Mohammad A. Alsmirat

Internet of surveillance: a cloud supported large-scale wireless surveillance system

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.

Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations

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.

Automated wireless video surveillance: an evaluation framework

AbstractIn 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.

SDMEC: Software Defined System for Mobile Edge Computing

Mobile Edge Computing (MEC) promises a paradigm shift in enabling efficient Mobile Cloud Computing (MCC) services by providing storage and processing capacity within the access range of the mobile devices. In MEC, Mobile Edge (ME) servers are placed at the edge of the mobile networks eliminating the need to offload compute-/storage-intensive tasks of the mobile devices to the core of the network (the centralized cloud data center). This reduces the network latency and enhances the quality of service provided for the mobile end users. Different applications can benefit from the large scale deployments of ME servers such as smart grid applications, content delivery networks, content sharing, traffic management, and E-health applications. This promising paradigm comes with its own downside related to the management complexity of large scale deployments that offers hundreds of applications to millions of users. In this paper, we introduce a software defined based framework to enable efficient MCC services through the integration of different software defined system components with the MEC system.

Impact of digital fingerprint image quality on the fingerprint recognition accuracy

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.

Analysis of waiting-time predictability in scalable media streaming

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 they will start to receive services. In this paper, we analyze waiting-time predictability in scalable video streaming. We present three 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.

Computer-aided diagnosis of lumbar disc herniation

Computer-aided diagnosis systems have been the focus of many research endeavors. They are based on the idea of processing and analyzing various types of input (such as patients medical history, physical examination results, images of different parts of the human body, etc.) to generate a quick and accurate diagnosis. In this work, we propose a system that follows the aforementioned approach to diagnose lumbar disk herniation from a top-down Magnetic Resonance Imaging (MRI) spine view of the suspected region. To the best of our knowledge, this is the first work to consider this type of images for the diagnosis of lumbar disk herniation. The proposed system consists of several stages that include image acquisition and annotation, Region Of Interest (ROI) extraction and enhancement, feature extraction, and classification. The experiments conducted to evaluate the system show that the system is quick and accurate making it a great aid in the diagnosis process as well as an invaluable platform for educational and research purposes.

Cross-Layer Optimization and Effective Airtime Estimation for Wireless Video Streaming

This paper develops a cross-layer optimization framework for video streaming from multiple sources to a central proxy station over a wireless network. The proposed framework 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 distortion. The framework utilizes a novel online approach for estimating the effective airtime of the network. We demonstrate the effectiveness of the proposed framework and effective airtime estimation approach through extensive experiments.

Waiting-time prediction in scalable on-demand video streaming

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.

Accelerating Levenshtein and Damerau edit distance algorithms using GPU with unified memory

String matching problems such as sequence alignment is one of the fundamental problems in many computer since fields such as natural language processing (NLP) and bioinformatics. Many algorithms have been proposed in the literature to address this problem. Some of these algorithms compute the edit distance between the two strings to perform the matching. However, these algorithms usually require long execution time. Many researches use high performance computing to reduce the execution time of many string matching algorithms. In this paper, we use the CUDA based Graphics Processing Unit (GPU) and the newly introduced Unified Memory(UM) to speed up the most common algorithms to compute the edit distance between two string. These algorithms are the Levenshtein and Damerau distance algorithms. Our results show that using GPU to implement the Levenshtein and Damerau distance algorithms improvements their execution times of about 11X and 12X respectively when compared to the sequential implementation. And an improvement of about 61X and 71X respectively can be achieved when GPU is used with unified memory.