A simple but efficient voice activity detection algorithm through Hilbert transform and dynamic threshold for speech pathologies

A simple but efficient voice activity detector based on the Hilbert transform and a dynamic threshold is presented to be used on the pre-processing of audio signals -- The algorithm to define the dynamic threshold is a modification of a convex combination found in literature -- This scheme allows the detection of prosodic and silence segments on a speech in presence of non-ideal conditions like a spectral overlapped noise -- The present work shows preliminary results over a database built with some political speech -- The tests were performed adding artificial noise to natural noises over the audio signals, and some algorithms are compared -- Results will be extrapolated to the field of adaptive filtering on monophonic signals and the analysis of speech pathologies on futures works

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

Response times in computerized adaptive testing: a method for cheating detection

In the field of educational and psychological measurement, the shift from paper-based to computerized tests has become a prominent trend in recent years. Computerized tests allow for more complex and personalized test administration procedures, like Computerized Adaptive Testing (CAT). CAT, following the Item Response Theory (IRT) models, dynamically generates tests based on test-taker responses, driven by complex statistical algorithms. Even if CAT structures are complex, they are flexible and convenient, but concerns about test security should be addressed. Frequent item administration can lead to item exposure and cheating, necessitating preventive and diagnostic measures. In this thesis a method called "CHeater identification using Interim Person fit Statistic" (CHIPS) is developed, designed to identify and limit cheaters in real-time during test administration. CHIPS utilizes response times (RTs) to calculate an Interim Person fit Statistic (IPS), allowing for on-the-fly intervention using a more secret item bank. Also, a slight modification is proposed to overcome situations with constant speed, called Modified-CHIPS (M-CHIPS). A simulation study assesses CHIPS, highlighting its effectiveness in identifying and controlling cheaters. However, it reveals limitations when cheaters possess all correct answers. The M-CHIPS overcame this limitation. Furthermore, the method has shown not to be influenced by the cheaters’ ability distribution or the level of correlation between ability and speed of test-takers. Finally, the method has demonstrated flexibility for the choice of significance level and the transition from fixed-length tests to variable-length ones. The thesis discusses potential applications, including the suitability of the method for multiple-choice tests, assumptions about RT distribution and level of item pre-knowledge. Also limitations are discussed to explore future developments such as different RT distributions, unusual honest respondent behaviors, and field testing in real-world scenarios. In summary, CHIPS and M-CHIPS offer real-time cheating detection in CAT, enhancing test security and ability estimation while not penalizing test respondents.

Data-driven Signal Processing per Jamming Detection in Sistemi Satellitari

In questo elaborato vengono analizzate differenti tecniche per la detection di jammer attivi e costanti in una comunicazione satellitare in uplink. Osservando un numero limitato di campioni ricevuti si vuole identificare la presenza di un jammer. A tal fine sono stati implementati i seguenti classificatori binari: support vector machine (SVM), multilayer perceptron (MLP), spectrum guarding e autoencoder. Questi algoritmi di apprendimento automatico dipendono dalle features che ricevono in ingresso, per questo motivo è stata posta particolare attenzione alla loro scelta. A tal fine, sono state confrontate le accuratezze ottenute dai detector addestrati utilizzando differenti tipologie di informazione come: i segnali grezzi nel tempo, le statistical features, le trasformate wavelet e lo spettro ciclico. I pattern prodotti dall’estrazione di queste features dai segnali satellitari possono avere dimensioni elevate, quindi, prima della detection, vengono utilizzati i seguenti algoritmi per la riduzione della dimensionalità: principal component analysis (PCA) e linear discriminant analysis (LDA). Lo scopo di tale processo non è quello di eliminare le features meno rilevanti, ma combinarle in modo da preservare al massimo l’informazione, evitando problemi di overfitting e underfitting. Le simulazioni numeriche effettuate hanno evidenziato come lo spettro ciclico sia in grado di fornire le features migliori per la detection producendo però pattern di dimensioni elevate, per questo motivo è stato necessario l’utilizzo di algoritmi di riduzione della dimensionalità. In particolare, l'algoritmo PCA è stato in grado di estrarre delle informazioni migliori rispetto a LDA, le cui accuratezze risentivano troppo del tipo di jammer utilizzato nella fase di addestramento. Infine, l’algoritmo che ha fornito le prestazioni migliori è stato il Multilayer Perceptron che ha richiesto tempi di addestramento contenuti e dei valori di accuratezza elevati.

