An actuated larynx phantom for pre-clinical evaluation of droplet-based reflex-stimulating laryngoscopes

The laryngeal adductor reflex (LAR) is an important protective function of the larynx to prevent aspiration and potentially fatal aspiration pneumonia by rapidly closing the glottis. Recently, a novel method for targeted stimulation and evaluation of the LAR has been proposed to enable non-invasive and reproducible LAR performance grading and to extend the understanding of this reflexive mechanism. The method relies on the laryngoscopically controlled application of accelerated water droplets in association with a high-speed camera system for LAR stimulation site and reflex onset latency identification. Prototype laryngoscopes destined for this method require validation prior to extensive clinical trials. Furthermore, demonstrations using a realistic phantom could increase patient compliance in future clinical settings. For these purposes, a model of the human larynx including vocal fold actuation for LAR simulation was developed in this work. The combination of image processing based on a custom algorithm and individual motorization of each vocal fold enables spatio-temporal droplet impact detection and controlled vocal fold adduction. To simulate different LAR pathologies, the current implementation allows to individually adjust the reflex onset latency of the ipsi- and contralateral vocal fold with respect to the automatically detected impact location of the droplet as well as the maximum adduction angle of each vocal fold. An experimental study of the temporal offset between desired and observed LAR onset latency due to image processing was performed for three average droplet masses based on highspeed recordings of the phantom. Median offsets of 100, 120 and 128 ms were found (n=16). This offset most likely has a multifactorial cause (image processing delay, inertia of the mechanical components, droplet motion). The observed offset increased with increasing droplet mass, as fluid oscillations after impact may have been detected as motion. In future work, alternative methods for droplet impact detection could be explored and the observed offset could be used for compensation of this undesirable delay

Stereo Laryngoscopic Impact Site Prediction for Droplet-Based Stimulation of the Laryngeal Adductor Reflex

The laryngeal adductor reflex (LAR) is a vital reflex of the human larynx. LAR malfunctions may cause life-threatening aspiration events. An objective, noninvasive, and reproducible method for LAR assessment is still lacking. Stimulation of the larynx by droplet impact, termed Microdroplet Impulse Testing of the LAR (MIT-LAR), may remedy this situation. However, droplet instability and imprecise stimulus application thus far prevented MIT-LAR from gaining clinical relevance. We present a system comprising two alternative, custom-built stereo laryngoscopes, each offering a distinct set of properties, a droplet applicator module, and image/point cloud processing algorithms to enable a targeted, droplet-based LAR stimulation. Droplet impact site prediction (ISP) is achieved by droplet trajectory identification and spatial target reconstruction. The reconstruction and ISP accuracies were experimentally evaluated. Global spatial reconstruction errors at the glottal area of (0.3±0.3) mm and (0.4±0.3) mm and global ISP errors of (0.9±0.6) mm and (1.3±0.8) mm were found for a rod lens-based and an alternative, fiberoptic laryngoscope, respectively. In the case of the rod lens-based system, 96% of all observed ISP error values are inferior to 2 mm; a value of 80% was found with the fiberoptic assembly. This contribution represents an important step towards introducing a reproducible and objective LAR screening method into the clinical routine

Intelligent systems for welding process automation

This paper presents and evaluates the concept and implementation of two distinct multi-sensor systems for the automated manufacturing based on parallel hardware. In the most sophisticated implementation, 12 processors had been integrated in a parallel multi-sensor system. Some specialized nodes implement an Artificial Neural Network, used to improve photogrammetry-based computer vision, and Fuzzy Logic supervision of the sensor fusion. Trough the implementation of distributed and intelligent processing units, it was shown that parallel architectures can provide significant advantages compared to conventional bus-based systems. The paper concludes with the comparison of the main aspects of the transputer and the DSP-based implementation of sensor guided robots

The IFMIF-DONES remote handling control system: Experimental setup for OPC UA integration

The devices used to carry out Remote Handling (RH) manipulation tasks in radiation environments address requirements that are significantly different from common robotic and industrial systems due to the lack of repetitive operations and incompletely specified control actions. This imposes the need of control with human-in -the-loop operations. These RH systems are used on facilities such PRIDE, CERN, ESS, ITER or IFMIF-DONES, the reference used for this work. For the RH system is crucial to provide high availability, robustness against radiation, haptic devices for teleoperation and dexterous operation, and smooth coordination and integration with the centralized control room. To achieve this purpose is necessary to find the best approach towards a standard control framework capable of providing a standard set of functionalities, tools, interfaces, communications, and data formats to the different types of mechatronic devices that are usually considered for Remote Handling tasks. This previous phase of homogenization is not considered in most facilities, which leads towards a costly integration process during the commissioning phase of the facility.In this paper, an approach to the IFMIF-DONES RH Control framework with strong standard support based on protocols such as OPC UA has been described and validated through an experimental setup. This test bench includes a set of physical devices (PLC, conveyor belt and computers) and a set of OPC UA compatible software tools, configured and operable from any node of the University of Granada network. This proof-of-concept mockup provides flexibility to modify the dimension and complexity of the setup by using new virtual or physical devices connected to a unique backbone. Besides, it will be used to test different aspects such as control schemes, failure injection, network modeling, predictive maintenance studies, operator training on simulated/ real scenarios, usability or ergonomics of the user interfaces before the deployment. In this contribution, the results are described and illustrated using a conveyor belt set-up, a small but representative reference used to validate the RH control concepts here proposed.European Union via the Euratom Research and Training Programme 101052200 - EUROfusio

Automated Intelligent Real-Time System For Aggregate Classification

This research focuses on developing an intelligent real-time classification system called NeuralAgg. Penyelidikan ini memfokuskan untuk membina sistem pengkelasan pintar secara masa nyata dipanggil NeuralAgg