Nonsingular systems of generalized Sylvester equations: An algorithmic approach

We consider the uniqueness of solution (i.e., nonsingularity) of systems of r generalized Sylvester and ⋆-Sylvester equations with n×n coefficients. After several reductions, we show that it is sufficient to analyze periodic systems having, at most, one generalized ⋆-Sylvester equation. We provide characterizations for the nonsingularity in terms of spectral properties of either matrix pencils or formal matrix products, both constructed from the coefficients of the system. The proposed approach uses the periodic Schur decomposition and leads to a backward stable O(n3r) algorithm for computing the (unique) solution

Wireless Sensor Network Deployment for Monitoring Soil Moisture Dynamics at the Field Scale

AbstractWe describe the deployment of a Wireless Sensor Network (WSN), composed of 135 soil moisture and 27 temperature sensors, in an apple tree orchard of about 5000 m2, located in the municipality of Cles, a small town in the Alpine region, northeastern Italy. The orchard is divided into three parcels each one subjected to a different irrigation schedule. The objective of the present work is to monitor soil moisture dynamics in the top soil to a detail, in both space and time, suitable to analyze the interplay between soil moisture dynamics and plant physiology. The deployment consists of 27 locations (verticals) connected by a multi hop WSN, each one equipped with 5 soil moisture sensors deployed at the depths of 10, 20, 30, 50 and 80 cm, and a temperature sensor at the depth of 20 cm. The proposed monitoring system is based on totally independent sensor nodes, which allow both real time and historic data management and are connected through an input/output interface to a WSN platform. Meteorological data are monitored by a weather station located at a distance of approximately 100 m from the experimental site.Great care has been posed to calibration of the capacitance sensors, both in the laboratory, with soil samples, and on site, after deployment, in order to minimize the noise caused by small oscillations in the input voltage and uncertainty in the calibration curves. In this work we report the results of a preliminary analysis on the data collected during the growing season 2009. We observed that the WSN greatly facilitates the collection of detailed measurements of soil moisture, thereby increasing the amount of information useful for exploring hydrological processes, but they should be used with care since the accuracy of collected data depends critically on the capability of the system to maintain constant the input voltage and on the reliability of calibration curves. Finally, we studied the spatial and temporal distribution of soil moisture in all the irrigated parcels, and explored how different irrigation schedules influence orchard's production

A class of quasi-sparse companion pencils

In this paper, we introduce a general class of quasi-sparse potential companion pencils for arbitrary square matrix polynomials over an arbitrary field, which extends the class introduced in [B. Eastman, I.-J. Kim, B. L. Shader, K.N. Vander Meulen, Companion matrix patterns. Linear Algebra Appl. 436 (2014) 255-272] for monic scalar polynomials. We provide a canonical form, up to permutation, for companion pencils in this class. We also relate these companion pencils with other relevant families of companion linearizations known so far. Finally, we determine the number of different sparse companion pencils in the class, up to permutation.This work has been partially supported by theMinisterio de Economía y Competitividad of Spain through grants MTM2015-68805-REDT and MTM2015-65798-P

Verification and Compliance in Collaborative Processes

Evidently, COVID-19 has changed our lives and is likely to make a lasting impact on our economic development and our industry and services. With the ongoing process of digital transformation in industry and services, Collaborative Networks (CNs) is required to be more efficient, productive, flexible, resilient and sustainable according to change of situations and related rules applied afterwards. Although the CN area is relatively young, it requires the previous research to be extended, i.e. business process management from dealing with processes within a single organization into processes across different organizations. In this paper, we review current business process verification and compliance research. Different tools approaches and limitations of them are compared. The further research issues and potential solutions of business process verification and compliance check are discussed in the context of CNs

Wireless monitoring of heterogeneous parameters in complex museum scenario

Monitoring systems designed for museum scenarios are nowadays largely employed in order to monitor environmental parameters for artworks conservation and to control exhibitions in order to prevent and avoid critical events (e.g., theft, damages). The use of wireless sensor network technology allows the non-invasive integration of such monitoring functionalities in a great variety of complex museum scenarios, often hosted in historic buildings. The proposed system addresses the problem of monitoring multiple physical parameters of interest for museum curators, exploiting the advantages of a flexible, cooperative, and pervasive WSN architecture

Random Bad State Estimator to Address False Data Injection in Critical Infrastructures

Given their crucial role for a society and economy, an essential component of critical infrastructures is the Bad State Estimator (BSE), responsible for detecting malfunctions affecting elements of the physical infrastructure. In the past, the BSE has been conceived to mainly cope with accidental faults, under assumptions characterizing their occurrence. However, evolution of the addressed systems category consisting in pervasiveness of ICT-based control towards increasing smartness, paired with the openness of the operational environment, contributed to expose critical infrastructures to intentional attacks, e.g. exploited through False Data Injection (FDI). In the flow of studies focusing on enhancements of the traditional BSE to account for FDI attacks, this paper proposes a new solution that introduces randomness elements in the diagnosis process, to improve detection abilities and mitigate potentially catastrophic common-mode errors. Differently from existing alternatives, the strength of this new technique is that it does not require any additional components or alternative source of information with respect to the classic BSE. Numerical experiments conducted on two IEEE transmission grid tests, taken as representative use cases, show the applicability and benefits of the new solution

Solution Bundles of Markov Performability Models through Adaptive Cross Approximation

A technique to approximate solution bundles, i.e., solutions of a parametric model where parameters are treated as independent variables instead of constants, is presented for Markov models. Analyses based on an approximated solution bundle are more efficient than those that solve the model for all combinations of parameters' values separately. In this paper the idea is to properly adapt low rank tensor approximation techniques, and in particular Adaptive Cross Approximation, to the evaluation of performability attributes. Application on exemplary case studies confirms the advantages of the new solution technique with respect to solving the model for all time and parameters' combinations

Implicit Reward Structures for Implicit Reliability Models

A new methodology for effective definition and efficient evaluation of dependability-related properties is proposed. The analysis targets the systems composed of a large number of components, each one modeled implicitly through high-level formalisms, such as stochastic Petri nets. Since the component models are implicit, the reward structure that characterizes the dependability properties has to be implicit as well. Therefore, we present a new formalism to specify those reward structures. The focus here is on component models that can be mapped to stochastic automata with one or several absorbing states so that the system model can be mapped to a stochastic automata network with one or several absorbing states. Correspondingly, the new reward structure defined on each component's model is mapped to a reward vector so that the dependability-related properties of the system are expressed through a newly introduced measure defined starting from those reward vectors. A simple, yet representative, case study is adopted to show the feasibility of the method

TAPAS: a Tool for Stochastic Evaluation of Large Interdependent Composed Models with Absorbing States

TAPAS is a new tool for efficient evaluation of dependability and performability attributes of systems composed of many interconnected components. The tool solves homogeneous continuous time Markov chains described by stochastic automata network models structured in submodels with absorbing states. The measures of interest are defined by a reward structure based on submodels composed through transition-based synchronization. The tool has been conceived in a modular and flexible fashion, to easily accommodate new features. Currently, it implements an array of state-based solvers that addresses the state explosion problem through powerful mathematical techniques, including Kronecker algebra, Tensor Trains and Exponential Sums. A simple, yet representative, case study is adopted, to present the tool and to show the feasibility of the supported methods, in particular frommemory consumption point of view