SPECT and PET serve as molecular imaging techniques and in vivo biomarkers for brain metastases

Nuclear medicine techniques (single photon emission computerized tomography, SPECT, and positron emission tomography, PET) represent molecular imaging tools, able to provide in vivo biomarkers of different diseases. To investigate brain tumours and metastases many different radiopharmaceuticals imaged by SPECT and PET can be used. In this review the main and most promising radiopharmaceuticals available to detect brain metastases are reported. Furthermore the diagnostic contribution of the combination of SPECT and PET data with radiological findings (magnetic resonance imaging, MRI) is discussed

Performance Oriented Adaptive Architectures with Guaranteed Bounds

While adaptive control has been used in numerous applications to achieve given system stabilization or command following criteria, the ability to obtain a predictable transient performance is a challenging problem when there is no a priori knowledge about system uncertainties (e.g., their upper bounds and/or domains). In order to address this problem, a new method is presented in [1, 2] utilizing artificial basis functions in the update law of an adaptive control design. This approach is predicated on a gradient minimization procedure and achieves a predictable transient performance without inducing oscillations in the system response as the constant gain due to the nature of this minimization approach is judiciously increased. However, selection of this gain is problem dependent and may need to be adjusted for each different design. To address this problem, we present a new approach which has an ability to auto-tune an adaptive control design with artificial basis functions employed when the controlled system is about to violate a given design constraint on error dynamics (i.e., only when it is necessary). In particular, our approach is based on a controller architecture that allows the assignment of a priori known (user-defined) transient performance bounds. These bounds are constructed through a restricted potential function approach [3] that yields to an error dependent gain to adjust system performance for time instants when it is required to meet given design criteria. In addition to the theoretical results based on Lyapunov stability arguments highlighting transient performance of an uncertain system that stays within a priori given performance bounds, an illustrative example is provided to demonstrate the efficacy of the proposed framework

A Data-Driven Slip Estimation Approach for Effective Braking Control under Varying Road Conditions

The performances of braking control systems for robotic platforms, e.g., assisted and autonomous vehicles, airplanes and drones, are deeply influenced by the road-tire friction experienced during the maneuver. Therefore, the availability of accurate estimation algorithms is of major importance in the development of advanced control schemes. The focus of this paper is on the estimation problem. In particular, a novel estimation algorithm is proposed, based on a multi-layer neural network. The training is based on a synthetic data set, derived from a widely used friction model. The open loop performances of the proposed algorithm are evaluated in a number of simulated scenarios. Moreover, different control schemes are used to test the closed loop scenario, where the estimated optimal slip is used as the set-point. The experimental results and the comparison with a model based baseline show that the proposed approach can provide an effective best slip estimation

Analysis and Design of Adaptive Control Systems with Unmodeled Input Dynamics Via Multiobjective Convex Optimization

A challenging problem for adaptive control systems is the accurate characterization of the transient response in the presence of dynamic uncertainties such as a partially known actuator. Considering an actuator modelled as a first order filter with an uncertain control effectiveness and using a projection mechanism for parameters adaptation, we show that the tracking error dynamics behaves as a linear system perturbed by bounded uncertainties. This brings the advantage that the stability analysis can be cast in terms of LMIs so that convex optimization tools can be used for analysis and design. In this framework we propose a mixed linear/adaptive control strategy whose parameters are computed via a convex Mult objective optimization in order to ensure, at the same time, the evolution of the error within a minimal size invariant set, while the added linear gain is minimized. A Numerical example is provided to demonstrate the efficacy of the method

Experimental Analysis of Neural Approaches for Synthetic Angle-of-Attack Estimation

Synthetic sensors enable flight data estimation without devoted physical sensors. Within modern digital avionics, synthetic sensors can be implemented and used for several purposes such as analytical redundancy or monitoring functions. The angle-of-attack, measured at air data system level, can be estimated using synthetic sensors exploiting several solutions, e.g. model-based, data-driven and model-free state observers. In the class of data-driven observers, multilayer perceptron neural networks are widely used to approximate the input-output mapping angle-of-attack function. Dealing with experimental flight test data, the multilayer perceptron can provide reliable estimation even though some issues can arise from noisy, sparse and unbalanced training domain. An alternative is offered by regularisation networks, such as radial basis function, to cope with training domain based on real flight data. The present work's objective is to evaluate performances of a single layer feed-forward generalised radial basis function network for AoA estimation trained with a sequential algorithm. The proposed analysis is performed comparing results obtained using a multilayer perceptron network adopting the same training and validation data

Verifiable Frequency-limited Adaptive Control Performance based on Linear Matrix Inequalities

Adaptive controllers are often criticized for the lack of clear and easy verification procedures that relate design parameters to time domain performance especially during transients. For this reason, it is a challenge to rigorously certify existing adaptive control algorithms. To that end, we propose a validation framework where stability and performance requirements are formulated in terms of Linear Matrix Inequalities. This brings the advantage that adaptive controller design and verification can be analyzed and optimized via the solution of a convex optimization whose objective is to guarantee the evolution of the error components within an a-priori specified domain. This approach was cast to verify the performance of the recently introduced Frequency-limited adaptive control scheme. A detailed case study is presented to show the efficacy of the proposed verification architecture

Interval Fuzzy Model for Robust Aircraft IMU Sensors Fault Detection

This paper proposes a data-based approach for a robust fault detection (FD) of the inertial measurement unit (IMU) sensors of an aircraft. Fuzzy interval models (FIMs) have been introduced for coping with the significant modeling uncertainties caused by poorly modeled aerodynamics. The proposed FIMs are used to compute robust prediction intervals for the measurements provided by the IMU sensors. Specifically, a nonlinear neural network (NN) model is used as central prediction of the sensor response while the uncertainty around the central estimation is captured by the FIM model. The uncertainty has been also modelled using a conventional linear Interval Model (IM) approach; this allows a quantitative evaluation of the benefits provided by the FIM approach. The identification of the IMs and of the FIMs was formalized as a linear matrix inequality (LMI) optimization problem using as cost function the (mean) amplitude of the prediction interval and as optimization variables the parameters defining the amplitudes of the intervals of the IMs and FIMs. Based on the identified models, FD validation tests have been successfully conducted using actual flight data of a P92 Tecnam aircraft by artificially injecting additive fault signals on the fault free IMU readings