Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Demo: automatic personal identification system for security in critical services: a case study

The demonstration proposal moves from the capabilities of a wireless biometric badge [4], which integrates a localization and tracking service along with an automatic personal identification mechanism, to show how a full system architecture is devised to enable the control of physical accesses to restricted areas. The system leverages on the availability of a novel IEEE 802.15.4/Zigbee Cluster Tree network model, on enhanced security levels and on the respect of all the users' privacy issues.

The contribution of multitemporal information from multispectral satellite images for automatic land cover classification at the national scale

Thesis submitted to the Instituto Superior de Estatística e Gestão de Informação da Universidade Nova de Lisboa in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in Information Management – Geographic Information Systems

On performance of distributed model predictive control in power system frequency regulation

This paper describes the implementation of a distributed model predictive approach for automatic generation control. Performance results are discussed by comparing classical techniques (based on integral control) with model predictive control solutions (centralized and distributed) for different operational scenarios with two interconnected networks. These scenarios include variable load levels (ranging from a small to a large unbalance generated power to power consumption ratio) and simultaneously variable distance between the interconnected networks systems. For the two networks the paper also examines the impact of load variation in an island context (a network isolated from each other).

Unsupervised automatic music genre classification

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

A semi-automatic approach to code smells detection

Eradication of code smells is often pointed out as a way to improve readability, extensibility and design in existing software. However, code smell detection remains time consuming and error-prone, partly due to the inherent subjectivity of the detection processes presently available. In view of mitigating the subjectivity problem, this dissertation presents a tool that automates a technique for the detection and assessment of code smells in Java source code, developed as an Eclipse plugin. The technique is based upon a Binary Logistic Regression model that uses complexity metrics as independent variables and is calibrated by expert‟s knowledge. An overview of the technique is provided, the tool is described and validated by an example case study.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Automatic generation of ETL physical systems from BPMN conceptual models

ETL conceptual modeling is a very important activity in any data warehousing system project implementation. Owning a high-level system representation allowing for a clear identification of the main parts of a data warehousing system is clearly a great advantage, especially in early stages of design and development. However, the effort to model conceptually an ETL system rarely is properly rewarded. Translating ETL conceptual models directly into something that saves work and time on the concrete implementation of the system process it would be, in fact, a great help. In this paper we present and discuss a hybrid approach to this problem, combining the simplicity of interpretation and power of expression of BPMN on ETL systems conceptualization with the use of ETL patterns to produce automatically an ETL skeleton, a first prototype system, which has the ability to be executed in a commercial ETL tool like Kettle.

Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale: some case studies

Every year, debris flows cause huge damage in mountainous areas. Due to population pressure in hazardous zones, the socio-economic impact is much higher than in the past. Therefore, the development of indicative susceptibility hazard maps is of primary importance, particularly in developing countries. However, the complexity of the phenomenon and the variability of local controlling factors limit the use of processbased models for a first assessment. A debris flow model has been developed for regional susceptibility assessments using digital elevation model (DEM) with a GIS-based approach.. The automatic identification of source areas and the estimation of debris flow spreading, based on GIS tools, provide a substantial basis for a preliminary susceptibility assessment at a regional scale. One of the main advantages of this model is its workability. In fact, everything is open to the user, from the data choice to the selection of the algorithms and their parameters. The Flow-R model was tested in three different contexts: two in Switzerland and one in Pakistan, for indicative susceptibility hazard mapping. It was shown that the quality of the DEM is the most important parameter to obtain reliable results for propagation, but also to identify the potential debris flows sources.

Distinguishing general concepts from individuals: an automatic coarse-grained classifier

Named entity recognizers are unable to distinguish if a term is a general concept as "scientist" or an individual as "Einstein". In this paper we explore the possibility to reach this goal combining two basic approaches: (i) Super Sense Tagging (SST) and (ii) YAGO. Thanks to these two powerful tools we could automatically create a corpus set in order to train the SuperSense Tagger. The general F1 is over 76% and the model is publicly available.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Decentralized model reference control of flexible cable-stayed beam structures

A decentralized model reference controller is designed to reduce the magnitude of the transversal vibration of a flexible cable-stayed beam structure induced by a seismic excitation. The controller design is made based on the principle of sliding mode such that a priori knowledge

Anti-vascular endothelial growth factor acts on retinal microglia/macrophage activation in a rat model of ocular inflammation.

PURPOSE: To evaluate whether anti-vascular endothelial growth factor (VEGF) neutralizing antibodies injected in the vitreous of rat eyes influence retinal microglia and macrophage activation. To dissociate the effect of anti-VEGF on microglia and macrophages subsequent to its antiangiogenic effect, we chose a model of acute intraocular inflammation. METHODS: Lewis rats were challenged with systemic lipopolysaccharide (LPS) injection and concomitantly received 5 µl of rat anti-VEGF-neutralizing antibody (1.5 mg/ml) in the vitreous. Rat immunoglobulin G (IgG) isotype was used as the control. The effect of anti-VEGF was evaluated at 24 and 48 h clinically (uveitis scores), biologically (cytokine multiplex analysis in ocular media), and histologically (inflammatory cell counts on eye sections). Microglia and macrophages were immunodetected with ionized calcium-binding adaptor molecule 1 (IBA1) staining and counted based on their differential shapes (round amoeboid or ramified dendritiform) on sections and flatmounted retinas using confocal imaging and automatic quantification. Activation of microglia was also evaluated with inducible nitric oxide synthase (iNOS) and IBA1 coimmunostaining. Coimmunolocalization of VEGF receptor 1 and 2 (VEGF-R1 and R2) with IBA1 was performed on eye sections with or without anti-VEGF treatment. RESULTS: Neutralizing rat anti-VEGF antibodies significantly decreased ocular VEGF levels but did not decrease the endotoxin-induced uveitis (EIU) clinical score or the number of infiltrating cells and cytokines in ocular media (interleukin [IL]-1β, IL-6, tumor necrosis factor [TNF]-α, and monocyte chemoattractant protein [MCP]-1). Eyes treated with anti-VEGF showed a significantly decreased number of activated microglia and macrophages in the retina and the choroid and decreased iNOS-positive microglia. IBA1-positive cells expressed VEGF-R1 and R2 in the inflamed retina. CONCLUSIONS: Microglia and macrophages expressed VEGF receptors, and intravitreous anti-VEGF influenced the microglia and macrophage activation state. Taking into account that anti-VEGF drugs are repeatedly injected in the vitreous of patients with retinal diseases, part of their effects could result from unsuspected modulation of the microglia activation state. This should be further studied in other ocular pathogenic conditions and human pathology.

Modelling the cardiovascular system for automatic interpretation of the blood pressure curve

A four compartment model of the cardiovascular system is developed. To allow for easy interpretation and to minimise the number of parameters, an effort was made to keep the model as simple as possible. A sensitivity analysis is first carried out to determine which are the most important model parameters to characterise the blood pressure signal. A four stage process is then described which accurately determines all parameter values. This process is applied to data from three patients and good agreement is shown in all cases.