Abstract: The system can include a set of joints, a controller, and a model engine; and can optionally include a support structure and an end effector. Joints can include: a motor, a transmission mechanism, an input sensor, and an output sensor. The system can enable articulation of the plurality of joints.

Abstract: Methods and Systems are provided for performing early onset diagnostics for clogged injectors using fuel trims collected from the Engine Management System and evaluated in different operating modes of the engine. Example implementations use fuel trims that can estimate the contrast in combustion ratio ? across different operating modes defined on the engine speed and calculated engine load plane. Features are extracted, then classified using machine learning to output a diagnosis according to different levels of fuel injector clogging.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: A method (400) for providing continuous wireless connectivity for monitoring a patient throughout a healthcare facility, including: providing (40) 2) an in-room access point in a room defined at least in part by shielding walls and an access point outside the room and connecting the in-room access point and the access point; (ii) transmitting (404), by a communication unit of a first source device, information related to the patient; (iii) receiving (406), by the access point outside the room, information from the first source device; (iv) receiving (408), by the in-room access point, additional information from the first source device; (v) combining (410), the information and the additional information from the first source device to generate a digital data stream; and (vi) transmitting (412), by an access point communication unit of the access point system, the digital data stream to an information system of the healthcare facility.

Abstract: A common Data Analytics Service (DAS) at service layer is designed to use underlying existing/future data analytics technologies or tools and provide them to service layer entities that need those data analytics operations with uniform access approach. A general operation framework/interface design is used for enabling DAS and the operation details within DAS. Related procedures for interacting with DAS, including the new parameters in service layer request/response messages can be used.

Abstract: Systems and methods for generating artificial intelligence (AI) writing assistance include receiving a writing prompt to be used by an AI writing engine from a writing assistance client. The writing prompt is processed to provide indication of relevant user content items that can be referenced in the writing prompt. The content and metadata for any content items referenced in the writing prompts is retrieved and aggregated with the writing prompt to generate a request for writing assistance for the AI writing engine. Once writing feedback is received, the feedback is provided to the writing assistance wherein it can be displayed in the UI for acceptance by a user.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a writing assistant configured to provide suggestions for improving the textual content. The textual content is analyzed by one or more NLP models configured to receive the textual content as an input and to provide candidate suggestions for correcting errors and/or otherwise improving the textual content. The candidate suggestions are associated with a suggestion category, and a determination is made by an autocorrection model whether autocorrection is appropriate for the suggestion category. The textual content is autocorrected based on those candidate suggestions for which autocorrection is appropriate while the remainder of the candidate suggestions may be presented to the user for approval prior to applying the candidate suggestions to the textual content.

Abstract: A common Data Analytics Service (DAS) at service layer is designed to use underlying existing/future data analytics technologies or tools and provide them to service layer entities that need those data analytics operations with uniform access approach. A general operation framework/interface design is used for enabling DAS and the operation details within DAS. Related procedures for interacting with DAS, including the new parameters in service layer request/response messages can be used.

Abstract: An example method includes: executing, by application analysis software executing in the virtualized computing system, process discovery agents on the VMs; receiving, at the application analysis software from the process discovery agents, process metadata describing processes executing on the VMs; generating signatures for the processes based on the process metadata; determining components of an application based on the signatures; determining components of an application based on the signatures; identifying, for a first component of the components, a component-specific metadata collector; executing, by the application analysis software, the component-specific metadata collector on a first VM of the VMs; and receiving, at the application analysis software from the component-specific metadata collector, custom metadata further describing a first process of the processes associated with the first component.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.

Abstract: An example method of determining application topology in a virtualized computing system having a cluster of hosts with hypervisors supporting virtual machines (VMs), the method including: executing agents on the VMs to obtain process metadata describing processes executing in the VMs; receiving, at an application analysis system, the process metadata; receiving network flow metadata from the agents on the VMs and/or from a network analyzer in the virtualized computing system; parsing the network flow metadata to identify a source VM and a destination VM of the VMs; relating the network flow metadata to portions of the process metadata associated with the source and the destination VMs to identify a source process and a destination process; and generating a topology of a source component connected to a destination component, the source component identifying the source VM and the source process, the destination component identifying the destination VM and the destination process.

Abstract: Sub-resolution measurement of fuel in fuel tank. In an embodiment, data, which comprise discrete fuel levels in a fuel tank and fuel injection rates for an internal combustion engine, are received. The fuel injection rates are integrated over a traversed distance to produce a fuel consumption series, and the discrete fuel levels are clustered over the traversed distance to produce a fuel level series. The fuel consumption and fuel level series are synchronized into a model that is used to generate sub-resolution measurements of fuel levels between the discrete fuel levels.

Abstract: An example method includes: executing, by application analysis software executing in the virtualized computing system, process discovery agents on the VMs; receiving, at the application analysis software from the process discovery agents, process metadata describing processes executing on the VMs; generating signatures for the processes based on the process metadata; determining components of an application based on the signatures; determining components of an application based on the signatures; identifying, for a first component of the components, a component-specific metadata collector; executing, by the application analysis software, the component-specific metadata collector on a first VM of the VMs; and receiving, at the application analysis software from the component-specific metadata collector, custom metadata further describing a first process of the processes associated with the first component.

Abstract: Sub-resolution measurement of fuel in fuel tank. In an embodiment, data, which comprise discrete fuel levels in a fuel tank and fuel injection rates for an internal combustion engine, are received. The fuel injection rates are integrated over a traversed distance to produce a fuel consumption series, and the discrete fuel levels are clustered over the traversed distance to produce a fuel level series. The fuel consumption and fuel level series are synchronized into a model that is used to generate sub-resolution measurements of fuel levels between the discrete fuel levels.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.

Abstract: A modular and distributed architecture for data stream processing and analysis is described to incorporate data stream analytics capabilities, called Data Stream Analytics Service (DSAS) in the IoT/M2M service layer. Each service layer node hosting DSAS can be split into two independent modules, Stream Forwarder and Stream Analytics Engine. Stream Forwarder is a light weight processing modules that can be responsible for data preprocessing and routing. Stream Analytics Engine is responsible for performing actual analytics on the data stream. Separating the two functionalities enables the service layer nodes to efficiently distribute stream analytics tasks across multiple nodes.

Abstract: A common Data Analytics Service (DAS) at service layer is designed to use underlying existing/future data analytics technologies or tools and provide them to service layer entities that need those data analytics operations with uniform access approach. A general operation framework/interface design is used for enabling DAS and the operation details within DAS. Related procedures for interacting with DAS, including the new parameters in service layer request/response messages can be used.