Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Parameter Identification in a Tuberculosis Model for Cameroon

A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency-and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years.

Double Fourier analysis for Emotion Identification in Voiced Speech

We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech -- Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions -- A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds -- Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions -- Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it -- Finally features related with emotions in voiced speech are extracted and presented

Monitoramento e identificação numérico e experimental de danos em vigas e pontes de aço e concreto utilizando transformadas de wavelet

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Experimental and Numerical Evaluation of Impact Damage Tolerance in composite materials

Composite materials characteristics are highly influenced by foreign objects impacts. My research focused on how a Low Velocity Impact and, therefore, Barely Visible Impact Damages, can reduce carbon/epoxy laminates compressive residual characteristics and which could be an improvement of their impact resistance. Solution was found out in Fibre Metal Laminates. Experimental and numerical analysis were performed on Carbon/Epoxy and Fibre Metal Laminate.

Modelação de juntas adesivas por modelos de dano coesivo utilizando o método direto

A utilização de adesivos hoje em dia encontra-se de tal forma disseminada que é transversal a diversos setores do mercado, como a indústria aeroespacial, aeronáutica, automóvel e do desporto. De facto, o uso de ligações adesivas em estruturas mecânicas tem vindo a crescer, na medida em que estes têm substituído os métodos de ligação convencionais, tais como brasagem, rebitagem, ligações aparafusadas e soldadura. No geral, as ligações adesivas apresentam diversas vantagens, desde a diminuição do peso, redução da concentração de tensões, facilidade de fabrico, bom comportamento a solicitações cíclicas e capacidade de unir materiais dissimilares. O crescente interesse da indústria nas ligações adesivas tem por base o aumento da confiabilidade nos métodos de previsão de resistência de estruturas adesivas. Neste contexto surgem os Modelos de Dano Coesivo, que permitem simular o crescimento do dano em estruturas, após introdução das leis coesivas previamente estimadas nos modelos numéricos. Uma das fases mais importantes neste método de previsão é a estimativa das leis coesivas em tração e corte, pelo que se torna de grande relevância a existência e validação de métodos precisos para a obtenção destas leis. Este trabalho visa a validação de leis coesivas em tração e corte, estimadas pela aplicação do método direto, na previsão da resistência de juntas com geometria de solicitação mista. Neste âmbito, ensaiaram-se JSS e JSD com diferentes comprimentos de sobreposição e com adesivos de diferente ductilidade. Foram considerados os adesivos Araldite® AV138, de elevada resistência e baixa ductilidade, o Araldite® 2015, de moderada ductilidade e resistência intermédia, e o SikaForce® 7752, de baixa resistência e elevada ductilidade. As leis coesivas em modo puro serviram de base para a criação de leis simplificadas triangulares, trapezoidais e linearesexponenciais, que foram testadas para cada um dos adesivos. A validação das mesmas consumou-se por comparação das previsões numéricas com os resultados experimentais. Procedeu-se também a uma análise de tensões de arrancamento e de corte no adesivo, de modo a compreender a influência das tensões na resistência das juntas. A utilização do método direto permitiu obter previsões de resistência bastante precisas, indicando as formas de leis coesivas mais adequadas para cada conjunto adesivo/geometria de junta. Para além disso, para as condições geométricas e materiais consideradas, este estudo permitiu concluir que não se cometem erros significativos na escolha de uma lei menos adequada.

Identification of structural damage in flexible structures using system norm and neural networks

Nowadays there is great interest in damage identification using non destructive tests. Predictive maintenance is one of the most important techniques that are based on analysis of vibrations and it consists basically of monitoring the condition of structures or machines. A complete procedure should be able to detect the damage, to foresee the probable time of occurrence and to diagnosis the type of fault in order to plan the maintenance operation in a convenient form and occasion. In practical problems, it is frequent the necessity of getting the solution of non linear equations. These processes have been studied for a long time due to its great utility. Among the methods, there are different approaches, as for instance numerical methods (classic), intelligent methods (artificial neural networks), evolutions methods (genetic algorithms), and others. The characterization of damages, for better agreement, can be classified by levels. A new one uses seven levels of classification: detect the existence of the damage; detect and locate the damage; detect, locate and quantify the damages; predict the equipment's working life; auto-diagnoses; control for auto structural repair; and system of simultaneous control and monitoring. The neural networks are computational models or systems for information processing that, in a general way, can be thought as a device black box that accepts an input and produces an output. Artificial neural nets (ANN) are based on the biological neural nets and possess habilities for identification of functions and classification of standards. In this paper a methodology for structural damages location is presented. This procedure can be divided on two phases. The first one uses norms of systems to localize the damage positions. The second one uses ANN to quantify the severity of the damage. The paper concludes with a numerical application in a beam like structure with five cases of structural damages with different levels of severities. The results show the applicability of the presented methodology. A great advantage is the possibility of to apply this approach for identification of simultaneous damages.

