Current Issue : October - December Volume : 2014 Issue Number : 4 Articles : 5 Articles
We present a new one-dimensional chaotic map, suitable for real-time image encryption. Its theoretical analysis, performed using\nsome specific tools from the chaos theory, shows that the proposed map has a chaotic regime and proves its ergodicity, for a large\nspace of values of the control parameter. In addition, to argue for the good cryptographic properties of the proposed map, we have\ntested the randomness of the values generated by its orbit using NIST statistical suite.Moreover, we present a new image encryption\nscheme with a classic bimodular architecture, in which the confusion and the diffusion are assured by means of two maps of the\npreviously proposed type. The very good cryptographic performances of the proposed scheme are proved by an extensive analysis,\nwhich was performed regarding the latest methodology in this field....
Wireless Ad hoc networks provide a flexible and adaptable infrastructure to transport data over a great variety of environments.\nRecently, real-time audio and video data transmission has been increased due to the appearance of many multimedia applications.\nOne of themajor challenges is to ensure the quality ofmultimedia streamswhen they have passed through a wireless ad hoc network.\nIt requires adapting the network architecture to the multimedia QoS requirements. In this paper we propose a new architecture\nto organize and manage cluster-based ad hoc networks in order to provide multimedia streams. Proposed architecture adapts the\nnetwork wireless topology in order to improve the quality of audio and video transmissions. In order to achieve this goal, the\narchitecture uses some information such as each node�s capacity and the QoS parameters (bandwidth, delay, jitter, and packet loss).\nThe architecture splits the network into clusters which are specialized in specific multimedia traffic. The real system performance\nstudy provided at the end of the paper will demonstrate the feasibility of the proposal....
The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called\nhigh efficiency video coding (HEVC). In theHEVC, there are three units in block structure: coding unit (CU), prediction unit (PU),\nand transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting\ninto four blocks with equal size, starting fromthe tree block. In this paper, we propose a fast CU depth decision algorithm forHEVC\ntechnology to reduce its computational complexity. In 2???? Ã?â?? 2????PU, the proposed method compares the rate-distortion (RD) cost\nand determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge\nSKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental\nresult shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main\nprofile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate\nloss of 0.17% is also observed without significant loss of image quality....
Due to its flexibility, scalability, real-time, and rich QoS features, Data Distribution Service (DDS) middleware provides seamless\nintegration with high-performance, real-time, and mission-critical networks. Unlike traditional client-server communication\nmodels, DDS is based on the publish/subscribe communication model. DDS improves video streaming quality through its efficient\nand high-performance data delivery mechanism. This paper studies and investigates how DDS is suitable for streaming real-time\nfull-motion video over a communication network. Experimental studies are conducted to compare video streaming using a the\nVLC player with the DDS overlay. Our results depict the superiority of DDS in provisioning quality video streams at the cost of low\nnetwork bandwidth.The results also showthatDDS is more scalable and flexible and is a promised technology for video distribution\nover IP networks where it uses much less bandwidth while maintaining high quality video stream delivery.\n1. Introduction\nVideo streaming applications are experiencing fast growth\nand demand for diverse business needs. Applications of video\nstreaming include, for example, commercial applications\nsuch as e-learning, video conferencing, stored-video streaming;\nand military applications such as video surveillance of\ntargeted field or specific objects. Video traffic is resource\nintensive and consumes a lot of network bandwidth; therefore\nit is challenging issue to streamvideo over limited-bandwidth\nnetworks, for example, WSN or Bluetooth. In many cases,\nbandwidth usage implies direct cost on end-users. In this\nwork, we try to enhance the end-user experience both in\nterms of quality and cost, through the deployment of theDDS\nmiddleware.\n1.1. DDS Overview and Video QoS Polices. DDS stands\nfor Data Distribution Service. It is a set of specifications\nstandardized by the Object Management Group (OMG).\nThe DDS middleware is a known standard with built-in\ndata-structures and attributes specified by meta-information\ncalled topics. Every topic describes a set of associated datasamples\nwith the same data-property and data-structure. For\nexample, a topic named ââ?¬Å?temperatureââ?¬Â can...
We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large\nscale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For\nthe first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role\nbased access controlmodel.Verification experiments were conducted on the CloudSim simulation platform, and the results showed\nthat the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the\nobject-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were\nextracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such\nas the ImageNet dataset.Aspatial bag ofwords tiling approach was then adopted to encode these feature vectors for bridging the gap\nbetween the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging\nTRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art\napproaches....
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