Search

Wireless sensor networks connect a set of highly flexible wireless devices with small weight and size. They are used to monitor and control the environment by organizing the acquired data at a central device. Constructing fully connected networks using low power consumption sensors, devices, and protocols is one of the main challenges facing wireless sensor networks, especially in places where it is difficult to establish wireless networks in a normal way, such as military areas, archaeological sites, agricultural districts, construction sites, and so on. This paper proposes an approach for constructing and extending Bi-Directional mesh networks using low power consumption technologies inside various indoors and outdoors architectures called “an adaptable Spider-Mesh topology”. The use of ESP-NOW protocol as a communication technology added an advantage of longer communication distance versus a slight increase of consumed power. It provides 15 times longer distance compared to BLE protocol while consuming only twice as much power. Therefore, according to our theoretical and experimental comparisons, the proposed approach could provide higher network coverage while maintaining an acceptable level of power consumption.

Temporal consistency stands as a vital property in semantic video retrieval. Few research studies can exploit this useful property. Most of the used methods in those studies depend on rules defined by experts and use ground-truth annotation. The Ground-truth annotation is time-consuming, labor intensive and domain specific. Additionally, it involves a limited number of annotated concepts and a limited number of annotated shots. Video concepts have interrelated relations, so the extracted temporal rules from ground-truth annotation are often inaccurate and incomplete. However, concept detection score data are a huge high-dimensional continuous-valued dataset and generated automatically. Temporal association rules algorithms are efficient methods in revealing the temporal relations, but they have some limitations when applied to high-dimensional and continuous-valued data. These constraints have led to a lack of research used temporal association rules. So, we propose a novel framework to encode the high-dimensional continuous-valued concept detection scores data into a single stream of numbers without loss of important information and to predict the neighbouring shots’ behavior by generating temporal association rules. Experiments on TRECVID 2010 dataset show that the proposed framework is both efficient and effective in encoding the dataset which reduces the dimensionality of the dataset matrix from 130×150000 dimensions to 130×1 dimensions without loss of important information and in predicting the behavior of neighbouring shots, the number of which can be 10 or more, using the extracted temporal rules.

There is a tremendous growth of digital data due to the stunning progress of digital devices which facilitates capturing them. Digital data include image, text, and video. Video represents a rich source of information. Thus, there is an urgent need to retrieve, organize, and automate videos. Video retrieval is a vital process in multimedia applications such as video search engines, digital museums, and video-on-demand broadcasting. In this paper, the different approaches of video retrieval are outlined and briefly categorized. Moreover, the different methods that bridge the semantic gap in video retrieval are discussed in more details.

The internet of things (IoT) and cloud computing are evolving technologies in the information technology field. Merging the pervasive IoT technology with cloud computing is an innovative solution for better analytics and decision-making. Deployed IoT devices offload different types of data to the cloud, while cloud computing converges the infrastructure, links up the servers, analyzes information obtained from the IoT devices, reinforces processing power, and offers huge storage capacity. However, this merging is prone to various cyber threats that affect the IoT-Cloud environment. Mutual authentication is considered as the forefront mechanism for cyber-attacks as the IoT-Cloud participants have to ensure the authenticity of each other and generate a session key for securing the exchanged traffic. While designing these mechanisms, the constrained nature of the IoT devices must be taken into consideration. We proposed a novel lightweight protocol (Light-AHAKA) for authenticating IoT-Cloud elements and establishing a key agreement for encrypting the exchanged sensitive data was proposed. In this paper, the formal verification of (Light-AHAKA) was presented to prove and verify the correctness of our proposed protocol to ensure that the protocol is free from design flaws before the deployment phase. The verification is performed based on two different approaches, the strand space model and the automated validation of internet security protocols and applications (AVISPA) tool.

This paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image was partitioned into five disjoint clusters based on this information via the unsupervised k-means algorithm. Among the clustered images, the one having the brightest average intensity was chosen to be the best cluster containing all the bright yellowish pixels. Using this cluster, a threshold value was estimated via statistic-based metrics and subsequently applied to remove any non-bright clustered pixels and preserve only the relatively bright ones within the image. Finally, the optic disc was eliminated from the thresholded image, leaving out only the bright abnormalities. This approach was evaluated over a total of 1419 images retrieved from three heterogeneous datasets: DIARETDB0, DIARETDB1 and MESSIDOR. The proposed segmentation algorithm was fully-automated, non-customized, simple and straightforward, regardless of the heterogeneity of the datasets. The proposed system correctly detected the bright abnormalities achieving an average sensitivity and specificity of 85.08% and 56.77%, respectively.

