Abstract: A computer-implemented method is described for detecting, identifying and locating an object feature in a ferromagnetic object. At least one hardware processor executes program instructions to: define a planned scan trajectory for scanning the ferromagnetic object with a sensor array comprising a plurality of magnetometer sensors, measure magnetic fields of the ferromagnetic object with the sensor array along an actual scan trajectory at locations adjacent to the ferromagnetic material to produce object scanning data representing magnetic characteristics of the ferromagnetic object along the actual scan trajectory. The actual scan trajectory includes deviation motion of the scanning array from the planned scan trajectory. The deviation motion is then compensated for to identify and locate the object feature in the ferromagnetic object. The compensation includes adjusting the object scanning data for the deviation motion and/or using a feature model that reflects the deviation motion.

Abstract: Methods and systems enabling rapid application development, verification, and deployment requiring only knowledge of high level languages. Two aspects of the disclosed methods and systems are called Machine Intelligence and Learning for Graphic chip Accessibility (MILeGrA) and Machine Intelligence and Learning for Graphic chip Execution (MILeGrE). Using MILeGrA and MILeGrE, high-level language programmers do not need to learn complex coprocessor programming languages, but can still use coprocessors (e.g., GPU processors) to benefit from results-in-seconds big data capabilities through the translation of coprocessor-unaware code to coprocessor-aware code. Execution of such coprocessor-unaware code on coprocessors includes parsing the coprocessor-unaware code to generate intermediate code, analyzing the intermediate code to determine a model for coprocessor-aware code generation, and generating coprocessor-aware code based on the model using machine learning techniques.

Abstract: A system and method using magnetic sensing to non-intrusively and non-destructively characterize ferromagnetic material within infrastructure. The system includes sensors for measuring magnetic field gradients from a standoff distance adjacent to ferromagnetic material. The method includes using the system to measure magnetic fields, determining magnetic field gradients measured by a sensor array, and comparing measured and modeled or historical magnetic field gradients at the same or similar positions to identify differences caused by a phenomenon in the ferromagnetic material, and, in a particular embodiment, to recognize defects and developing defects.

Abstract: A computer-implemented method is described for detecting, identifying and locating an object feature in a ferromagnetic object. At least one hardware processor executes program instructions to: define a planned scan trajectory for scanning the ferromagnetic object with a sensor array comprising a plurality of magnetometer sensors, measure magnetic fields of the ferromagnetic object with the sensor array along an actual scan trajectory at locations adjacent to the ferromagnetic material to produce object scanning data representing magnetic characteristics of the ferromagnetic object along the actual scan trajectory. The actual scan trajectory includes deviation motion of the scanning array from the planned scan trajectory. The deviation motion is then compensated for to identify and locate the object feature in the ferromagnetic object.

Abstract: Methods and systems enabling rapid application development, verification, and deployment requiring only knowledge of high level languages. Two aspects of the disclosed methods and systems are called Machine Intelligence and Learning for Graphic chip Accessibility (MILeGrA) and Machine Intelligence and Learning for Graphic chip Execution (MILeGrE). Using MILeGrA and MILeGrE, high-level language programmers do not need to learn complex coprocessor programming languages, but can still use coprocessors (e.g., GPU processors) to benefit from results-in-seconds big data capabilities through the translation of coprocessor-unaware code to coprocessor-aware code. Execution of such coprocessor-unaware code on coprocessors includes parsing the coprocessor-unaware code to generate intermediate code, analyzing the intermediate code to determine a model for coprocessor-aware code generation, and generating coprocessor-aware code based on the model using machine learning techniques.

Abstract: A system and method using magnetic sensing to non-intrusively and non-destructively characterize ferromagnetic material within infrastructure. The system includes sensors for measuring magnetic field gradients from a standoff distance adjacent to ferromagnetic material. The method includes using the system to measure magnetic fields, determining magnetic field gradients measured by a sensor array, and comparing measured and modeled or historical magnetic field gradients at the same or similar positions to identify differences caused by a phenomenon in the ferromagnetic material, and, in a particular embodiment, to recognize defects and developing defects.

Abstract: A method and device are provided for multilingual speech recognition. In one example, a speech recognition method includes receiving a multilingual input speech signal, extracting a first phoneme sequence from the multilingual input speech signal, determining a first language likelihood score indicating a likelihood that the first phoneme sequence is identified in a first language dictionary, determining a second language likelihood score indicating a likelihood that the first phoneme sequence is identified in a second language dictionary, generating a query result responsive to the first and second language likelihood scores, and outputting the query result.

Abstract: A system and method using magnetic sensing to non-intrusively and non-destructively characterize ferromagnetic material within infrastructure. The system includes sensors for measuring magnetic field gradients from a standoff distance adjacent to ferromagnetic material. The method includes using the system to measure magnetic fields, determining magnetic field gradients measured by a sensor array, and comparing measured and modeled or historical magnetic field gradients at the same or similar positions to identify differences caused by a phenomenon in the ferromagnetic material, and, in a particular embodiment, to recognize defects and developing defects.

Abstract: A method of imaging an organism includes introducing a composite nanoparticle into a circulating fluid of an organism to form a circulating fluid mixture in the organism is provided. The composite nanoparticle comprises a core comprising at least one of a contrast agent and a magnetic material, and at least one layer of biocompatible material surrounding the core. The method further includes receiving an image of at least a portion of the organism where the circulating fluid has circulated, removing at least a portion of the circulating fluid mixture from the organism at a first rate, applying a magnetic field to the removed portion of the circulating fluid mixture to selectively remove the composite nanoparticle from the circulating fluid mixture and to produce a filtered fluid mixture, and returning the filtered fluid mixture to the circulating fluid of the organism at a second rate.

Abstract: A system and method using magnetic sensing to non-intrusively and non-destructively characterize ferromagnetic material within infrastructure. The system includes sensors for measuring magnetic field gradients from a standoff distance adjacent to ferromagnetic material. The method includes using the system to measure magnetic fields, determining magnetic field gradients measured by a sensor array, and comparing measured and modeled or historical magnetic field gradients at the same or similar positions to identify differences caused by a phenomenon in the ferromagnetic material, and, in a particular embodiment, to recognize defects and developing defects.