Influencia de la temperatura en el comportamiento y color de Palaemon elegans (Rathke, 1837) (Crustacea, Decapoda)

[ES] El camarón de charco (Palaemon elegans) soporta altos rangos de temperatura, pero a medida que ésta baja (hasta 5 ºC) comienza a reducir sus movimientos hasta quedar completamente inmóvil, casi en letargo. A este cambio de comportamiento va unido un cambio en la coloración del cuerpo del animal, que pasa de ser completamente transparente con tonos vivos azules y amarillos a ser translúcido, con azules y amarillos mucho más pálidos

Efecto del color del oponente en el desencadenamiento de la agresividad en los machos de Betta splendens

[ES] Los machos de Betta splendens se muestran más agresivos ante la presencia de otros machos de la misma especie y color similar al suyo, en comparación con la agresividad desplegada ante machos de tonalidad distinta

3-D geometry reconstruction using a color image stereo pair and partial differential equations

[EN] In this work, we present a new model for a dense disparity estimation and the 3-D geometry reconstruction using a color image stereo pair. First, we present a brief introduction to the 3-D Geometry of a camera system. Next, we propose a new model for the disparity estimation based on an energy functional. We look for the local minima of the energy using the associate Euler-Langrage partial differential equations. This model is a generalization to color image of the model developed in, with some changes in the strategy to avoid the irrelevant local minima. We present some numerical experiences of 3-D reconstruction, using this method some real stereo pairs.

Reconstrucción 3-D a partir de un par estéreo de imágenes en color utilizando ecuaciones en derivadas parciales

[ES] En este trabajo proponemos un nuevo modelo para el cálculo de la disparidad y la reconstrucción 3-D a partir de un sistema estéreo compuesto por 2 imágenes en color. Proponemos un nuevo modelo para el cálculo de la disparidad basado en un criterio de energía. Para calcular los mínimos de este funcional de energía utilizamos la ecuación en derivadas parciales de Euler-Langrage asociada. Este modelo es una extensión a imágenes color del modelo desarrollado en "L. Alvarez, R. Deriche, J. Sánchez and J. Weickert, Dense disparity map estimation respecting image discontinuities : A PDE and Scale-Space Based Approach. INRIA Rapport de Recherche Nº 3874, 2000". Con algunos cambios en la estrategia parav evitar caer en mínimos locales de la energía. Por último presentamos algunas experiencias numéricas de la reconstrucción 3-D obtenida con este método en algunos pares estéreos de imágenes reales.

Implementation of a robust optical flow method for color images

We analyse the influence of colour information in optical flow methods. Typically, most of these techniques compute their solutions using grayscale intensities due to its simplicity and faster processing, ignoring the colour features. However, the current processing systems have minimized their computational cost and, on the other hand, it is reasonable to assume that a colour image offers more details from the scene which should facilitate finding better flow fields. The aim of this work is to determine if a multi-channel approach supposes a quite enough improvement to justify its use. In order to address this evaluation, we use a multi-channel implementation of a well-known TV-L1 method. Furthermore, we review the state-of-the-art in colour optical flow methods. In the experiments, we study various solutions using grayscale and RGB images from recent evaluation datasets to verify the colour benefits in motion estimation.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Adaptive multiscale biological signal processing

Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

Studio del microcircolo della parete gastrica affetta da patologia infiammatoria e neoplastica mediante Color-Power Doppler ed ecografia con mezzo di contrasto endovenoso

