Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches

Abstract

Downlink data rates can vary significantly in cellular networks, with a potentially non-negligible effect on the user experience. Content providers address this problem by using different representations (e.g., picture resolution, video resolution and rate) of the same content and switch among these based on measurements collected during the connection. Knowing the achievable data rate before the connection establishment should definitely help content providers to choose the most appropriate representation from the very beginning. We have conducted several large measurement campaigns involving a panel of users connected to a production network in France, to determine whether it is possible to predict the achievable data rate using measurements collected, before establishing the connection to the content provider, on the operator's network and on the mobile node. We establish evidence that it is indeed possible to exploit these measurements to predict, with an acceptable accuracy, the achievable data rate.We thus introduce cooperation strategies between the mobile user, the network operator and the content provider to implement such anticipatory solution.

Cellular Neural Networks Templates Learning Approach Based on Mutual Information and Firefly Algorithm for X-Ray Images de-noising

“…demonstrates the contaminated image with 5% noise corruption on the left as the input of the CeNN, on the right Figure the denoised image after total number of iterations using the proposed methodology In Figure(5), a comparison between the 10% contaminated image and the output image were illustrated. The final CeNN template obtained by the proposed methodology is shown in (18) and (19): For the Poisson noise, the proposed algorithm produced different templates, as shown below.…”