Finite-time control of discrete-time linear systems: Analysis and design conditions

In this paper we deal with some finite-time control problems for discrete-time, time-varying linear systems. First we provide necessary and sufficient conditions for finite-time stability; these conditions require either the computation of the state transition matrix of the system or the solution of a certain difference Lyapunov inequality. Then we address the design problem. The proposed conditions allow us to find output feedback controllers which stabilize the closed loop system in the finite-time sense; all these conditions can be expressed in terms of LMIs and therefore are numerically tractable, as shown in the example included in the paper.     

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

Anonymization of moving objects databases by clustering and perturbation

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k−1 other individuals.     

A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm

The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In this paper, a two-objective evolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality. Compression is measured in terms of difference in percentage between the sizes of the original and compressed images, whereas quality is computed as mean squared error between the reconstructed and the original images. We discuss the application of the proposed approach to well-known benchmark images and show how the quantization tables determined by our method improve the performance of the JPEG algorithm with respect to the default tables suggested in Annex K of the JPEG standard.     

Exploration strategies based on multi-criteria decision making for searching environments in rescue operations

Some applications require autonomous robots to search an initially unknown environment for static targets, without any a priori information about environment structure and target locations. Targets can be human victims in search and rescue or materials in foraging. In these scenarios, the environment is incrementally discovered by the robots exploiting exploration strategies to move around in an autonomous and effective way. Most of the strategies proposed in literature are based on the idea of evaluating a number of candidate locations on the frontier between the known and the unknown portions of the environment according to ad hoc utility functions that combine different criteria. In this paper, we show some of the advantages of using a more theoretically-grounded approach, based on Multi-Criteria Decision Making (MCDM), to define exploration strategies for robots employed in search and rescue applications. We implemented some MCDM-based exploration strategies within an existing robot controller and we evaluated their performance in a simulated environment.     

The labeled maximum matching problem

Given a graph G where a label is associated with each edge, we address the problem of looking for a maximum matching of G using the minimum number of different labels, namely the labeled maximum matching problem. It is a relatively new problem whose application is related to the timetabling problem. We prove it is NP-complete and present four different mathematical formulations. Moreover, we propose an exact algorithm based on a branch-and-bound approach to solve it. We evaluate the performance of our algorithm on a wide set of instances and compare our computational times with the ones required by CPLEX to solve the proposed mathematical formulations. Test results show the effectiveness of our procedure, that hugely outperforms the solver.     

An haptic-based immersive environment for shape analysis and modelling

Currently, the design of aesthetic products is a process that requires a set of activities where digital models and physical mockups play a key role. Typically, these are modified (and built) several times before reaching the desired design, increasing the development time and, consequently, the final product cost. In this paper, we present an innovative design environment for computer-aided design (CAD) surface analysis. Our system relies on a direct visuo-haptic display system, which enables users to visualize models using a stereoscopic view, and allows the evaluation of sectional curves using touch. Profile curves are rendered using an haptic device that deforms a plastic strip, thanks to a set of actuators, to reproduce the curvature of the shape co-located with the virtual model. By touching the strip, users are able to evaluate shape characteristics, such as curvature or discontinuities (rendered using sound), and to assess the surface quality. We believe that future computer-aided systems (CAS)/CAD systems based on our approach will contribute in improving the design process at industrial level. Moreover, these will allow companies to reduce the product development time by reducing the number of physical mockups necessary for the product design evaluation and by increasing the quality of the final product, allowing a wider exploration and comparative evaluation of alternatives in the given time.     

Analysis and synthesis of attractive quantum Markovian dynamics

We propose a general framework for investigating a large class of stabilization problems in Markovian quantum systems. Building on the notions of invariant and attractive quantum subsystem, we characterize attractive subspaces by exploring the structure of the invariant sets for the dynamics. Our general analysis results are exploited to assess the ability of open-loop Hamiltonian and output-feedback control strategies to synthesize Markovian generators which stabilize a target subsystem, subspace, or pure state. In particular, we provide an algebraic characterization of the manifold of stabilizable pure states in arbitrary finite-dimensional Markovian systems, that leads to a constructive strategy for designing the relevant controllers. Implications for stabilization of entangled pure states are addressed by example.