{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T15:33:04Z","timestamp":1768318384278,"version":"3.49.0"},"reference-count":16,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T00:00:00Z","timestamp":1572480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.<\/jats:p>","DOI":"10.3390\/s19214727","type":"journal-article","created":{"date-parts":[[2019,10,31]],"date-time":"2019-10-31T06:33:29Z","timestamp":1572503609000},"page":"4727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Special Issue \u201cAdvanced Signal Processing in Intelligent Systems for Health Monitoring\u201d"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8515-7933","authenticated-orcid":false,"given":"Maysam","family":"Abbod","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiann-Shing","family":"Shieh","sequence":"additional","affiliation":[{"name":"Department of Mechanical engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1109\/JIOT.2018.2872389","article-title":"Review of Smart Health Monitoring Approaches with Survey Analysis and Proposed Framework","volume":"6","author":"Gahlot","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_2","unstructured":"Rose, T., Jasper, J.K., and Iyappan, L. (2018, January 27\u201328). Developments of Structural Health Monitoring System, State-of-the-Art Review. Proceedings of the International Conference on Emerging and Sustainable Trends in Civil Engineering (ESCE-2018), Shivamogga, India."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alokita, S., Rahul, V., Jayakrishna, K., Kar, V.R., M Rajesh, S.T., and Manikandan, M. (2019). Recent Advances and Trends in Structural Health Monitoring. Structural Health Monitoring of Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, Woodhead Publishing.","DOI":"10.1016\/B978-0-08-102291-7.00004-6"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2321","DOI":"10.1002\/stc.2321","article-title":"A Literature Review of Next-Generation Smart Sensing Technology in Structural Health Monitoring","volume":"26","author":"Shea","year":"2019","journal-title":"J. Struct. 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Bearing Fault Diagnosis Based on the Switchable Normalization SSGAN with 1-D Representation of Vibration Signals as Input. Sensors, 19.","DOI":"10.3390\/s19092000"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, H., Li, Y., and Yu, H. (2019). A Novel Health Indicator Based on Cointegration for Rolling Bearings\u2019 Run-To-Failure Process. Sensors, 19.","DOI":"10.3390\/s19092151"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tao, J., Qin, C., Li, W., and Liu, C. (2019). Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time-Frequency Information of Vibration Signals. Sensors, 19.","DOI":"10.3390\/s19153280"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, H., Qin, C., and Liu, M. (2019). A Rail Fault Diagnosis Method Based on Quartic C2 Hermite Improved Empirical Mode Decomposition Algorithm. 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