Perceptual learning reflects external noise filtering and internal noise reduction through channel reweighting

To investigate the nature of plasticity in the adult visual system, perceptual learning was measured in a peripheral orientation discrimination task with systematically varying amounts of external (environmental) noise. The signal contrasts required to achieve threshold were reduced by a factor or two or more after training at all levels of external noise. The strong quantitative regularities revealed by this novel paradigm ruled out changes in multiplicative internal noise, changes in transducer nonlinearites, and simple attentional tradeoffs. Instead, the regularities specify the mechanisms of perceptual learning at the behavioral level as a combination of external noise exclusion and stimulus enhancement via additive internal noise reduction. The findings also constrain the neural architecture of perceptual learning. Plasticity in the weights between basic visual channels and decision is sufficient to account for perceptual learning without requiring the retuning of visual mechanisms.

Projection and interpolation based techniques for structured and impulsive noise filtering

In this chapter we present the relevant mathematical background to address two well defined signal and image processing problems. Namely, the problem of structured noise filtering and the problem of interpolation of missing data. The former is addressed by recourse to oblique projection based techniques whilst the latter, which can be considered equivalent to impulsive noise filtering, is tackled by appropriate interpolation methods.

Influência de filtros de ruído dinâmico na perceção das cores utilizando um novo teste de visão das cores

Dissertação de mestrado em Optometria Avançada

High capacity audio watermarking using the high frequency band of the wavelet domain

This paper proposes a novel high capacity robust audio watermarking algorithm by using the high frequency band of the wavelet decomposition at which the human auditory system (HAS) is not very sensitive to alteration. The main idea is to divide the high frequency band into frames and, for embedding, to change the wavelet samples depending on the average of relevant frame¿s samples. The experimental results show that the method has a very high capacity (about 11,000 bps), without significant perceptual distortion (ODG in [¿1 ,0] and SNR about 30dB), and provides robustness against common audio signal processing such as additive noise, filtering, echo and MPEG compression (MP3).

High capacity FFT-based audio watermarking

Peer-reviewed

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed

Fenix-järjestelmän suorituskyvyn analysointi

Kasvavat käyttäjämäärät TietoEnatorin Fenix-tietojärjestelmässä ovat nostaneet suorituskyvyn tutkinnan tärkeään asemaan. Suorituskykymittausten tekeminen, analysoiminen ja ennen kaikkea säännöllinen seuranta ovat ensiarvioisen tärkeitätoimenpiteitä järjestelmän tehokkuuden proaktiiviseen kehittämiseen. Fenixin suorituskykymittausjärjestelmän tuottamaa aineistoa on runsaasti tarjolla. Tämän työn tavoitteena on edistää suorituskykymittausjärjestelmän tuottaman aineiston käyttöä erilaisissa raportointi- ja tutkimustilanteissa. Työssäperehdytään aineiston keräämiseen, häiriösuodatukseen ja tulosten visualisointiin. Työn tuloksena syntyy automaattinen raportointijärjestelmä, joka tuottaa palvelukohtaisia tilastollisia suorituskykyraportteja. Raporttien tarkoituksena on tuoda kehittäjien nähtäville palveluista kerätyt mittaustulokset ja osoittaa mahdollisia suorituskykyparannuskohteita.

Secure logarithmic audio watermarking scheme based on the human auditory system

Peer-reviewed

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Deconvolução de perfis de poço

