Keyword search (4,164 papers available)

"Ahmad MO" Authored Publications:

Title Authors PubMed ID
1 Age estimation via electrocardiogram from smartwatches Adib A; Zhu WP; Ahmad MO; 41142465
ENCS
2 Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection Pirhadi A; Salari S; Ahmad MO; Rivaz H; Xiao Y; 36306056
PERFORM
3 DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR Masoumi N; Rivaz H; Ahmad MO; Xiao Y; 36173541
ENCS
4 Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms Masoumi N; Belasso CJ; Ahmad MO; Benali H; Xiao Y; Rivaz H; 33683544
PERFORM
5 Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video. Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO 29407997
IMAGING
6 A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing. Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO 29762493
ENCS
7 Image denoising via overlapping group sparsity using orthogonal moments as similarity measure. Kumar A, Ahmad MO, Swamy MNS 30392726
ENCS
8 Big Data-Driven Cellular Information Detection and Coverage Identification. Wang H, Xie S, Li K, Ahmad MO 30813353
ENCS

 

Title:A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.
Authors:Li KWang HXu XDu YLiu YAhmad MO
Link:https://www.ncbi.nlm.nih.gov/pubmed/29762493?dopt=Abstract
Publication:
Keywords:
PMID:29762493 Category:Sensors (Basel) Date Added:2019-06-04
Dept Affiliation: ENCS
1 College of Smart City, Beijing Union University, Beijing 100101, China. like@buu.edu.cn.
2 College of Smart City, Beijing Union University, Beijing 100101, China. whyxdt@163.com.
3 College of Smart City, Beijing Union University, Beijing 100101, China. 151081210202@buu.edu.cn.
4 College of Robotics, Beijing Union University, Beijing 100101, China. duyu@buu.edu.cn.
5 College of Robotics, Beijing Union University, Beijing 100101, China. yuansheng@buu.edu.cn.
6 Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G IM8, Canada. omair@ece.concordia.ca.

Description:

A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Sensors (Basel). 2018 May 15;18(5):

Authors: Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Abstract

Service perception analysis is crucial for understanding both user experiences and network quality as well as for maintaining and optimizing of mobile networks. Given the rapid development of mobile Internet and over-the-top (OTT) services, the conventional network-centric mode of network operation and maintenance is no longer effective. Therefore, developing an approach to evaluate and optimizing users' service perceptions has become increasingly important. Meanwhile, the development of a new sensing paradigm, mobile crowdsensing (MCS), makes it possible to evaluate and analyze the user's OTT service perception from end-user's point of view other than from the network side. In this paper, the key factors that impact users' end-to-end OTT web browsing service perception are analyzed by monitoring crowdsourced user perceptions. The intrinsic relationships among the key factors and the interactions between key quality indicators (KQI) are evaluated from several perspectives. Moreover, an analytical framework of perceptional degradation and a detailed algorithm are proposed whose goal is to identify the major factors that impact the perceptional degradation of web browsing service as well as their significance of contribution. Finally, a case study is presented to show the effectiveness of the proposed method using a dataset crowdsensed from a large number of smartphone users in a real mobile network. The proposed analytical framework forms a valuable solution for mobile network maintenance and optimization and can help improve web browsing service perception and network quality.

PMID: 29762493 [PubMed]





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