An FPGA-based 3D image processor with median and convolution filters for real-time applications

2005 ◽  
Author(s):  
Sharmila Venugopal ◽  
Carlos R. Castro-Pareja ◽  
Omkar S. Dandekar
Keyword(s):  
Real Time ◽  
3D Image ◽  
Image Processor ◽  
Real Time Applications ◽  
Convolution Filters

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications

Peak-Power-Aware Energy Management for Periodic Real-Time Applications

2020 ◽  
Vol 39 (4) ◽  
pp. 779-788 ◽  
Author(s):  
Mohsen Ansari ◽  
Amir Yeganeh-Khaksar ◽  
Sepideh Safari ◽  
Alireza Ejlali
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Peak Power ◽  
Real Time Applications

Effects of multilevel security on real-time applications

2002 ◽  
Author(s):  
R.K. Clark ◽  
I.B. Greenberg ◽  
P.K. Boucher ◽  
T.F. Lunt ◽  
P.G. Neumann ◽  
...  
Keyword(s):  
Real Time ◽  
Multilevel Security ◽  
Real Time Applications

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

Efficient placement of audio data on optical disks for real-time applications

1989 ◽  
Vol 32 (7) ◽  
pp. 862-871 ◽  
Author(s):  
Clement Yu ◽  
Wei Sun ◽  
Dina Bitton ◽  
Qi Yang ◽  
Richard Bruno ◽  
...  
Keyword(s):  
Real Time ◽  
Audio Data ◽  
Optical Disks ◽  
Real Time Applications
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