Abstract: A method for allocating computing resources for a vehicle includes determining an optimal task configuration for a computing task based at least in part on a task constraint of the computing task. The method further may include determining a criticality level of the computing task based at least in part on the optimal task configuration and the task constraint of the computing task. The method further may include routing the computing task to one of a plurality of remote server systems based at least in part on the criticality level of the computing task.

Abstract: A resource sharing marketplace platform includes a back-office server including a controller adapted to run a plurality of integrated systems and a wireless communication module in communication with the controller, wherein, the resource sharing marketplace platform is adapted to receive a request from a user, collect data from a plurality of acceptable service providers, and provide a response to the user.

Abstract: A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A system for CI/CD of AI/ML based components includes a cloud infrastructure which receives multiple new requirements from a vehicle designer or a developer, with the new requirements adapted for artificial intelligence/machine learning (AI/ML) based components of a vehicle. A dataset is provided. A metamorphic relations (MR) module receives input information from the dataset and sends MR information to the dataset. A components requirements database includes the new requirements in addition to existing requirements for the AI/ML based components. The MR module also receives components requirements data from the components requirements database and sends the MR information to the components requirements database. An AI/ML algorithm analyzes the input information from the dataset and prepares an updated component dataset.

Abstract: A resource sharing marketplace platform includes a back-office server in wireless communication with a controller adapted to run a plurality of integrated systems, and is adapted to authenticate identification of the user and facilitate user log-in, receive a request from a user, establish dynamic parameters related to the request, break down the request from the user into a plurality of serviceable micro-queries by decomposing at least one request for multivariate data into a plurality of requests for multivariate and univariate data, and develop a contract including key clauses and reward constraints, identify, from a group of registered service providers, a sub-group of registered service providers that are capable of providing data related to at least one micro-query, and send a request for data to the sub-group, collect data from a plurality of acceptable service providers, and repack the individual data to provide a response to the user.

Abstract: A computing system including a cooperative system architecture for cataloging, informing, sharing, and managing engineering assets within an organization includes a user management subsystem for creating a registered user profile associated with a specific user of the computing system. The registered user profile includes user metadata that provides identifying characteristics of the specific user. The computing system includes an asset management subsystem including an asset repository for recording one or more engineering assets. The asset management subsystem modifies the user metadata of the registered user profile in response to the specific user recording an engineering asset in the asset repository. The computing system includes a test management subsystem including a test repository for recording one or more test bill of materials.

Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: generate a resource query, the resource query comprising a request for resources for offloading at least one computation process and including at least one termination condition. The processor is also programmed to transmit the resource query to at least one remote resource provider and initiate an offload operation to offload the at least one computation process based on a received resource request.

Abstract: A resource sharing marketplace platform includes a back-office server including a controller adapted to run a plurality of integrated systems and a wireless communication module in communication with the controller, wherein, the resource sharing marketplace platform is adapted to receive a request from a user, collect data from a plurality of acceptable service providers, and provide a response to the user.

Abstract: Methods and apparatus are provided for selecting a computational platform. The method includes collecting platform feature data for each of a plurality of existing computational platforms in a particular class of computational platforms. The platform feature data for each of the plurality of existing computational platforms includes a plurality of design parameters and a performance measurement for the computational platform to complete a benchmarking software component. The method further includes training a mathematical model using the collected platform feature data to devise a predictive model for predicting a predicted performance measurement for a computational platform in the particular class of computational platforms when provided with design parameters, predicting, with the predictive model, the predicted performance measurement of a new computational platform that is not available for testing, and selecting a computational platform using the predicted performance measurement.

Abstract: A vehicle, control system for the vehicle and method of operating the vehicle. The control system includes a target processor, a binary executable program and an instruction set simulator (ISS). The binary executable program is compiled to run on a legacy processor in order to operate the vehicle. The ISS is configured to run on the target processor and emulate operation of the legacy processor. The binary executable program runs on the target processor via the ISS in order to operate the vehicle.

Abstract: A system and method for configuring a filter object for a controller area network is disclosed. The method includes determining, by a processor, a plurality of message identifiers of messages that are to be captured by a filter object. The method also includes performing factorization of a function that represents the plurality of message identifiers to generate a simplified function. The method also includes configuring at least one filter object based on the generated simplified function.

Abstract: A system and method for configuring a filter object for a controller area network is disclosed. The method includes determining, by a processor, a plurality of message identifiers of messages that are to be captured by a filter object. The method also includes performing factorization of a function that represents the plurality of message identifiers to generate a simplified function. The method also includes configuring at least one filter object based on the generated simplified function.

Abstract: Methods and apparatus are provided for selecting a computational platform. The method includes collecting platform feature data for each of a plurality of existing computational platforms in a particular class of computational platforms. The platform feature data for each of the plurality of existing computational platforms includes a plurality of design parameters and a performance measurement for the computational platform to complete a benchmarking software component. The method further includes training a mathematical model using the collected platform feature data to devise a predictive model for predicting a predicted performance measurement for a computational platform in the particular class of computational platforms when provided with design parameters, predicting, with the predictive model, the predicted performance measurement of a new computational platform that is not available for testing, and selecting a computational platform using the predicted performance measurement.

Abstract: Managing task execution in a multi-core processor may be achieved by employing a spinlock and a multi-processor priority ceiling protocol. The spinlock may be employed to effect a dynamically enforceable mutual exclusion constraint. The multi-processor priority ceiling protocol may be employed to effect the dynamically enforceable mutual exclusion constraint to synchronize a plurality of tasks executing in the first and second processing cores of the multi-core processor.

Abstract: A method for managing task execution in a multi-core processor includes employing a spinlock to effect a dynamically enforceable mutual exclusion constraint and employing a multi-processor priority ceiling protocol to effect the dynamically enforceable mutual exclusion constraint to synchronize a plurality of tasks executing in the first and second processing cores of the multi-core processor.

Abstract: A statistical approach to estimating software execution times is implemented by preparing a model of a target processing device, correlating the software to be estimated to benchmark programs used in the preparation of the model, and then applying the software to be estimated to the model. The model is developed by determining the actual execution times of the benchmark programs, determining a number of virtual instructions in the benchmark programs and determining a predictor equation that correlates the virtual instructions to the actual execution time. The predictor equation is determined by a linear regression technique that includes a correlation analysis of the virtual instructions, removal of highly correlated instructions, and a combination of stepwise linear regression and multiple linear regression to determine the predictor equation.

Abstract: A statistical approach to estimating software execution times is implemented by preparing a model of a target processing device, correlating the software to be estimated to benchmark programs used in the preparation of the model, and then applying the software to be estimated to the model. The model is developed by determining the actual execution times of the benchmark programs, determining a number of virtual instructions in the benchmark programs and determining a predictor equation that correlates the virtual instructions to the actual execution time. The predictor equation is determined by a linear regression technique that includes a correlation analysis of the virtual instructions, removal of highly correlated instructions, and a combination of stepwise linear regression and multiple linear regression to determine the predictor equation. A 2-sample t-test is utilized to evaluate whether the software to be capable of being estimated by the model developed from the benchmark programs.