Real-Time Data Compression System for Data-Intensive Scientific Applications Using FPGA Architecture

Author(s):  
Mohammed Bawatna ◽  
Oliver Knodel ◽  
Rainer G. Spallek
2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Woochul Kang ◽  
Jaeyong Chung

With ubiquitous deployment of sensors and network connectivity, amounts of real-time data for embedded systems are increasing rapidly and database capability is required for many embedded systems for systematic management of real-time data. In such embedded systems, supporting the timeliness of tasks accessing databases is an important problem. However, recent multicore-based embedded architectures pose a significant challenge for such data-intensive real-time tasks since the response time of accessing data can be significantly affected by potential intercore interferences. In this paper, we propose a novel feedback control scheme that supports the timeliness of data-intensive tasks against unpredictable intercore interferences. In particular, we use multiple inputs/multiple outputs (MIMO) control method that exploits multiple control knobs, for example, CPU frequency and the Quality-of-Data (QoD) to handle highly unpredictable workloads in multicore systems. Experimental results, using actual implementation, show that the proposed approach achieves the target Quality-of-Service (QoS) goals, such as task timeliness and Quality-of-Data (QoD) while consuming less energy compared to baseline approaches.


2016 ◽  
Author(s):  
Dilworth Y. Parkinson ◽  
Keith Beattie ◽  
Xian Chen ◽  
Joaquin Correa ◽  
Eli Dart ◽  
...  

2014 ◽  
Vol 519-520 ◽  
pp. 70-73 ◽  
Author(s):  
Jing Bai ◽  
Tie Cheng Pu

Aiming at storing and transmitting the real time data of energy management system in the industrial production, an online data compression technique is proposed. Firstly, the auto regression model of a group of sequence is established. Secondly, the next sampled data can be predicted by the model. If the estimated error is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied in electricity energy data compression of a beer production. The application result verifies the effectiveness of the proposed method.


Author(s):  
Fang Zhang ◽  
Lin Cheng ◽  
Xiong Li ◽  
Yuanzhang Sun ◽  
Wenzhong Gao ◽  
...  

1992 ◽  
Author(s):  
Shen-en Qian ◽  
Ruqin Wang ◽  
Shuqiu Li ◽  
Yisong Dai

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