c469f6f4d489de4b7c80 · Machine Learning in a Quality Managed RF Measurement Workflow

{
  "title_en": "Machine Learning in a Quality Managed RF Measurement Workflow",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2019-07-06",
  "date_original": "July 6, 2019",
  "url": "https://www.nist.gov/publications/machine-learning-quality-managed-rf-measurement-workflow",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Aric Sanders , John Bass, Arpita Bhutani, Mary A. Ho, Jim Booth",
  "abstract_excerpt_en": "Advances in artificial intelligence, or more specifically machine learning, have made it possible for computers to recognize patterns as well or better than humans. The process of quality management in radio-frequency measurements is an arduous one that",
  "id": "c469f6f4d489de4b7c80",
  "sources": [
    "NIST Publications"
  ],
  "source_records": [
    {
      "title_en": "Machine Learning in a Quality Managed RF Measurement Workflow",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2019-07-06",
      "date_original": "July 6, 2019",
      "url": "https://www.nist.gov/publications/machine-learning-quality-managed-rf-measurement-workflow",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Aric Sanders , John Bass, Arpita Bhutani, Mary A. Ho, Jim Booth",
      "abstract_excerpt_en": "Advances in artificial intelligence, or more specifically machine learning, have made it possible for computers to recognize patterns as well or better than humans. The process of quality management in radio-frequency measurements is an arduous one that"
    }
  ],
  "translation_state": "none"
}