Hybrid soft computing systems: industrial and commercial applications

1999 ◽  
Vol 87 (9) ◽  
pp. 1641-1667 ◽  
Author(s):  
P.P. Bonissone ◽  
Yu-To Chen ◽  
K. Goebel ◽  
P.S. Khedkar
Keyword(s):  
Soft Computing ◽  
Computing Systems ◽  
Commercial Applications

Appraisal of Soft Computing Methods in Collaboration With Smart City Applications and Wireless Network

2020 ◽  
pp. 1273-1285
Author(s):  
Mamata Rath ◽  
Bibudhendu Pati
Keyword(s):  
Wireless Network ◽  
Soft Computing ◽  
Health Care Systems ◽  
Computing Methods ◽  
Computing Systems ◽  
Probabilistic Thinking ◽  
Research Article ◽  
Intelligent Analysis ◽  
Care Systems ◽  
Soft Computing Methods

Applications of soft computing methods are spread in fields that deal with intelligent analysis. As the human intelligence can survey the likelihood of some occasions in possibilities, comparatively soft computing systems additionally utilize some smart-based strategies to evaluate ongoing issues with diagnostic models. Fundamental segments of soft computing incorporate machine learning, probabilistic thinking, swarm intelligence, and ANN algorithms. In this research article, there is a broad analysis of these intelligence-based soft computing strategies connected as different operational parts of a wireless network and there is a scheme of a soft computing-based method for smart and safe health care systems.

On the Application of Embedded Computer System in Radar Scanning System

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Jia Ai
Keyword(s):  
Computer Systems ◽  
Scanning System ◽  
Security Risks ◽  
Good Reliability ◽  
Computing Systems ◽  
Computing Power ◽  
Embedded Computer ◽  
Radar Systems ◽  
Commercial Applications ◽  
Development Prospects

The development of computer technology is extremely rapid in today's world. The faster it develops, the more security risks it is exposed to and the stronger computing power of the computing systems and software it requires. There are many advantages of embedded computer systems, including not only good reliability, but also strong practicability. Therefore, embedded computer systems are widely applied in business and industry. Open or commercial applications allow computers to embed radar systems in many industries. Therefore, the computer embedded radar operating system has bright development prospects.

Data-Aware Distributed Computing

2012 ◽  
pp. 1-27 ◽  
Author(s):  
Esma Yildirim ◽  
Mehmet Balman ◽  
Tevfik Kosar
Keyword(s):  
Distributed Computing ◽  
Workflow Management ◽  
Data Access ◽  
New Paradigm ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Data Movement ◽  
End To End ◽  
Commercial Applications ◽  
Main Components

With the continuous increase in the data requirements of scientific and commercial applications, access to remote and distributed data has become a major bottleneck for end-to-end application performance. Traditional distributed computing systems closely couple data access and computation, and generally, data access is considered a side effect of computation. The limitations of traditional distributed computing systems and CPU-oriented scheduling and workflow management tools in managing complex data handling have motivated a newly emerging era: data-aware distributed computing. In this chapter, the authors elaborate on how the most crucial distributed computing components, such as scheduling, workflow management, and end-to-end throughput optimization, can become “data-aware.” In this new computing paradigm, called data-aware distributed computing, data placement activities are represented as full-featured jobs in the end-to-end workflow, and they are queued, managed, scheduled, and optimized via a specialized data-aware scheduler. As part of this new paradigm, the authors present a set of tools for mitigating the data bottleneck in distributed computing systems, which consists of three main components: a data-aware scheduler, which provides capabilities such as planning, scheduling, resource reservation, job execution, and error recovery for data movement tasks; integration of these capabilities to the other layers in distributed computing, such as workflow planning; and further optimization of data movement tasks via dynamic tuning of underlying protocol transfer parameters.

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