Wireless multimedia sensor networks (WMSNs), is an ad hoc network of wirelessly connected sensor nodes that allow retrieving video and audio streams, still images, and scalar sensor data but such sensors are limited in energy, memory, communication, and computational power. Multimedia transmission over wireless sensor network (WSN) is a challenging task due to quality-of-service(QoS) guarantees such as huge amount of bandwidth, strict delay, and lower loss ratio. Recently, cross-layer approach adopted by WMSNs shows a promising approach that improves quality of multimedia transmitted over WSNs under different wireless conditions. In this paper, an energy aware and adaptive cross layer scheme to transmit multimedia content over WSNs is presented. It provides packet, queue, and path scheduling, so that it selects optimal video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type through an adaptive priority video queue so that less important packets are dropped in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority are routed through different paths with different QoSs considering network lifetime. Simulation results show that new scheme transmits video over WSNs efficiently and meets QoS requirements and uses energy wisely to prolongs network lifetime.

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.

Cloud computing becomes the next-generation architecture of IT Enterprises. Cloud computing is a technology. It enables clients to use high-end services in a form of software as a service. These reside on different servers all over the world. There are many security threats in cloud computing. Data security is one of them. Data security raises client concerns. There are many issues of data security. It named maintenance of data integrity, data hiding and data safety. These threats dominate clients concerns when the issue of cloud come up. Cloud computing has a big data. Traditional encryption method is time-consuming in this environment. Cloud computing has a single security architecture. It has also many customers with different demands of security. In this case, data security is considered one of the most important issues in cloud computing. The proposed work focused on accessing data securely in cloud and desktop environment. It depends on public key cryptosystem. Generally, Data security is an important factor for both cloud computing and traditional desktop applications. Client needs to have the highest possible level of privacy. Public key cryptosystem is a good candidate for this purpose. It plays a vital role in cloud computing security and desktop. The paper presents an evaluation for selected public key cryptosystem techniques. It named ElGamal cryptosystem, modified chaotic cryptosystem and chaotic cryptosystem. A modified chaotic cryptosystem was presented. The modification enabled the same message to have different cipher version. The experiments implemented at two independent platforms namely desktop computer and Amazon EC2 Micro Instance cloud computing environment. In this paper, the selected algorithms compared according to randomness testing. A standardized NIST statistical testing is used in both desktop environment and cloud computing environment. The algorithms are implemented using python charm Cryptography framework. Simulation results are shown to demonstrate the effectiveness of each algorithm. NIST statistical tests are used to determine suitable technique for cloud computing environment and desktop. It also used to study the performance of the selected encryption techniques in both environment. General Terms Cloud computing, Security. NIST statistical testing, chaotic, a Modified chaotic, ElGamal

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors, which are limited in terms of memory, computing, bandwidth, and battery lifetime. Multimedia transmission over WSN requires certain QoS guarantees such as huge amount of bandwidth, strict delay and lower loss ratio that makes transmitting multimedia is a complicated task. However, adopting cross-layer approach in WMSNs improves quality of service of WSN under different environmental conditions. In this work, an energy efficient and QoS aware framework for transmitting multimedia content over WSN (EQWSN) is presented, where packet, queue and path scheduling were introduced. It adapts the application layer parameter of video encoder to current wireless channel state, and drops less important packets in case of network congestion according to packet type. Finally, the path scheduling differentiates packets types/priority and route them through different paths with different QoS considering network lifetime. Simulation results show that the new scheme EQWSN transmits video quality with QoS guarantees in addition to prolonging network lifetime.

