The arrival of smart phones has created the new era in communication between users and internet. Smart phone users are able to run their own application along with enterprise applications. In case of personal application, most of them are downloaded from public market, resulting in challenge for the security frame work by threat of losing sensitive user data. So, ARM introduces the virtualization technique in hardware level to prevent the application process completely isolated from the normal world. However, understanding ARM architecture and internal working is still black box for the user as well as developers. So, in this thesis, by using the qualitative approach like examine the pre research work in open source and ARM trust zone, white paper, internal knowledge from Sony security team, we take a deep look at the architecture of the ARM trust zone in hardware level to analyze and evaluate their implementation. We describe the design and implementation of trust zone features in android emulator with advantages and disadvantages of it in analysis and result phase and conclude with annotation of suitable design on future use to enhance the security domain for secure processing and utility in Android emulator to benefit the user and developer community. The contribution of this thesis project can be summarized as following: 1) reviewing current practices and theories on implementation of ARM Trust zone; 2) creating a common methodology for handle the research problem; 3) proposing step-by-step approaches by comparing actual working of Trust zone in hardware level with design and idea of emulated one; 4) Analysis and design the appropriate model to solve the research question.

As the mobile communication world expands in size and complexity, the need for testing the solutions in a controlled environment before deployment is as pronounced as ever. Simultaneously, new tools and products are developed to optimize the process and reduce costs. One tool that allows for testing the system under varying network conditions during development is a network emulator, which lies in between a classic network simulator and real-world network testing, providing high flexibility and repeatability in experiments and is expected to behave closer to a real-world network than a network simulator would. One established network emulator is the Itrinegy Network Emulator (INE). This paid product comes with a physical device and a software GUI and has been on the market for almost two decades. On the other end of the network emulation spectrum, the Znail Network Emulator is found: an open-source software-based network emulator designed to run on a single-board computer (e.g., a Raspberry Pi), which has been developed in the last five years in a nonenterprise-manner. In this thesis, these two network emulators are evaluated and compared against each other, aiming to investigate what functionality they offer and evaluate their emulation accuracy level. To do this, a testbed is set up to perform experiments by sending network traffic from a client to a server. The network emulator is placed between the hosts, acting as a bridge and applying different network conditions to the passing network traffic. The functionality comparison demonstrates that Znail offers minimal configuration options compared to the INE: only the most basic network conditions can be achieved with Znail. The results from the testbed experiments show that both emulators perform well when only one network parameter is applied at a time. However, when using more complex configurations with combinations of multiple network parameters, INE is overall superior in accuracy and stability, and Znail is deficient in several sectors. Although, the results also show that INE has some frailties in achieving correct emulation accuracy when combined with low bandwidth levels. Through the emulator functionality comparison and accuracy evaluation, this thesis argues that Znail can be a good choice of emulator in smaller establishments and for the at-home developer, while the INE is a better choice for larger organizations where stability and reliability are crucial.

Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model non-stationary dynamics. Recent applications in machine learning favor approximate, optimization-based inference for fast predictions, but applications to computer surrogate modeling - with an eye towards downstream tasks like Bayesian optimization and reliability analysis - demand broader uncertainty quantification (UQ). I prioritize UQ through full posterior integration in a Bayesian scheme, hinging on elliptical slice sampling of latent layers. I demonstrate how my DGP's non-stationary flexibility, combined with appropriate UQ, allows for active learning: a virtuous cycle of data acquisition and model updating that departs from traditional space-filling designs and yields more accurate surrogates for fixed simulation effort. I propose new sequential design schemes that rely on optimization of acquisition criteria through evaluation of strategically allocated candidates instead of numerical optimizations, with a motivating application to contour location in an aeronautics simulation. Alternatively, when simulation runs are cheap and readily available, large datasets present a challenge for full DGP posterior integration due to cubic scaling bottlenecks. For this case I introduce the Vecchia approximation, popular for ordinary GPs in spatial data settings. I show that Vecchia-induced sparsity of Cholesky factors allows for linear computational scaling without compromising DGP accuracy or UQ. I vet both active learning and Vecchia-approximated DGPs on numerous illustrative examples and real computer experiments. I provide open-source implementations in the "deepgp" package for R on CRAN. / Doctor of Philosophy / Scientific research hinges on experimentation, yet direct experimentation is often impossible or infeasible (practically, financially, or ethically). For example, engineers designing satellites are interested in how the shape of the satellite affects its movement in space. They cannot create whole suites of differently shaped satellites, send them into orbit, and observe how they move. Instead they rely on carefully developed computer simulations. The complexity of such computer simulations necessitates a statistical model, termed a "surrogate", that is able to generate predictions in place of actual evaluations of the simulator (which may take days or weeks to run). Gaussian processes (GPs) are a common statistical modeling choice because they provide nonlinear predictions with thorough estimates of uncertainty, but they are limited in their flexibility. Deep Gaussian processes (DGPs) offer a more flexible alternative while still reaping the benefits of traditional GPs. I provide an implementation of DGP surrogates that prioritizes prediction accuracy and estimates of uncertainty. For computer simulations that are very costly to run, I provide a method of sequentially selecting input configurations to maximize learning from a fixed budget of simulator evaluations. I propose novel methods for selecting input configurations when the goal is to optimize the response or identify regions that correspond to system "failures". When abundant simulation evaluations are available, I provide an approximation which allows for faster DGP model fitting without compromising predictive power. I thoroughly vet my methods on both synthetic "toy" datasets and real aeronautic computer experiments.