False alarms in damage detection approaches

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in structures in order to improve reliability and reduce life-cycle costs. The greatest challenge for designing a SHM system is knowing what changes to look for and how to classify them. Different approaches for SHM have been proposed for damage identification, each one with advantages and drawbacks. This paper presents a methodology for improvement in vibration signal analysis using statistics information involving the probability density. Generally, the presence of noises in input and output signals results in false alarms, then, it is important that the methodology can minimize this problem. In this paper, the proposed approach is experimentally tested in a flexible plate using a piezoelectric (PZT) actuator to provide the disturbance.

Application of lumped dissipation model in nonlinear analysis of reinforced concrete structures

This paper deals with the application of the lumped dissipation model in the analysis of reinforced concrete structures, emphasizing the nonlinear behaviour of the materials The presented model is based on the original models developed by Cipollina and Florez-Lopez (1995) [12]. Florez-Lopez (1995) [13] and Picon and Florez-Lopez (2000) [14] However, some modifications were introduced in the functions that control the damage evolution in order to improve the results obtained. The efficiency of the new approach is evaluated by means of a comparison with experimental results on reinforced concrete structures such as simply supported beams, plane frames and beam-to-column connections Finally, the adequacy of the numerical model representing the global behaviour of framed structures is investigated and the limits of the analysis are discussed (C) 2009 Elsevier Ltd All rights reserved

Strength prediction of adhesively-bonded scarf repairs in composite structures under bending

This work reports on the experimental and numerical study of the bending behaviour of two-dimensional adhesively-bonded scarf repairs of carbon-epoxy laminates, bonded with the ductile adhesive Araldite 2015®. Scarf angles varying from 2 to 45º were tested. The experimental work performed was used to validate a numerical Finite Element analysis using ABAQUS® and a methodology developed by the authors to predict the strength of bonded assemblies. This methodology consists on replacing the adhesive layer by cohesive elements, including mixed-mode criteria to deal with the mixed-mode behaviour usually observed in structures. Trapezoidal laws in pure modes I and II were used to account for the ductility of the adhesive used. The cohesive laws in pure modes I and II were determined with Double Cantilever Beam and End-Notched Flexure tests, respectively, using an inverse method. Since in the experiments interlaminar and transverse intralaminar failures of the carbon-epoxy components also occurred in some regions, cohesive laws to simulate these failure modes were also obtained experimentally with a similar procedure. A good correlation with the experiments was found on the elastic stiffness, maximum load and failure mode of the repairs, showing that this methodology simulates accurately the mechanical behaviour of bonded assemblies.

An exploratory study on facial emotion recognition capacity in beginning Alzheimer's disease.

Objective: It was the aim of this study to investigate facial emotion recognition (FER) in the elderly with cognitive impairment. Method: Twelve patients with Alzheimer's disease (AD) and 12 healthy control subjects were asked to name dynamic or static pictures of basic facial emotions using the Multimodal Emotion Recognition Test and to assess the degree of their difficulty in the recognition task, while their electrodermal conductance was registered as an unconscious processing measure. Results: AD patients had lower objective recognition performances for disgust and fear, but only disgust was accompanied by decreased subjective FER in AD patients. The electrodermal response was similar in all groups. No significant effect of dynamic versus static emotion presentation on FER was found. Conclusion: Selective impairment in recognizing facial expressions of disgust and fear may indicate a nonlinear decline in FER capacity with increasing cognitive impairment and result from progressive though specific damage to neural structures engaged in emotional processing and facial emotion identification. Although our results suggest unchanged unconscious FER processing with increasing cognitive impairment, further investigations on unconscious FER and self-awareness of FER capacity in neurodegenerative disorders are required.

Patterns of calcium-binding proteins in human inferior colliculus: identification of subdivisions and evidence for putative parallel systems.

The subdivisions of human inferior colliculus are currently based on Golgi and Nissl-stained preparations. We have investigated the distribution of calcium-binding protein immunoreactivity in the human inferior colliculus and found complementary or mutually exclusive localisations of parvalbumin versus calbindin D-28k and calretinin staining. The central nucleus of the inferior colliculus but not the surrounding regions contained parvalbumin-positive neuronal somata and fibres. Calbindin-positive neurons and fibres were concentrated in the dorsal aspect of the central nucleus and in structures surrounding it: the dorsal cortex, the lateral lemniscus, the ventrolateral nucleus, and the intercollicular region. In the dorsal cortex, labelling of calbindin and calretinin revealed four distinct layers.Thus, calcium-binding protein reactivity reveals in the human inferior colliculus distinct neuronal populations that are anatomically segregated. The different calcium-binding protein-defined subdivisions may belong to parallel auditory pathways that were previously demonstrated in non-human primates, and they may constitute a first indication of parallel processing in human subcortical auditory structures.