Introduzione: Negli ultimi anni, il color-power Doppler si è dimostrato un utile strumento per valutare le alterazioni della vascolarizzazione della parete intestinale nelle patologie del tratto gastro-enterico. Più di recente, i mezzi di contrasto ecografici di II generazione associati all’ecografia real-time con basso indice meccanico (CEUS) hanno permesso di valutare ecograficamente il microcircolo, consentendo la valutazione della vascolarizzazione di parete nelle patologie associate a flogosi e neoangiogenesi. Studi recenti hanno documentato i pattern Doppler e CEUS nella malattia infiammatoria intestinale. Le alterazioni della vascolarizzazione di parete nella patologia neoplastica, invece, sono state finora valutate con sola tecnica Doppler. Recenti studi basati sull’impiego di tale metodica hanno in effetti dimostrato che l’intensità del segnale vascolare di parete correla con la variante istologica della neoplasia e con il suo grado di invasione vascolare costituendo così un parametro di neoangiogenesi tumorale. Pertanto, ottenere mediante CEUS una più accurata definizione del microcircolo di parete potrebbe aiutare nella diagnosi differenziale tra patologia infiammatoria e neoplastica dello stomaco e fornire utili informazioni per valutare l’ aggressività del cancro gastrico.

Circoli collaterali porto-sistemici all'eco-color-Doppler addominale in pazienti con ipertensione portale cirrotica: correlazione con il gradiente porto-epatico e valore prognostico

Background. Abdominal porto-systemic collaterals (APSC) on Color-Doppler ultrasound are a frequent finding in portal hypertensive cirrhotic patients. In patients with cirrhosis, an HVPG ≥ 16mmHg has been shown to be associated with increased mortality in two studies. Non-invasive indicators of HVPG ≥ 16 mmHg might define a subgroup of high-risk patients, but data on this aspect are lacking. Aims. We aimed to investigate whether HVPG predicts mortality in patients with clinically significant portal hypertension, and if APSC may predict a severe portal hypertensive state (i.e. HVPG≥16mmHg) in patients with cirrhosis and untreated portal hypertension. Methods. We analysed paired HVPG and ultrasonographic data of 86 untreated portal hypertensive cirrhotic patients. On abdominal echo-color-Doppler data on presence, type and number of APSC were prospectively collected. HVPG was measured following published guidelines. Clinical, laboratory and endoscopic data were available in all cases. First decompensation of cirrhosis and liver-disease related mortality on follow-up (mean 28±20 months) were recorded. Results. 73% of patients had compensated cirrhosis, while 27% were decompensated. All patients had an HVPG≥10 mmHg (mean 17.8±5.1 mmHg). 58% of compensated patients and 82% of decompensated patients had an HVPG over 16 mmHg. 25% had no varices, 28% had small varices, and 47% had medium/large varices. HVPG was higher in patients with esophageal varices vs. patients without varices (19.0±4.8 vs. 14.1±4.2mmHg, p<0.0001), and correlated with Child-Pugh score (R=0.494,p=0.019). 36 (42%) patients had APSC were more frequent in decompensated patients (60% vs. 35%, p=0.03) and in patients with esophageal varices (52% vs. 9%,p=0.001). HVPG was higher in patients with APSC compared with those without PSC (19.9± 4.6 vs. 16.2± 4.9mmHg, p=0.001). The prevalence of APSC was higher in patients with HVPG≥16mmHg vs. those with HVPG<16mmHg (57% vs. 13%,p<0.0001). Decompensation was significantly more frequent in patients with HVPG≥16mmHg vs. HVPG<16mmHg (35.1% vs. 11.5%, p=0.02). On multivariate analysis only HVPG and bilirubin were independent predictors of first decompensation. 10 patients died during follow-up. All had an HVPG≥16 mmHg (26% vs. 0% in patients with HVPG <16mmHg,p=0.04). On multivariate analysis only MELD score and HVPG ≥16mmHg were independent predictors of mortality. In compensated patients the detection of APSC predicted an HVPG≥16mmHg with 92% specificity, 54% sensitivity, positive and negative likelihood ratio 7.03 and 0.50, which implies that the demonstration of APSC on ultrasound increased the probability of HVPG≥16mmHg from 58% to 91%. Conclusions. HVPG maintains an independent prognostic value in the subset of patients with cirrhosis and clinically significant portal hypertension. The presence of APSC is a specific indicator of severe portal hypertension in patients with cirrhosis. Detection of APSC on ultrasound allows the non-invasive identification of a subgroup of compensated patients with bad prognosis, avoiding the invasive measurement of HVPG.

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.