Search results for " network"

Prediction of peak shape in hydro-organic and micellar-organic liquid chromatography as a function of mobile phase composition

2007

A simple model is proposed that relates the parameters describing the peak width with the retention time, which can be easily predicted as a function of mobile phase composition. This allows the further prediction of peak shape with global errors below 5%, using a modified Gaussian model with a parabolic variance. The model is useful in the optimisation of chromatographic resolution to assess an eventual overlapping of close peaks. The dependence of peak shape with mobile phase composition was studied for mobile phases containing acetonitrile in the presence and absence of micellised surfactant (micellar-organic and hydro-organic reversed-phase liquid chromatography, RPLC). In micellar RPLC…

Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19

2021

Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with …

An ASSOM neural network to represent actions performed by an autonomous agent

1997

An ASSOM neural network to describe the action performed by an autonomous reactive agent is proposed. The neural network receives in input the sequences of data acquired by the agent internal sensors and it classifies them by generating the corresponding symbolic assertions. Experimental results performed on a RWI B12 autonomous robot are reported.

Dropping out of school as a meaningful action for adolescents with social, emotional and behavioural difficulties

2013

This study examines and discusses dropping out of school related to adolescents with social, emotional and behavioural difficulties (SEBD). It is based on in-depth interviews of 10 adolescents between the ages of 16 and 20, three girls and two boys with internalised problems, and two girls and three boys with extroverted behavioural problems. Given this group of students' challenges at school, the aim of this paper is to explore the narratives of this adolescent group as they relate to the significance they attach to their dropout behaviour. An additional objective is to draw attention to what these findings are likely to mean for implementing preventive practices in school. Results show th…

Rapid developmental switch in the mechanisms driving early cortical columnar networks

2006

The immature cerebral cortex self-organizes into local neuronal clusters long before it is activated by patterned sensory inputs. In the cortical anlage of newborn mammals, neurons coassemble through electrical or chemical synapses either spontaneously or by activation of transmitter-gated receptors. The neuronal network and the cellular mechanisms underlying this cortical self-organization process during early development are not completely understood. Here we show in an intact in vitro preparation of the immature mouse cerebral cortex that neurons are functionally coupled in local clusters by means of propagating network oscillations in the beta frequency range. In the newborn mouse, this…

Network of small towns. Themes and projects in the territory of Tindari

2019

The protection of natural and cultural resources in small towns is a topic of growing interest within the European context and aims to enhance the local heritage toward an interaction consciously balanced between human activities. The project Tindari 2030: Natural emotion led by the research group LabCity Architecture aims to indicate and enhance the territory of Tindari with regard to its natural, cultural and religious resources, such as the Sanctuary of the Black Madonna, an archaeological site (396 BC), the sandy naturalistic system of the 'Laghetti di Marinello',the 'Coda di Volpe' trail, which is the remaining part of the 'Via Francigena Palermo-Messina per la marina'. This project in…

Rete di centri minori. Temi e progetti nel territorio di Tindari

2019

The protection of natural and cultural resources in small towns is a topic of growing interest within the European context and aims to enhance the local heritage toward an interaction consciously balanced between human activities. The project Tindari 2030: Natural emotion led by the research group LabCity Architecture aims to indicate and enhance the territory of Tindari with regard to its natural, cultural and religious resources, such as the Sanctuary of the Black Madonna, an archaeological site (396 BC), the sandy naturalistic system of the 'Laghetti di Marinello',the 'Coda di Volpe' trail, which is the remaining part of the 'Via Francigena Palermo-Messina per la marina'. This project in…

Interference and Communications among Active Network Applications

1999

A New Adaptive Neural Harmonic Compensator for Inverter Fed Distributed Generation

2004

This paper deals with the command of inverters in DG (distributed generation) systems by use of linear neural networks in such a way that, with a slight upgrade of their control software, they can be used also to compensate for the harmonic distortion in the node where they are connected (local compensation), that is in the in the point of common coupling (PCC). To this purpose a neural estimator based on linear neurons (ADALINEs) has been developed which is able to act as a selective noise cancellers for each harmonic of the node voltage. The use of linear neurons permits the drawbacks of classical neural networks to be overcome and moreover the neural estimator is easy to implement, thus …

Active Learning for Monitoring Network Optimization

2012

Kernel-based active learning strategies were studied for the optimization of environmental monitoring networks. This chapter introduces the basic machine learning algorithms originated in the statistical learning theory of Vapnik (1998). Active learning is closer to an optimization done using sequential Gaussian simulations. The chapter presents the general ideas of statistical learning from data. It derives the basics of kernel-based support vector algorithms. The active learning framework is presented and machine learning extensions for active learning are described in the chapter. Kernel-based active learning strategies are tested on real case studies. The chapter explores the use of a c…