Roberto Gallea

Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids

In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.

Noise Filtering Using Edge-Driven Adaptive Anisotropic Diffusion

This paper presents a method aimed to noise removal in MRI (magnetic resonance imaging). We propose an improvement of Perona and Maliks anisotropic diffusion filter. In our schema, the diffusion equation of the filter has been modified to take into account the edges direction. This allows the filter to blur uniform areas, while it better preserves the edges. Both quantitative and qualitative evaluation is presented and the results are compared with other methods.

Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data

Abstract In this work a general framework for non-rigid 3D medical image registration is presented. It relies on two pattern recognition techniques: kernel regression and fuzzy c-means clustering. The paper provides theoretic explanation, details the framework, and illustrates its application to implement three registration algorithms for CT/MR volumes as well as single 2D slices. The first two algorithms are landmark-based approaches, while the third one is an area-based technique. The last approach is based on iterative hierarchical volume subdivision, and maximization of mutual information. Moreover, a high performance Nvidia CUDA based implementation of the algorithm is presented. The framework and its applications were evaluated with a number of tests, which show that the proposed approaches achieve valuable results when compared with state-of-the-art techniques. Additional assessment was taken by expert radiologists, providing performance feedback from the final user perspective.

Physical Metaphor for Streaming Media Retargeting

We here introduce an image/video retargeting method that operates arbitrary aspect ratios resizing achieved in real-time performances. Most of the literature retargeting approaches sacrifice real-time performances in behalf of quality. On the other hand, existing fast methods provide arguable results. We can obtain a valuable trade-off between effectiveness and efficiency. The method named Spring Simulation Retargeting (SSR) is mainly based on a physical springs-based simulation. The media are assumed as flexible objects composed of particles and springs with different local stiffness properties, related to the visual importance of the content. The variation of the object size generates elastic forces which determine a new arrangement of the particles, according to the elongation of their connected springs. The deformations are mostly introduced in regions where low importance content is present, while high saliency regions are preserved as desired. The proposed method is evaluated and compared both for images and videos, against several state of the art methods and a user study is taken to assess the results, showing the value of the approach.

Multi-modal image registration using fuzzy kernel regression

This paper presents a study aimed to the realization of a novel multiresolution registration framework. The transformation function is computed iteratively as a composition of local deformations determined by the maximization of mutual information. At each iteration, local transformations are joint together using fuzzy kernel regression. This technique represents the core of the mothod and its formally described from a probabilistic perspective. It avoids blocking artifacts and allows to keep the final deformation spatially congruent and smooth. Both qualitative and quantitative experimental results show that this approach is equally effective for registering datasets acquired from both single and multiple diagnostic modalities.

Effective and efficient interpolation for mutual information based multimodality elastic image registration

Mutual information (MI) is a popular similarity metric for multimodality image registration purpose. However, it is negatively influenced by artifacts due to interpolation effects. As a result, registration algorithms performance could be affected. In this paper a novel interpolation scheme is presented. It is both effective and efficient. Effective because it limits the presence of local maxima in the mutual information curve, efficient because it is simple to compute being based on simple and optimized distance measures. The method is validated and compared against other techniques both from performance and time complexity persepectives. Differently from other reference works, which perform tests using rigid transformations, method performance was measured using non-rigid parametric transformation. Experimental results show that the proposed scheme represents a good tradeoff between performance and computing requirements, making it suitable for actual elastic image registration purposes.

Pareto-optimal glowworm swarms optimization for smart grids management

This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.

A Java-based Wrapper for Wireless Communications

The increasing number of new applications for mobile devices in pervasive environments, do not cope with changes in the wireless communications. Developers of such applications have to deal with problems arising from the available wireless connections in the given environment. A middleware is a solution that allows to overcome some of these problems. It provides to the applications a set of functions that facilitate their development. In this paper we present a Java-based communication wrapper, called SmartTraffic, which allows programmers to seamlessly use TCP or UDP protocols over Bluetooth or any IP-based wireless network. Developers can use SmartTraffic within their Java applications, thus focusing on the application goals, and leaving out details about how it should interact with the available wireless connection.

Combining top-down and bottom-up visual saliency for firearms localization

Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless information and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the peoples face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms.

Automatic Aesthetic Photo Composition

A proper aesthetic composition of photographic content does result in an actual emotional response from the watcher. In this work we propose a fully automatic computational approach to photo composition. This method takes into account well-known and widely adopted aesthetic guidelines relative to picture content as a mean for guiding an optimization framework. The resulting composition is produced as the optimal combination of cropping and retargeting.