Paolo Di Giamberardino

New MEMS Tweezers for the Viscoelastic Characterization of Soft Materials at the Microscale

As many studies show, there is a relation between the tissue’s mechanical characteristics and some specific diseases. Knowing this relationship would help early diagnosis or microsurgery. In this paper, a new method for measuring the viscoelastic properties of soft materials at the microscale is proposed. This approach is based on the adoption of a microsystem whose mechanical structure can be reduced to a compliant four bar linkage where the connecting rod is substituted by the tissue sample. A procedure to identify both stiffness and damping coefficients of the tissue is then applied to the developed hardware. Particularly, stiffness is calculated solving the static equations of the mechanism in a desired configuration, while the damping coefficient is inferred from the dynamic equations, which are written under the hypothesis that the sample tissue is excited by a variable compression force characterized by a suitable wave form. The whole procedure is implemented by making use of a control system.

Isotropy in any RR planar dyad under active joint stiffness regulation

The present investigation is dedicated to the study of the static balance at the tip of a planar RR robot. For this case, a configuration can be interpreted, in the static sense, as isotropic when any force applied to the robot wrist yields a small displacement which is theoretically parallel to the applied force (no matter how the force is directed on the plane). This characteristic offers many advantages and is considered as an optimal design goal. Unfortunately, the conditions to achieve such property in RR manipulators are very restrictive, and until now, only one solution is adopted, with a fixed lengths ratio. The present paper reveals how any RR planar robot can achieve isotropy at the tip by using a feedback action at the joints to gain arbitrary elastic coefficients. The new approach of design brings to less restrictive conditions than the previous ones.

The Area Coverage Problem for Dynamic Sensor Networks

In this section a brief description of area coverage and connectivity maintenance for sensor networks is given together with their collocation in the scientific literature. Particular attention is given to dynamic sensor networks, such as sensor networks in witch sensing nodes moves continuously, under the assumption, reasonable in many applications, that synchronous or asynchronous discrete time measures are acceptable instead of continuous ones.

Modeling the Effects of Prevention and Early Diagnosis on HIV/AIDS Infection Diffusion

In this paper, a new model describing the human immunodeficiency virus (HIV)-acquired immuno deficiency syndrome (AIDS) epidemic spread is proposed. The improvement with respect to the known models has been driven by recent results obtained from historical data collection and the suggestions given by the World Health Organization: the characteristics of the virus diffusion, mainly by body fluids, imply the trivial fact that wise behaviors of healthy subjects and fast timely recognition of a new positive diagnosis should reduce the spread quite fast. Therefore, the set of susceptible subjects is divided into two categories: the wise people that, suitably informed, avoid dangerous behaviors, and the ones that, with irresponsible acts, could get the infection. The set of infected subjects is constituted by people who are still not aware of being infected (and therefore are responsible of the HIV spread), along with the subjects aware of being infected by HIV or AIDS. Inspired by the international guidelines suggestions, three controls are introduced, aiming both at the prevention and at the cure: an informative campaign, a test campaign, and an HIV/AIDS therapy action. Among them, the core of the control effort is a fast HIV diagnosis. The equilibrium points, their stability, and the influences of the introduced inputs to the system behavior are studied, yielding to preliminary statements for prospective works on suitable control design approaches.

Data processing for industrial automation

Abstract The processing of sampled variables directly acquired from a controlled plant is highly recommended before their utilisation for implementing a control strategy at the field level and the supervisory one. In this paper the algorithms for implementing some pre-processing procedures are presented. The first one allows to determine the bandwidth of the signal utilised for activating a control strategy. A new procedure for designing a non-recursive low-pass filter is then presented. Since the filter model is given by an analytical equation, the computation of its first order derivative and the second order one is direct and very simple. Finally, an algorithm for the on line computation of the estimated mean value is given. The validation tests of all the proposed algorithms conclude the paper.

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.

Stiffness Characterization of Biological Tissues by Means of MEMS-Technology Based Micro Grippers Under Position Control

This paper presents a method for detecting the mechanical stiffness of micro-metric biological tissues by means of compliance tests performed with a MEMS-Technology based microgripper. Thanks to an actuating rotary comb drive working in cooperation with another sensing rotary comb drive, the system is able to recognize the tissue sample stiffness. Such characterization is possible thanks to a proper control system that is applied to the whole mechanical structure.

Classification of emotional signals from the DEAP dataset

A Brain Computer Interface (BCI) is a useful instrument to support human communication, frequently implemented by using electroencephalography (EEG). Regarding the used communication paradigm, a very large number of strategies exist and, recently, self-induced emotions have been introduced. However, in general the actual emotion-based BCIs are just binary, since they are capable of recognizing just a single emotion. A crucial node is the introduction of more than a single emotional state for improving the efficiency of a BCI. In order to be used in BCIs, signals from different emotional states have to be collected, recognized and classified. In the present paper, a method for mapping several emotional states was described and tested on EEG signals collected from a publicly available dataset for emotion analysis using physiological signals (DEAP). The proposed method, its experimental protocol, and preliminary numerical results on three different emotional states were presented and discussed. The method, based on multiple binary classification, was capable of optimizing the most discriminative channels and the features combination for each emotional state and of recognizing between several emotional states through a polling

Computational Modeling of Objects Presented in Images: Fundamentals, Methods and Applications

This book contains extended versions of selected papers from the 3rd edition of the International Symposium CompIMAGE. These contributions include cover methods of signal and image processing and analysis to tackle problems found in medicine, material science, surveillance, biometric, robotics, defence, satellite data, traffic analysis and architecture, image segmentation, 2D and 3D reconstruction, data acquisition, interpolation and registration, data visualization, motion and deformation analysis and 3D vision.

Input-state matching under piecewise constant control for systems on matrix Lie groups

The constructive controllability problem under piecewise constant control is discussed for systems evolving on matrix Lie groups. Two approaches are followed: an indirect one, in which a continuous time control is matched by a multi-rate sampled data controller ensuring coincidence at the sampling instants and a direct one, in which a digital point to point maneuver is designed. Simulations are performed to compare the continuous and digital approaches.