Deep Learning and Reconfigurable Platforms in the Internet of Things: Challenges and Opportunities in Algorithms and Hardware
| Autor: | Milos Manic, Juan J. Rodriguez-Andina, Roberto Fernandez Molanes, Kasun Amarasinghe |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Government
Artificial neural network Situation awareness business.industry Computer science Deep learning 020208 electrical & electronic engineering Big data 02 engineering and technology Terabyte Data science Industrial and Manufacturing Engineering Predictive maintenance Task (project management) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | IEEE Industrial Electronics Magazine. 12:36-49 |
| ISSN: | 1941-0115 1932-4529 |
| Popis: | As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of today, the discussion really turns from how the massive data sets are collected to how value can be derived from them, i.e., how to extract knowledge out of such (big) data. IoT devices are used in an ever-growing number of application domains (see Figure 1), ranging from sports gadgets (e.g., Fitbits and Apple Watches) or more serious medical devices (e.g., pacemakers and biochips) to smart homes, cities, and self-driving cars, to predictive maintenance in missioncritical systems (e.g., in nuclear power plants or airplanes). Such applications introduce endless possibilities for better understanding, learning, and informedly acting (i.e., situational awareness and actionable information in government lingo). Although rapid expansion of devices and sensors brings terrific opportunities for taking advantage of terabytes of machine data, the mind-boggling task of understanding growth of data remains and heavily relies on artificial intelligence and machine learning [1], [2]. |
| Databáze: | OpenAIRE |
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