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<p>Our interest is the risk assessment of rare natural hazards, such as</p><p>large volcanic pyroclastic flows. Since catastrophic consequences of</p><p>volcanic flows are rare events, our analysis benefits from the use of</p><p>a computer model to provide information about these events under</p><p>natural conditions that may not have been observed in reality.</p><p>A common problem in the analysis of computer experiments, however, is the high computational cost associated with each simulation of a complex physical process. We tackle this problem by using a statistical approximation (emulator) to predict the output of this computer model at untried values of inputs. Gaussian process response surface is a technique commonly used in these applications, because it is fast and easy to use in the analysis.</p><p>We explore several aspects of the implementation of Gaussian process emulators in a Bayesian context. First, we propose an improvement for the implementation of the plug-in approach to Gaussian processes. Next, we also evaluate the performance of a spatial model for large data sets in the context of computer experiments.</p><p>Computer model data can also be combined to field observations in order to calibrate the emulator and obtain statistical approximations to the computer model that are closer to reality. We present an application where we learn the joint distribution of inputs from field data and then bind this auxiliary information to the emulator in a calibration process.</p><p>One of the outputs of our computer model is a surface of maximum volcanic flow height over some geographical area. We show how the topography of the volcano area plays an important role in determining the shape of this surface, and we propose methods</p><p>to incorporate geophysical information in the multivariate analysis of computer model output.</p> / Dissertation

Embedded in circuit emulator (EICE) is the most common and widely used debugging techniques for microprocessors. Because the ICE is capable to provide diverse debugging and testing mechanisms, such as: single-step debugging, breakpoints setting and detection, monitoring, and modification of internal resources. However, the shortcoming of the conventional embedded in circuit emulator (EICE) is that the operation of the processor has to be suspended during debugging, which is categorized as static debugging (Static Debug) and is infeasible for real-time debugging. Therefore, this paper proposes a design alternative to support the real-time system debugging without suspending the microprocessor via the debug hardware Coprocessor14 (the Debug Coprocessor). In this paper, the embedded in circuit emulator is combined with Coprocessor 14 to provide both the static debugging and Run-time system debugging. After incorporating CP14 with the debugging mechanism, the control of the debug hardware is no longer limited to use the IEEE 1149.1 test port during debugging. On the other hand, the set of debugging constraints and the observation of the internal state of the microprocessor can be achieved by inserting the Coprocessor instruction at the program level.

This thesis presents a neuron emulator and headstage circuit for patch clamp setups and provides simulation, measurement and verification results. The circuit implemented on a printed circuit board (PCB) is battery powered and portable. The emulator provides both passive (resting potential) and active (action potential) electrical properties of a live neuron as seen from a single electrode by using the headstage circuit. It can be used to test electrophysiological equipment such as current-clamp, voltage-clamp or patch-clamp amplifiers. The action potentials (APs) are generated with a voltage-dependent frequency controlled by a microcontroller implementing a firing range from -60 mV to -30 mV and firing frequency from 1 Hz to10 Hz. The charge released by firing the neuron is initially stored on a 110 pC capacitor. Compared to directly using a current or voltage source, this design results in a more realistic simulation of the APs generated by ionic currents in a live neuron. The measured results from a prototype demonstrate that the neuron emulator meets the design specifications and it is capable of performing voltage clamp and rate responsive current clamp functionality. Measured results using a commercial clamp amplifier are provided to confirm the emulator operation in a practical recording environment.

