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Virtual and augmented reality (VR/AR), teleoperation, and telepresence technologies heavily depend on video streaming and playback to enable immersive user experiences. However, the substantial bandwidth requirements and file sizes associated with VR/AR and 360-degree video content present significant challenges for efficient transmission and storage. Modern video coding standards, including HEVC, AV1, VP9, VVC, and EVC, have been designed to address these issues by enhancing coding efficiency while maintaining video quality on par with the H.264 standard. Nonetheless, the adaptive block structures inherent to these video coding standards introduce increased computational complexity, necessitating additional intra-prediction modes. The integration of AI in video coding has the potential to substantially improve video compression efficiency, reduce file sizes, and enhance video quality, making it a crucial area of research and development within the video coding domain. As AI systems can execute a wide array of tasks and adapt to new challenges, their incorporation into video coding may result in even more advanced compression techniques and innovative solutions to meet the ever-evolving demands of the industry. In this study, we introduce a state-of-the-art adaptive instant learning-based model, named iHELP, developed to address the computational complexity arising from encoders' adaptive block structures. The iHELP model achieves outstanding coding efficiency and quality while considerably improving encoding speed. iHELP model has been tested on HEVC, but it applies to other encoders with similar adaptive block structures. iHELP model employs entropy-based block similarity to predict the splitting decision of the LCU, determining whether to divide the block based on the correlation between the block content and previously adjacent encoded blocks in both spatial and temporal dimensions. Our methodology has been rigorously evaluated using the HEVC standard's common test conditions, and the results indicate that iHELP serves as an effective solution for efficient video coding in bandwidth-constrained situations, making it suitable for real-time video applications. The proposed method achieves an 80% reduction in encoding time while maintaining comparable PSNR performance relative to the RDO approach. The exceptional potential of the iHELP model calls for further exploration, as no other existing methods have demonstrated such a high level of performance. [ABSTRACT FROM AUTHOR]

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment. [ABSTRACT FROM AUTHOR]

The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the "golden record" that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality. [ABSTRACT FROM AUTHOR]

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Advancement of the prediction models used in a variety of fields is a result of the contribution of machine learning approaches. Utilizing such modeling in feature engineering is exceptionally imperative and required. In this research, we show how to utilize machine learning to save time in research experiments, where we save more than five thousand hours of measuring the energy consumption of enc()ding record ings. Since measuring the energy consumption has got to be done by humans and since we require more than eleven thousand experiments to cover all the combinations of video sequences, video biLrate, and video encoding settings, we utilize machine learning to model the energy consumption utilizing linear regression. VP8 codec has been offered by Google as a free video encoder in an effort to replace the popular H.264 video encoder standard. This research model energy consumption and describes the major differences between H.264/AVC and VP8 encoders based on of energy consumption and performance through experiments that are machine learning-based modeling. Twentynine unc()mpressed video segments from a standard data-set are used, with several sizes, details, and dynamics, where the frame sizes ranging from QCIF(176x144) to 2160p(3840x2160). For fairness in comparison analysis, we use seven settings in VP8 encoder and fifteen types of tun ing in H.264/AVC. The settings cover various video qualities. The performance metrics include video qualities, enc()ding time, and encoding energy consumption. [ABSTRACT FROM AUTHOR]

Summary: White Blood Cell (WBC) segmentation is one of the important topics in the medical image processing field. Many researchers proposed several clustering approaches to segment WBC from blood smear microscopic images. However, a fast and robust segmentation of WBCs is still a challenging task. In this work, we propose parallel algorithms that utilize the parallelism capabilities of the Graphics Processing Units (GPUs) to accelerate the segmentation of WBC from microscopic images. In this research, we implement the main image segmentation clustering algorithms using one thread that we run on a single CPU (sequential implementation) and using multiple threads that we run on both the CPU and the GPU (hybrid CPU‐GPU). We focus our work on the most common four segmentation algorithms: Standard K‐means (SKM), Adaptive K‐means (AKM), Fuzzy C‐means (FCM), and Fuzzy Possibilistic C‐means (FPCM). We implement these algorithms and the pre‐processing steps for WBC image segmentation in CUDA programming to take the advantages of the large number of cores in GPUs. In this work, our hybrid implementation accelerated the four studied sequential algorithms by 4X, 3.8X, 3.4X, and 3.4X, respectively, without affecting WBC segmentation quality. [ABSTRACT FROM AUTHOR]

Summary: Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi‐orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy. [ABSTRACT FROM AUTHOR]

Despite the large body of work on fingerprint identification systems, most of it focused on using specialized devices. Due to the high price of such devices, some researchers directed their attention to digital cameras as an alternative source for fingerprints images. However, such sources introduce new challenges related to image quality. Specifically, most digital cameras compress captured images before storing them leading to potential losses of information. This study comes to address the need to determine the optimum ratio of the fingerprint image compression to ensure the fingerprint identification system's high accuracy. This study is conducted using a large in-house dataset of raw images. Therefore, all fingerprint information is stored in order to determine the compression ratio accurately. The results proved that the used software functioned perfectly until a compression ratio of (30-40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system. [ABSTRACT FROM AUTHOR]

This paper develops a cross-layer optimization solution for video streaming from multiple sources to a central proxy station over a wireless network. The proposed solution manages the application rates and transmission opportunities of various video sources based on the dynamic network conditions in such a way that minimizes the overall video distortion. The solution includes a new online approach for estimating the effective network airtime and a new video bitrate-distortion model. We demonstrate the effectiveness of the proposed solution through extensive experiments. The results show that the proposed solution substantially enhances the perceptual video quality while reducing the power consumed by the video sources and that the solution is highly adaptable to the existence of interfering network traffic. [ABSTRACT FROM AUTHOR]

