Cognitive radios (CRs) have been proposed as a promising solution for improving spectrum utilization via opportunistic spectrum sharing. In a CR network environment, primary (licensed) users have priority over secondary (unlicensed) users when accessing the wireless channel. Thus, if a malicious secondary user exploits this spectrum access etiquette by mimicking the spectral characteristics of a primary user, it can gain priority access to a wireless channel over other secondary users. This scenario is referred to in the literature as primary user emulation (PUE). This dissertation first covers three approaches for detecting primary user emulation attacks in cognitive radio networks, which can be classified in two categories. The first category is based on cyclostationary features, which employs a cyclostationary calculation to represent the modulation features of the user signals. The calculation results are then fed into an artificial neural network for classification. The second category is based on video processing method of action recognition in frequency domain, which includes two approaches. Both of them analyze the FFT sequences of wireless transmissions operating across a cognitive radio network environment, as well as classify their actions in the frequency domain. The first approach employs a covariance descriptor of motion-related features in the frequency domain, which is then fed into an artificial neural network for classification. The second approach is built upon the first approach, but employs a relational database system to record the motion-related feature vectors of primary users on this frequency band. When a certain transmission does not have a match record in the database, a covariance descriptor will be calculated and fed into an artificial neural network for classification. This dissertation is completed by a novel PUE detection approach which employs a distributed sensor network, where each sensor node works as an independent PUE detector. The emphasis of this work is how these nodes collaborate to obtain the final detection results for the whole network. All these proposed approaches have been validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform.

In this thesis, we present novel methodology for emulating and calibrating computer models with high-dimensional output. Computer models for complex physical systems, such as climate, are typically expensive and time-consuming to run. Due to this inability to run computer models efficiently, statistical models ('emulators') are used as fast approximations of the computer model, fitted based on a small number of runs of the expensive model, allowing more of the input parameter space to be explored. Common choices for emulators are regressions and Gaussian processes. The input parameters of the computer model that lead to output most consistent with the observations of the real-world system are generally unknown, hence computer models require careful tuning. Bayesian calibration and history matching are two methods that can be combined with emulators to search for the best input parameter setting of the computer model (calibration), or remove regions of parameter space unlikely to give output consistent with the observations, if the computer model were to be run at these settings (history matching). When calibrating computer models, it has been argued that fitting regression emulators is sufficient, due to the large, sparsely-sampled input space. We examine this for a range of examples with different features and input dimensions, and find that fitting a correlated residual term in the emulator is beneficial, in terms of more accurately removing regions of the input space, and identifying parameter settings that give output consistent with the observations. We demonstrate and advocate for multi-wave history matching followed by calibration for tuning. In order to emulate computer models with large spatial output, projection onto a low-dimensional basis is commonly used. The standard accepted method for selecting a basis is to use n runs of the computer model to compute principal components via the singular value decomposition (the SVD basis), with the coefficients given by this projection emulated. We show that when the n runs used to define the basis do not contain important patterns found in the real-world observations of the spatial field, linear combinations of the SVD basis vectors will not generally be able to represent these observations. Therefore, the results of a calibration exercise are meaningless, as we converge to incorrect parameter settings, likely assigning zero posterior probability to the correct region of input space. We show that the inadequacy of the SVD basis is very common and present in every climate model field we looked at. We develop a method for combining important patterns from the observations with signal from the model runs, developing a calibration-optimal rotation of the SVD basis that allows a search of the output space for fields consistent with the observations. We illustrate this method by performing two iterations of history matching on a climate model, CanAM4. We develop a method for beginning to assess model discrepancy for climate models, where modellers would first like to see whether the model can achieve certain accuracy, before allowing specific model structural errors to be accounted for. We show that calibrating using the basis coefficients often leads to poor results, with fields consistent with the observations ruled out in history matching. We develop a method for adjusting for basis projection when history matching, so that an efficient and more accurate implausibility bound can be derived that is consistent with history matching using the computationally prohibitive spatial field.

