A Mechatronic Cardiovascular Simulation System for Jugular Venous Echo-Doppler Training

Echo-doppler examination of the jugular vessel is a powerful tool for the early diagnosis of cardiovascular disorders that can be further related to central nervous system diseases. Unfortunately, the ultrasound technique is strongly operator-dependent, so the quality of the scan, the accuracy of the measurement, and therefore the rapidity and robustness of the diagnosis reflect the degree of training. The paper presents the development of a mechatronic simulation system for improving the skill of novice physicians in echo-doppler procedures. The patient is simulated by a silicone manikin whose materials are designed to have a realistic ultrasound response. Two tubes allow blood-mimicking fluid to flow inside the manikin, simulating the hemodynamics of the internal jugular vein. The mechatronic system is designed for controlling the flow waveform, to reproduce several clinical cases of interest for diagnosis. The experiments investigate the accuracy of the echo-doppler measurements performed on the proposed system by novice operators using a real ultrasound scanner

Concurrent and Reactive Constraint Programming

The Italian Logic Programming community has given several contributions to the theory of Concurrent Constraint Programming. In particular, in the topics of semantics, verification, and timed extensions. In this paper we review the main lines of research and contributions of the community in this fiel

An algorithm computing the Pareto frontier in constraint satisfaction problems

Real-life problems often exhibit a multi-criteria structure: user requirements are many and possibly conflicting. In combinatorial optimization, criteria are functions, ranging on the set of possible solutions. A widely used approach suggests to compute a weighted sum of the different criteria. Unluckily, deciding the weights beforehand is not always straightforward; moreover, the weighted sum approach often provides extreme solutions, while the user usually prefers balanced solutions. In this paper, we propose an algorithm to compute Pareto optimal solutions in Constraint Satisfaction Problems. A solution is Pareto optimal if it is not possible an improvement in one criterion without worsening other criteria. The algorithm is complete, and can provide the whole set of Pareto optimal solutions. Since the set of Pareto optimal solutions can be huge and the algorithm needs to access them efficiently, we propose to arrange them in a suitable data structure, namely Point Quad-Trees. Experimental results show the effectiveness of the proposed method

University timetabling in ECLiPSe

This paper describes how the university timetabling problem is addressed in the Laurea course Ingegneria dell'Informazione (Information Engineering) for the University of Ferrara, Italy. The university timetabling problem is modelled as a Constraint Optimisation Problem and addressed with ECLiPSe, one of the leading Constraint Logic Programming languages

Improving the efficiency of euclidean TSP solving in constraint programming by predicting effective nocrossing constraints

The Traveling Salesperson Problem (TSP) is a well-known problem addressed in the literature through various techniques, including Integer Linear Programming, Constraint Programming (CP) and Local Search. Many real life instances belong to the subclass of Euclidean TSPs, in which the nodes to be visited are associated with points in the Euclidean plane, and the distance between them is the Euclidean distance. A well-known property of the Euclidean TSP is that no crossings can exist in an optimal solution. In a previous publication, we exploited this property to speedup the solution of Euclidean instances in CP, by imposing a number of so-called no-overlapping constraints. The number of imposed constraints is quadratic in the number of nodes of the TSP. In this work, we observe that not all the no-overlapping constraints are equally useful: by experimental analysis, some of them provide a speedup, while others only introduce overhead. We use a supervised machine learning approach on them to learn a binary classifier, with the objective to impose only those no-overlapping constraints that have been classified as effective. Preliminary experiments support the validity of the idea

Cost-based filtering for determining the Pareto frontier

Real life problems involve seldom only one criterion, but multiple criteria should be optimised at the same time. The criteria can even be conflicting each other, so the classical techniques used in single criteria optimization cannot be simply reused. Many ideas have been presented in the literature for addressing multiple criteria optimization problems; one of the ideas is to provide the so-called Pareto frontier, i.e., the set of solutions that are not dominated by other feasible solutions. The Pareto frontier provides a lot of information to the decision-maker, that could be used to select the best preferred solution. On the other hand, finding the Pareto frontier is a time-consuming task. In single-criteria optimization, integration of Constraint Programming and Operations Research techniques has often been a successful approach to tackle difficult problems. Information derived by a solver that handles the linear relaxation of the original problem (like lower bounds, reduced costs, dual solution) has been used to improve the bounding and pruning capabilities of a Constraint Programming solver. In this paper, we propose an approach that integrates linear programming in Constraint Programming to speedup the search process in multiple-criteria optimization. We extend a Constraint Programming algorithm for finding the Pareto frontier, integrate it with a linear solver that provides bounds and reduced costs. Promising preliminary experiments show the effectiveness of the approach