scholarly journals Quality of Experience for Large Ultra-High-Resolution Tiled Displays with Synchronization Mismatch

2011 ◽  
Vol 2011 ◽  
pp. 1-14
Author(s):  
Sachin Deshpande ◽  
Scott Daly
Keyword(s):  
High Resolution ◽  
Quality Of Experience ◽  
Tiled Displays

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Quality-of-Experience-Aware Incentive Mechanism for Workers in Mobile Device Cloud

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sajeeb Saha ◽  
Md. Ahsan Habib ◽  
Tamal Adhikary ◽  
Md. Abdur Razzaque ◽  
Md. Mustafizur Rahman ◽  
...  
Keyword(s):  
Mobile Device ◽  
Quality Of Experience ◽  
Incentive Mechanism ◽  
Mobile Device Cloud

What's my App?

2021 ◽  
Vol 48 (4) ◽  
pp. 41-44
Author(s):  
Dena Markudova ◽  
Martino Trevisan ◽  
Paolo Garza ◽  
Michela Meo ◽  
Maurizio M. Munafo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Real Time ◽  
Language Processing ◽  
Traffic Management ◽  
Quality Of Experience ◽  
Broadband Internet ◽  
Management Policies ◽  
Control Traffic

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

scholarly journals Differentiation of Mitragyna speciosa, a narcotic plant, from allied Mitragyna species using DNA barcoding-high-resolution melting (Bar-HRM) analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chayapol Tungphatthong ◽  
Santhosh Kumar J. Urumarudappa ◽  
Supita Awachai ◽  
Thongchai Sooksawate ◽  
Suchada Sukrong
Keyword(s):  
High Resolution ◽  
Dna Barcoding ◽  
High Resolution Melting ◽  
Dna Barcode ◽  
Herbal Medicines ◽  
Efficient Tool ◽  
Hrm Analysis ◽  
Mitragyna Speciosa ◽  
Leaf Powder

AbstractMitragyna speciosa (Korth.) Havil. [MS], or “kratom” in Thai, is the only narcotic species among the four species of Mitragyna in Thailand, which also include Mitragyna diversifolia (Wall. ex G. Don) Havil. [MD], Mitragyna hirsuta Havil. [MH], and Mitragyna rotundifolia (Roxb.) O. Kuntze [MR]. M. speciosa is a tropical tree belonging to the Rubiaceae family and has been prohibited by law in Thailand. However, it has been extensively covered in national and international news, as its abuse has become more popular. M. speciosa is a narcotic plant and has been used as an opium substitute and traditionally used for the treatment of chronic pain and various illnesses. Due to morphological disparities in the genus, the identification of plants in various forms, including fresh leaves, dried leaf powder, and finished products, is difficult. In this study, DNA barcoding combined with high-resolution melting (Bar-HRM) analysis was performed to differentiate M. speciosa from allied Mitragyna and to assess the capability of Bar-HRM assays to identify M. speciosa in suspected kratom or M. speciosa-containing samples. Bar-HRM analysis of PCR amplicons was based on the ITS2, rbcL, trnH-psbA, and matK DNA barcode regions. The melting profiles of ITS2 amplicons were clearly distinct, which enabled the authentication and differentiation of Mitragyna species from allied species. This study reveals that DNA barcoding coupled with HRM is an efficient tool with which to identify M. speciosa and M. speciosa-containing samples and ensure the safety and quality of traditional Thai herbal medicines.

Concealment Support and Error Resilience for HEVC to Improve Consumer Quality of Experience

2021 ◽  
pp. 1-1
Author(s):  
Gosala Kulupana ◽  
Dumidu S. Talagala ◽  
Hemantha Kodikara Arachchi ◽  
Mobolaji Akinola ◽  
Anil Fernando
Keyword(s):  
Quality Of Experience ◽  
Error Resilience
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