Alberto Landi

A discrete-time localization method for capsule endoscopy based on on-board magnetic sensing

Recent achievements in active capsule endoscopy have allowed controlled inspection of the bowel by magnetic guidance. Capsule localization represents an important enabling technology for such kinds of platforms. In this paper, the authors present a localization method, applied as first step in time-discrete capsule position detection, that is useful for establishing a magnetic link at the beginning of an endoscopic procedure or for re-linking the capsule in the case of loss due to locomotion. The novelty of this approach consists in using magnetic sensors on board the capsule whose output is combined with pre-calculated magnetic field analytical model solutions. A magnetic field triangulation algorithm is used for obtaining the position of the capsule inside the gastrointestinal tract. Experimental validation has demonstrated that the proposed procedure is stable, accurate and has a wide localization range in a volume of about 18 × 103 cm3. Position errors of 14 mm along the X direction, 11 mm along the Y direction and 19 mm along the Z direction were obtained in less than 27 s of elaboration time. The proposed approach, being compatible with magnetic fields used for locomotion, can be easily extended to other platforms for active capsule endoscopy.

Cuk converter global control via fuzzy logic and scaling factors

A successful implementation of a low-cost PI-fuzzy controller for a Cuk converter is presented in this paper. Properties of the proposed controller are robustness around the operating point, good performance of transient responses in changing load conditions and/or input voltage, and invariant dynamic performance in the face of varying output operating points. Simulations and experimental results have been obtained via a suitable variation of the scaling factors related to the input variables of the fuzzy controller.

Automatic nonlinear auto-tuning method for Hammerstein modeling of electrical drives

Accurate modeling of electrical drives for online testing is a relevant problem, because of their nonlinear behavior. Efficient modeling for simulation, performance evaluation, and testing must consider accurate as well as simple models. This paper proposes the application of auto-tune methods to identify equivalent Hammerstein models, where the nonlinear process is approximated by a static nonlinear element followed by a linear dynamic second or third-order model. The effectiveness of the presented procedure is first verified by simulation results, showing that Hammerstein models overcome the limitations inherent to small-signal linearizations. A standard implementation of such technique considers a relay adjustment for attempts in a heuristic way. In this paper, two innovations are proposed: the relay adjustment is automatically shifted and the method is applied for complex electric drives. Experimental results are shown in the case of a drive constituted by a DC/AC inverter supplying a single-phase induction motor and of a step-down chopper.

Innovative Solutions for Overhead Catenary-Pantograph System: Wire Actuated Control and Observed Contact Force

In this paper an innovative active pantograph for high-speed trains is proposed. The results presented are based on extensive simulation tests. The parameters used in the simulation are those of a real pantograph for high-speed trains: the pantograph model is modified by adding a wire actuation, in order to exert a constant contact force between the moving pantograph and the overhead contact wire. A wire-actuated control and contact force observers are proposed as effective solutions in the case of a possible implementation.

Functional Structure of Spontaneous Sleep Slow Oscillation Activity in Humans

Background During non-rapid eye movement (NREM) sleep synchronous neural oscillations between neural silence (down state) and neural activity (up state) occur. Sleep Slow Oscillations (SSOs) events are their EEG correlates. Each event has an origin site and propagates sweeping the scalp. While recent findings suggest a SSO key role in memory consolidation processes, the structure and the propagation of individual SSO events, as well as their modulation by sleep stages and cortical areas have not been well characterized so far. Methodology/Principal Findings We detected SSO events in EEG recordings and we defined and measured a set of features corresponding to both wave shapes and event propagations. We found that a typical SSO shape has a transition to down state, which is steeper than the following transition from down to up state. We show that during SWS SSOs are larger and more locally synchronized, but less likely to propagate across the cortex, compared to NREM stage 2. Also, the detection number of SSOs as well as their amplitudes and slopes, are greatest in the frontal regions. Although derived from a small sample, this characterization provides a preliminary reference about SSO activity in healthy subjects for 32-channel sleep recordings. Conclusions/Significance This work gives a quantitative picture of spontaneous SSO activity during NREM sleep: we unveil how SSO features are modulated by sleep stage, site of origin and detection location of the waves. Our measures on SSOs shape indicate that, as in animal models, onsets of silent states are more synchronized than those of neural firing. The differences between sleep stages could be related to the reduction of arousal system activity and to the breakdown of functional connectivity. The frontal SSO prevalence could be related to a greater homeostatic need of the heteromodal association cortices.

