DC electrical surveying involves the injection of current into the earth, and the measurement of the electrical potential differences this produces. A number of electrode configurations such as the Schlumberger and Wenner arrays, dipole-dipole and pole-pole geometries are in common use for electrical surveying. New acquisition systems enable the convenient collection of data with a number of common configurations at the same time. It is found however that while the recovery of layered structure from electrical surveys can be effective, the sensitivity and resolving power of such systems in detecting the presence of anomalous three-dimensional (3-D) bodies is poor. This is mainly due to the dominance of conduction pathways through the layered earth compared to the influence of small 3-D conductivity anomalies.Theoretical relationships between the responses of various survey geometries to the layered earth may be established as is shown in this thesis, but their response to 3-D targets differs strongly. This thesis introduces a new procedure for anomalous target detection by the computation of an apparent resistivity residual using multi-electrode configuration survey data. This procedure, applicable to a variety of electrode geometries, reduces the dominance of the layered earth response and enhances the signal from 3-D structures.In the development and testing of this new apparent resistivity residual, numerically modelled data were used. In order to obtain suitable test data of high accuracy it was necessary to make improvements to modelling software. For this purpose, recently developed techniques in numerical modelling such as the biconjugate gradient method, new digital linear filters for computation of Hankel transforms, and spectral formalism were employed in an integral equation approach for the software developed in this thesis.The computed apparent ++ / resistivity residual was found to depend on the array type and dimensions, the nature of the anomalous zone, geological layer geometries, and resistivity contrasts of the layers involved. While the apparent resistivity residual signature requires some measure of interpretation, it is shown to enhance the resolution and detectability of 3-D targets in a layered environment.The presence of random noise produces some degradation in the performance of the residual technique, but a normalisation procedure has been developed to alleviate the problem. A preliminary field trial showed that survey profiles of apparent resistivity residual were able to locate a subsurface conductive anomaly in an area in Western Australia.A transitional zone is defined as a layer in the earth where resistivity varies as a continuous function of depth. A theoretical formulation for the electrical response of an earth structure composed of anomalous 3-D bodies in the presence of transitional layers is introduced. Tests on synthetic survey data showed that the apparent resistivity residual is an effective anomaly detector in transitional layer environments.A multi-system method of computing an apparent resistivity residual has been developed theoretically and tested on both synthetic and field data. This new approach when applied to resistivity profiling is more sensitive to, and gives greater resolution of, localised anomalies than is possible using conventional profiling procedures.

Book Summary: Chermak and Musiek's two-volume, award-winning handbooks are back in newly revised editions. Extensively revised and expanded, Volume II provides expanded coverage of rehabilitative and professional issues, detailing intervention strategies for children and adults. Volume I provides comprehensive coverage of the auditory neuroscience and clinical science needed to accurately diagnose the range of developmental and acquired central auditory processing disorders in children, adults, and older adults. Building on the excellence achieved with the best-selling 1st editions which earned the 2007 Speech, Language, and Hearing Book of the Year Award the second editions include contributions from world-renowned authors detailing major advances in auditory neuroscience and cognitive science; diagnosis; best practice intervention strategies in clinical and school settings; as well as emerging and future directions in diagnosis and intervention.

