The performance of modern robotic manipulators has allowed research in recent years, for the development of fast automated non-destructive testing (NDT) of complex geometries. Contemporary robots are well suited for their accuracy and flexibility when adapting to new tasks. Several robotic inspection prototype systems and a number of commercial products have been created around the world. This paper describes the latest progress of a new phase of the research applied to a composite aerospace component of size 1 by 3 metres. A multi robot flexible inspection cell was used to take the fundamental research and the feasibility studies to higher technology readiness levels, all set for future industrial exploitation. The robot cell was equipped with high accuracy and high payload robots, mounted on 7 metre tracks, and an external rotary axis.A robotically delivered photogrammetry technique was first used to assess the position of the components placed within the robot working envelope and their deviation to CAD. Offline programming was used to generate a scan path for phased array ultrasonics testing (PAUT) which was implemented using high data rate acquisition from a conformable wheel probe.Real-time robot path-correction, based on force-torque control (FTC), was deployed to achieve the optimum ultrasonic coupling and repeatable data quality. New communication software was developed that enabled the simultaneous control of the multiple robots performing different tasks and the reception of accurate positional feedback positions. All aspects of the system were controlled through a purposely developed graphic user interface that enabled the flexible use of the unique set of hardware resources, the data acquisition, visualisation and analysis.Acknowledgement:

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one's movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such realtime BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.

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Background Children with Down syndrome have poorer functional and sensory skills compared to children with typical development. Virtual reality (VR) training could help improve these skills. Moreover, transcranial direct current stimulation (tDCS) has achieved promising results in terms of enhancing the effects of physical and sensory therapy by modulating cortical excitability. Methods/design Two investigations are proposed: (1) an observational study with a convenience sample consisting of children with Down syndrome (group 1—cognitive age of 6 to 12 years according to the Wechsler Abbreviated Scale of Intelligence) and children with typical development 6 to 12 years of age (group 2). Both groups will undergo evaluations on a single day involving a three-dimensional analysis of upper limb movements, an analysis of muscle activity of the biceps and brachial triceps muscles and an analysis of visuospatial and cognitive-motor variables. (2) Analysis of clinical intervention: a pilot study and clinical trial will be conducted involving individuals with Down syndrome (cognitive age of 6 to 12 years according to the Wechsler Abbreviated Scale of Intelligence). The sample will be defined after conducting a pilot study with the same methodology as that to be used in the main study. The participants will be randomly allocated to two groups: An experimental group submitted to anodal tDCS combined with a VR game and a manual motor task and a control group submitted to sham tDCS combined with a VR game and a manual motor task. The training protocol will involve 10 sessions of active or sham tDCS during memory and motor task games. Three 20-min sessions will be held per week for a total of 10 sessions. Evaluations will be performed on three different occasions: pre-intervention, post-intervention (after 10 sessions) and follow-up (1 month after the intervention). Evaluations will consist of analyses of electroencephalographic signals, electromyographic signals of the biceps and triceps brachii, and the three-dimensional reconstruction of the reaching movement. The results will be analyzed statistically with the significance level set at 5% (p ≤ 0.05). Discussion The optimization of the results obtained with virtual reality training is believed to be related to the interactive experience with a wide range of activities and scenarios involving multiple sensory channels and the creation of exercises, the intensity of which can be adjusted to the needs of children. Therefore, the proposed study aims to complement the literature with further information on tDCS and VR training considering different variables to provide the scientific community with clinical data on this combination of interventions. Trial registration Brazilian Clinical Trials Registry (REBEC) protocol number RBR-43pk59 registered on 2019 March 27 https://ensaiosclinicos.gov.br/rg/RBR-43pk59 and Human Research Ethics Committee number 3.608.521 approved on 2019 September 30. Protocol version 2021 October 20. Any changes to the protocol will be reported to the committees and approved. Informed consent will be obtained from all participants by the clinical research coordinator and principal investigator.