, 14-hydroxy docosahexaenoic acid (14-HDHA) levels were also increased in severe vs control and mild-affected patients [24.7 (38.2) vs 2.4 (2.2) and 3.7 (6.4) ng/mL, respectively]. Resolvin D5 was also significantly elevated in both moderate [15.0 (22.4) pg/ml] and severe patients [24.0 (24.1) pg/ml] versus controls [0.0 (0.0) pg/ml]. These results were confirmed by sparse partial least squares discriminant analysis which highlighted the contribution of these mediators to the separation between each of the groups. In conclusion, the potent inflammatory response to SARS-CoV-2 infection involves not only pro- but also anti-inflammatory lipid mediators that can be quantified in easily accessible serum samples, suggesting the need to perform future research on their generation pathways that will help us to discover new therapeutic targets

Cell-Free Nucleic Acids

The deficits of mammography and the potential of noninvasive diagnostic testing using circulating miRNA profiles are presented in our first review article. Exosomes are important in the transfer of genetic information. The current knowledge on exosome-associated DNAs and on vesicle-associated DNAs and their role in pregnancy-related complications is presented in the next article. The major obstacle is the lack of a standardized technique for the isolation and measurement of exosomes. One review has summarized the latest results on cell-free nucleic acids in inflammatory bowel disease (IBD). Despite the extensive research, the etiology and exact pathogenesis are still unclear, although similarity to the cell-free ribonucleic acids (cfRNAs) observed in other autoimmune diseases seems to be relevant in IBD. Liquid biopsy is a useful tool for the differentiation of leiomyomas and sarcomas in the corpus uteri. One manuscript has collected the most important knowledge of mesenchymal uterine tumors and shows the benefits of noninvasive sampling. Microchimerism has also recently become a hot topic. It is discussed in the context of various forms of transplantation and transplantation-related advanced therapies, the available cell-free nucleic acid (cfNA) markers, and the detection platforms that have been introduced. Ovarian cancer is one of the leading serious malignancies among women, with a high incidence of mortality; the introduction of new noninvasive diagnostic markers could help in its early detection and treatment monitoring. Epigenetic regulation is very important during the development of diseases and drug resistance. Methylation changes are important signs during ovarian cancer development, and it seems that the CDH1 gene is a potential candidate for being a noninvasive biomarker in the diagnosis of ovarian cancer. Preeclampsia is a mysterious disease—despite intensive research, the exact details of its development are unknown. It seems that cell-free nucleic acids could serve as biomarkers for the early detection of this disease. Three research papers deal with the prenatal application of cfDNA. Copy number variants (CNVs) are important subjects for the study of human genome variations, as CNVs can contribute to population diversity and human genetic diseases. These are useful in NIPT as a source of population specific data. The reliability of NIPT depends on the accurate estimation of fetal fraction. Improvement in the success rate of in vitro fertilization (IVF) and embryo transfer (ET) is an important goal. The measurement of embryo-specific small noncoding RNAs in culture media could improve the efficiency of ET

Approaches for Outlier Detection in Sparse High-Dimensional Regression Models

Modern regression studies often encompass a very large number of potential predictors, possibly larger than the sample size, and sometimes growing with the sample size itself. This increases the chances that a substantial portion of the predictors is redundant, as well as the risk of data contamination. Tackling these problems is of utmost importance to facilitate scientific discoveries, since model estimates are highly sensitive both to the choice of predictors and to the presence of outliers. In this thesis, we contribute to this area considering the problem of robust model selection in a variety of settings, where outliers may arise both in the response and the predictors. Our proposals simplify model interpretation, guarantee predictive performance, and allow us to study and control the influence of outlying cases on the fit. First, we consider the co-occurrence of multiple mean-shift and variance-inflation outliers in low-dimensional linear models. We rely on robust estimation techniques to identify outliers of each type, exclude mean-shift outliers, and use restricted maximum likelihood estimation to down-weight and accommodate variance-inflation outliers into the model fit. Second, we extend our setting to high-dimensional linear models. We show that mean-shift and variance-inflation outliers can be modeled as additional fixed and random components, respectively, and evaluated independently. Specifically, we perform feature selection and mean-shift outlier detection through a robust class of nonconcave penalization methods, and variance-inflation outlier detection through the penalization of the restricted posterior mode. The resulting approach satisfies a robust oracle property for feature selection in the presence of data contamination – which allows the number of features to exponentially increase with the sample size – and detects truly outlying cases of each type with asymptotic probability one. This provides an optimal trade-off between a high breakdown point and efficiency. Third, focusing on high-dimensional linear models affected by meanshift outliers, we develop a general framework in which L0-constraints coupled with mixed-integer programming techniques are used to perform simultaneous feature selection and outlier detection with provably optimal guarantees. In particular, we provide necessary and sufficient conditions for a robustly strong oracle property, where again the number of features can increase exponentially with the sample size, and prove optimality for parameter estimation and the resulting breakdown point. Finally, we consider generalized linear models and rely on logistic slippage to perform outlier detection and removal in binary classification. Here we use L0-constraints and mixed-integer conic programming techniques to solve the underlying double combinatorial problem of feature selection and outlier detection, and the framework allows us again to pursue optimality guarantees. For all the proposed approaches, we also provide computationally lean heuristic algorithms, tuning procedures, and diagnostic tools which help to guide the analysis. We consider several real-world applications, including the study of the relationships between childhood obesity and the human microbiome, and of the main drivers of honey bee loss. All methods developed and data used, as well as the source code to replicate our analyses, are publicly available