Aquesta tesi doctoral mostra la recerca feta en l'àrea de la diarització de locutor per a sales de reunions. En la present s'estudien els algorismes i la implementació d'un sistema en diferit de segmentació i aglomerat de locutor per a grabacions de reunions a on normalment es té accés a més d'un micròfon per al processat. El bloc més important de recerca s'ha fet durant una estada al International Computer Science Institute (ICSI, Berkeley, Caligornia) per un període de dos anys.La diarització de locutor s'ha estudiat força per al domini de grabacions de ràdio i televisió. La majoria dels sistemes proposats utilitzen algun tipus d'aglomerat jeràrquic de les dades en grups acústics a on de bon principi no se sap el número de locutors òptim ni tampoc la seva identitat. Un mètode molt comunment utilitzat s'anomena "bottom-up clustering" (aglomerat de baix-a-dalt), amb el qual inicialment es defineixen molts grups acústics de dades que es van ajuntant de manera iterativa fins a obtenir el nombre òptim de grups tot i acomplint un criteri de parada. Tots aquests sistemes es basen en l'anàlisi d'un canal d'entrada individual, el qual no permet la seva aplicació directa per a reunions. A més a més, molts d'aquests algorisms necessiten entrenar models o afinar els parameters del sistema usant dades externes, el qual dificulta l'aplicabilitat d'aquests sistemes per a dades diferents de les usades per a l'adaptació.La implementació proposada en aquesta tesi es dirigeix a solventar els problemes mencionats anteriorment. Aquesta pren com a punt de partida el sistema existent al ICSI de diarització de locutor basat en l'aglomerat de "baix-a-dalt". Primer es processen els canals de grabació disponibles per a obtindre un sol canal d'audio de qualitat major, a més dínformació sobre la posició dels locutors existents. Aleshores s'implementa un sistema de detecció de veu/silenci que no requereix de cap entrenament previ, i processa els segments de veu resultant amb una versió millorada del sistema mono-canal de diarització de locutor. Aquest sistema ha estat modificat per a l'ús de l'informació de posició dels locutors (quan es tingui) i s'han adaptat i creat nous algorismes per a que el sistema obtingui tanta informació com sigui possible directament del senyal acustic, fent-lo menys depenent de les dades de desenvolupament. El sistema resultant és flexible i es pot usar en qualsevol tipus de sala de reunions pel que fa al nombre de micròfons o la seva posició. El sistema, a més, no requereix en absolute dades d´entrenament, sent més senzill adaptar-lo a diferents tipus de dades o dominis d'aplicació. Finalment, fa un pas endavant en l'ús de parametres que siguin mes robusts als canvis en les dades acústiques. Dos versions del sistema es van presentar amb resultats excel.lents a les evaluacions de RT05s i RT06s del NIST en transcripció rica per a reunions, a on aquests es van avaluar amb dades de dos subdominis diferents (conferencies i reunions). A més a més, es fan experiments utilitzant totes les dades disponibles de les evaluacions RT per a demostrar la viabilitat dels algorisms proposats en aquesta tasca. / This thesis shows research performed into the topic of speaker diarization for meeting rooms. It looks into the algorithms and the implementation of an offline speaker segmentation and clustering system for a meeting recording where usually more than one microphone is available. The main research and system implementation has been done while visiting the International Computes Science Institute (ICSI, Berkeley, California) for a period of two years. Speaker diarization is a well studied topic on the domain of broadcast news recordings. Most of the proposed systems involve some sort of hierarchical clustering of the data into clusters, where the optimum number of speakers of their identities are unknown a priory. A very commonly used method is called bottom-up clustering, where multiple initial clusters are iteratively merged until the optimum number of clusters is reached, according to some stopping criterion. Such systems are based on a single channel input, not allowing a direct application for the meetings domain. Although some efforts have been done to adapt such systems to multichannel data, at the start of this thesis no effective implementation had been proposed. Furthermore, many of these speaker diarization algorithms involve some sort of models training or parameter tuning using external data, which impedes its usability with data different from what they have been adapted to.The implementation proposed in this thesis works towards solving the aforementioned problems. Taking the existing hierarchical bottom-up mono-channel speaker diarization system from ICSI, it first uses a flexible acoustic beamforming to extract speaker location information and obtain a single enhanced signal from all available microphones. It then implements a train-free speech/non-speech detection on such signal and processes the resulting speech segments with an improved version of the mono-channel speaker diarization system. Such system has been modified to use speaker location information (then available) and several algorithms have been adapted or created new to adapt the system behavior to each particular recording by obtaining information directly from the acoustics, making it less dependent on the development data.The resulting system is flexible to any meetings room layout regarding the number of microphones and their placement. It is train-free making it easy to adapt to different sorts of data and domains of application. Finally, it takes a step forward into the use of parameters that are more robust to changes in the acoustic data. Two versions of the system were submitted with excellent results in RT05s and RT06s NIST Rich Transcription evaluations for meetings, where data from two different subdomains (lectures and conferences) was evaluated. Also, experiments using the RT datasets from all meetings evaluations were used to test the different proposed algorithms proving their suitability to the task.

Book Summary: Chermak and Musiek's two-volume, award-winning handbooks are back in newly revised editions. Extensively revised and expanded, Volume II provides expanded coverage of rehabilitative and professional issues, detailing intervention strategies for children and adults. Volume I provides comprehensive coverage of the auditory neuroscience and clinical science needed to accurately diagnose the range of developmental and acquired central auditory processing disorders in children, adults, and older adults. Building on the excellence achieved with the best-selling 1st editions which earned the 2007 Speech, Language, and Hearing Book of the Year Award the second editions include contributions from world-renowned authors detailing major advances in auditory neuroscience and cognitive science; diagnosis; best practice intervention strategies in clinical and school settings; as well as emerging and future directions in diagnosis and intervention.

