Integrating Sparse Learning-Based Feature Detectors into Simultaneous Localization and Mapping-A Benchmark Study.

Simultaneous localization and mapping (SLAM) is one of the cornerstones of autonomous navigation systems in robotics and the automotive industry. Visual SLAM (V-SLAM), which relies on image features, such as keypoints and descriptors to estimate the pose transformation between consecutive frames, is a highly efficient and effective approach for gathering environmental information. With the rise of representation learning, feature detectors based on deep neural networks (DNNs) have emerged as an alternative to handcrafted solutions.

Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods.

Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem.

Application of conditional robust calibration to ordinary differential equations models in computational systems biology: a comparison of two sampling strategies.

Mathematical modelling is a widely used technique for describing the temporal behaviour of biological systems. One of the most challenging topics in computational systems biology is the calibration of non-linear models; i.e.

CRA toolbox: software package for conditional robustness analysis of cancer systems biology models in MATLAB.

Background: In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters.

An approach for optimally extending mathematical models of signaling networks using omics data.

Mathematical modeling is a key process in Systems Biology and the use of computational tools such as Cytoscape for omics data processing, need to be integrated in the modeling activity. In this paper we propose a new methodology for modeling signaling networks by combining ordinary differential equation models and a gene recommender system, GeneMANIA. We started from existing models, that are stored in the BioModels database, and we generated a query to use as input for the GeneMANIA algorithm.

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.

Background: The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer.

Results: We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty.

Computational model of EGFR and IGF1R pathways in lung cancer: a Systems Biology approach for Translational Oncology.

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration.

An architecture for rapid prototyping of control schemes for artificial ventricles.

This paper presents an experimental system aimed at rapid prototyping of feedback control schemes for ventricular assist devices, and artificial ventricles in general. The system comprises a classical mock circulatory system, an actuated bellow-based ventricle chamber, and a software architecture for control schemes implementation and experimental data acquisition, visualization and storing. Several experiments have been carried out, showing good performance of ventricular pressure tracking control schemes.

A Kalman filtering approach to estimation of maximum ventricle elastance.

In this paper a method for estimating maximum ventricular elastance through an extended Kalman filter is proposed, based on measurement of ventricular volume and aortic pressure. The Kalman filter is particularly well suited to this task, since it produces an optimal estimate (in the sense that the error is statistically minimized) given noise corrupted data. The EKF model is derived from an electrical-analog model of the left ventricle and systemic load.