Development of a Multicommutated Flow System with Chemiluminometric Detection for Quantification of Gentamicin in Pharmaceuticals

A new flow procedure based on multicommutation with chemiluminometric detection was developed to quantify gentamicin sulphate in pharmaceutical formulations. This approach is based on gentamicin's ability to inhibit the chemiluminometric reaction between luminol and hypochlorite in alkaline medium, causing a decrease in the analytical signal. The inhibition of the analytical signal is proportional to the concentration of gentamicin sulphate, within a linear range of 1 to 4 mu g mL(-1) with a coefficient variation <3%. A sample throughput of 55 samples h(-1) was obtained. The developed method is sensitive, simple, with low reagent consumption, reproducible, and inexpensive, and when applied to the analysis of pharmaceutical formulations (eye drops and injections) it gave results with RSD between 1.10 and 4.40%.

Chemical evolution of the Galactic bulge as traced by microlensed dwarf and subgiant stars III. Detection of lithium in the metal-poor bulge dwarf MOA-2010-BLG-285S

Context. To study the evolution of Li in the Galaxy it is necessary to observe dwarf or subgiant stars. These are the only long-lived stars whose present-day atmospheric chemical composition reflects their natal Li abundances according to standard models of stellar evolution. Although Li has been extensively studied in the Galactic disk and halo, to date there has only been one uncertain detection of Li in an unevolved bulge star. Aims. Our aim with this study is to provide the first clear detection of Li in the Galactic bulge, based on an analysis of a dwarf star that has largely retained its initial Li abundance. Methods. We performed a detailed elemental abundance analysis of the bulge dwarf star MOA-2010-BLG-285S using a high-resolution and high signal-to-noise spectrum obtained with the UVES spectrograph at the VLT when the object was optically magnified during a gravitational microlensing event (visual magnification A similar to 550 during observation). The Li abundance was determined through synthetic line profile fitting of the (7)Li resonance doublet line at 670.8 nm. The results have been corrected for departures from LTE. Results. MOA-2010-BLG-285S is, at [Fe/H] = -1.23, the most metal-poor dwarf star detected so far in the Galactic bulge. Its old age (12.5 Gyr) and enhanced [alpha/Fe] ratios agree well with stars in the thick disk at similar metallicities. This star represents the first unambiguous detection of Li in a metal-poor dwarf star in the Galactic bulge. We find an NLTE corrected Li abundance of log epsilon(Li) = 2.16, which is consistent with values derived for Galactic disk and halo dwarf stars at similar metallicities and temperatures. Conclusions. Our results show that there are no signs of Li enrichment or production in the Galactic bulge during its earliest phases. Observations of Li in other galaxies (omega Cen) and other components of the Galaxy suggest further that the Spite plateau is universal.

NEX-LMS: A novel adaptive control scheme for harmonic sound quality control

This paper presents a novel adaptive control scheme. with improved convergence rate, for the equalization of harmonic disturbances such as engine noise. First, modifications for improving convergence speed of the standard filtered-X LMS control are described. Equalization capabilities are then implemented, allowing the independent tuning of harmonics. Eventually, by providing the desired order vs. engine speed profiles, the pursued sound quality attributes can be achieved. The proposed control scheme is first demonstrated with a simple secondary path model and, then, experimentally validated with the aid of a vehicle mockup which is excited with engine noise. The engine excitation is provided by a real-time sound quality equivalent engine simulator. Stationary and transient engine excitations are used to assess the control performance. The results reveal that the proposed controller is capable of large order-level reductions (up to 30 dB) for stationary excitation, which allows a comfortable margin for equalization. The same holds for slow run-ups ( > 15s) thanks to the improved convergence rate. This margin, however, gets narrower with shorter run-ups (<= 10s). (c) 2010 Elsevier Ltd. All rights reserved.

Ultrasound, eddy current and magnetic Barkhausen noise as tools for sigma phase detection on a UNS S31803 duplex stainless steel

Sigma phase is a deleterious one which can be formed in duplex stainless steels during heat treatment or welding. Aiming to accompany this transformation, ferrite and sigma percentage and hardness were measured on samples of a UNS S31803 duplex stainless steel submitted to heat treatment. These results were compared to measurements obtained from ultrasound and eddy current techniques, i.e., velocity and impedance, respectively. Additionally, backscattered signals produced by wave propagation were acquired during ultrasonic inspection as well as magnetic Barkhausen noise during magnetic inspection. Both signal types were processed via a combination of detrended-fluctuation analysis (DFA) and principal component analysis (PCA). The techniques used were proven to be sensitive to changes in samples related to sigma phase formation due to heat treatment. Furthermore, there is an advantage using these methods since they are nondestructive. (C) 2010 Elsevier B.V. All rights reserved.

