Hyperspectral subspace identification

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.

Validação do método de deteção de eritropoietina recombinante humana em urina humana por focalização isoelétrica

A eritropoietina (EPO) é uma substância que estimula a produção de eritrócitos, aumentando a oxigenação muscular, sendo segregada de forma natural pelo organismo e excretada na urina em baixas concentrações. Devido às suas propriedades e características, a EPO foi rapidamente introduzida no mundo do desporto, como substância ilícita, proporcionando vantagens no rendimento desportivo. No início de 2000 foi desenvolvido um método de deteção direta de EPO Recombinante (rHuEPO) em urina humana por Lasne, baseado na focalização isoelétrica (IEF) em gel de poliacrilamida, seguido de duplo blote, tendo este sido publicado e validado. Em 2002, a Agência Mundial Antidopagem (AMA) implementou este mesmo método, sendo atualmente um dos métodos oficiais utilizado pelos laboratórios acreditados pela AMA. Desta forma, o ponto de partida para a realização deste trabalho consistiu na necessidade de implementar e validar o método de referência de IEF para a deteção de rHuEPO em urina humana. O trabalho foi realizado no Laboratório de Análises e Dopagem (LAD) do Instituto do Desporto de Portugal (IDP), atual Instituto Português do Desporto e Juventude (IPDJ). O principal objetivo deste trabalho consistiu no estudo/investigação de diferentes parâmetros de validação (especificidade/seletividade; capacidade de identificação; limite de deteção; exatidão e repetibilidade), de acordo com o protocolado no Procedimento Geral interno do Laboratório de Análises de Dopagem de Lisboa (LAD). O referido método de triagem e confirmação revelou possuir características de desempenho conformes com os requisitos aplicáveis, pelo que é considerado validado e apto.

Unsupervised hyperspectral signal subspace identification

Hyperspectral imaging sensors provide image data containing both spectral and spatial information from the Earth surface. The huge data volumes produced by these sensors put stringent requirements on communications, storage, and processing. This paper presents a method, termed hyperspectral signal subspace identification by minimum error (HySime), that infer the signal subspace and determines its dimensionality without any prior knowledge. The identification of this subspace enables a correct dimensionality reduction yielding gains in algorithm performance and complexity and in data storage. HySime method is unsupervised and fully-automatic, i.e., it does not depend on any tuning parameters. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Effects of fungal contamination on respiratory symptoms of poultry workers

Exposure to certain fungi (molds) can cause human illness by 3 specific mechanisms: generation of a harmful immune response, direct infection by the organism or/and toxic-irritant effects from mold byproducts. Moulds are considered central elements in daily exposure of poultry workers and can be the cause of an increased risk of occupational respiratory diseases, like allergic and non-allergic rhinitis and asthma.

Unveiling the Biometric Potential of Finger-Based ECG Signals

The ECG signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at the chest. In this paper, we propose a finger-based ECG biometric system, that uses signals collected at the fingers, through a minimally intrusive 1-lead ECG setup recurring to Ag/AgCl electrodes without gel as interface with the skin. The collected signal is significantly more noisy than the ECG acquired at the chest, motivating the application of feature extraction and signal processing techniques to the problem. Time domain ECG signal processing is performed, which comprises the usual steps of filtering, peak detection, heartbeat waveform segmentation, and amplitude normalization, plus an additional step of time normalization. Through a simple minimum distance criterion between the test patterns and the enrollment database, results have revealed this to be a promising technique for biometric applications.

Antineoplastic drugs contamination of workplace surfaces in two Portuguese hospitals

Despite the classification as known or suspected human carcinogens, by the International Agency for Research on Cancer, the antineoplastic drugs are extensively used in cancer treatment due to their specificity and efficacy. As human carcinogens, these drugs represent a serious threat to the healthcare workers involved in their preparation and administration. This work aims to contribute to better characterize the occupational exposure of healthcare professionals to antineoplastic drugs, by assessing workplace surfaces contamination of pharmacy and administration units of two Portuguese hospitals. Surface contamination was assessed by the determination of cyclophosphamide, 5-fluorouracil, and paclitaxel. These three drugs were used as surrogate markers for surfaces contamination by cytotoxic drugs. Wipe samples were taken and analyzed by HPLCDAD. From the total of 327 analyzed samples, in 121 (37%) was possible to detect and quantify at least one drug. Additionally, 28 samples (8.6 %) indicate contamination by more than one antineoplastic drug, mainly in the administration unit, in both hospitals. Considering the findings in both hospitals, specific measures should be taken, particularly those related with the promotion of good practices and safety procedures and also routine monitoring of surfaces contamination in order to guarantee the appliance of safety measures.

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.