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Chiari I Malformation (CM-I) is a disorder characterized by herniation of the cerebellar tonsils through the foramen magnum. The choice of the optimal surgical management of CM-I is still an open problem, as there are no guidelines for the most suitable approach in symptomatic patients. We assessed the possibility of predicting the optimal intervention on the basis of morphological properties of the brain, posterior fossa and cerebellum. The boundaries of these regions were estimated by non-rigid registration of test sagittal magnetic resonance images (MRI) over an atlas. Different features characterizing the estimated regions were extracted and used to develop Naive Bayes classifiers. Classification was tested on a database of 30 patients, which were either healthy or pathological, with either mild or severe manifestation of CM-I. The best cross-validated classifier selected input features related to the shape and relative dimension of different brain regions (specifically, cerebellum and posterior fossa), the possible presence and dimension of the hernia and the regularity of the boundary of the cerebellum. The best classification accuracy (tested with a leave-one-out approach) was about 83%. These promising results suggest investigating further the possibility of defining the surgical treatment of CM-I on the basis of morphometric parameters from sagittal MRIs.

Spontaneous activity of neural networks depends on their stage of development. Computational performances of a network increase when the maturation leads to a self-organized criticality. Thus, an increasing complexity in the behavior of the network is expected when it enters in this developmental stage, called critical state. We tested this hypothesis investigating with a Micro-Electrodes Array of 60 electrodes a neuronal culture that during maturation exhibited first a subcritical and then a critical state. We found that in the critical state the local complexity (measured in terms of Approximate Entropy) was larger than in subcritical conditions ($\mathbf{mean}\pm \mathbf{std}$, ApEn about $\mathbf{1.03}+\mathbf{0.10},\mathbf{0.77}+\mathbf{0.18}$ in critical and sub-critical states, respectively; differences statistically significant), but only if the embedding dimension is at least 3 and the tolerance is fixed (we considered it equal to 1 ms, which is close to the characteristic time of neural communications).

The acceptance of wireless sensor networks (WSN) has increased greatly due to their comprehensive capabilities. Since WSNs are generally battery-powered networks, reducing energy consumption is critical to improve their lifetime and, in turn, their performance and reliability. Recently, smart processing, especially neural networks, has been employed to efficiently manage the power consumed by Wireless Sensor Networks (WSN). Data driven approaches and, in particular, data reduction schemes can reduce the energy spent for communication by judicious selection of the time in which specific sensors of the network are interrogated. In this paper, a multi-layer perceptron (MLP) is used to decide on the data samples required. To justify the usefulness of our idea, we conduct an experiment for effective monitoring of environmental conditions. Results show that our method reduces the number of required samples while not menacing the accuracy needed for practical purposes.

Quality assessment and standardization of the property of the final product is fundamental in food industry. Coffee particle granulometry and density are continuously monitored during coffee beans grinding and grinders are controlled by operators in order to keep coffee particle granulometry within specific thresholds. In this work, a neural system is used to learn how to control two grinders used for coffee production at LAVAZZA factory, obtaining average control error of the order of a few µm. The results appear promising for the future development of an automatic decision support system.

The reliable detection of water and ice over road surfaces is an important issue in improving the traffic safety and in reducing the costs for maintenance, particularly during winter. A low-cost capacitive sensor for the estimation of road conditions is studied. A simulation model was developed to investigate the capacitance of the sensor when air, water, or ice is covering its surface and to assess the effect of the variation of an environmental temperature or of the thickness of water or ice. An algorithm for the estimation of the state of the sensor (dry, wet, or icy) was developed based on the results of the simulations, which indicated that the time derivative of the estimated capacitance provided optimal information. The accuracy and reliability of the estimates provided by the sensor were assessed in laboratory experiments, placing more sensors in a climatic chamber and investigating the estimated state of the sensors and the timing of the identification of wet-icy or icy-wet transitions. Reliable estimates were obtained by all the sensors, with a dispersion of the transition times on the order of a few minutes. The sensor was also investigated in the field. Two sensors (one of which was bituminized) were embedded in a road pavement to monitor continuously the road surface condition for a month. Both sensors provided indications in line with the environmental conditions, identifying properly the icy condition and indicating the wet state of the road during both rain and fog. Thus, the sensor is suggested as a feasible tool for monitoring the road conditions to support information systems improving the security and efficient maintenance of roads during winter.

The issue of air quality is now a major concern for many citizens worldwide. Local air quality forecasting can be made on the basis of meteorological variables and air pollutants concentration time series. We propose an adaptive filter technique based on an artificial neural network (ANN) to make 24-hours maximal daily ozone-concentrations forecasts.

This study analytically addresses the problem of neuromuscular electrical stimulation for a planar, multilayer, anisotropic model of a physiological tissue (referred to as volume conductor). Both conductivity and permittivity of the volume conductor are considered, including dispersive properties. The analytical solution is obtained in the 2- D Fourier transform domain, transforming in the planes parallel to the volume conductor surface. The model is efficient in terms of computational cost, as the solution is analytical (only numerical Fourier inversion is needed). It provides the current distribution in a physiological tissue induced by an electrical current delivered at the skin surface. Three representative examples of application of the model are considered. (1) The simulation of stimulation artefact during transcutaneous electrical stimulation and EMG detection. Only the effect of the volume conductor is considered, neglecting the other sources of artefact (such as the capacitive coupling between the stimulating and recording electrodes). (2) The simulation of the electrical current distribution within the muscle and the low-pass filter effect of the volume conductor on sinusoidal stimulation currents with different stimulation frequencies. (3) The estimation of the amplitude modulated current distribution within the muscle for interferential stimulation. The model is devoted to the simulation of neuromuscular stimulation, but the same method could be applied in other fields in which the estimation of the electrical current distribution in a medium induced by the injection of a current from the boundary of the medium is of interest.

A finite-element model for the generation of single fiber action potentials in a muscle undergoing various degrees of fiber shortening is developed. The muscle is assumed fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. In each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with a finite-element approach using the analytically derived conductivity tensor. Representative simulations of single fiber action potentials with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model provides a new tool for interpreting surface EMG signal features with changes in muscle geometry, as it happens during dynamic contractions.

Surface electromyographic (EMG) signal modeling has important applications in the interpretation of experimental EMG data. Most models of surface EMG generation considered volume conductors homogeneous in the direction of propagation of the action potentials. However, this may not be the case in practice due to local tissue inhomogeneities or to the fact that there may be groups of muscle fibers with different orientations. This study addresses the issue of analytically describing surface EMG signals generated by bi-pinnate muscles, i.e., muscles which have two groups of fibers with two orientations. The approach will also be adapted to the case of a muscle with fibers inclined in the depth direction. Such muscle anatomies are inhomogeneous in the direction of propagation of the action potentials with the consequence that the system can not be described as space invariant in the direction of source propagation. In these conditions, the potentials detected at the skin surface do not travel without shape changes. This determines numerical issues in the implementation of the model which are addressed in this work. The study provides the solution of the nonhomogenous, anisotropic problem, proposes an implementation of the results in complete surface EMG generation models (including finite-length fibers), and shows representative results of the application of the models proposed.