Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)

Transmission of video content over wireless access networks (in particular, Wireless Local Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing video quality prediction models. The main aim of the project is the development of novel and efficient models for video quality prediction in a non-intrusive way for low bitrate and resolution videos and to demonstrate their application in QoS-driven adaptation schemes for mobile video streaming applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content type was found to be the most important parameter. (3) Efficient regression-based and artificial neural network-based learning models were developed for video quality prediction over WLAN and UMTS access networks. The models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and optimization in network planning and content provisioning for network/service providers.(4) The applications of the proposed regression-based models were investigated in (i) optimization of content provisioning and network resource utilization and (ii) A new fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks. (5) Finally, Internet-based subjective tests that captured distortions caused by the encoder and the wireless access network for different types of contents were designed. The database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked

Quality of Assessment in Connected Vehicles

In recent years, there has been a huge interest in Machine-to-Machine connectivity under the umbrella of Internet of Things (IoT). With the UK Government looking to trial autonomous (driverless) cars this year, connected vehicles will play a key part in improving and managing existing road safety and congestion, leading to a new generation of intelligent transport systems. This is also well aligned to the current initiatives by the automotive industry to improve the driver’s experience on-board. However, the wireless channels most suitable for this application have not been standardized. In this paper, we review the wireless channels suitable for vehicle-2-vehicle (V2V) and Vehicle–to-x (V2x) connectivity. We further present preliminary analysis on the factors that impact the Quality of Service (QoS) of connected vehicles. We use the open access GEMV2 data to carry out Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) on the link quality and found that both line of sight and non line of sight has a significant impact on the link quality. The work presented here will help in the development of connected vehicle network (CVN) prediction model and control for V2V and V2x connectivity. It will further contribute towards unfolding and testing key research questions in the context of connected vehicles which may otherwise be overlooked

Web Usage Mining and User Behaviour Prediction

Today, Internet is playing such a significant role in our day-to-day life. We have witnessed the evermore- interesting and upcoming publishing medium is the World Wide Web (WWW). The rapid growth in the volume of information available over the WWW and number of its� potential users� has leads to difficulties in providing effective search service for users�, resulting in decrease in the web performance. Web Usage Mining is an area, where the navigational access behaviour of users� over the web is tracked and analyzed. So that websites owner can easily identify the access patterns of its users�. By collecting and analyzing this behaviour of user activities, websites owner can enhance the quality and performance of services to catch the attention of existing as well as new customers. This research paper intends to provide an overview of past and current evaluation in users� future request prediction using Web Usage Mining

Development and Standardization of the Gratitude Scale

Abstract The Gratitude Scale (GS) developed by the authors was administered to 456 adults to determine the psychometric characteristics i.e. reliability and validity. Cronbach’s Alpha of the scale was found 0.91. Content validity of the scale was verified by some experts, academicians, and professionals. For testing multicollinearity and singularity ‘Determinant’ of the R-matrix was estimated and it was greater than 0.00001. The items having factor loading greater than or equal to 0.40 were selected. Total 26 items with five dimensions emerged through Exploratory Factor Analysis explaining 58.14% of the variance, which provided the evidence of factorial/construct validity of the scale. The scale can be used for research and human resource development programs in school/university and organizations. Keywords: Gratitude, content validity, Factor analysis, Multicollinearity, R-matri

Navigating Civic Spaces During a Pandemic: Pakistan Report

The Covid-19 pandemic exacerbated trends in civic spaces underway before 2020 in Pakistan, reinforcing the state’s on-going deep discomfort with rights-based actors and mobilizations whilst allowing the divisive rhetoric and mass gathering of sectarian forces to flourish. The crisis afforded some new opportunities, too. Diverse interest groups gained recognition of some rights or tried to stave off further hardship during the economic downturn. Digital spaces became an increasingly powerful arena for activism and dissent. They were also targeted by security agencies, as women journalists who reported against the government’s narrative on its Covid-19 response discovered. This report shows how different actors responded to the pandemic in ways that affect civic spaces. The research identified areas of civil society which underwent some forms of public mobilization, whether through articulating new or ongoing claims, forming new associations, or using innovative strategies to respond to shrinking civic spaces. The discussion captures new forms of social and political action, in particular the opposition political alliance (Pakistan Democratic Alliance) formed in September, 2020. It ends with an assessment of the implications of these changes in civic spaces for the governance context in Pakistan

Predicting Types of Failures in Wireless Sensor Networks Using an Adaptive Neuro-fuzzy Inference System

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models

De-Noising Signals using Wavelet Transform in Internet of Underwater Things

Internet of Underwater Things (IoUT) is an emerging field within Internet of Things (IoT) towards smart cities. IoUT has applications in monitoring underwater structures as well as marine life. This paper presents preliminary work where sensor nodes were built on Arduino Uno platform with temperature and pressure sensors with wireless capability. The sensors nodes were then tested in the Flumes of the COAST laboratory to determine the maximum depth achievable in fresh water before the signal is lost as radio frequencies are susceptible to interference under water. Further, the received signals were de-noised using Wavelet Transform, Daubechies thresholding techniques at level 5. Preliminary results suggest that at a depth of 30 cm, signal was lost, de-noising of the signal was achieved with very small errors (a mean squared error of 0.106 and 0.000446 and Peak-Sign-to-Noise Ratios of 70.18 dB and 58.83 dB for the pressure and temperature signals, respectively. Results from this study will lay the foundation to further investigations in wireless sensor networks in IoUT integrating the de-noising techniques

A Phenomenological Analysis of Challenges and Benefits of Online Learning Transformation in the Masters of Health Professions Education

Learning that challenges preconceived notions and inspires the development of fresh perspectives on the world has been referred to as transformative learning. This study offers a thorough analysis of how changes in learning have been reflected, including how they have an impact on curricula, in order to guide the master of health professions education’s potential field applications. Through purposive sampling, 15 students of MHPE from Islamic International Medical College, Rawalpindi, Pakistan have been chosen. The study design is phenomenological in nature. A semi-structured interview has been used. All the interviews were audio-recorded and separately transcribed. The transcribed interviews were then imported into NVivo software version 11 for analysis. A thematic analysis has been done and six themes are generated. The applications of distance learning positively related to teaching and learning practices and students identified a change in their attitudes toward distance learning. Major factors recognized were student-centered learning, small group discussions, peer-assisted learning, technology awareness, personal grooming, and motivation. About three-fourths of the sample population experienced a transformation in their distance learning after going through a Master’s in Health Professionals Education. The impact of hands-on activities and small group discussions turned out to be the strongest factors that caused the transformation in distance learning