Papers by Nawaf O. Alsrehin
Video transcoding can cause degradation to an original video. Currently, there is no general mode... more Video transcoding can cause degradation to an original video. Currently, there is no general model that assesses the impact of video transcoding on video quality. Such a model could play a critical role in I would like to thank my family: my parents (Omar Alsrehin and Najah Alzoubi), my brothers (Ala'a and Dea'a), and my sisters (Ghada, Manar, and Nowar) for their love, encouragement, constant prayers (doas), guidance, and unconditional affection that made all of this possible. Last but not the least, I would especially like to thank my wife (Nosaybah Alzoubi) for her patience and unending support during the most challenging periods of my Ph.D. research. Thanks to my daughters: Ayah, Yarah, Zainah, Hala, and Saba, who have missed me a lot during my study.
Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies
Expert Systems with Applications, 2021
Abstract Eye tracking is the process of measuring where one is looking (point of gaze) or the mot... more Abstract Eye tracking is the process of measuring where one is looking (point of gaze) or the motion of an eye relative to the head. Researchers have developed different algorithms and techniques to automatically track the gaze position and direction, which are helpful in different applications. Research on eye tracking is increasing owing to its ability to facilitate many different tasks, particularly for the elderly or users with special needs. This study aims to explore and review eye tracking concepts, methods, and techniques by further elaborating on efficient and effective modern approaches such as machine learning (ML), Internet of Things (IoT), and cloud computing. These approaches have been in use for more than two decades and are heavily used in the development of recent eye tracking applications. The results of this study indicate that ML and IoT are important aspects in evolving eye tracking applications owing to their ability to learn from existing data, make better decisions, be flexible, and eliminate the need to manually re-calibrate the tracker during the eye tracking process. In addition, they show that eye tracking techniques have more accurate detection results compared with traditional event-detection methods. In addition, various motives and factors in the use of a specific eye tracking technique or application are explored and recommended. Finally, some future directions related to the use of eye tracking in several developed applications are described.
Smart Traffic Light Management Systems
International Journal of Technology Diffusion, 2020
Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforceme... more Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforcement, and bad traffic light management are main factors that have led to traffic congestion. One of the most important problems in cities is the difficulty of further expanding the existing infrastructures. Having that in mind, the main accessible and available alternatives that could provide better management of the traffic lights is to use technological systems. There are many methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, and a few other technologies. This research is focused on reviewing all these existing methods and studies using a systematic literature review (SLR). The SLR was intended to improve the synthesis of research by introducing a systematic process. This article aims at analyzing and assessing the existing studies against selected factors of comparison. The study achieves these aim...
Journal of Communications, 2019
Eye tracking has become increasingly important in many sectors because of its ability to facilita... more Eye tracking has become increasingly important in many sectors because of its ability to facilitate day to day activities, especially for users with special needs, where tasks as simple as turning on a light require effort. To tackle similar issues, we propose a model that utilizes Video Oculography approach through Tobii technology with added voice interfaces using Azure cloud to help control home appliances. This model traces the user via reflected infrared light patterns and calculates the gaze position automatically. This method uses no wearable technology through Video Oculography with multiple cameras which deliver several advantages; it provides accurate gaze estimates, portability of the video recording system, and fully remote recordings. Eye tracking facilitates interactions to control home appliances when the user cannot or does not wish to use their hands by means of the IoT and cloud Technologies. Eye tracking has great impact in countless fields such as neurology, cognition, communication and security of different categories. Focus groups and usability tests show that many users were satisfied with the straightforward use of the model, flexibility and reliability in interacting with the system, and accuracy in the movement of the pointer, make this model a reliable solution to simplifying some daily tasks.
IEEE Access, 2019
Traffic congestion is becoming the issues of the entire globe. This study aims to explore and rev... more Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study's methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.
