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Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate.
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The Postdoctoral Blueprint Part Two: The Faculty Application Jun;32(6):463-466
To prepare for an academic career requires a postdoctoral position that provides an advanced research experience, which leads to increasing independence. However, it is critical to develop other parts of your academic portfolio to create a robust application. Here, we discuss the critical steps in preparing a competitive faculty application.
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- Award ID(s):
- 2011577
- PAR ID:
- 10326375
- Date Published:
- Journal Name:
- Trends in cell biology
- Volume:
- 32
- Issue:
- 6
- ISSN:
- 0962-8924
- Page Range / eLocation ID:
- 463-466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/doi.org/10.1016/j.tcb.2022.01.008
