Abstract: Through-the-road (TTR) hybrid designs using control strategies such as an equivalent consumption minimization strategy (ECMS) or adaptive ECMS are implemented at the supplemental torque delivering electrically-powered drive axle (or axles) in a manner that follows operational parameters or computationally estimates states of the primary drivetrain and/or fuel-fed engine, but does not itself participate in control of the fuel-fed engine or primary drivetrain. Crowdsourced distributions of adaptations and/or selections of BSFC type data allow ECMS implementations (or other similar control strategies) employ efficiency curves for a particular paired-with fuel-fed engine and/or operating conditions and to improve overall efficiencies of the TTR hybrid configuration. In some cases, signatures are used to identify appropriate BSFC type data for crowdsourced dissemination.

Abstract: Through-the-road (TTR) hybrid designs using control strategies such as an equivalent consumption minimization strategy (ECMS) or adaptive ECMS are implemented at the supplemental torque delivering electrically-powered drive axle (or axles) in a manner that follows operational parameters or computationally estimates states of the primary drivetrain and/or fuel-fed engine, but does not itself participate in control of the fuel-fed engine or primary drivetrain. On vehicle adaptation of BSFC type data for paired-with fuel-fed engine allows an ECMS implementation (or other similar control strategy) to refine efficiency curves for the particular fuel-fed engine and/or operating conditions in a manner that can improve overall efficiencies of a TTR hybrid configuration.

Abstract: Through-the-road (TTR) hybrid designs using control strategies such as an equivalent consumption minimization strategy (ECMS) or an adaptive ECMS are implemented at the supplemental torque delivering electrically-powered drive axle (or axles) in a manner that follows operational parameters or computationally estimates states of the primary drivetrain and/or fuel-fed engine, but does not itself participate in control of the fuel-fed engine or primary drivetrain. BSFC type data particular to the paired-with fuel-fed engine allows an ECMS implementation (or other similar control strategy) to adapt to efficiency curves for the particular fuel-fed engine and to improve overall efficiencies of the TTR hybrid configuration.

Abstract: Systems and methods to control recapture and use of energy to provide an APU include a vehicle having an electrically powered drive axle to provide supplemental torque to the vehicle to supplement primary motive forces applied through a separate drivetrain powered by a fuel-fed engine of the vehicle. The vehicle further includes an energy store to supply the electrically powered drive axle with electrical power or receive energy recovered using the electrically powered drive axle. The vehicle also includes the APU coupled to receive electrical power from the energy store for stopover operation and without idling of the fuel-fed engine. Further, the vehicle includes a hybrid control system for managing, based on an estimated travel time to a stopover location, an SoC of the energy store while the vehicle travels over a roadway to provide a target SoC of the energy store when the vehicle arrives at the stopover location.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. Recommendations of candidate computing platforms may be based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: A through the road (TTR) hybridization strategy is proposed to facilitate introduction of hybrid electric vehicle technology in a significant portion of current and expected trucking fleets. In some cases, the technologies can be retrofitted onto an existing vehicle (e.g., a truck, a tractor unit, a trailer, a tractor-trailer configuration, at a tandem, etc.). In some cases, the technologies can be built into new vehicles. In some cases, one vehicle may be built or retrofitted to operate in tandem with another and provide the hybridization benefits contemplated herein. By supplementing motive forces delivered through a primary drivetrain and fuel-fed engine with supplemental torque delivered at one or more electrically-powered drive axles, improvements in overall fuel efficiency and performance may be delivered, typically without significant redesign of existing components and systems that have been proven in the trucking industry.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: This disclosure sets forth systems and methods for recommending candidate computing platforms for migration of data and data-related workload from an original computing platform. The systems and methods further describe determining recommendations of candidate computing platforms based on a comparison of key performance and utilization statistics of the original computing platform under a user-generated workload with candidate computing platforms under a synthetic workload. Key performance and utilization statistics may relate to CPU, memory, file I/O, network I/O, and database I/O operations on the respective computing platforms. The synthetic workload may be defined by parameters that simulate the key performance and utilization statistics of the original computing platform under the user-generated workload. Further, the synthetic workloads may be executed on individual candidate computing platforms to determine service level capabilities that are ultimately used to form the recommendation.

Abstract: Benchmarking of a cloud computing instance is performed by a benchmarking application via direct system calls and locally stored measures to lower impact on benchmarking. Furthermore, stored measures are uploaded to a server when benchmarking is not being performed, so as not to have the uploading impact measurement. The benchmarking is performed via an application profile comprising a plurality of benchmark indicia. Benchmarking indicia may be specific to 64-bit operating systems. Benchmarking indicia may be variable, in which a thread pool in the benchmarking application increases or decreases active threads based on the variance of the benchmarking indicia. In this way, a benchmarking application can simulate an application load not only by benchmarking indicia, but also by time.

Abstract: Systems and methods for selecting reception of video streams in an adaptive fashion, for example, from a multi-stream video source. In one implementation, reception of the video streams may be dynamically switched such that optimal bandwidth is selected in adaptive fashion using a set of video parameters, such as the size or dimensions of the viewing window, and/or a set of system and network parameters, to ascertain the optimal stream selection.

Abstract: Systems and methods for providing high-quality region of interest (HQ-ROI) viewing within an overall scene by enabling one or more HQ-ROIs to be viewed in a controllable fashion, as higher quality ‘windows-within-a-window’ of regions (spatial subsets) of a scene.

Abstract: A system and method for remediation of contaminated soil are provided. The system comprises a soil remediation cell of contaminated soil and a plurality of multi-functional conduits located within the contaminated soil. Each multi-functional conduit defines a reaction housing. The multi-functional conduits includes heating elements for introducing heat into the contaminated soil for volatilizing the contaminants located within the contaminated soil, without utilizing mechanically driven forced air. This 10 produces a contaminated vapor. Flow channels extending therethrough remove the contaminated vapor from within the soil remediation cell. A substantial portion of the contaminants in the contaminated vapor are destroyed in the reaction housing. A substantially non-contaminated vapor is produced in which at least about 80% by weight of the contaminants have been destroyed.

Abstract: A network switch including a central memory that stores device identification information, port numbers, control information, and packet data received at the ports of the switch. The memory includes a packet section that stores packet data and a device identification section that stores identification entries, where each entry corresponds to a network device coupled to a port of the switch. The switch includes a switch manager to control data flow between the ports and the central memory. Each of the identification entries includes a unique network address to identify one of the network devices and a port number to identify one of the network ports. Each of the identification entries is located within the central memory at a hash address derived by hashing the unique network address. Hash logic receives and hashes each network address to determine a hash address, which is used to access the identification entries. The memory is organized into a chain structure to enable quick access of entries.

Abstract: A network system that provides a scaleable and distributed architecture to enable a plurality of remote clients to access a local area network (LAN). The network system includes an access server that executes a network operating system (NOS), such as any standard NOS, to enable communication with the LAN, an access bus, an access device coupled to the access server and the access bus that cooperates with the NOS to establish a communication link between the access server and the clients, and at least one multiport device coupled to the access bus and coupled to one or more clients through corresponding wide area network (WAN) connections that cooperates with the access device to establish a communication link between clients and the access device. The access server is preferably implemented on an industry standard platform using industry standard components. The access bus is either a dedicated bus or a shared media bus, such as Ethernet.