Ardelio Galletti

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.

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.

A smoothing spline that approximates Laplace transform functions only known on measurements on the real axis

The scientific and application-oriented interest in the Laplace transform and its inversion is testified by more than 1000 publications in the last century. Most of the inversion algorithms available in the literature assume that the Laplace transform function is available everywhere. Unfortunately, such an assumption is not fulfilled in the applications of the Laplace transform. Very often, one only has a finite set of data and one wants to recover an estimate of the inverse Laplace function from that. We propose a fitting model of data. More precisely, given a finite set of measurements on the real axis, arising from an unknown Laplace transform function, we construct a dth degree generalized polynomial smoothing spline, where d = 2m − 1, such that internally to the data interval it is a dth degree polynomial complete smoothing spline minimizing a regularization functional, and outside the data interval, it mimics the Laplace transform asymptotic behavior, i.e. it is a rational or an exponential function (the end behavior model), and at the boundaries of the data set it joins with regularity up to order m − 1, with the end behavior model. We analyze in detail the generalized polynomial smoothing spline of degree d = 3. This choice was motivated by the (ill)conditioning of the numerical computation which strongly depends on the degree of the complete spline. We prove existence and uniqueness of this spline. We derive the approximation error and give a priori and computable bounds of it on the whole real axis. In such a way, the generalized polynomial smoothing spline may be used in any real inversion algorithm to compute an approximation of the inverse Laplace function. Experimental results concerning Laplace transform approximation, numerical inversion of the generalized polynomial smoothing spline and comparisons with the exponential smoothing spline conclude the work.

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.

Numerical regularization of a real inversion formula based on the Laplace transform's eigenfunction expansion of the inverse function

We describe the numerical approximation of the inverse Laplace function based on the Laplace transforms eigenfunction expansion of the inverse function, in a real case. The error analysis allows us to introduce a regularization technique involving computable upper bounds of amplification factors of local errors introduced by the computational process. A regularized solution is defined as one which is obtained within the maximum attainable accuracy. Moreover the regularization parameter, that in this case coincides with the truncation parameter of the eigenfunction expansion, is dynamically computed by the algorithm itself in such a way that it provides the minimum of the global error bound.

Visitor Dynamics in a Cultural Heritage Scenario

We propose a biologically inspired mathematical model to simulate the personalized interactions of users with cultural heritage objects and spaces in the real case of an exhibition. The main idea is to measure the interests of a spectator with respect to an artwork by means of a model able to describe the users behavioural dynamics. In our approach, the user is assimilated to a computational neuron, and its interests are deduced by counting potential spike trains, generated by external currents. As an effort, we relies on an huge amount of log files that store visitors movements and interactions within a beautiful art exhibition named The Beauty or the Truth located in Naples, Italy. The technological tools deployed within the exhibition aim to create a novel metaphor stimulating user enjoyment and knowledge diffusion and the collected log files are useful data to analyse how such technology an influence and modify user behaviours. We also performed an experimental analysis exploiting clustering facilities to discover natural groups that reflect visiting styles. This is particularly suitable to provide the tuning of a heuristic classifier. The obtained results revealed to be particularly interesting also to understand other important aspects hidden in the data and unattended in our first analysis.

An error estimate of Gaussian recursive filter in 3Dvar problem

Computational kernel of the three-dimensional variational data assimilation (3D-Var) problem is a linear system, generally solved by means of an iterative method. The most costly part of each iterative step is a matrix-vector product with a very large covariance matrix having Gaussian correlation structure. This operation may be interpreted as a Gaussian convolution, that is a very expensive numerical kernel. Recursive Filters (RFs) are a well known way to approximate the Gaussian convolution and are intensively applied in the meteorology, in the oceanography and in forecast models. In this paper, we deal with an oceanographic 3D-Var data assimilation scheme, named OceanVar, where the linear system is solved by using the Conjugate Gradient (GC) method by replacing, at each step, the Gaussian convolution with RFs. Here we give theoretical issues on the discrete convolution approximation with a first order (1st-RF) and a third order (3rd-RF) recursive filters. Numerical experiments confirm given error bounds and show the benefits, in terms of accuracy and performance, of the 3-rd RF.

A GPU parallel implementation of the Local Principal Component Analysis overcomplete method for DW image denoising

We focus on the Overcomplete Local Principal Component Analysis (OLPCA) method, which is widely adopted as denoising filter. We propose a programming approach resorting to Graphic Processor Units (GPUs), in order to massively parallelize some heavy computational tasks of the method. In our approach, we design and implement a parallel version of the OLPCA, by using a suitable mapping of the tasks on a GPU architecture with the aim to investigate the performance and the denoising features of the algorithm. The experimental results show improvements in terms of GFlops and memory throughput.