Salvatore Cuomo

IoT-based collaborative reputation system for associating visitors and artworks in a cultural scenario

We design a CRS to establish the people reputation within Cultural spaces.The system is able to classify visitor behaviours.Our approach is suitable for both nonprofit and business oriented organizations. In this paper, starting from a comprehensive mathematical model of a Collaborative Reputation Systems (CRSes), we present a research study within the Cultural Heritage domain. The main goal of this study has been the evaluation and classification of the visitors behaviour during a cultural event. By means of mobile technological instruments, opportunely deployed within the environment, it is possible to collect data representing the knowledge to be inferred and give a reliable rate for both visitors and exposed artworks. Discussed results, confirm the reliability and the usefulness of CRSes for deeply understand dynamics related to people visiting styles.

3D data denoising via Nonlocal Means filter by using parallel GPU strategies.

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.

A Regularized MRI Image Reconstruction based on Hessian Penalty Term on CPU/GPU Systems

In this paper we investigate an inverse reconstruction problem of Magnetic Resonance Imaging with few acquired body scanner samples. The missing information in the Fourier domain causes image artefacts, therefore iterative computationally expensive recovery techniques are needed. We propose a regularization approach based on second order derivative of both simulated and real images with highly undersampled data, obtaining a good reconstruction accuracy. Moreover, an accelerated regularization algorithm, by using a projection technique combined with an implementation on Graphics Processing Unit (GPU) computing environment, is presented. The numerical experiments give clinically-feasible reconstruction runtimes with an increase in speed and accuracy of the MRI dataset reconstructions.

A revised scheme to compute horizontal covariances in an oceanographic 3D-VAR assimilation system

We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of an RF with first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances. An advantage of the proposed scheme is that the CPUs time can be substantially reduced with benefits on the large scale applications. Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications. The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation.

A revised scheme for real time ECG Signal denoising based on recursive filtering

Abstract In many healthcare applications, artifacts mask or corrupt important features of Electrocardiogram (ECG) signals. In this paper we describe a revised scheme for ECG signal denoising based on a recursive filtering methodology. We suggest a suitable class of kernel functions in order to remove artifacts in the ECG signal, starting from noise frequencies in the Fourier domain. Our approach does not require high computational requirements and this feature offers the possibility of an implementation of the scheme directly on mobile computing devices. The proposed scheme allows local denoising and hence a real time visualization of the signal by means of a strategy based on boundary conditions. Experiments on real datasets have been carried out in order to test, in terms of computation and accuracy, the proposed algorithm. Finally, comparative results with other well-known denoising methods are shown.

Visiting Styles in an Art Exhibition Supported by a Digital Fruition System

We investigate the user dynamics related to the interaction with artworks in an exhibition. In a first step, we characterize visitors in a cultural heritage scenario and after, we study how these interact with available technologies. Accordingly with the fact that the technology plays a crucial role in supporting spectators and enhancing their experiences, the starting point of this research is the analysis of real data coming from visitors of the art exhibition named The Beauty or the Truth that was located in Naples, Italy. The event was equipped with several technological tools arranged within the halls of the exhibition, with the aim to create a novel metaphor that stimulates the user enjoyment and the knowledge diffusion. The collected log files from a suitable expert software system are used in a flexible framework in order to analyse how the supporting pervasive technology influence and modify behaviours and visiting styles. Finally, we carried out some experiments to exploit the clustering facilities for finding groups that reflect visiting styles. The obtained results have revealed interesting issues also to understand hidden aspects in the data and unattended in the analysis.

A Novel O(n) Numerical Scheme for ECG Signal Denoising

Abstract High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. This paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noises frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

Effects of increasing CREB-dependent transcription on the storage and recall processes in a hippocampal CA1 microcircuit.

The involvement of the hippocampus in learning processes and major brain diseases makes it an ideal candidate to investigate possible ways to devise effective therapies for memory‐related pathologies like Alzheimers Disease (AD). It has been previously reported that augmenting CREB activity increases the synaptic Long‐Term Potentiation (LTP) magnitude in CA1 pyramidal neurons and their intrinsic excitability in healthy rodents. It has also been suggested that hippocampal CREB signaling is likely to be down‐regulated during AD, possibly degrading memory functions. Therefore, the concept of CREB‐based memory enhancers, i.e. drugs that would boost memory by activation of CREB, has emerged. Here, using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD‐like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB‐based therapies could provide a new approach to treat AD.

A numerical approach to nonlinear two-point boundary value problems for ODEs

In this paper we propose a numerical approach to solve some problems connected with the implementation of the Newton type methods for the resolution of the nonlinear system of equations related to the discretization of a nonlinear two-point BVPs for ODEs with mixed linear boundary conditions by using the finite difference method.

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

In this work we present a multi-level parallel framework for the Optical Flow computation on a GPUs cluster, equipped with a scientific computing middleware (the PetSc library). Starting from a flow-driven isotropic method, which models the optical flow problem through a parabolic partial differential equation (PDE), we have designed a parallel algorithm and its software implementation that is suitable for heterogeneous computing environments (multiprocessor, single GPU and cluster of GPUs). The proposed software has been tested on real SAR images sequences. Numerical experiments highlight the performance of the proposed software framework, which can reach a gain of about 95% with respect to the sequential implementation.