The objective of the thesis is utilizing body sensing technology to develop a more intuitive and convenient way to control robots. The idea is to build a body sensing control system based on Kinect framework. Through Kinect, users from different age groups can achieve the desired purposes through motion demonstration without complicated programming. The system can accurately calculate angle change from users¡¦ gestures in a motion and identify key-postures which can compose an emulation motion similar to the presenting one. In other words, from analyzing these key postures, the demonstrated behaviors are able to be represented internally. Therefore, the system, consisting of a kinematics computational module and a representation algorithm, not only provides the function of behavior emulation but also behavior representation. By representation algorithm, the system extracts the features of combined behaviors. Besides, with the modular programming methodology, different behaviors can be reorganized to generate new behaviors based on the set of key poses represented by the extracted features. The application implemented in this system is within the OpenNI and NITE environment. OpenNI is used to retrieve information that the Kinect captured. NITE is used to track the user skeleton. The system is demonstrated by a play of ¡§Tai-Ji-Advancer¡¨ and at http://www.youtube.com/watch?v=cSYS49JKVAA.

Majority of the modern day compute intensive applications are heterogeneous in nature. To support their ever increasing computational requirements, present day System-on-Chip (SoC) architectures have adapted multicore style of modeling, thereby incorporating multiple, heterogeneous processing cores on a single chip. The emerging Network-On-Chip (NoC) interconnect paradigm provides a scalable and power-efficient solution for communication among multiple cores, serving as a powerful replacement for traditional bus based architectures. A fast, robust and exible emulation platform is the key to successful realization and validation of such architectures within a very short span of time. This research focuses on various aspects of Hardware/Software (HW/SW) codesign for AcENoCs (Accelerated Emulation Platform for NoCs), a Field Programmable Gate Array (FPGA) accelerated, con gurable, cycle accurate platform for emulation and validation of NoC architectures. This work also details the design, implementation and evaluation of AcENoCs' software framework along with the various design optimizations carried out and tradeoffs considered in AcENoCs' HW/SW codesign for achieving an optimum balance between emulated network dimensions and emulation performance. AcENoCs emulation platform is realized on a Xilinx Virtex-5 FPGA. AcENoCs' hardware framework consists of the NoC built using configurable hardware library components, while the software framework consists of Traffic Generators (TGs) and their associated source queues, Traffic Receptors (TRs) along with statistics analysis module and dynamically controlled emulation clock generator. The software framework is implemented using on-chip Xilinx MicroBlaze processor. This report also describes the interaction between various HW/SW events in an emulation cycle and assesses AcENoCs' performance speedup and tradeoffs over existing FPGA emulators and software simulators. FPGA synthesis results showed that networks with dimensions upto 5x5 could be accommodated inside the device. Varying synthetic traffic workloads, generated by TGs, were used to evaluate the network. Real application based traces were also run on AcENoCs platform to evaluate the performance improvement achieved in comparison to software simulators. For improving the emulator performance, software profiling was carried out to identify and optimize the software components consuming highest number of processor cycles in an emulation cycle. Emulation testcases were run and latency values recorded for varying traffic patterns in order to evaluate AcENoCs platform. Experimental results showed emulation speedups in order of 10000-12000X over HDL (Hardware Description Language) simulators and 14-47X over software simulators, without sacri cing cycle accuracy.

