Alberto De Santis

Robust real time eye tracking for computer interface for disabled people

Gaze is a natural input for a Human Computer Interface (HCI) for disabled people, who have of course an acute need for a communication system. An efficient eye tracking procedure is presented providing a non-invasive method for real time detection of a subject eyes in a sequence of frames captured by low cost equipment. The procedure can be easily adapted to any subject and is adequately insensitive to changing of the illumination. The eye identification is performed on a piece-wise constant approximation of the frames. It is based on a discrete level set formulation of the variational approach to the optimal segmentation problem. This yields a simplified version of the original data retaining all the information relevant to the application. Tracking is obtained by a fast update of the optimal segmentation between successive frames. No eye movement model is required being the procedure fast enough to obtain the current frame segmentation as one step update from the previous frame segmentation.

Collective behavior in gene regulation: Post-transcriptional regulation and the temporal compartmentalization of cellular cycles

Self‐sustained oscillations are perhaps the most studied objects in science. The accomplishment of such a task reliably and accurately requires the presence of specific control mechanisms to face the presence of variable and largely unpredictable environmental stimuli and noise. Self‐sustained oscillations of transcript abundance are, in fact, widespread and are not limited to the reproductive cycle but are also observed during circadian rhythms, metabolic cycles, developmental cycles and so on. To date, much of the literature has focused on the transcriptional machinery underlying control of the basic timing of transcript abundance. However, mRNA abundance is known to be regulated at the post‐transcriptional level also and the relative contribution of the two mechanisms to gene‐expression programmes is currently a major challenge in molecular biology. Here, we review recent results showing the relevance of the post‐transcriptional regulation layer and present a statistical reanalysis of the yeast metabolic cycle using publicly available gene‐expression and RNA‐binding data. Taken together, the recent theoretical and experimental developments reviewed and the results of our reanalysis strongly indicate that regulation of mRNA stability is a widespread, phase‐specific and finely tuned mechanism for the multi‐layer control of gene expression needed to achieve high flexibility and adaptability to external and internal signals.

A discrete level set approach to image segmentation

Models and algorithms in image processing are usually defined in the continuum and then applied to discrete data, that is the signal samples over a lattice. In particular, the set up in the continuum of the segmentation problem allows a fine formulation basically through either a variational approach or a moving interfaces approach. In any case, the image segmentation is obtained as the steady-state solution of a nonlinear PDE. Nevertheless the application to real data requires discretization schemes where some of the basic image geometric features have a loose meaning. In this paper, a discrete version of the level set formulation of a modified Mumford and Shah energy functional is investigated, and the optimal image segmentation is directly obtained through a nonlinear finite difference equation. The typical characteristics of a segmentation, such as its component domains area and its boundary length, are all defined in the discrete context thus obtaining a more realistic description of the available data. The existence and uniqueness of the optimal solution is proved in the class of piece wise constant functions, but with no restrictions on the nature of the segmentation boundary multiple points. The proposed algorithm compared to a standard segmentation procedure in the continuum generally provides a more accurate segmentation, with a much lower computational cost.

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex.

Brief On model consistency in compartmental systems identification

Compartmental systems are composed of a finite number of subsystems, called compartments, interacting by exchanging material. We propose a set of constraints ensuring compartmentality in an identification process.

Brief Identification of positive linear systems with Poisson output transformation

Positive systems are systems in which the input/state/output variables are always positive since they represent quantities. We propose an identification procedure for a class of positive linear systems.

Graphite nodules features identifications and damaging micromechanims in ductile irons

Ductile irons mechanical properties are strongly influenced by the metal matrix microstructure and on the graphite elements morphology. Depending on the chemical composition, the manufacturing process and the heat treatments, these graphite elements can be characterized by different shape, size and distribution. These geometrical features are usually evaluated by the experts visual inspection, and some commercial softwares are also available to assist this activity. In this work, an automatic procedure based on an image segmentation technique is applied: this procedure is validated not only considering spheroidal graphite elements, but also considering other morphologies (e.g. lamellae).

Design of a Classification Strategy for Light Microscopy Images of the Human Liver.

Light Microscopy (LM) represents the method by which pathologists study histological sections; the observations by LM can be considered the gold standard for making diagnosis and for its diagnostic accuracy. The classes that can be defined through the observation of LM images of the liver are: normal, steatosis, fibrosis, cirrhosis and hepatocarcinoma (HCC). Normally, a pathologist has to examine by LM many histological sections to perform a complete and accurate diagnosis. For this reason, an automatic system for the analysis of LM images of the liver would be particularly useful. Goal of this paper is to propose an automatic multi-stage procedure to classify the normal tissue, and the pathologic ones from human liver microphotographs. Due to the articulated nature of the examined images, the analysis will first assess if steatosis is present, by using objects analysis, and then determine whether the image belongs to a normal tissue or to one of the other pathologic ones, by using a machine learning based technique. To this aim some texture features are calculated, and the Principal Component Analysis is applied to derive the best representation of the data. Four binary Support Vector Machines classifiers are trained, one for each kind the four classes of liver conditions to be identified. Experimental results show the classification capability of the proposed system, with promising theoretical and experimental basis for developing a fully automatic decision support system.

Disturbance attenuation for a class of distributed parameter systems

This paper deals with the problem of robust stabilization and disturbance attenuation via measured feedback, for a class of dissipative collocated distributed systems with disturbances affecting both the input and the measured output. The proposed solution is based on a direct ℒ2-gain characterization which avoids the usual Riccati equation argument. For this purpose only strong stabilizability of the semigroup is required. A flexible slewing link is chosen as an application example.

Segmentation Based Pattern Recognition for Peri-Urban Areas in X Band SAR Images

In this paper Synthetic Aperture Radar (SAR) images in X-band were analyzed in order to infer ground properties from data. The aim was to classify different zones in peri-urban forestries integrating information from different sources. In particular the X band is sensitive to the moisture content of the ground that can be therefore put into relation with the gray level of the image; moreover, the gray level is related to the smoothness and roughness of the ground. An integration of image segmentation and machine learning methods is studied to classify different zones of peri-urban forestries, such as trees canopies, lawns, water pounds, roads, etc., directly from the gray level signal properties. As case study the X-SAR data of a forest near Rome, the Castel Fusano area, are analyzed.