Bulletin of Electrical Engineering and Informatics, 2019
This paper proposes a new full-reference algorithm, called Video Motion Quality (VMQ) that evalua... more This paper proposes a new full-reference algorithm, called Video Motion Quality (VMQ) that evaluates the relative motion quality of the distorted video generated from the reference video based on all the frames from both videos. VMQ uses any frame-based metric to compare frames from the original and distorted videos. It uses the time stamp for each frame to measure the intersection values. VMQ combines the comparison values with the intersection values in an aggregation function to produce the final result. To explore the efficiency of the VMQ, we used a set of raw, uncompressed videos to generate a new set of encoded videos. These encoded videos are then used to generate a new set of distorted videos which have the same video bit rate and frame size but with reduced frame rate. To evaluate the VMQ, we applied the VMQ by comparing the encoded videos with the distorted videos and recorded the results. The initial evaluation results showed compatible trends with most of subjective evaluation results.
Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study
2020 3rd International Conference on Information and Computer Technologies (ICICT), 2020
Face recognition is the process of identifying a person by their facial characteristics from a di... more Face recognition is the process of identifying a person by their facial characteristics from a digital image or a video frame. Face recognition has extensive applications and there will be a massive development in future technologies. The main contribution of this research is to perform a comparative study between different statistical-based face recognition techniques, namely: Eigen-faces, Fisher-faces, and Local Binary Patterns Histograms (LBPH) to measure their effectiveness and efficiency using real-database images. These recognizers still used on top of commercial face recognition products. Additionally, this research is comprehensively comparing 17 face-recognition techniques adopted in research and industry that use artificial-neural network, criticize and categories them into an understandable category. Also, this research provides some directions and suggestions to overcome the direct and indirect issues for face recognition. It has found that there is no existing recognition method that the community of face recognition has agreed on and solves all the issues that face the recognition, such as different pose variation, illumination, blurry and low-resolution images. This study is important to the recognition communities, software companies, and government security officials. It has a direct impact on drawing clear path for new face recognition propositions. This study is one of the studies with respect to the size of its reviewed approaches and techniques.
International Journal of Innovative Research in Computer and Communication Engineering, May 30, 2015
In this paper, we address the problem of selecting and composing video transcoding services in a ... more In this paper, we address the problem of selecting and composing video transcoding services in a distributed cloud environment. One of the challenging issues for video transcoding service composition is how to find the best transcoding path to route the data flow through while satisfying the viewer requirements and specifications. In a cloud environment, video transcoding service providers provide different video transcoding services that have similar functionality (i.e., format conversion), but with different Quality of Services (QoS) specifications. Since the combination of the QoS specifications, such as frame size, frame rate, video bit rate, and transcoding delay might affect the end user's experience in non-intuitive and subjective way and also might affect the delivering of a high quality video content over any type of network, we propose a QoS-aware model to select and compose the best video transcoding services to satisfy hard constraints on the input and output video formats and comes as close as possible to satisfying soft constraints on the QoS. This model uses an aggregate function to evaluate the QoS for each transcoding service and for each viewer request to explore the best composition path. In this paper, we adapt the Simulated Annealing (SA) algorithm and the Genetic Algorithm (GA) as candidate solutions to help in the composition process. The SA/GA algorithms provide multi-constraints QoS assurance for video transcoding service composition. They also support directed acyclic graph composition topology. We have implemented a prototype of the proposed algorithms and conducted experiments using small-, medium-, and large-scale graphs of video transcoders and sample viewer requests to measure the performance and the quality of the results. The experimental results show that the SA outperforms the GA in terms of performance and success ratio for small-scale graph, while GA outperforms the SA algorithm in terms of performance for medium-and large-scale graphs. The success ratio for the SA and GA algorithms are close to each other for medium-and large-scale graphs. At the end, we provide several directions and suggestions for future work.
QoS-Aware Video Transcoding Service Selection Process
Journal of Media & Mass Communication, 2015
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Papers by Nawaf O. Alsrehin