A Model Predictive Control Strategy Toward Optimal Structured Treatment Interruptions in Anti-HIV Therapy

In this paper, model predictive control (MPC) strategies are applied to the control of human immunodeficiency virus infection, with the final goal of implementing an optimal structured treatment interruptions protocol. The MPC algorithms proposed in this paper use a dynamic model recently developed in order to mimic both transient responses and ultimate behavior, and to describe accordingly the different effect of commonly used drugs in highly active antiretroviral therapy (HAART). Simulation studies show that the proposed methods achieve the goal of reducing the drug consumption (thus minimizing the severe side effects of HAART drugs) while respecting the desired constraints on CD4+ cells and free virions concentration. Such promising results are obtained with realistic assumptions of infrequent (possibly noisy) measurements of a subset of model state variables. Furthermore, the control objectives are achieved even in the presence of mismatch between the dynamics of true patients and that of the MPC model.

Artificial Neural Networks in the Outcome Prediction of Adjustable Gastric Banding in Obese Women

Background Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure. Methodology/Principal Findings The study population consisted of 172 obese women, with a mean±SD presurgical and postsurgical Body Mass Index (BMI) of 42.5±5.1 and 32.4±4.8 kg/m2, respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively). Conclusions/Significance ANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.

Phototube sensor for monitoring the quality of current collection on overhead electrified railways

Abstract A new measurement system able to detect the occurrence of arcing between the pantograph and the overhead contact line was studied and set up. The system uses photomultiplier tubes to measure the duration of the ultraviolet emission due to electric arcing. The major advantages of the system proposed are its non-invasivity with respect to the pantograph equipment, its reliability and its low cost. Correlation with data acquired during high-speed test runs from different sensors (e.g. currents measured in an equipotential wire between the front and the rear pantographs, images acquired from a TV camera), provided the opportunity to perform an efficient calibration and an analysis of selectivity of the phototube sensor. Data from the phototube sensor along with various other information lead to the definition of a reliable index for evaluating the quality of the current collection. The determination of such an index has primary relevance for maintenance activities, for computing the minimal thrusts to be applied to the pantograph at different speeds and for testing new materials for overhead contact wires and collector strips.

SINGULAR SPECTRUM ANALYSIS AND ADAPTIVE FILTERING ENHANCE THE FUNCTIONAL CONNECTIVITY ANALYSIS OF RESTING STATE fMRI DATA

Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.

ErpICASSO: A tool for reliability estimates of independent components in EEG event-related analysis

Independent component analysis and blind source separation methods are steadily gaining popularity for separating individual brain and non-brain source signals mixed by volume conduction in electroencephalographic data. Despite the advancements on these techniques, determining the number of embedded sources and their reliability are still open issues. In particular to date no method takes into account trial-to-trial variability in order to provide a reliability measure of independent components extracted in Event Related Potentials (ERPs) studies. In this work we present ErpICASSO, a new method which modifies a data-driven approach named ICASSO for the analysis of trials (epochs). In addition to ICASSO the method enables the user to estimate the number of embedded sources, and provides a quality index of each extracted ERP component by combining trial-to-trial bootstrapping and CCA projection. We applied ErpICASSO on ERPs recorded from 14 subjects presented with unpleasant and neutral pictures. We separated potentials putatively related to different systems and identified the four primary ERP independent sources. Standing on the confidence interval estimated by ErpICASSO, we were able to compare the components between neutral and unpleasant conditions. ErpICASSO yielded encouraging results, thus providing the scientific community with a useful tool for ICA signal processing whenever dealing with trials recorded in different conditions.