No description available.

This thesis describes methods for improving sensitivity in rapid singleplex and multiplex microarray assays. The assays utilize the optical characteristics of colloidal gold nanoparticles for the colorimetric detection of proteins. Multiplexed detection in sandwich immunoassays is limited by cross-reactivity between different detection antibodies. The cross-reactivity between antibodies can contribute to increased background noise - decreasing the Limit-of-Detection of the assay - or generate false positive signals. Paper I shows improved assay sensitivity in a multiplexed vertical flow assay by the application of ultrasonic energy to the gold nanoparticles functionalized with detection antibodies. The ultrasonication of the antibody conjugated gold nanoparticles resulted in a 10 000 fold increase in sensitivity in a 3-plex assay. COMSOL Multiphysics was used to simulate the acoustical energy of the probe used in Paper I for obtaining an indication of the size and direction of the forces acting upon the functionalized gold nanoparticles. In Paper II, it was studied if different gold nanoparticle conjugation methods and colorimetric signal enhancement of the gold nanoparticle conjugates could influence the sensitivity of a paper-based lateral flow microarray assay, targeting cardiac troponin T for the rapid diagnostics of acute myocardial infarction. Ultrasonication and signal enhancement of the detection gold nanoparticles has the potential of improving the sensitivity of paper based assays and expanding their potential future applications. / <p>QC 20170911</p>

No description available.

<p>In 2021, an estimated 1.5 million people were diagnosed with HIV globally, increasing the total to 38.4 million people. Approximately 16% of this population were unaware of their infected status and required HIV testing, which is a critical first step in HIV prevention, treatment, and care. Hence, there is a need to develop a rapid, user-friendly, and cost-effective point-of-care test for HIV detection. The time between HIV infection and a detectable host HIV antibody concentration can extend up to 90 days. By incorporating more sensitive testing for the HIV p24 antigen on the virus, the diagnosis lag can be reduced to 17 days. This window could be further shortened by using horseradish peroxidase (HRP) enzyme as a signal enhancement technique. The work herein focuses on developing an enzymatically signal-enhanced lateral flow assay test for the p24 antigen to detect HIV during the acute phase of infection. Conjugation chemistry for the sandwich assay was characterized using DLS and UV-Vis. Dot blots were then used to assess and enhance the functionality of the individual components via a visual color gradient formed by the protein coupled with antibody-conjugated gold nanoparticles. A quantitative analysis was performed using ImageJ software through signal pixel intensity analysis. A limit of detection (LoD) of 6 ng/mL was obtained for the detection of the p24 antigen. This LoD was improved to 0.2 ng/mL by incorporating HRP signal enhancement with the diaminobenzidine substrate. This 30x signal improvement could drive down the LoD even further to improve the sensitivity of the commercial p24 antigen tests. Different fabrication and scalability studies were performed to produce a cost- efficient, fully functional prototype of a paper-based lateral flow device incorporating the signal- enhanced p24 assay. This study serves as a solid foundation to research focused on creating more efficient point-of-care tests that can be used in resource-limited settings to provide early detection of HIV for the 6 million individuals who are currently unaware of their HIV status. </p>

No description available.

