Enhanced fast digital integrator for magnetic measurements

An enhanced Fast Digital Integrator (eFDI) was prototyped to satisfy the new requirements arising from current on-field exploitation of the previous Fast Digital Integrator in magnetic measurements for particle accelerators at CERN. In particular, the prototype achieves improved performance in terms of offset (5 ppm on 10 V fullscale), self-calibration accuracy (1 ppm of residual error), and data throughput (100 MB/s), by simultaneously preserving high-level signal-to-noise and distortion ratio (SINAD 105 dB at 10 Hz). In this paper, initially, the specifications, the design solutions, and the main features of the eFDI are illustrated. Then, the experimental results of the metrological characterization are compared with the CERN state-of-the-art integrator FDI performance in order to highlight the achieved improvements

Energy chirp measurements by means of an RF deflector: a case study the gamma beam source LINAC at ELI-NP

RF Deflector (RFD) based measurements are widely used in high–brightness electron LINAC around the world in order to measure the ultra–short electron bunch length. The RFD provides a vertical kick to the particles of the electron bunch according to their longitudinal positions. In this paper, a measurement technique for the bunch length and other bunch proprieties, based on the usage of an RFD, is proposed. The basic idea is to obtain information about the bunch length, energy chirp, and energy spread from vertical spot size measurements varying the RFD phase, because they add contributions on this quantity. The case study is the Gamma Beam System (GBS), the Compton Source being built in the Extreme Light Infrastructure–Nuclear Physics (ELI–NP) facility. The ELEctron Generation ANd Tracking (ELEGANT) code is used for tracking the particles from RFD to the measurement screen

Digital Integrator for Fast Accurate Measurement of Magnetic Flux by Rotating Coils

A fast digital integrator (FDI) with dynamic accuracy and a trigger frequency higher than those of a portable digital integrator (PDI), which is a state-of-the-art instrument for magnetic measurements based on rotating coils, was developed for analyzing superconducting magnets in particle accelerators. Results of static and dynamic metrological characterization show how the FDI prototype is already capable of overcoming the dynamic performance of PDI as well as covering operating regions that used to be inaccessibl

A Flexible Software Framework for Magnetic Measurements at CERN: a Prototype for the new Generation of Rotating Coils

A new software platform named FFMM (Flexible Framework for Magnetic Measurements) is under development at CERN (European Organization for Nuclear Research) in cooperation with the University of Sannio. The FFMM is aimed at facing the new test requirements arising after the production series of the Large Hadron Collider magnets. In particular, the basic concepts of the FFMM, its architecture, and the experimental implementation of a demonstrator are illustrated in order to show how the quality requirements of software flexibility and scalability are met

Performance Analysis of a Fast Digital Integrator for Magnetic Field Measurements at CERN

A Fast Digital Integrator (FDI) has been designed at CERN for increasing performance of state-of-art instruments analyzing superconducting magnets in particle accelerators. In particular, in flux measurement, a bandwidth up to 50-100 kHz and an accuracy of 10 ppm has to be targeted. In this paper, basic concepts and architecture of the developed FDI are highlighted. Numerical metrological analysis of the instrument performance is shown, by focusing both on deterministic errors and on uncertainty in time and amplitude domains

Uncertainty Reduction Via Parameter Design of A Fast Digital Integrator for Magnetic Field Measurement

At European Centre of Nuclear Research (CERN), within the new Large Hadron Collider (LHC) project, measurements of magnetic flux with uncertainty of 10 ppm at a few of decades of Hz for several minutes are required. With this aim, a new Fast Digital Integrator (FDI) has been developed in cooperation with University of Sannio, Italy [1]. This paper deals with the final design tuning for achieving target uncertainty by means of experimental statistical parameter design

A Wearable Brain-Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0

This paper proposes a wearable monitoring system for inspection in the framework of Industry 4.0. The instrument integrates augmented reality (AR) glasses with a noninvasive single-channel brain-computer interface (BCI), which replaces the classical input interface of AR platforms. Steady-state visually evoked potentials (SSVEP) are measured by a single-channel electroencephalography (EEG) and simple power spectral density analysis. The visual stimuli for SSVEP elicitation are provided by AR glasses while displaying the inspection information. The real-time metrological performance of the BCI is assessed by the receiver operating characteristic curve on the experimental data from 20 subjects. The characterization was carried out by considering stimulation times from 10.0 down to 2.0 s. The thresholds for the classification were found to be dependent on the subject and the obtained average accuracy goes from 98.9% at 10.0 s to 81.1% at 2.0 s. An inspection case study of the integrated AR-BCI device shows encouraging accuracy of about 80% of lab values

Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces

A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces

High-wearable EEG-based distraction detection in motor rehabilitation

A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness

Wearable Brain-Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality

An instrument for remote control of the robot by wearable brain-computer interface (BCI) is proposed for rehabilitating children with attention-deficit/hyperactivity disorder (ADHD). Augmented reality (AR) glasses generate flickering stimuli, and a single-channel electroencephalographic BCI detects the elicited steady-state visual evoked potentials (SSVEPs). This allows benefiting from the SSVEP robustness by leaving available the view of robot movements. Together with the lack of training, a single channel maximizes the device's wearability, fundamental for the acceptance by ADHD children. Effectively controlling the movements of a robot through a new channel enhances rehabilitation engagement and effectiveness. A case study at an accredited rehabilitation center on ten healthy adult subjects highlighted an average accuracy higher than 83%, with information transfer rate (ITR) up to 39 b/min. Preliminary further tests on four ADHD patients between six- and eight-years old provided highly positive feedback on device acceptance and attentional performance