Abstract: A method and a system for calibrating a measuring arrangement on the basis of a 16-term error model determines a matrix (A) with measured scattering parameters (Sm) from different calibration standards (3) and with associated actual scattering parameters (Sa) of the calibration standards (3) and determines linear-in-T system errors (Ti) for the calibration of a network analyzer (1) by solving a linear equation system with the determined matrix (A). To solve the linear equation system, a first and a second linear-in-T system error (k, p) are freely selected in each case. With use of reciprocal calibration standards, the determined linear-in-T system errors are weighted with the freely selected first linear-in-T system error (Ti) or with a correct second linear-in-T system error pkor(k)) dependent upon the first linear-in-T system error (k).

Abstract: A method and a system for calibrating a measuring arrangement on the basis of a 16-term error model determines a matrix (A) with measured scattering parameters (Sm) from different calibration standards (3) and with associated actual scattering parameters (Sa) of the calibration standards (3) and determines linear-in-T system errors ({tilde over (T)}i) for the calibration of a network analyzer (1) by solving a linear equation system with the determined matrix (A). To solve the linear equation system, a first and a second linear-in-T system error (k,p) are freely selected in each case. With use of reciprocal calibration standards, the determined linear-in-T system errors are weighted with the freely selected first linear-in-T system error ({tilde over (T)}i) or with a correct second linear-in-T system error (pkor(k) dependent upon the first linear-in-T system error (k).

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.