For time-varying signals such as speech and audio, short-time analysis becomes necessary to compute specific signal attributes and to keep track of their evolution. The standard technique is the short-time Fourier transform (STFT), using which one decomposes a signal in terms of windowed Fourier bases. An advancement over STFT is the wavelet analysis in which a function is represented in terms of shifted and dilated versions of a localized function called the wavelet. A specific modeling approach particularly in the context of speech is based on short-time linear prediction or short-time Wiener filtering of noisy speech. In most nonstationary signal processing formalisms, the key idea is to analyze the properties of the signal locally, either by first truncating the signal and then performing a basis expansion (as in the case of STFT), or by choosing compactly-supported basis functions (as in the case of wavelets). We retain the same motivation as these approaches, but use polynomials to model the signal on a short-time basis (“short-time polynomial representation”). To emphasize the local nature of the modeling aspect, we refer to it as “local polynomial modeling (LPM).” We pursue two main threads of research in this thesis: (i) Short-time approaches for speech enhancement; and (ii) LPM for enhancing smooth signals, with applications to ECG, noisy nonuniformly-sampled signals, and voiced/unvoiced segmentation in noisy speech. Improved iterative Wiener filtering for speech enhancement A constrained iterative Wiener filter solution for speech enhancement was proposed by Hansen and Clements. Sreenivas and Kirnapure improved the performance of the technique by imposing codebook-based constraints in the process of parameter estimation. The key advantage is that the optimal parameter search space is confined to the codebook. The Nonstationary signal enhancement solutions assume stationary noise. However, in practical applications, noise is not stationary and hence updating the noise statistics becomes necessary. We present a new approach to perform reliable noise estimation based on spectral subtraction. We first estimate the signal spectrum and perform signal subtraction to estimate the noise power spectral density. We further smooth the estimated noise spectrum to ensure reliability. The key contributions are: (i) Adaptation of the technique for non-stationary noises; (ii) A new initialization procedure for faster convergence and higher accuracy; (iii) Experimental determination of the optimal LP-parameter space; and (iv) Objective criteria and speech recognition tests for performance comparison. Optimal local polynomial modeling and applications We next address the problem of fitting a piecewise-polynomial model to a smooth signal corrupted by additive noise. Since the signal is smooth, it can be represented using low-order polynomial functions provided that they are locally adapted to the signal. We choose the mean-square error as the criterion of optimality. Since the model is local, it preserves the temporal structure of the signal and can also handle nonstationary noise. We show that there is a trade-off between the adaptability of the model to local signal variations and robustness to noise (bias-variance trade-off), which we solve using a stochastic optimization technique known as the intersection of confidence intervals (ICI) technique. The key trade-off parameter is the duration of the window over which the optimum LPM is computed. Within the LPM framework, we address three problems: (i) Signal reconstruction from noisy uniform samples; (ii) Signal reconstruction from noisy nonuniform samples; and (iii) Classification of speech signals into voiced and unvoiced segments. The generic signal model is x(tn)=s(tn)+d(tn),0 ≤ n ≤ N - 1. In problems (i) and (iii) above, tn=nT(uniform sampling); in (ii) the samples are taken at nonuniform instants. The signal s(t)is assumed to be smooth; i.e., it should admit a local polynomial representation. The problem in (i) and (ii) is to estimate s(t)from x(tn); i.e., we are interested in optimal signal reconstruction on a continuous domain starting from uniform or nonuniform samples. We show that, in both cases, the bias and variance take the general form: The mean square error (MSE) is given by where L is the length of the window over which the polynomial fitting is performed, f is a function of s(t), which typically comprises the higher-order derivatives of s(t), the order itself dependent on the order of the polynomial, and g is a function of the noise variance. It is clear that the bias and variance have complementary characteristics with respect to L. Directly optimizing for the MSE would give a value of L, which involves the functions f and g. The function g may be estimated, but f is not known since s(t)is unknown. Hence, it is not practical to compute the minimum MSE (MMSE) solution. Therefore, we obtain an approximate result by solving the bias-variance trade-off in a probabilistic sense using the ICI technique. We also propose a new approach to optimally select the ICI technique parameters, based on a new cost function that is the sum of the probability of false alarm and the area covered over the confidence interval. In addition, we address issues related to optimal model-order selection, search space for window lengths, accuracy of noise estimation, etc. The next issue addressed is that of voiced/unvoiced segmentation of speech signal. Speech segments show different spectral and temporal characteristics based on whether the segment is voiced or unvoiced. Most speech processing techniques process the two segments differently. The challenge lies in making detection techniques offer robust performance in the presence of noise. We propose a new technique for voiced/unvoiced clas-sification by taking into account the fact that voiced segments have a certain degree of regularity, and that the unvoiced segments do not possess any smoothness. In order to capture the regularity in voiced regions, we employ the LPM. The key idea is that regions where the LPM is inaccurate are more likely to be unvoiced than voiced. Within this frame-work, we formulate a hypothesis testing problem based on the accuracy of the LPM fit and devise a test statistic for performing V/UV classification. Since the technique is based on LPM, it is capable of adapting to nonstationary noises. We present Monte Carlo results to demonstrate the accuracy of the proposed technique.