Wireless multimedia sensor networks (WMSNs), have limited resources in terms of computational, memory, bandwidth, and battery capability, which makes transmitting multimedia content over it, is a challenge as multimedia requires QoS guarantee. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an adaptive cross layer framework for transmitting multimedia content over WSN (ACWSN) is presented, it is based on an extensive study of Group Of Picture structure effect on quality of multimedia transmission in various wireless channel states that is executed at sink node. It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state which is communicated from physical layer and recommendations communicated from sink node; in addition an AVQ (adaptive video queue) which schedule packets according to its type to drop less important packets in case of network congestion. Simulation results show that ACWSN optimizes video quality in different wireless channel conditions.

Breast cancer or malignant breast neoplasm is the most common type of cancer in women. Researchers are not sure of the exact cause of breast cancer. If the cancer can be detected early, the options of treatment and the chances of total recovery will increase. Computer Aided Diagnostic (CAD) systems can help the researchers and specialists in detecting the abnormalities early. The main goal of computerized breast cancer detection in digital mammography is to identify the presence of abnormalities such as mass lesions and Micro calcification Clusters (MCCs). Early detection and diagnosis of breast cancer represent the key for breast cancer control and can increase the success of treatment. This chapter investigates a new CAD system for the diagnosis process of benign and malignant breast tumors from digital mammography. X-ray mammograms are considered the most effective and reliable method in early detection of breast cancer. In this chapter, the breast tumor is segmented from medical image using Fuzzy Clustering Means (FCM) and the features for mammogram images are extracted. The results of this work showed that these features are used to train the classifier to classify tumors. The effectiveness and performance of this work is examined using classification accuracy, sensitivity and specificity and the practical part of the proposed system distinguishes tumors with high accuracy.

advancements in cloud computing due to increased connectivity and exponentially proliferating data has resulted in migration towards cloud architecture that employ dynamic load and access balancing. Cloud computing is technology where the clients" can use high end services in form of software that reside on different servers and access data from all over the world. With a promising technology like this, it certainly abandons clients" privacy, management of data and services, putting new security threats towards the assurance of data in cloud. However, there are some security concerns when clients handle and share data in the cloud- computing environment. The security threats such as maintenance of data integrity, data hiding and data safety dominate clients concerns when the issues of cloud security come up. The big data and time-consuming encryption calculations related to applying any encryption method have proved as a hindrance in this field. Cryptography is knowledge of protecting the information for providing encryption techniques In this paper, Cloud computing security framework was tested. The framework ensures a trusted cloud environment that controlled by both the client and the cloud environment. The proposed solution secures the movement of data between client and cloud end. the non-breakability of ElGamal based on Elliptic curve cryptography for data encryption/decryption was used along with Diffie Hellman based on elliptic curve mechanism for connection establishment, The data integrity verification is done by introducing a message digest tag for updating data based on SHA-256. The proposed encryption mechanism uses the combination of elliptical cryptography and SHA-256 methods.

Tactical Networks rely on modern communication technologies to connect soldiers and commanders engaged in the battlefield and enable enhanced information flow and data gathering, allowing for decision making based on real-time data analysis. Tactical network is a wireless communication network where wireless tactical network security is a milestone because a soldier does not know from where and when the attacks come.In this paper, a dangerous attack over the deployed tactical network is proposed through group of drones which are sent to the tactical network headquarter. The drone’s mission is to identify every device MAC, IP, Name and Location to intercept data or destroy the devices. Also, in this paper, a proposed solution is given for this proposed attack through changing MAC, IP, Name and Location for every device in the headquarter periodically according to the attack time and the devices importance. The MAC, IP, Name and Location must be changed before the drones return to enemy territories.A software program is developed to change MAC, IP, and Name periodically from centered server with no conflicts. The performance analysis for the proposed solution have been taken into consideration the metrics of packets drops, time delay, communication overhead and storage overhead during the changing time. A software tool verifies the changing of IP, MAC and Name for every device. This solution ensures that before the drones return to enemy territories all devices fixed parameters of MAC, IP, and Name are changed.