Wireless research and development requires effective and efficient simulation and emulation tools to validate and evaluate wireless designs. Wireless channel models are used in the tools to simulate signal propagation properties in the real physical world. However, due to practical issues, these models are often too generalized and simplified in large scale experiments, and they only provide limited realism. In this thesis, a novel world model is proposed for simulation and emulation of wireless networks. The proposed model includes the design and implementation of a variety of environment models that enhance realism in simulation. These models capture realistic signal propagation properties across multiple connections, and over time: first, the impact of realistic physical world features, such as channel dynamics and cross link correlation are characterized at different time scales; then, both geometrical and statistical simulation models are developed to recreate desired channel dynamics among wireless network links efficiently. Three major components of the proposed design are described in this thesis: 1) a flexible channel simulation model, 2) improvement of parameter accuracy in geometric channel models, and 3) wireless link correlation models with a case study in vehicular networks. The flexible channel simulation model supports fast generation of channel updates for complicated channel models, including small-scale fast fading, large-scale path loss and multi-path delay and attenuation. To achieve high realism, a variety of techniques are developed to obtain high parameter accuracy in geographic channel models. Link correlation models are developed for simulating wireless channels within a network context, where adjacent wireless links share the same propagation medium. The wireless link correlation model handles both temporal and spatial correlations, to reflect properties at different time scales and location-based similarities. A case study in vehicular networks illustrates the effectiveness of using the proposed environment model to improve the realism of wireless simulation and emulation platforms. Simulation results from implemented models are compared against the measurement data from physical world vehicle-to-vehicle channels, and show good approximation to reality. The evaluation results of correlated channel models show improved realism in channel properties and corresponding impact on the performance of a gossip protocol.

Stuttgart, Univ., Diplomarbeit, 2007.

This masters thesis deals with updating the Linux emulation layer (so called Linuxulator). The task was to update the layer to match the functionality of Linux 2.6. As a reference implementation, the Linux 2.6.16 kernel was chosen. The concept is loosely based on the NetBSD implementation. Most of the work was done in the summer of 2006 as a part of the Google Summer of Code students program. The focus was on bringing the NPTL (new posix thread library) support into the emulation layer, including TLS (thread local storage), futexes (fast user space mutexes), PID mangling, and some other minor things. Many small problems were identified and fixed in the process. My work was integrated into the main FreeBSD source repository and will be shipped in the upcoming 7.0R release. We, the emulation development team, are working toward making the Linux 2.6 emulation the default emulation layer in FreeBSD.

No description available.

No description available.

With the continuing growth of the Internet and network protocols, there is a need for Protocol Development Environments. Simulation environments like ns and OPNET require protocol code to be rewritten in a discrete event model. Direct Code Execution Environments (DCEE) solve the Verification and Validation problems by supporting the execution of unmodified protocol code in a controlled environment. Open Network Emulator (ONE) is a system supporting Direct Code Execution in a parallel environment - allowing unmodified protocol code to run on top of a parallel simulation layer, capable of simulating complex network topologies. Traditional approaches to the problem of Parallel Discrete Event Simulation (PDES) broadly fall into two categories. Conservative approaches allow processing of events only after it has been asserted that the event handling would not result in a causality error. Optimistic approaches allow for causality errors and support means of restoring state — i.e., rollback. All standard approaches to the problem of PDES are either flawed by their assumption of existing event patterns in the system or cannot be applied to ONE due to their restricted analysis on simplified models like queues and Petri-nets. The Adaptive Time Window algorithm is a bounded optimistic parallel simulation algorithm with the capability to change the degree of optimism with changes in the degree of causality in the network. The optimism at any instant is bounded by the amount of virtual time called the time window. The algorithm assumes efficient rollback capabilities supported by the â Weaves' framework. The algorithm is reactive and responds to changes in the degree of causality in the system by adjusting the length of its time window. With sufficient history gathered the algorithm adjusts to the increasing causality in the system with a small time window (conservative approach) and increases to a higher value (optimistic approach) during idle periods. The problem of splitting the entire simulation run into time windows of arbitrary length, whereby the total number of rollbacks in the system is minimal, is NP-complete. The Adaptive Time Window algorithm is compared against offline greedy approaches to the NP-complete problem called Oracle Computations. The total number of rollbacks in the system and the total execution time for the Adaptive Time Window algorithm were comparable to the ones for Oracle Computations. / Master of Science