Giovanni Costantini

Determination of enteroviruses, hepatitis A virus, bacteriophages and Escherichia coli in Adriatic Sea mussels

The aim of the present study was to evaluate the incidence of enteric viruses in mussels and to verify the possibility of using phages as indirect indicators of mussel viral contamination. Mussels (36 samples) collected from three different areas of the Adriatic Sea were analysed to determine the following parameters: Escherichia coli, somatic coliphage (T6 phage), F‐Plus (MS2 phage), B40‐8 (phage of Bacteroides fragilis), enteroviruses and hepatitis A virus. Most of the results of the bacteriological analysis (most probable number (MPN) ml−1) were in accordance with the bacteriological limits established by European law, with the exception of seven samples. The bacteriophage analyses were always negative for F‐Plus and B40‐8, with the exception of a few samples, whereas the somatic coliphages were generally between 0 and 20 MPN g−1, with the exception of two samples (110 MPN g−1). The virological analysis showed five samples positive for the presence of enteroviruses and 13 for the presence of hepatitis A virus (in three samples both viruses were present). Most of these samples presented acceptable bacteriological parameters and the bacteriophages were absent or their value was generally very low. The results show that the detection of E. coli and phages does not seem to be a good indicator of viral contamination.

Cellular Neural Networks With Virtual Template Expansion for Retinal Vessel Segmentation

A retinal vessel segmentation method based on cellular neural networks (CNNs) is proposed. The CNN design is characterized by a virtual template expansion obtained through a multistep operation. It is based on linear space-invariant 3times3 templates and can be realized using existing chip prototypes like the ACE16K. The proposed design is capable of performing vessel segmentation within a short computation time. It was tested on a publicly available database of color images of the retina, using receiver operating characteristic curves. The simulation results show good performance comparable with that of the best existing methods

Neural associative memory storing gray-coded gray-scale images

We present a neural associative memory storing gray-scale images. The proposed approach is based on a suitable decomposition of the gray-scale image into gray-coded binary images, stored in brain-state-in-a-box-type binary neural networks. Both learning and recall can be implemented by parallel computation, with time saving. The learning algorithm, used to store the binary images, guarantees asymptotic stability of the stored patterns, low computational cost, and control of the weights precision. Some design examples and computer simulations are presented to show the effectiveness of the proposed method.

Toward the design of a wearable system for fall-risk detection in telerehabilitation.

Telemedicine represents a valid aid in rehabilitation process. A remote therapist in a telerehabilitation program could monitor daily motion activity and assign motion-rehabilitation tools on the basis of the fall risk. However, one problem is detection of the fall risk itself. Web-based video-camera images alone do not help the remote assessment of the fall risk using the most commonly used qualitative tests based on visual observation. A novel wearable system to assess fall risk in telerehabilitation has been proposed based on an Inertial Measurement Unit and a medical protocol. It provides a score in four levels (1: no fall risk; 4: major fall risk). The telemedicine tool is integrated to the Global System for Mobile communication (GSM) net. Each component of the wearable system has been designed and integrated. Each component in the system has been tested individually and in a closed loop. One subject was monitored in a telemedicine link. The test showed a high degree of acceptance. The tool will be furnished to subjects along with a homecare device for daily routine monitoring of motion activity and could eventually be integrated with other systems designed to monitor other physiological parameters along with different aids and monitoring tools.

Speech emotion recognition using amplitude modulation parameters and a combined feature selection procedure

Speech emotion recognition (SER) is a challenging framework in demanding human machine interaction systems. Standard approaches based on the categorical model of emotions reach low performance, probably due to the modelization of emotions as distinct and independent affective states. Starting from the recently investigated assumption on the dimensional circumplex model of emotions, SER systems are structured as the prediction of valence and arousal on a continuous scale in a two-dimensional domain. In this study, we propose the use of a PLS regression model, optimized according to specific features selection procedures and trained on the Italian speech corpus EMOVO, suggesting a way to automatically label the corpus in terms of arousal and valence. New speech features related to the speech amplitude modulation, caused by the slowly-varying articulatory motion, and standard features extracted from the pitch contour, have been included in the regression model. An average value for the coefficient of determination R2 of 0.72 (maximum value of 0.95 for fear and minimum of 0.60 for sadness) is obtained for the female model and a value for R2 of 0.81 (maximum value of 0.89 for anger and minimum value of 0.71 for joy) is obtained for the male model, over the seven primary emotions (including the neutral state).

Event based transcription system for polyphonic piano music

Music transcription consists in transforming the musical content of audio data into a symbolic representation. The objective of this study is to investigate a transcription system for polyphonic piano, triggered by events corresponding to the played notes. The proposed method focuses on note events and their main characteristics: the attack instant, the pitch and the final instant. Onset detection exploits a binary time-frequency representation of the audio signal. Note classification and offset detection are based on constant Q transform (CQT) and support vector machines (SVMs). We present a collection of experiments using synthesized MIDI files and piano recordings, and compare the results with existing approaches.

Associative Memory Design Using Support Vector Machines

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebbs law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples

Quasi-Lagrangian Neural Network for Convex Quadratic Optimization

A new neural network for convex quadratic optimization is presented in this brief. The proposed network can handle both equality and inequality constraints, as well as bound constraints on the optimization variables. It is based on the Lagrangian approach, but exploits a partial dual method in order to keep the number of variables at minimum. The dynamic evolution is globally convergent and the steady-state solutions satisfy the necessary and sufficient conditions of optimality. The circuit implementation is simpler with respect to existing solutions for the same class of problems. The validity of the proposed approach is verified through some simulation examples.

Associative memory design for 256 gray-level images using a multilayer neural network

A design procedure is presented for neural associative memories storing gray-scale images. It is an evolution of a previous work based on the decomposition of the image with 2/sup L/ gray levels into L binary patterns, stored in L uncoupled neural networks. In this letter, an L-layer neural network is proposed with both intralayer and interlayer connections. The connections between different layers introduce interactions among all the neurons, increasing the recall performance with respect to the uncoupled case. In particular, the proposed network can store images with the commonly used number of 256 gray levels instead of 16, as in the previous approach.

Multiplierless digital learning algorithm for cellular neural networks

A new learning algorithm is proposed for space-varying cellular neural networks, used to implement associative memories. The algorithm exhibits some peculiar features which make it very attractive: the finite precision of connection weights is automatically taken into account as a design constraint; no multiplication is needed for weight computation; learning can be implemented in fixed point digital hardware or simulated on a digital computer without numerical errors.