Benedetto Intrigila, Igor Melatti, Alberto Tofani, and Guido Macchiarelli. "Computational models of myocardial endomysial collagen arrangement." Computer Methods and Programs in Biomedicine 86, no. 3 (2007): 232–244. Elsevier North-Holland, Inc.. ISSN: 0169-2607. DOI: 10.1016/j.cmpb.2007.03.004.
Abstract: Collagen extracellular matrix is one of the factors related to high passive stiffness of cardiac muscle. However, the architecture and the mechanical aspects of the cardiac collagen matrix are not completely known. In particular, endomysial collagen contribution to the passive mechanics of cardiac muscle as well as its micro anatomical arrangement is still a matter of debate. In order to investigate mechanical and structural properties of endomysial collagen, we consider two alternative computational models of some specific aspects of the cardiac muscle. These two models represent two different views of endomysial collagen distribution: (1) the traditional view and (2) a new view suggested by the data obtained from scanning electron microscopy (SEM) in NaOH macerated samples (a method for isolating collagen from the other tissue). We model the myocardial tissue as a net of spring elements representing the cardiomyocytes together with the endomysial collagen distribution. Each element is a viscous elastic spring, characterized by an elastic and a viscous constant. We connect these springs to imitate the interconnections between collagen fibers. Then we apply to the net of springs some external forces of suitable magnitude and direction, obtaining an extension of the net itself. In our setting, the ratio forces magnitude /net extension is intended to model the stress /strain ratio of a microscopical portion of the myocardial tissue. To solve the problem of the correct identification of the values of the different parameters involved, we use an artificial neural network approach. In particular, we use this technique to learn, given a distribution of external forces, the elastic constants of the springs needed to obtain a desired extension as an equilibrium position. Our experimental findings show that, in the model of collagen distribution structured according to the new view, a given stress /strain ratio (of the net of springs, in the sense specified above) is obtained with much smaller (w.r.t. the other model, corresponding to the traditional view) elasticity constants of the springs. This seems to indicate that by an appropriate structure, a given stiffness of the myocardial tissue can be obtained with endomysial collagen fibers of much smaller size.
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Giuseppe Della Penna, Benedetto Intrigila, Igor Melatti, Enrico Tronci, and Marisa Venturini Zilli. "Finite Horizon Analysis of Markov Chains with the Mur$\varphi$ Verifier." In Correct Hardware Design and Verification Methods, 12th IFIP WG 10.5 Advanced Research Working Conference, CHARME 2003, L'Aquila, Italy, October 21-24, 2003, Proceedings, edited by D. Geist and E. Tronci, 394–409. Lecture Notes in Computer Science 2860. Springer, 2003. ISSN: 3-540-20363-X. DOI: 10.1007/978-3-540-39724-3_34.
Abstract: In this paper we present an explicit disk based verification algorithm for Probabilistic Systems defining discrete time/finite state Markov Chains. Given a Markov Chain and an integer k (horizon), our algorithm checks whether the probability of reaching an error state in at most k steps is below a given threshold. We present an implementation of our algorithm within a suitable extension of the Mur$\varphi$ verifier. We call the resulting probabilistic model checker FHP-Mur$\varphi$ (Finite Horizon Probabilistic Mur$\varphi$). We present experimental results comparing FHP-Mur$\varphi$ with (a finite horizon subset of) PRISM, a state-of-the-art symbolic model checker for Markov Chains. Our experimental results show that FHP-Mur$\varphi$ can handle systems that are out of reach for PRISM, namely those involving arithmetic operations on the state variables (e.g. hybrid systems).
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. On Model Based Synthesis of Embedded Control Software. Vol. abs/1207.4474. CoRR, Technical Report, 2012.
Abstract: Many Embedded Systems are indeed Software Based Control Systems (SBCSs), that is control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for control software. Given the formal model of a plant as a Discrete Time Linear Hybrid System and the implementation specifications (that is, number of bits in the Analog-to-Digital (AD) conversion) correct-by-construction control software can be automatically generated from System Level Formal Specifications of the closed loop system (that is, safety and liveness requirements), by computing a suitable finite abstraction of the plant.
With respect to given implementation specifications, the automatically generated code implements a time optimal control strategy (in terms of set-up time), has a Worst Case Execution Time linear in the number of AD bits $b$, but unfortunately, its size grows exponentially with respect to $b$. In many embedded systems, there are severe restrictions on the computational resources (such as memory or computational power) available to microcontroller devices.
This paper addresses model based synthesis of control software by trading system level non-functional requirements (such us optimal set-up time, ripple) with software non-functional requirements (its footprint). Our experimental results show the effectiveness of our approach: for the inverted pendulum benchmark, by using a quantization schema with 12 bits, the size of the small controller is less than 6% of the size of the time optimal one.
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software. Vol. abs/1210.2276. CoRR, Technical Report, 2012.
Abstract: Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software.
Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis.
In this paper, we present a map-reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis.
We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Control Software Visualization." In Proceedings of INFOCOMP 2012, The Second International Conference on Advanced Communications and Computation, 15–20. ThinkMind, 2012. ISSN: 978-1-61208-226-4.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Undecidability of Quantized State Feedback Control for Discrete Time Linear Hybrid Systems." In Theoretical Aspects of Computing – ICTAC 2012, edited by A. Roychoudhury and M. D'Souza, 243–258. Lecture Notes in Computer Science 7521. Springer Berlin Heidelberg, 2012. DOI: 10.1007/978-3-642-32943-2_19.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Linear Constraints as a Modeling Language for Discrete Time Hybrid Systems." In Proceedings of ICSEA 2012, The Seventh International Conference on Software Engineering Advances, 664–671. ThinkMind, 2012.
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "On Model Based Synthesis of Embedded Control Software." In Proceedings of the 12th International Conference on Embedded Software, EMSOFT 2012, part of the Eighth Embedded Systems Week, ESWeek 2012, Tampere, Finland, October 7-12, 2012, edited by Ahmed Jerraya and Luca P. Carloni and Florence Maraninchi and John Regehr, 227–236. ACM, 2012. Notes: Techreport version can be found at arxiv.org. DOI: 10.1145/2380356.2380398.
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Automatic Control Software Synthesis for Quantized Discrete Time Hybrid Systems." In Proceedings of the 51th IEEE Conference on Decision and Control, CDC 2012, December 10-13, 2012, Maui, HI, USA, 6120–6125. IEEE, 2012. Notes: Techreport version can be found at http://arxiv.org/abs/1207.4098. DOI: 10.1109/CDC.2012.6426260.
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Giuseppe Della Penna, Daniele Magazzeni, Alberto Tofani, Benedetto Intrigila, Igor Melatti, and Enrico Tronci. "Automatic Synthesis of Robust Numerical Controllers." In Icas '07, 4. IEEE Computer Society, 2007. ISSN: 0-7695-2859-5. DOI: 10.1109/CONIELECOMP.2007.59.
Abstract: A major problem of numerical controllers is their robustness, i.e. the state read from the plant may not be in the controller table, although it may be close to some states in the table. For continuous systems, this problem is typically handled by interpolation techniques. Unfortunately, when the plant contains both continuous and discrete variables, the interpolation approach does not work well. To cope with this kind of systems, we propose a general methodology that exploits explicit model checking in an innovative way to automatically synthesize a (time-) optimal numerical controller from a plant specification and apply an optimized strengthening algorithm only on the most significant states, in order to reach an acceptable robustness degree. We implemented all the algorithms within our CGMurphi tool, an extension of the well-known CMurphi verifier, and tested the effectiveness of our approach by applying it to the well-known truck and trailer obstacles avoidance problem.
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