Protein structure prediction is a very challenging problem in drug discovery and computational biology. To achieve better multi-class classification model for fold recognition problem, a combination between semi- supervised and boosting techniques is proposed into Semi- supervised Boosting Fold Recognition (SB-FR) algorithm. In addition, a testing method "TreeTest" is introduced for improving the overall accuracy of SB-FR algorithm. To benchmark the performance of the proposed SB-FR algorithm, a famous challengeable "Ding and Dubchak" dataset is used for training and testing. In addition, different parameters are applied to the same random sets of labeled and unlabeled sequences. To benchmark the "TreeTest" testing method, All-versus-All (AvA) testing method is used for comparison. Finally, using the proposed SB-FR algorithm along with the proposed "TreeTest" method for fold recognition multi-class classification, a 5.6% improvement is recorded in the overall accuracy for three- class and 8.2% for five-class classifications when compared to the base classifier. 

During the last few years, the Segmentation problem has been tackled from different disciplines. Many algorithms have been developed to solve this problem. AntClust algorithm is an antbased algorithm that uses the self-organizing and autonomous brood sorting behavior observed in real ants for unsupervised partitioning. A population of artificial ants provides an image segmentation of the relevant classes without any previous knowledge about the number of classes needed. This paper proposes a hybrid solution based on AntClust algorithm and data mining (e.g., Kmeans). Experimental results demonstrate that the proposed solution is able to extract the correct number of clusters with better clustering quality and execution time compared to the results obtained from AntClust algorithm.

The immense growth in wireless network applications has encouraged the researchers to enhance and propose new approaches that facilitate the deployment of the widely used services, protocols, and applications of the wired networking area in the wireless networks. Among which, the Session Initiation Protocol (SIP) is a signaling protocol that allows the establishment of multimedia sessions and calls between different parties. SIP functionality totally depends on a centralized infrastructure, and complexity arises when deploying such a protocol in a special type of wireless networks, namely Mobile Ad-hoc Networks (MANET), due to the absence of a centralized infrastructure, and the dynamic topology of the later. This paper highlights the the challenges arising when deploying SIP in MANETs and presents an overview of different proposed approaches to overcome these challenges.

Optic disc segmentation is a key element in automatic screening systems, which facilitates the detection of lesions that affect the interior surface of the eye (i.e. fundus). Therefore, this paper aims to provide a fully automated technique for detecting and segmenting the optic disc. First, the fundus image is preprocessed in order to estimate the approximate location of the optic disc, excluding the positions that doubtfully contain the optic disc. Consequently, the top candidates for the optic disc are detected and then ranked based on their strengths. Afterwards, the vessels density within each candidate is calculated and then weighted according to the candidate's strength, in which the one having the highest score is chosen to be the segmented optic disc. The performance of the proposed segmentation algorithm is evaluated over nine heterogeneous datasets of fundus images, achieving a sensitivity of 94.72%.

although the tendency towards a Semantic Web is increasing, the current Web is keen on more semantic data. There are several web application domains which need to present their data in the form of graphs to get information and knowledge to allow semantic web searching to deal with them. These domains can be for example geographic data, data in the area of bioinformatics, social networks or even corporate search engines. Most of these domains already store their data in relational database management systems which are not capable of being queried directly by semantic web applications. In this paper we will propose a semi-automated method to map the data from relational databases to RDF structures. This is true for both a single source database as well as different source databases. The results show a complexity of order n2 where n is the number of records processed, when it comes to the performance in creating the output from mapping the relational database to RDF files.

Thesis documents are underestimated even though they hold large sets of useful information -- as they include most of the research information -- , but since they are harder to obtain, researchers were lead to depend on research papers even though they have a size limitation and lack elaboration. A lot of time and effort are invested in research, so having a linkage among researchers based on their work would somehow facilitate solving the research problem process. A major step to tackle this goal is to structure thesis documents by extracting some fields such as title, author and abstract. This paper presents a way to structure a semi-structured thesis documents using decision trees in 4 different ways (Simple, Medium, Complex and using KNIME), they scored an overall accuracy of 99.2%.

