A hardware-efficient parallel architecture for real-time blob analysis based on run-length code

2017 ◽  
Vol 15 (3) ◽  
pp. 657-672 ◽  
Author(s):  
Bingjie Li ◽  
Cunguang Zhang ◽  
Bo Li ◽  
Hongxu Jiang ◽  
Qizhi Xu
Keyword(s):  
Real Time ◽  
Parallel Architecture ◽  
Run Length ◽  
Blob Analysis ◽  
Length Code

Method and parallel architecture for extracting the image fractal dimension in real time

1998 ◽  
Author(s):  
Xiangdong Chen ◽  
Wensen Chang ◽  
Zheng Gao
Keyword(s):  
Fractal Dimension ◽  
Real Time ◽  
Parallel Architecture

Real-Time Implementation of Parallel Architecture Based Noise Minimization from Speech Signals on FPGA

2018 ◽  
Vol 103 (3) ◽  
pp. 1941-1963 ◽  
Author(s):  
Deepak Kumar Gupta ◽  
Vijay Kumar Gupta ◽  
Mahesh Chandra ◽  
Gaurav Verma
Keyword(s):  
Real Time ◽  
Parallel Architecture ◽  
Speech Signals

Test data compression using alternating variable run-length code

Integration ◽  
2011 ◽  
Vol 44 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Bo Ye ◽  
Qian Zhao ◽  
Duo Zhou ◽  
Xiaohua Wang ◽  
Min Luo
Keyword(s):  
Data Compression ◽  
Test Data ◽  
Test Data Compression ◽  
Run Length ◽  
Length Code

Feature-Based Object Location of IC Pins by Using Fast Run Length Encoding BLOB Analysis

2014 ◽  
Vol 4 (11) ◽  
pp. 1887-1898 ◽  
Author(s):  
Qiusheng Zhong ◽  
Zhong Chen ◽  
Xianmin Zhang ◽  
Guanghua Hu
Keyword(s):  
Object Location ◽  
Run Length ◽  
Blob Analysis ◽  
Feature Based

scholarly journals A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4129 ◽  
Author(s):  
Hassan Mushtaq ◽  
Sajid Gul Khawaja ◽  
Muhammad Usman Akram ◽  
Amanullah Yasin ◽  
Muhammad Muzammal ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Parallel Architecture ◽  
Partitioning Around Medoids ◽  
Proposed Model ◽  
Data Points ◽  
Data Objects ◽  
Natural Groups ◽  
Multiple Processing ◽  
Multi Core Processor

Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but due to its high time complexity, the PAM algorithm cannot be used with large datasets or in any embedded or real-time application. In this work, we propose a simple and scalable parallel architecture for the PAM algorithm to reduce its running time. This architecture can easily be implemented either on a multi-core processor system to deal with big data or on a reconfigurable hardware platform, such as FPGA and MPSoCs, which makes it suitable for real-time clustering applications. Our proposed model partitions data equally among multiple processing cores. Each core executes the same sequence of tasks simultaneously on its respective data subset and shares intermediate results with other cores to produce results. Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel. It is also observed that the speedup graph of our proposed model becomes more linear with the increase in number of data points and as the clusters become more uniform. The results also demonstrate that the proposed architecture produces the same results as the actual PAM algorithm, but with reduced computational complexity.

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