As an enormous computing power is required to get knowledge from a large volume of data, the parallel and distributed computing is highly recommended to process them. Artificial Neural Networks (ANNs) need as much as possible data to have... more
As an enormous computing power is required to get knowledge from a large volume of
data, the parallel and distributed computing is highly recommended to process them.
Artificial Neural Networks (ANNs) need as much as possible data to have high accuracy,
whereas parallel processing can help us to save time in ANNs training. In
this paper, exemplary parallelization of artificial neural network training by dint of
Java and its native socket libraries has been implemented. During the experiments, it
has been noticed that Java native socket implementation tends to have memory issues
when a large amount of training datasets are involved in training. It has been remarked
that exemplary parallelization of artificial neural network training cannot outperform
drastically when additional nodes are introduced into the system after a certain point.
This is comprehensively due to the network communication complexity in the system.
data, the parallel and distributed computing is highly recommended to process them.
Artificial Neural Networks (ANNs) need as much as possible data to have high accuracy,
whereas parallel processing can help us to save time in ANNs training. In
this paper, exemplary parallelization of artificial neural network training by dint of
Java and its native socket libraries has been implemented. During the experiments, it
has been noticed that Java native socket implementation tends to have memory issues
when a large amount of training datasets are involved in training. It has been remarked
that exemplary parallelization of artificial neural network training cannot outperform
drastically when additional nodes are introduced into the system after a certain point.
This is comprehensively due to the network communication complexity in the system.
Research Interests:
Laser scanners have a lot of advantages over conventional video cameras. Data processing in laser scanner system becomes faster and easier because there is no need to record real world videos. Besides, the problem of private life... more
Laser scanners have a lot of advantages over conventional video cameras. Data processing in laser scanner system becomes faster and easier because there is no need to record real world videos. Besides, the problem of private life conservation is taken away. This paper proposes an approach to track objects from laser scanned dataset. Data points collected by each scan of laser scanners are considered as video frame. Support vector machine (SVM) along with Hungarian algorithm and Kalman filter are used to obtain trajectories of objects from the laser scanned dataset. Experimental results on the same laser scanned datasetshow that the method of SVM with Hungarian algorithm and Kalman filter performs better than that of its alternative having various thresholds with Hungarian algorithm and Kalman filter.