This thesis is composed of three independent parts, which are respectively focused on three different applications. 1. A Circuit Design of Fast Bipolar Inner Product Processor for Neural Associative Memory Networks¡G A novel and high-speed realization of the bipolar-valued inner product processor for associative memory networks is presented. The proposed design is verified to speed up the inner product computation compared with prior works. 2. An Area-Saving 8-bit A/D Converter Using A Binary Search Scheme¡G A fast and area-saving analog-to-digital converter using DFFs and a digital-to-analog converter is proposed. This design provides a reasonably fast solution for the embedded ADC with the area penalty growing linearly with the data length. 3. A Smart Battery Monitor Emulator System¡G An efficient smart battery monitor emulator system is designed by using the bq2018 IC of Benchmarq company. This system is aimed to improve the battery monitoring efficiency such that the exact remaining power and time of the battery can be estimated.

Die PDAs von Palm Inc. und seinen Lizenznehmern werden gerne zur Adress- und Terminverwaltung eingesetzt. Damit ist ihr Leistungspotential jedoch nicht erschöpft. Wer gerne selbst Applikationen für Palm PDAs entwickeln möchte, ist dabei nicht zwingend auf eine windowsbasierte Entwicklungsumgebung angewiesen. Unter Linux gibt es Compiler, Ressourceeditoren und Emulatoren für PalmOS. Ich werde in meinem Vortrag diese Werkzeuge vorstellen, demonstrieren und ein Beispielprogramm zeigen.

The concept of cable television, originally called community antenna television (CATV), began in the 1940's. The information and services provided by cable operators have changed drastically since the early days. Cable service providers are no longer simply providing their customers with broadcast television but are providing a multi-purpose, two-way link to the digital world. Custom programming, telephone service, radio, and high-speed internet access are just a few of the services offered by cable service providers in the 21st century. At the dawn of the internet the dominant mode of access was through telephone lines. Despite advances in dial-up modem technology, the telephone system was unable to keep pace with the demand for data throughput. In the late 1990's an industry consortium known as Cable Television Laboratories, Inc. developed a standard protocol for providing high-speed internet access through the existing CATV infrastructure. This protocol is known as Data Over Cable Service Interface Specification (DOCSIS) and it helped to usher in the era of the information superhighway. CATV systems use different parts of the radio frequency (RF) spectrum for communication to and from the user. The downstream portion (data destined for the user) consumes the bulk of the spectrum and is located at relatively high frequencies. The upstream portion (data destined to the network from the user) of the spectrum is smaller and located at the low end of the spectrum. This lower frequency region of the RF spectrum is particularly prone to impairments such as micro-reflections, which can be viewed as a type of multipath interference. Upstream data transfer in the presence of these impairments is therefore problematic and requires complex signal correction algorithms to be employed in the receiver. The quality of a receiver is largely determined by how well it mitigates the signal impairments introduced by the channel. For this reason, engineers developing a receiver require a piece of equipment that can emulate the channel impairments in any permutation in order to test their receiver. The conventional test methodology uses a hardware RF channel emulator connected between the transmitter and the receiver under test. This method not only requires an expensive RF channel emulator, but a functioning analog front-end as well. Of these two problems, the expense of the hardware emulator is likely less important than the delay in development caused by waiting for a functional analog front-end. Receiver design is an iterative, time consuming process that requires the receiver's digital signal processing (DSP) algorithms be tested as early as possible to reduce the time-to-market. This thesis presents a digital implementation of a DOCSIS-compliant channel emulator whereby cable micro-reflections and thermal noise at the analog front-end of the receiver are modelled digitally at baseband. The channel emulator and the modulator are integrated into a single hardware structure to produce a compact circuit that, during receiver testing, resides inside the same field programmable gate array (FPGA) as the receiver. This approach removes the dependence on the analog front-end allowing it to be developed concurrently with the receiver's DSP circuits, thus reducing the time-to-market. The approach taken in this thesis produces a fully programmable channel emulator that can be loaded onto FPGAs as needed by engineers working independently on different receiver designs. The channel emulator uses 3 independent data streams to produce a 3-channel signal, whereby a main channel with micro-reflections is flanked on either side by adjacent channels. Thermal noise normally generated by the receiver's analog front-end is emulated and injected into the signal. The resulting structure utilizes 43 dedicated multipliers and 401.125 KB of RAM, and achieves a modulation error ratio (MER) of 55.29 dB.