Space Shuttle/TDRSS communication and tracking systems analysis

In order to evaluate the technical and operational problem areas and provide a recommendation, the enhancements to the Tracking and Data Delay Satellite System (TDRSS) and Shuttle must be evaluated through simulation and analysis. These enhancement techniques must first be characterized, then modeled mathematically, and finally updated into LinCsim (analytical simulation package). The LinCsim package can then be used as an evaluation tool. Three areas of potential enhancements were identified: shuttle payload accommodations, TDRSS SSA and KSA services, and shuttle tracking system and navigation sensors. Recommendations for each area were discussed

Scalable reliable on-demand media streaming protocols

This thesis considers the problem of delivering streaming media, on-demand, to potentially large numbers of concurrent clients. The problem has motivated the development in prior work of scalable protocols based on multicast or broadcast. However, previous protocols do not allow clients to efficiently: 1) recover from packet loss; 2) share bandwidth fairly with competing flows; or 3) maximize the playback quality at the client for any given client reception rate characteristics. In this work, new protocols, namely Reliable Periodic Broadcast (RPB) and Reliable Bandwidth Skimming (RBS), are developed that efficiently recover from packet loss and achieve close to the best possible server bandwidth scalability for a given set of client characteristics. To share bandwidth fairly with competing traffic such as TCP, these protocols can employ the Vegas Multicast Rate Control (VMRC) protocol proposed in this work. The VMRC protocol exhibits TCP Vegas-like behavior. In comparison to prior rate control protocols, VMRC provides less oscillatory reception rates to clients, and operates without inducing packet loss when the bottleneck link is lightly loaded. The VMRC protocol incorporates a new technique for dynamically adjusting the TCP Vegas threshold parameters based on measured characteristics of the network. This technique implements fair sharing of network resources with other types of competing flows, including widely deployed versions of TCP such as TCP Reno. This fair sharing is not possible with the previously defined static Vegas threshold parameters. The RPB protocol is extended to efficiently support quality adaptation. The Optimized Heterogeneous Periodic Broadcast (HPB) is designed to support a range of client reception rates and efficiently support static quality adaptation by allowing clients to work-ahead before beginning playback to receive a media file of the desired quality. A dynamic quality adaptation technique is developed and evaluated which allows clients to achieve more uniform playback quality given time-varying client reception rates

Optimized algorithms for multimedia streaming

Ph.DDOCTOR OF PHILOSOPH

Channel Allocation for Smooth Video Delivery over Cognitive Radio Networks

Video applications normally demand stringent quality-of-service (QoS) for the high quality and smooth video playback at the receiver. Since the network is usually shared by multiple applications with diverse QoS requirements, QoS provisioning is an important and difficult task for the efficient and smooth video delivery. In the context of cognitive radio (CR) networks, as the secondary or unlicensed users share a pool of bandwidth that is temporarily being unused by the primary or licensed users, there is an inevitable interference between the licensed primary users and the unlicensed CR devices. As a result, efficient and smooth video delivery becomes even more challenging as the channel spectrum is not only a precious resource, but also much more dynamic and intermittently available to secondary users. In this thesis, we focus on the provision of guaranteed QoS to video streaming subscribers in CR network. In video streaming applications, a playout buffer is typically deployed at the receiver to deal with the impact of the network dynamics. With different buffer storage, users can have different tolerance to the network dynamics. We exploit this feature for channel allocation in CR network. To this end, we model the channel availability as an on-off process which is stochastically known. Based on the bandwidth capacity and the specific buffer storage of users, we intelligently allocate the channels to maximize the overall network throughput while providing users with the smooth video playback, which is formulated as an optimization framework. Given the channel conditions and the video packet storage in the playout buffer, we propose a centralized scheme for provisioning the superior video service to users. Simulation results demonstrate that by exploiting the playout buffer of users, the proposed channel allocation scheme is robust against intense network dynamics and provides users with the elongated smooth video playback

Enabling Large-Scale Peer-to-Peer Stored Video Streaming Service with QoS Support

This research aims to enable a large-scale, high-volume, peer-to-peer, stored-video streaming service over the Internet, such as on-line DVD rentals. P2P allows a group of dynamically organized users to cooperatively support content discovery and distribution services without needing to employ a central server. P2P has the potential to overcome the scalability issue associated with client-server based video distribution networks; however, it brings a new set of challenges. This research addresses the following five technical challenges associated with the distribution of streaming video over the P2P network: 1) allow users with limited transmit bandwidth capacity to become contributing sources, 2) support the advertisement and discovery of time-changing and time-bounded video frame availability, 3) Minimize the impact of distribution source losses during video playback, 4) incorporate user mobility information in the selection of distribution sources, and 5) design a streaming network architecture that enables above functionalities.To meet the above requirements, we propose a video distribution network model based on a hybrid architecture between client-server and P2P. In this model, a video is divided into a sequence of small segments and each user executes a scheduling algorithm to determine the order, the timing, and the rate of segment retrievals from other users. The model also employs an advertisement and discovery scheme which incorporates parameters of the scheduling algorithm to allow users to share their life-time of video segment availability information in one advertisement and one query. An accompanying QoS scheme allows reduction in the number of video playback interruptions while one or more distribution sources depart from the service prematurely.The simulation study shows that the proposed model and associated schemes greatly alleviate the bandwidth requirement of the video distribution server, especially when the number of participating users grows large. As much as 90% of load reduction was observed in some experiments when compared to a traditional client-server based video distribution service. A significant reduction is also observed in the number of video presentation interruptions when the proposed QoS scheme is incorporated in the distribution process while certain percentages of distribution sources depart from the service unexpectedly

