Mehdi Agha Sarram Articles List

List of Articles Mehdi Agha Sarram

Open Access Article
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1 - Improving the Architecture of Convolutional Neural Network for Classification of Images Corrupted by Impulse Noise
Mohammad Momeny M. Agha Sarram A. M. Latif R. Sheikhpour
20.1001.1.16823745.1398.17.4.10.9

Impulse noise is one the common noises which reduces the performance of convolutional neural networks (CNNs) in image classification. Preprocessing for removal of impulse noise is a costly process which may have a destructive effect on the training and validation of the More

Impulse noise is one the common noises which reduces the performance of convolutional neural networks (CNNs) in image classification. Preprocessing for removal of impulse noise is a costly process which may have a destructive effect on the training and validation of the convolutional neural networks due to insufficient improvement of noisy images. In this paper, a convolutional neural network is proposed which is robust to impulse noise. Proposed CNN classify images corrupted by impulse noise without any preprocessing for noise removal. A noise detection layer is placed at the beginning of the proposed CNN to prevent the processing of noisy values. The ILSVRC-2012 database is used to train the proposed CNN. Experimental results show that preventing the impact of impulse noise on the training process and classification of CNN can increase the accuracy and speed of the network training. The proposed CNN with error of 0.24 is better than other methods in classification of noisy image corrupted by impulse noise with 10% density. The time complexity of O(1) in the proposed CNN for robustness to noise indicates the superiority of the proposed CNN. Manuscript profile
Open Access Article
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2 - Optimal Resource Allocation in Multi-Task Software-Defined Sensor Networks
S. A. Mostafavi M. Agha Sarram T. Salimian
20.1001.1.16823745.1398.17.4.13.2

Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program wh More

Unlike conventional wireless sensor networks which are designed for a specific application, Software-Defined Wireless Sensor Networks (SDSN) can embed multiple sensors on each node, defining multiple tasks simultaneously. Each sensor node has a virtualization program which serves as a common communication infrastructure for several different applications. Different sensor applications in the network can have different target functions and decision parameters. Due to the resource constraints of sensor network nodes, the multiplicity and variety of tasks in each application, requirements for different levels of quality of service, and the different target functions for different applications, the problem of allocating resources to the tasks on the sensors is complicated. In this paper, we formulate the problem of allocating resources to the sensors in the SDSN with different objective functions as a multi-objective optimization problem and provide an effective solution to solve it. Manuscript profile
Open Access Article
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3 - Load Balancing in Fog Nodes using Reinforcement Learning Algorithm
niloofar tahmasebi pouya Mehdi-Agha Sarram
20.1001.1.16823745.1401.20.4.3.3

Fog computing is an emerging research field for providing cloud computing services to the edges of the network. Fog nodes process data stream and user requests in real-time. In order to optimize resource efficiency and response time, increase speed and performance, task More

Fog computing is an emerging research field for providing cloud computing services to the edges of the network. Fog nodes process data stream and user requests in real-time. In order to optimize resource efficiency and response time, increase speed and performance, tasks must be evenly distributed among the fog nodes. Therefore, in this paper, a new method is proposed to improve the load balancing in the fog computing environment. In the proposed algorithm, when a task is sent to the fog node via mobile devices, the fog node using reinforcement learning decides to process that task itself, or assign it to one of the neighbor fog nodes or cloud for processing. The evaluation shows that the proposed algorithm, with proper distribution of tasks between nodes, has less delay to tasks processing than other compared methods. Manuscript profile

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List of Articles Mehdi Agha Sarram