Secure Image Transmission in Wireless Network Using Conventional Neural Network and DOST

2021 ◽  
pp. 91-101
Author(s):  
Manoj Diwakar ◽  
Pardeep Kumar
Keyword(s):  
Neural Network ◽  
Wireless Network ◽  
Image Transmission

scholarly journals Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Xiaonan Tan ◽  
Geng Chen ◽  
Hongyu Sun
Keyword(s):  
Neural Network ◽  
Wireless Network ◽  
Bp Neural Network ◽  
Transmission Rate ◽  
Back Propagation ◽  
Vertical Handover ◽  
Integrated Network ◽  
Neural Network Input ◽  
The Right ◽  
Handover Algorithm

Abstract A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

scholarly journals Image Compression Technique Based on Fractal Image Compression Using Neural Network – A Review

2021 ◽  
pp. 47-57
Author(s):  
Diyar Waysi Naaman
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Back Propagation ◽  
Image Transmission ◽  
Search Space ◽  
Compression Technique ◽  
Advanced Technique ◽  
Fractal Image ◽  
Network Algorithm ◽  
Neural Network Algorithm

Image compression research has increased dramatically as a result of the growing demands for image transmission in computer and mobile environments. It is needed especially for reduced storage and efficient image transmission and used to reduce the bits necessary to represent a picture digitally while preserving its original quality. Fractal encoding is an advanced technique of image compression. It is based on the image's forms as well as the generation of repetitive blocks via mathematical conversions. Because of resources needed to compress large data volumes, enormous programming time is needed, therefore Fractal Image Compression's main disadvantage is a very high encoding time where decoding times are extremely fast. An artificial intelligence technique similar to a neural network is used to reduce the search space and encoding time for images by employing a neural network algorithm known as the “back propagation” neural network algorithm. Initially, the image is divided into fixed-size and domains. For each range block its most matched domain is selected, its range index is produced and best matched domains index is the expert system's input, which reduces matching domain blocks in sets of results. This leads in the training of the neural network. This trained network is now used to compress other images which give encoding a lot less time. During the decoding phase, any random original image, converging after some changes to the Fractal image, reciprocates the transformation parameters. The quality of this FIC is indeed demonstrated by the simulation findings. This paper explores a unique neural network FIC that is capable of increasing neural network speed and image quality simultaneously.

scholarly journals MPDHD-A Handover Decision Scheme for Seamless Connectivity in Wireless Network

2020 ◽  
Vol 9 (9) ◽  
pp. 524-528
Keyword(s):  
Neural Network ◽  
Wireless Network ◽  
Intelligent System ◽  
Fuzzy Model ◽  
Base Station ◽  
Handover Decision ◽  
Novel Technologies ◽  
Multiple Parameters ◽  
Seamless Connectivity ◽  
Increasing Demand

Handover is one of the major concerns arising in wireless network due to increasing demand of services by the customers. Different studies have been performed to attain a seamless handover. Researchers are implementing novel technologies so that efficient decision can be made to maintain effective communication. Multilayer feed forward artificial neural network has been implemented in a recent study in which Received Signal strength indicator (RSSI), monetary cost, Data rate and Velocity of mobile users in the network are taken into account for handover decision in wireless network. Due to several limitations of this technique, a novel method- Multiple parameters dependent Handover decision (MPDHD) is presented in which Sugeno fuzzy model is amalgamated with neural network to form an intelligent system. In the system, neural network is trained by the fuzzy model which reduced the complexity of the existing work. Also along with the parameters used in existing work, a new user metric-Load is introduced to check the availability of the base station with minimum load of users connected to it. The simulation of the proposed work is carried out in the MATLAB environment. From, the experimental results, it is concluded that MPDHD is better than existing approaches and reduced the handover probability in the network.

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