Intel Corporation

Cloud Raxak, Inc.

Prasanna Mulgaonkar

Seshashayee Sankarshana Murthy

David M Durham

Ravi L Sahita

Hormuzd M Khosravi

Oscar Nestares

Scott Ettinger

Horst Haussecker

Yasser Rasheed

Michael P Dierks

Steve Orrin

Paul S Schmitz

Systems and methods for compute resource configuration, verification, and remediation are provided herein. An example method includes verifying compliance of an operating system and compute assets provisioned configured within a middleware of a computing device using a pre-defined configuration profile, the compliance being determined by comparison of run-time hardware and software attributes of the compute assets to the pre-defined configuration profile comprising hardware and software requirements for the client of a blockchain implementation.

Trust in block-chain implementations using compute resource configuration and verification systems and methods

Systems and methods for compute resource configuration, verification, and remediation are provided herein. An example method includes verifying compliance of an operating system and compute assets provisioned configured within a middleware of a computing device using a pre-defined configuration profile, the compliance being determined by comparison of run-time hardware and software attributes of the compute assets to the pre-defined configuration profile comprising hardware and software requirements for the client.

Compute resource configuration and verification systems and methods

In some embodiments, approaches may provide an out-of-band (OOB) agent to protect a platform. The OOB agent may be able to use non-TRS methods to measure and protect an in-band security agent. In some embodiments, a manageability engine can provide out of band connectivity to the in-band and out-of-band security agents and provide access to the system memory resources without having to rely on OS services. This can be used for a trusted anti-malware and remediation service.

Computer system and method with anti-malware

A result higher resolution (HR) image of a scene given multiple, observed lower resolution (LR) images of the scene is computed using a Bayesian estimation image reconstruction methodology. The methodology yields the result HR image based on a Likelihood probability function that implements a model for the formation of LR images in the presence of noise. This noise is modeled by a probabilistic, non-Gaussian, robust function. The image reconstruction methodology may be used to enhance the image quality of images or video captured using a low resolution image capture device. Other embodiments are also described and claimed.

Computing a higher resolution image from multiple lower resolution images using model-base, robust Bayesian estimation

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Saratoga, CA, United States of America

Prasanna Mulgaonkar