bahareh asadi
Abstract
One of the important challenges in Wireless Sensor Networks is to proceeds data transmission in a way that tries to increase the life of the network. One of the main issues is the reduction of latency in the node and energy in the sink nodes. Due to the limited energy of the nodes, data transmission ...
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One of the important challenges in Wireless Sensor Networks is to proceeds data transmission in a way that tries to increase the life of the network. One of the main issues is the reduction of latency in the node and energy in the sink nodes. Due to the limited energy of the nodes, data transmission has the largest share in energy consumption, so it is important to design a structure that has the least amount of energy in sending data to the base station. In this paper, we use fuzzy logic and Mamdani method for clustering to solve the challenge and time division multiplexing method to connect the nodes with the header. The proposed clustering is based on the use of the LEACH algorithm, the capability and reliability of which are improved by fuzzy systems, and the particle optimization algorithm is used to optimize the path of the networks. The simulation results show that energy consumption decreases with increasing number of cycles. For example, energy consumption reached 0.9 in the 2000 round and 0.1 in the 5000 round.
Zahra Aghajani; mostafa karbasi; Bahareh asadi
Abstract
Deaf people or people with hearing loss have a major problem in everyday communication. There are many applications available in the market to help blind people to interact with the world. Voice-based email and chatting systems are available to communicate with each other by blinds. This helps to interact ...
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Deaf people or people with hearing loss have a major problem in everyday communication. There are many applications available in the market to help blind people to interact with the world. Voice-based email and chatting systems are available to communicate with each other by blinds. This helps to interact with persons by blind people. Also, many attempts have been made with Sign Language (SL) translators to solve of communication gap between normal and deaf people and ease communication for deaf people. In this paper, the geometric feature is used as feature extraction for static sign recognition. Support Vector Machine (SVM) classifier is used for training and testing to develop a system using static signs. So, the accuracy result for static signs using the Geometric feature is 62.92\% which needs to be improved by other feature extraction and classifiers.