Wireless Multimedia Sensor Networks (WMSNs), is a network of sensors which are limited in terms of computational, memory, bandwidth, and battery capability. Multimedia transmission over WSN requires certain Qos guarantees such as huge amount of bandwidth, strict delay and lower loss ratio, which makes transmitting multimedia content over it, is a challenging problem. Recently adopting cross-layer design in WMSNs proved to be a promising approach, which improves quality of service of WSN under various operational conditions. In this work, an energy aware framework for transmitting multimedia content over WSN (ECWMSN) is presented, where packet and path scheduling were introduced, so that It adaptably selects optimum video encoding parameters at application layer according to current wireless channel state, and schedules packets according to its type to drop less important packets in case of network congestion. Finally, path scheduling is introduced so that different packets types/priority is routed through suitable path with suitable Qos taking into consideration the network lifetime. Simulation results show that ECWMSN optimizes video quality and prolongs network lifetime.

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fundus showing its main structural components along with their properties and functions. Consequently, the survey reviews the image enhancement techniques and also categorizes the image segmentation methodologies for the optic disc which include property-based methods, methods based on convergence of blood vessels, and model-based methods. The performance of segmentation algorithms is evaluated using a number of publicly available databases of retinal images via evaluation metrics which include accuracy and true positive rate (i.e. sensitivity). The survey, at the end, describes the different abnormalities occurring within the optic disc region.

Radio Frequency Identification (RFID) is a technology that has risen to prominence over the past decade. EPCglobal Network provides a standardized way to collect, store, and distribute data among various organizations. Low-Level Reader Protocol (LLRP) is a standard interface between RFID Readers and Clients. LLRP implements Transport Layer Security (TLS) to setup a secure connection. Authentication techniques in TLS protocol are mainly based on public key cryptography. Recently TLS supports Kerberos cipher suites that provide Kerberos-based authentication. It is a symmetric key cryptography. This paper introduces RFID Access Control system (RFID-AC) as approach and assesses its security vulnerabilities. The paper experimentally implements TLS LLRP endpoints and examines different possible security solutions. Experiments focus on comparing the commonly used authentication technique; authenticated certificate-based versus Kerberos credentials-based. Analysis was done to determine the most suitable one for access control that can satisfy the security concerns and Quality of Services (QoS) issues.

OUI (Organic User Interface) together with NUI (Natural User Interface) and TUI (Tangible User Interface) falls under the field of Ubiquitous Computing, aiming to give the digital life a deeper human sense. Accordingly, we presented an OUI framework for designing OUIs to formalize the input and output interaction techniques for OUI users, and discuss it based on novel perspective of the organic interaction possible styles. We enhance the SMaG (Speech/ Manipulation/ air-Gesture) interaction model for organic interaction styles, in addition to our OUI design principles and design-specific guidelines in a perspective based on the usability aspects of the look, feel and design. Moreover, we propose a new usability model in this paper to enable measuring and testing the usability standards of organic systems, in which we called the 3Es usability model, referring to Efficiency, Enjoyment and Easiness.

The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance). Expressions recognition is performed by using radial basis function (RBF) based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness) in addition to the natural. The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

A methodology for elucidation of structural, functional, and mechanistic knowledge on promiscuous proteins is proposed that constitutes a workflow of integrated bioinformatics analysis. Sequence alignments with closely related homologues can reveal conserved regions which are functionally important. Scanning protein motif databases, along with secondary and surface accessibility predictions integrated with post-translational modification sites (PTMs) prediction reveal functional and protein-binding motifs. Integrating this information about the protein with the GO, SCOP, and CATH annotations of the templates can help to formulate a 3D model with reasonable accuracy even in the case of distant sequence homology. A novel integrative model of the non-structural protein 5A of Hepatitis C virus: a hub promiscuous protein with roles in virus replication and host interactions is proposed. The 3D structure for domain II was predicted based on, the Homo sapiens Replication factor-A protein-1 (RPA1), as a template using consensus meta-servers results. Domain III is an intrinsically unstructured domain with a fold from the retroviral matrix protein, which conducts diverse protein interactions and is involved in viral replication and protein interactions. It also has a single-stranded DNA-binding protein motif (SSDP) signature for pyrimidine binding during viral replication. Two protein-binding motifs with high sequence conservation and disordered regions are proposed; the first corresponds to an Interleukin-8B receptor signature (IL-8R-B), while the second has a lymphotoxin beta receptor (LTβR) high local similarity. A mechanism is proposed to their contribution to NS5A Interferon signaling pathway interception. Lastly, the overlapping between LTβR and SSDP is considered as a sign for NS5A date hubs.