NDT flaw mapping of steel surfaces by continuous magnetic Barkhausen noise: Volumetric flaw detection case

This paper reports the use of a non-destructive, continuous magnetic Barkhausen noise (CMBN) technique to investigate the size and thickness of volumetric defects, in a 1070 steel. The magnetic behavior of the used probe was analyzed by numerical simulation, using the finite element method (FEM). Results indicated that the presence of a ferrite coil core in the probe favors MBN emissions. The samples were scanned with different speeds and probe configurations to determine the effect of the flaw on the CMBN signal amplitude. A moving smooth window, based on a second-order statistical moment, was used for analyzing the time signal. The results show the technique`s good repeatability, and high capacity for detection of this type of defect. (C) 2009 Elsevier Ltd. All rights reserved.

Residence time distribution in holding tubes using generalized convection model and numerical convolution for non-ideal tracer detection

A procedure is proposed for the determination of the residence time distribution (RTD) of curved tubes taking into account the non-ideal detection of the tracer. The procedure was applied to two holding tubes used for milk pasteurization in laboratory scale. Experimental data was obtained using an ionic tracer. The signal distortion caused by the detection system was considerable because of the short residence time. Four RTD models, namely axial dispersion, extended tanks in series, generalized convection and PER + CSTR association, were adjusted after convolution with the E-curve of the detection system. The generalized convection model provided the best fit because it could better represent the tail on the tracer concentration curve that is Caused by the laminar velocity profile and the recirculation regions. Adjusted model parameters were well cot-related with the now rate. (C) 2010 Elsevier Ltd. All rights reserved.

Adaptive Carrier Tracking for Mars to Earth Communications During Entry, Descent, and Landing

We propose a robust and low complexity scheme to estimate and track carrier frequency from signals traveling under low signal-to-noise ratio (SNR) conditions in highly nonstationary channels. These scenarios arise in planetary exploration missions subject to high dynamics, such as the Mars exploration rover missions. The method comprises a bank of adaptive linear predictors (ALP) supervised by a convex combiner that dynamically aggregates the individual predictors. The adaptive combination is able to outperform the best individual estimator in the set, which leads to a universal scheme for frequency estimation and tracking. A simple technique for bias compensation considerably improves the ALP performance. It is also shown that retrieval of frequency content by a fast Fourier transform (FFT)-search method, instead of only inspecting the angle of a particular root of the error predictor filter, enhances performance, particularly at very low SNR levels. Simple techniques that enforce frequency continuity improve further the overall performance. In summary we illustrate by extensive simulations that adaptive linear prediction methods render a robust and competitive frequency tracking technique.

Transient and Steady-State Analysis of the Affine Combination of Two Adaptive Filters

In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow adaptive filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed.

Distributed Estimation Over an Adaptive Incremental Network Based on the Affine Projection Algorithm

We study the problem of distributed estimation based on the affine projection algorithm (APA), which is developed from Newton`s method for minimizing a cost function. The proposed solution is formulated to ameliorate the limited convergence properties of least-mean-square (LMS) type distributed adaptive filters with colored inputs. The analysis of transient and steady-state performances at each individual node within the network is developed by using a weighted spatial-temporal energy conservation relation and confirmed by computer simulations. The simulation results also verify that the proposed algorithm provides not only a faster convergence rate but also an improved steady-state performance as compared to an LMS-based scheme. In addition, the new approach attains an acceptable misadjustment performance with lower computational and memory cost, provided the number of regressor vectors and filter length parameters are appropriately chosen, as compared to a distributed recursive-least-squares (RLS) based method.

Improving the tracking capability of adaptive filters via convex combination

As is well known, Hessian-based adaptive filters (such as the recursive-least squares algorithm (RLS) for supervised adaptive filtering, or the Shalvi-Weinstein algorithm (SWA) for blind equalization) converge much faster than gradient-based algorithms [such as the least-mean-squares algorithm (LMS) or the constant-modulus algorithm (CMA)]. However, when the problem is tracking a time-variant filter, the issue is not so clear-cut: there are environments for which each family presents better performance. Given this, we propose the use of a convex combination of algorithms of different families to obtain an algorithm with superior tracking capability. We show the potential of this combination and provide a unified theoretical model for the steady-state excess mean-square error for convex combinations of gradient- and Hessian-based algorithms, assuming a random-walk model for the parameter variations. The proposed model is valid for algorithms of the same or different families, and for supervised (LMS and RLS) or blind (CMA and SWA) algorithms.