A QoE adaptive management system for high definition video streaming over wireless networks

[EN] The development of the smart devices had led to demanding high-quality streaming videos over wireless communications. In Multimedia technology, the Ultra-High Definition (UHD) video quality has an important role due to the smart devices that are capable of capturing and processing high-quality video content. Since delivery of the high-quality video stream over the wireless networks adds challenges to the end-users, the network behaviors 'factors such as delay of arriving packets, delay variation between packets, and packet loss, are impacted on the Quality of Experience (QoE). Moreover, the characteristics of the video and the devices are other impacts, which influenced by the QoE. In this research work, the influence of the involved parameters is studied based on characteristics of the video, wireless channel capacity, and receivers' aspects, which collapse the QoE. Then, the impact of the aforementioned parameters on both subjective and objective QoE is studied. A smart algorithm for video stream services is proposed to optimize assessing and managing the QoE of clients (end-users). The proposed algorithm includes two approaches: first, using the machine-learning model to predict QoE. Second, according to the QoE prediction, the algorithm manages the video quality of the end-users by offering better video quality. As a result, the proposed algorithm which based on the least absolute shrinkage and selection operator (LASSO) regression is outperformed previously proposed methods for predicting and managing QoE of streaming video over wireless networks.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" with in the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Canovas, A.; Lloret, J.; Ali, A. (2021). A QoE adaptive management system for high definition video streaming over wireless networks. Telecommunication Systems. 77(1):63-81. https://doi.org/10.1007/s11235-020-00741-2638177

Determine the Role of Customer Engagement on Relationship Quality and Relationship Performance

This conceptual paper aims to investigating the potential effect of relationship market orientation upon buyer-seller relationship with particular emphases on customer engagement, relationship quality, and relationship performance. Engagement is a central concept in the social psychology/exchange literature. Customer engagement explains how social relationships initiate, endure and develop. The literature on buyer-seller relationships has been inspired by social psychology/exchange, adopting concepts such as trust and commitment, but overlooking the concept of customer engagement. In this study we propose a conceptual perspective on customer engagement that is based on the principle of social exchange. We demonstrate the relevance of customer engagement by linking it to relationship quality and relationship performance. More specifically, it is argued that the outcome of relationship quality from a lack of customer engagement by moderating effects of relationship involvement. Purpose of study to provide insight into customer engagement when viewed through perspective of social exchange lens. Objective of study to determine the major customer engagement is really matter in relationship quality and relationship performance. To determine the relationship involvement moderate between customer engagement and relationship quality Key words: Engagement, Relationship Quality, Relationship Involvement, Relationship Performance

The Impact of Service Quality Dimensions on Customer Satisfaction: Case Study of University Utara Malaysia

The purpose of this study is to investigate the effect of service quality (reliability, empathy, tangibility, assurance, responsiveness) on customer satisfaction in University Utara Malaysia (Education Service). Each variable is measured using 7-point interval scale: reliability (6 items), empathy (6 items), tangibility (5 items), assurance (10 items), and responsiveness (5 items) on customer satisfaction (6 items). Using the primary data collection method, 160 questionnaires were distributed to postgraduate students inside University Utara Malaysia (Sintok Campus), in north Malaysia. The responses collected were 98 completed questionnaires representing with 61.25 % response rate. The data were analyzed using Structural equation modeling (SEM) using AMOS 7. Confirmatory factor analysis of measurement models indicates adequate goodness of fit after a few items were eliminated through modification indices verifications. Goodness of fit for the revised structural model shows adequate fit. This study has established five direct effects: (1) reliability customer satisfaction; (2) empathy and customer satisfaction; (3) tangibility and customer satisfaction; (4) assurance and customer satisfaction; (5) and responsiveness on customer satisfaction. And, this study concludes that all hypotheses have been asserted in the revised model

IoT-Based Vision Techniques in Autonomous Driving

As more people drive vehicles, there is a corresponding increase in the number of deaths and injuries that happen due to road traffic accidents. Thus, various solutions have been proposed to reduce the impact of accidents. One of the most popular solutions is autonomous driving, which involves a series of embedded systems. These embedded systems assist drivers by providing crucial information on the traffic environment or by acting to protect the vehicle occupants in particular situations or to aid driving. Autonomous driving has the capacity to improve transportation services dramatically. Given the successful use of visual technologies and the implementation of driver assistance systems in recent decades, vehicles are prepared to eliminate accidents, congestion, collisions, and pollution. In addition, the IoT is a state-of-the-art invention that will usher in the new age of the Internet by allowing different physical objects to connect without the need for human interaction. The accuracy with which the vehicle's environment is detected from static images or videos, as well as the IoT connections and data management, is critical to the success of autonomous driving. The main aim of this review article is to encapsulate the latest advances in vision strategies and IoT technologies for autonomous driving by analysing numerous publications from well-known databases

An automated model for the assessment of QoE of adaptive video streaming over wireless networks

[EN] Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. In adaptive technology services, a client's application receives a streaming video through the adaptation of its quality to the network condition. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. The experiment's evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user's experience. The automated model could function to demonstrate the importance of correlation for network behaviors' parameters. Consequently, it directly influences the satisfaction of the end-user's perceptual quality. In comparison with other recent related works, the model provided a positive Pearson Correlation value. Simulated results give a better performance between objective Structural Similarity (SSIM) and subjective Mean Opinion Score (MOS) evaluation metrics for all video test samples.This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the Project under Grant TIN2017-84802-C2-1-P. This study has been partially done in the computer science departments at the (University of Sulaimani and Halabja).Taha, M.; Ali, A.; Lloret, J.; Gondim, PRL.; Canovas, A. (2021). An automated model for the assessment of QoE of adaptive video streaming over wireless networks. Multimedia Tools and Applications. 80(17):26833-26854. https://doi.org/10.1007/s11042-021-10934-92683326854801

Biotechnological and ecological potential of 'Micromonospora provocatoris' sp. nov., a gifted strain isolated from the Challenger Deep of the Mariana Trench

A Micromonospora strain, isolate MT25T, was recovered from a sediment collected from the Challenger Deep of the Mariana Trench using a selective isolation procedure. The isolate produced two major metabolites, n-acetylglutaminyl glutamine amide and desferrioxamine B, the chemical structures of which were determined using 1D and 2D-NMR, including 1H-15N HSQC and 1H-15N HMBC 2D-NMR, as well as high resolution MS. A whole genome sequence of the strain showed the presence of ten natural product-biosynthetic gene clusters, including one responsible for the biosynthesis of desferrioxamine B. Whilst 16S rRNA gene sequence analyses showed that the isolate was most closely related to the type strain of Micromonospora chalcea, a whole genome sequence analysis revealed it to be most closely related to Micromonospora tulbaghiae 45142T. The two strains were distinguished using a combination of genomic and phenotypic features. Based on these data, it is proposed that strain MT25T (NCIMB 15245T, TISTR 2834T) be classified as Micromonospora provocatoris sp. nov. Analysis of the genome sequence of strain MT25T (genome size 6.1 Mbp) revealed genes predicted to responsible for its adaptation to extreme environmental conditions that prevail in deep-sea sediments