Utilizing cloud services in running large-scale video surveillance systems is not uncommon. However, special attention should be given to data security and privacy because, typically, data travels over insecure public networks. In this work, we propose an end-to-end security framework for a cloud-based video surveillance system that supports a large number of cameras. Our framework provides mutual authentication, session key management, data confidentiality, and data integrity. Consequently, encrypted video frames can only be sourced from authenticated cameras and only destined to authenticated cloud devices where the integrity of such frames can also be verified against potential change. As video streaming is a very delay-sensitive application, we study different variations of the proposed framework to find security options that achieve the best trade-off between the added delay and the security of the system. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) enables ubiquitous and efficient cloud services to mobile users which facilitates mobile cloud computing (MCC) more easily by providing storage and processing capacity within the access range of the mobile devices. To achieve the goals of MEC, Mobile Edge (ME) servers are co-placed with the mobile network base station (at the edge of the mobile network). This eliminates the need to move computation and storage intensive tasks from a mobile device to a centralized cloud server. This in turn reduces the network communication load and delay and it also enhances the quality of service provided for the mobile end users. Applications, such as smart grid applications, content delivery networks, crowed sourcing, traffic management and E-health, will greatly benefit from such deployment. Unfortunately, a large-scale deployment of ME servers that is needed to make hundreds of applications available to millions of users, comes with great management complexity. The emerging technique of Software-Defined Systems (SDSys) abstracts the management complexities of many systems at different layers by utilizing software components. In this paper, we build a software-defined based framework that enable efficient and ubiquitous MCC services by integrating different SDSys components with the MEC system. The integrated framework is implemented and it is evaluated for its feasibility, flexibility and potential superiority. [ABSTRACT FROM AUTHOR]

In the past years, surveillance systems have attracted both industries and researchers due to its importance for security. Automated Video Surveillance (AVS) systems are established to automatically monitor objects in real-time. Employing wireless communication in an AVS system is an attractive solution due to its convenient installation and configuration. Unfortunately, wireless communication, in general, has limited bandwidth, not to mention the intrinsic dynamic conditions of the network (e.g., collision and congestion). Many solutions have been proposed in the literature to solve the bandwidth allocation problem in wireless networks, but much less work is done to design evaluation frameworks for such solutions. This paper targets the demand for a realistic wireless AVS system simulation framework that models and simulates most of the details in a typical wireless AVS framework. The proposed simulation framework is built over the well-known NS-3 network simulator. This framework also supports the testing and the evaluation of cross-layer solutions that manages many factors over different layers of AVS systems in the wireless 802.11 infrastructure network. Moreover, the simulation framework supports the collection of many used performance metrics that are usually used in AVS system performance evaluation. [ABSTRACT FROM AUTHOR]

Multicasting in Cognitive Radio Networks (CRNs) is receiving a great interest from researchers in both academia and industry. In this work, we investigate the problem of multi-hop multicasting routing in CRNs. To solve this problem, we propose a new multi-hop multicasting routing protocol for CRNs. The proposed protocol finds the best multi-hop path between a sender and a group of receivers and guarantees the use of the best channels along the path. We propose three variations of the proposed protocol depending on how the channel assignment is performed. In the first variation, the operating channel is determined based on channel quality only (measured in terms of the transmission time). The second variation chooses the best channel based on the average channel availability time only. The last variation determines the best channel based on the channel probability of success, which is a metric that combines both channel quality and average channel availability time. We conduct ns-2 simulation experiments to evaluate the performance of our proposed protocols under various network conditions. [ABSTRACT FROM AUTHOR]

Large-scale video surveillance systems are among the necessities for securing our life these days. The high bandwidth demand and the large storage requirements are the main challenges in such systems. To face these challenges, the system can be deployed as a multi-tier framework that utilizes different technologies. In such a framework, technologies proposed under the umbrella of the Internet of Things (IoT) can play a significant rule in facing the challenges. In video surveillance, the cameras can be considered as 'the things' that are streaming videos to a central processing and storage server (the cloud) through the Internet. Wireless technologies can be used to connect wireless cameras to the surveillance system more conveniently than wired cameras. Unfortunately, wireless communication in general tend to have limited bandwidth that needs careful management to achieve scalability. In this paper, we design and evaluate a reliable IoT-based wireless video surveillance system that provides an optimal bandwidth distribution and allocation to minimize the overall surveillance video distortion. We evaluate our system using NS-3 simulation. The results show that the proposed framework fully utilizes the available cloud bandwidth budget and achieves high scalability. [ABSTRACT FROM AUTHOR]

Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to propose, a new multi-path routing protocol that distributes packets over the available paths between a sender and a receiver in a multi-hop ad hoc network. We call this protocol Geometric Sequence Based Multipath Routing Protocol (GMRP).Design/methodology/approach GMRP distributes packets according to the geometric sequence. GMRP is evaluated using GloMoSim simulator. The authors use packet delivery ratio and end-to-end delay as the comparison performance metrics. They also vary many network configuration parameters such as number of nodes, transmission rate, mobility speed and network area.Findings The simulation results show that GMRP reduces the average end-to-end delay by up to 49 per cent and increases the delivery ratio by up to 8 per cent.Originality/value This study is the first to propose to use of geometric sequence in the multipath routing approach. [ABSTRACT FROM AUTHOR]

Providing video streaming users with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait. In the absence of any waiting-time feedback, users are more likely to defect because of the uncertainty as to when their services will start. We analyze waiting-time predictability in scalable video streaming. We propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. [ABSTRACT FROM AUTHOR]