Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

none9noThe pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.openPrist Mariorosario; Monteriu' Andrea; Pallotta Emanuele; Cicconi Paolo; Freddi Alessandro; Giuggioloni Federico; Caizer Eduard; Verdini Carlo; Longhi SauroPrist, Mariorosario; Monteriu', Andrea; Pallotta, Emanuele; Cicconi, Paolo; Freddi, Alessandro; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Saur

Sensory perceptual metrics: design and application of biologically based methods for the assessment of systemic cortical alterations

A large number of neurological disorders (neurodegenerative, neurodevelopmental or trauma induced) are difficult to diagnose or assess, thus limiting treatment efficacy. Existing solutions and products attempting to fill this gap are costly, extremely slow, often invasive, and in many cases fail to definitively (and quantitatively) diagnose or assess treatment. Our innovative low-cost sensory testing device and accompanying software package can be used to non-invasively assess the central nervous system (CNS) health status in minutes for numerous patient populations. The somatosensory system is ideally suited for the design of a CNS diagnostic system. First, the organization of the system is such that adjacent skin regions project to adjacent cortical regions (i.e., it is somatotopic). Second, ambient environmental noise in the system can be easily controlled (i.e., it is less likely that a patient will be exposed to distracting tactile input than auditory or visual input). Third, the somatosensory system is the only sensory system that is highly integrated with the pain system, and this is often an important aspect of a patient's diagnosis. The diagnostic system delivers a battery of somatosensory-based tests that are conducted rapidly, much like an eye exam with verbal feedback. Neuro-adaptation, functional connectivity (e.g. cortical synchronization), and feed-forward inhibition are just a few of the cortical mechanisms that can be quantified using somatosensory testing protocols. Many of these protocols leverage tactile illusions which act as confounds on top of a basic somatosensory test, allowing each subject to serve as his or her own control. Design and validation of the perceptual metrics was/is accomplished via correlative studies that compare non-invasive observations of human sensory percepts with non-human primate neurophysiological studies. Additional validation has been demonstrated through the use of a magnet-compatible version of the device in both functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) studies. Based on pilot data (currently an ontological database of roughly 3000 subjects), the system can be used to enable clinicians to have a much better view of a patient's CNS health status.Doctor of Philosoph