Ut-battelle, Inc.

University of Tennessee Research Foundation

Tsinghua University

Ali Passian

Thomas George Thundat

Joel A Dawson

Laurene Tetard

Milton Nance Ericson

Thomas L Ferrell

Michael D Iannacone

Joel M Asiamah

Gong Gu

Varisara Tansakul

Srikanth B Yoginath

Gavin B Long

Vineet K Khullar

Rubye H Farahi

Robert C Jordan

Additive manufacturing's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of additive manufacturing devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is challenging. A probabilistic-model-based approach addresses this challenge.

Stealthy process attack detection for automated manufacturing

A system senses analytes through one or more sensors that detect or measure a physical characteristic. The one or more sensor generate a spectroscopic-data signal corresponding to the detection. An edge device communicatively couples the one or more sensors that communicatively couples a wide-area network coupling a cloud service. The edge device includes a data acquisition device that receives spectroscopic data signals from the one or more sensor and a processor that processes the spectroscopic-data signals to identify an analyte. The edge device also includes a transceiver that transmits data identifying the analytes to the cloud service.

Edge-computing with enhanced standoff sensors

A system and method (referred to as the system) detect infectious code. The system injects a repetitive software code that causes malware in a monitored device to render a detectable direct current power consumption profile. A guide wave generator generates a guide wave signal that establishes an observational window that is applied to data that represent a direct current source power consumption of the monitored device. An extraction device extracts a portion of the data that represent the direct current source power consumption of the monitored device. A deviation engine identifies the malware on the monitored device without processing data associated with a prior identification of the malware or identifying a source of the malware or identifying a location of the malware on the monitored device.

Rootkit detection system

A Surface Plasmon Scanning-Tunneling Chemical Mapping (SPSTM) system is disclosed that determines identification characteristics of a target material. An optical beam source launches an optical beam that propagates through a transparent optical element to a material layer to excite surface plasmons of the material layer. An optical probe with a nanometer-sized tip is positioned over a nanometer-sized region of the target material, which is positioned on the material layer, to measure a probe signal associated only with the surface plasmons that tunnel from the material layer through the nanometer-sized region of the target material and collected by the optical probe. An optical property analyzer is configured to determine at least one optical property associated with the nanometer-sized region based on the probe signal associated with the surface plasmons collected by the optical probe. The optical properties identify identification characteristics associated with the nanometer-sized region of the target material.

Surface plasmon scanning-tunneling chemical mapping (SPSTM) system

A method of analyzing a sample that includes applying a first set of energies at a first set of frequencies to a sample and applying, simultaneously with the applying the first set of energies, a second set of energies at a second set of frequencies, wherein the first set of energies and the second set of energies form a multi-mode coupling. The method further includes detecting an effect of the multi-mode coupling.

Mode-synthesizing atomic force microscopy and mode-synthesizing sensing

Mode synthesizing atomic force microscopy and mode-synthesizing sensing

Low energy light illumination and either a doped semiconductor surface or a surface-plasmon supporting surface are used in combination for manipulating a fluid on the surface in the absence of any applied electric fields or flow channels. Precise control of fluid flow is achieved by applying focused or tightly collimated low energy light to the surface-fluid interface. In the first embodiment, with an appropriate dopant level in the semiconductor substrate, optically excited charge carriers are made to move to the surface when illuminated. In a second embodiment, with a thin-film noble metal surface on a dispersive substrate, optically excited surface plasmons are created for fluid manipulation. This electrode-less optical control of the Marangoni effect provides re-configurable manipulations of fluid flow, thereby paving the way for reprogrammable microfluidic devices.

Microscale fluid transport using optically controlled marangoni effect

Growing community of inventors

Knoxville, TN, United States of America

Ali Passian