{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:48:49Z","timestamp":1762004929864,"version":"build-2065373602"},"reference-count":11,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T00:00:00Z","timestamp":1507852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000141410719"],"award-info":[{"award-number":["N000141410719"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Microphone sensor systems provide information that may be used for a variety of applications. Such systems generate large amounts of data. One concern is with microphone failure and unusual values that may be generated as part of the information collection process. This paper describes methods and a MATLAB graphical interface that provides rapid evaluation of microphone performance and identifies irregularities. The approach and interface are described. An application to a microphone array used in a wind tunnel is used to illustrate the methodology.<\/jats:p>","DOI":"10.3390\/s17102329","type":"journal-article","created":{"date-parts":[[2017,10,13]],"date-time":"2017-10-13T11:34:09Z","timestamp":1507894449000},"page":"2329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality"],"prefix":"10.3390","volume":"17","author":[{"given":"Robert","family":"Vasta","sequence":"first","affiliation":[{"name":"Department of Mathematics, Virginia Tech, Blacksburg, VA 24061, USA"}]},{"given":"Ian","family":"Crandell","sequence":"additional","affiliation":[{"name":"Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA"}]},{"given":"Anthony","family":"Millican","sequence":"additional","affiliation":[{"name":"Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA"}]},{"given":"Leanna","family":"House","sequence":"additional","affiliation":[{"name":"Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA"}]},{"given":"Eric","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,13]]},"reference":[{"key":"ref_1","unstructured":"Rouse, M. (2015, March 23). Sensor Analytics. Available online: http:\/\/internetofthingsagenda.techtarget.com\/definition\/sensor-analytics."},{"key":"ref_2","unstructured":"Chen, I. (2015, May 29). Sensor Data Analytics - Unlocking Value in \u2018Big Data\u2019. Available online: http:\/\/www.eetimes.com\/author.asp?section_id=36&doc_id=1326715."},{"key":"ref_3","unstructured":"Vensi Inc. (2015, October 16). Sensors Data Analytics in the IoT World. Available online: http:\/\/blueapp.io\/blog\/sensors-data-analytics-in-the-iot-world\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, L., Liu, D., Zhang, Y., and Peng, Y. (2016). Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines. Sensors, 16.","DOI":"10.3390\/s16050623"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1016\/j.microrel.2015.06.076","article-title":"Entropy-based sensor selection for condition monitoring and prognostics of aircraft engine","volume":"55","author":"Liu","year":"2015","journal-title":"Microelectron. Reliab."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pang, J., Liu, D., Liao, H., Peng, Y., and Peng, X. (2014, January 22\u201325). Anomaly detection based on data stream monitoring and prediction with improved Gaussian process regression algorithm. Proceedings of the International Conference on Prognostics and Health Management, Spokane, WA, USA.","DOI":"10.1109\/ICPHM.2014.7036394"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hayes, M.A., and Capretz, M.A.M. (2014, January 27\u201330). Contextual Anomaly Detection in Big Sensor Data. Proceedings of the IEEE International Congress on Big Data, Washington, DC, USA.","DOI":"10.1109\/BigData.Congress.2014.19"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1214\/aos\/1176343411","article-title":"Asymptotic results for goodness-of-fit statistics with unknown parameters","volume":"4","author":"Stephens","year":"1976","journal-title":"Ann. Stat."},{"key":"ref_10","unstructured":"Stephens, M., and D\u2019Agostino, R. (1986). Goodness-of-Fit Techniques, Marcel Dekker."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1017\/jfm.2014.405","article-title":"Pressure fluctuations produced by forward steps immersed in a turbulent boundary layer","volume":"756","author":"Awasthi","year":"2014","journal-title":"J. Fluid Mech."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:47:08Z","timestamp":1760208428000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,13]]},"references-count":11,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["s17102329"],"URL":"https:\/\/doi.org\/10.3390\/s17102329","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,10,13]]}}}