White Matter Hyperintensity and Multi-region Brain MRI Segmentation Using Convolutional Neural Network

Accurate segmentation of WMH (white matter hyperintensity) from the magnetic resonance image is a prerequisite for many precise medical procedures, especially for the diagnosis of vascular dementia. Brain segmentation has important research significance and clinical application prospects especially for early detection of Alzheimer’s disease. In order to effectively perform accurate segmentation according to the MRI characteristics of different regions of the brain, this thesis proposed an optimized 3D u-net and used WHM segmentation as a pre-experiment to select the good hyperparameters (i.e. network depth, image fusion method, and the implementation of loss function) to construct an image feature learning network with both long and short skip connections. Soft voting is used as the postprocessing procedure. Our model is evaluated by a 10-fold cross-validation and achieved a dice score of 0.78 for binary segmentation (WMH segmentation) and accuracy of 0.96 for multi-class segmentation (139 regions brain segmentation), outperforming other methods

Dynamic Application Level Security Sensors

The battle for cyber supremacy is a cat and mouse game: evolving threats from internal and external sources make it difficult to protect critical systems. With the diverse and high risk nature of these threats, there is a need for robust techniques that can quickly adapt and address this evolution. Existing tools such as Splunk, Snort, and Bro help IT administrators defend their networks by actively parsing through network traffic or system log data. These tools have been thoroughly developed and have proven to be a formidable defense against many cyberattacks. However, they are vulnerable to zero-day attacks, slow attacks, and attacks that originate from within. Should an attacker or some form of malware make it through these barriers and onto a system, the next layer of defense lies on the host. Host level defenses include system integrity verifiers, virus scanners, and event log parsers. Many of these tools work by seeking specific attack signatures or looking for anomalous events. The defenses at the network and host level are similar in nature. First, sensors collect data from the security domain. Second, the data is processed, and third, a response is crafted based on the processing. The application level security domain lacks this three step process. Application level defenses focus on secure coding practices and vulnerability patching, which is ineffective. The work presented in this thesis uses a technique that is commonly employed by malware, dynamic-link library (DLL) injection, to develop dynamic application level security sensors that can extract fine-grain data at runtime. This data can then be processed to provide stronger application level defense by shrinking the vulnerability window. Chapters 5 and 6 give proof of concept sensors and describe the process of developing the sensors in detail

Video-on-Demand over Internet: a survey of existing systems and solutions

Video-on-Demand is a service where movies are delivered to distributed users with low delay and free interactivity. The traditional client/server architecture experiences scalability issues to provide video streaming services, so there have been many proposals of systems, mostly based on a peer-to-peer or on a hybrid server/peer-to-peer solution, to solve this issue. This work presents a survey of the currently existing or proposed systems and solutions, based upon a subset of representative systems, and defines selection criteria allowing to classify these systems. These criteria are based on common questions such as, for example, is it video-on-demand or live streaming, is the architecture based on content delivery network, peer-to-peer or both, is the delivery overlay tree-based or mesh-based, is the system push-based or pull-based, single-stream or multi-streams, does it use data coding, and how do the clients choose their peers. Representative systems are briefly described to give a summarized overview of the proposed solutions, and four ones are analyzed in details. Finally, it is attempted to evaluate the most promising solutions for future experiments. Résumé La vidéo à la demande est un service où des films sont fournis à distance aux utilisateurs avec u

Smart PIN: performance and cost-oriented context-aware personal information network

The next generation of networks will involve interconnection of heterogeneous individual networks such as WPAN, WLAN, WMAN and Cellular network, adopting the IP as common infrastructural protocol and providing virtually always-connected network. Furthermore, there are many devices which enable easy acquisition and storage of information as pictures, movies, emails, etc. Therefore, the information overload and divergent content’s characteristics make it difficult for users to handle their data in manual way. Consequently, there is a need for personalised automatic services which would enable data exchange across heterogeneous network and devices. To support these personalised services, user centric approaches for data delivery across the heterogeneous network are also required. In this context, this thesis proposes Smart PIN - a novel performance and cost-oriented context-aware Personal Information Network. Smart PIN's architecture is detailed including its network, service and management components. Within the service component, two novel schemes for efficient delivery of context and content data are proposed: Multimedia Data Replication Scheme (MDRS) and Quality-oriented Algorithm for Multiple-source Multimedia Delivery (QAMMD). MDRS supports efficient data accessibility among distributed devices using data replication which is based on a utility function and a minimum data set. QAMMD employs a buffer underflow avoidance scheme for streaming, which achieves high multimedia quality without content adaptation to network conditions. Simulation models for MDRS and QAMMD were built which are based on various heterogeneous network scenarios. Additionally a multiple-source streaming based on QAMMS was implemented as a prototype and tested in an emulated network environment. Comparative tests show that MDRS and QAMMD perform significantly better than other approaches