A maioria dos perfis de poço utilizados nas avaliações petrofísicas de reservatórios possuem uma resolução vertical na ordem de um metro. Isto cria um problema quando as espessuras típicas das camadas são inferiores a um metro, uma vez que não há correção das leituras. Os perfis de alta resolução vertical como da ferramenta de propagação eletromagnética (EPT, Schlumberger), o dipmeter (SHDT, Schlumberger) ou das ferramentas de varredura acústica ou elétrica possuem uma resolução vertical da ordem de centimetros, mas apresentam uma limitada aplicação para as avaliações petrofísicas. Nós apresentamos um método para a deconvolução de um perfil de baixa resolução vertical que utiliza informações de um perfil de alta resolução vertical para identificar uma nítida interface entre camadas que apresentam valores da propriedade petrofísica contrastante, mas localmente constante em ambos os lados. A partir desse intervalo de controle, nós determinamos a função resposta vertical da ferramenta sob as condições atuais do poço com base no teorema da convolução. Utilizamos várias interfaces de modo a obter valores mais representativos da resposta da ferramenta. O perfil de baixa resolução é então deconvoluido utilizando a transformada discreta de Fourier (FFT) sobre todo o intervalo de interesse. É importante destacar que a invasão do filtrado da lama e a presença do bolo de lama não produzem efeitos danosos sobre o método, que foi aplicado a perfis sintéticos e a dados de campo, onde a aplicação de filtros com um correto ajuste de profundidade, bem como a própria escolha do intervalo de controle, antes da deconvolução, são de extrema importância para o sucesso do método.

Development of a SPECT -based three-dimensional treatment planner for radionuclide therapy with iodine -131

Accurate calculation of absorbed dose to target tumors and normal tissues in the body is an important requirement for establishing fundamental dose-response relationships for radioimmunotherapy. Two major obstacles have been the difficulty in obtaining an accurate patient-specific 3-D activity map in-vivo and calculating the resulting absorbed dose. This study investigated a methodology for 3-D internal dosimetry, which integrates the 3-D biodistribution of the radionuclide acquired from SPECT with a dose-point kernel convolution technique to provide the 3-D distribution of absorbed dose. Accurate SPECT images were reconstructed with appropriate methods for noise filtering, attenuation correction, and Compton scatter correction. The SPECT images were converted into activity maps using a calibration phantom. The activity map was convolved with an $\sp{131}$I dose-point kernel using a 3-D fast Fourier transform to yield a 3-D distribution of absorbed dose. The 3-D absorbed dose map was then processed to provide the absorbed dose distribution in regions of interest. This methodology can provide heterogeneous distributions of absorbed dose in volumes of any size and shape with nonuniform distributions of activity. Comparison of the activities quantitated by our SPECT methodology to true activities in an Alderson abdominal phantom (with spleen, liver, and spherical tumor) yielded errors of $-$16.3% to 4.4%. Volume quantitation errors ranged from $-$4.0 to 5.9% for volumes greater than 88 ml. The percentage differences of the average absorbed dose rates calculated by this methodology and the MIRD S-values were 9.1% for liver, 13.7% for spleen, and 0.9% for the tumor. Good agreement (percent differences were less than 8%) was found between the absorbed dose due to penetrating radiation calculated from this methodology and TLD measurement. More accurate estimates of the 3-D distribution of absorbed dose can be used as a guide in specifying the minimum activity to be administered to patients to deliver a prescribed absorbed dose to tumor without exceeding the toxicity limits of normal tissues. ^

A comparison of open-source segmentation architectures for dealing with imperfect data from the media in speech synthesis

Traditional Text-To-Speech (TTS) systems have been developed using especially-designed non-expressive scripted recordings. In order to develop a new generation of expressive TTS systems in the Simple4All project, real recordings from the media should be used for training new voices with a whole new range of speaking styles. However, for processing this more spontaneous material, the new systems must be able to deal with imperfect data (multi-speaker recordings, background and foreground music and noise), filtering out low-quality audio segments and creating mono-speaker clusters. In this paper we compare several architectures for combining speaker diarization and music and noise detection which improve the precision and overall quality of the segmentation.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.

Non-Gaussian error probability in optical soliton transmission

We find the probability distribution of the fluctuating parameters of a soliton propagating through a medium with additive noise. Our method is a modification of the instanton formalism (method of optimal fluctuation) based on a saddle-point approximation in the path integral. We first solve consistently a fundamental problem of soliton propagation within the framework of noisy nonlinear Schrödinger equation. We then consider model modifications due to in-line (filtering, amplitude and phase modulation) control. It is examined how control elements change the error probability in optical soliton transmission. Even though a weak noise is considered, we are interested here in probabilities of error-causing large fluctuations which are beyond perturbation theory. We describe in detail a new phenomenon of soliton collapse that occurs under the combined action of noise, filtering and amplitude modulation. © 2004 Elsevier B.V. All rights reserved.