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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A Graphical Approach to Testing Real-Time Embedded Devices

Day, Steven M 01 June 2009 (has links)
Software Testing is both a vital and expensive part of the software development lifecycle. Improving the testing process has the potential for large returns. Current testing methodologies used to test real-time embedded devices are examined and the weaknesses in them are exposed. This leads to the introduction of a new graphical testing methodology based on flowcharts. The new approach is both a visual test creation program and an automated execution engine that together frame a new way of testing. The new methodology incorporates flow-based diagrams, visual layouts, and simple execution rules to improve upon traditional testing approaches. The new methodology is evaluated against other methodologies and is shown to provide significant improvements in the area of software testing.
2

A formal approach to hardware analysis

Traub, Niklas Gerard January 1986 (has links)
No description available.
3

Hardware-Efficient Scalable Reinforcement Learning Systems

Liu, Zhenzhen 01 December 2007 (has links)
Reinforcement Learning (RL) is a machine learning discipline in which an agent learns by interacting with its environment. In this paradigm, the agent is required to perceive its state and take actions accordingly. Upon taking each action, a numerical reward is provided by the environment. The goal of the agent is thus to maximize the aggregate rewards it receives over time. Over the past two decades, a large variety of algorithms have been proposed to select actions in order to explore the environment and gradually construct an e¤ective strategy that maximizes the rewards. These RL techniques have been successfully applied to numerous real-world, complex applications including board games and motor control tasks. Almost all RL algorithms involve the estimation of a value function, which indicates how good it is for the agent to be in a given state, in terms of the total expected reward in the long run. Alternatively, the value function may re‡ect on the impact of taking a particular action at a given state. The most fundamental approach for constructing such a value function consists of updating a table that contains a value for each state (or each state-action pair). However, this approach is impractical for large scale problems, in which the state and/or action spaces are large. In order to deal with such problems, it is necessary to exploit the generalization capabilities of non-linear function approximators, such as arti…cial neural networks. This dissertation focuses on practical methodologies for solving reinforcement learning problems with large state and/or action spaces. In particular, the work addresses scenarios in which an agent does not have full knowledge of its state, but rather receives partial information about its environment via sensory-based observations. In order to address such intricate problems, novel solutions for both tabular and function-approximation based RL frameworks are proposed. A resource-efficient recurrent neural network algorithm is presented, which exploits adaptive step-size techniques to improve learning characteristics. Moreover, a consolidated actor-critic network is introduced, which omits the modeling redundancy found in typical actor-critic systems. Pivotal concerns are the scalability and speed of the learning algorithms, for which we devise architectures that map efficiently to hardware. As a result, a high degree of parallelism can be achieved. Simulation results that correspond to relevant testbench problems clearly demonstrate the solid performance attributes of the proposed solutions.
4

A design methodology for self-timed VLSI systems

Al-Helwani, A. M. January 1985 (has links)
No description available.
5

A Power System-Based IoT Network for Remote Sensing Applications

Gaiero, Dominic 01 June 2021 (has links) (PDF)
Cities around the world are facing increasingly significant challenges, including rapid urbanization, resource management, and environmental threats. In California for example, wildfires present an ever-growing threat that gravely harms people, destroys communities, and causes billions of dollars in damages. The task of addressing these environmental threats and many other challenges is greatly aided with widespread data collection and real-time inference. However, as IoT networks scale and require more energy for near-data analytics, the IoT endpoints require more power and complexity, limiting their deployment. Additionally, deploying endpoints in remote locations creates further challenges with higher reliability and communication constraints. In this thesis, we propose an approach for building scalable and reliable near-data analytics systems by leveraging existing power systems. The insight for this approach is that power transmission and distribution systems provide 1) an elevated vantage ideal for sensing, 2) wide coverage of remote and urban areas, 3) cost effective power supply via energy harvesting, and 4) the ability to use existing power infrastructures to further improve application accuracy. We describe an implementation of our approach using power system-based sensor and gateway nodes, and their integration with cloud processing resources. We evaluate the cost, power, and communication of this approach in the context of a remote wildfire sensing application, and demonstrate that this approach provides improved accuracy and scalability with significantly lower costs as compared to conventional approaches.
6

GPU-based Implementation of the Variational Path Integral Method

Mudhasani, Shanthan 01 May 2011 (has links)
Any system in the world constitutes particles like electrons. To analyze the behaviors of these systems the behavior of these particles must be predicted. The ground state energy of a molecule is the most important information about a molecule and can calculate by solving the Schrodinger equation. But as the number of atoms increase, the number of variable (coordinates of the atom) that the equation represent increases by three times. Due to the large state space and the nonlinear nature of the Schrodinger equation, it is very difficult to solver this equation. Quantum Monte Carlo (QMC) is a very efficient method to solve the Schrodinger equation for accurate results. This methods uses random numbers to sample the complex equation and get very accurate results. Due to the large data involved in this method, it exhibits rich amount of data parallelism. Variational path integral (VPI) simulations are a class of QMC methods that permit direct computation of expectation values of coordinate-space observables for the nodeless ground states of many-body quantum systems. High degree of data parallelism involved in this method facilitates the use of Graphical Processing Units (GPUs), a powerful type of processor well known to computer gamers. In comparison to the other parallel systems, like CPU clusters, GPU hardware can be much faster and is significantly cheaper. The goal of this thesis is to implement the VPI simulation algorithm on GPU to compute the coordinate-space observables of a Neon cluster.
7

Low power design of a 916 MHz Gilbert Cell Mixer and a Class-A Power Amplifier for Bioluminescent Bioreporter Integrated Circuit Transmitter

Kilambi, Supriya 01 May 2011 (has links)
This thesis presents the low power design of a 916MHz Gilbert cell mixer and a Class-A power amplifier for the Bioluminescent Bioreporter Integrated Circuit (BBIC) transmitter. There has been increased use in the man-made sensors which can operate in environments unsuitable for humans and at locations remote from the observer. One such sensor is the bioluminescent bioreporter integrated circuit (BBIC). Bioluminescent bioreporters are the bacteria that are genetically engineered in order to achieve bioluminescence when in contact with the target substance. The BBIC has bioreporters placed on a single CMOS integrated circuit (IC) that detects the bioluminescence, performs the signal processing and finally transmits the senor data. The wireless transmission allows for remote sensing by eliminating the need of costly cabling to communicate with the sensor. The wireless data transmission is performed by the transmitter system. The digital data stream generated by the signal processing circuitry of the BBIC is ASK modulated for transmission. The direct conversion transmitter used in this design includes a PLL, Mixer and a Power amplifier. The PLL is used to generate a 916MHz frequency signal. This signal is mixed with the digital data signal generated from the signal processing circuitry of the BBIC. A double balanced Gilbert cell is used to perform the mixing operation. The mixer output is applied to a power amplifier which provides amplification of the RF output power. The Gilbert cell mixer and the power amplifier have been implemented in 90nm CMOS process available through MOSIS.
8

PVT Compensation for Single-Slope Measurement Systems

Tham, Kevin Vun Kiat 01 May 2011 (has links)
A pulse-width locked loop (PWLL) circuit is reported that compensates for process, voltage, and temperature (PVT) variations of a linear ramp generator within a 12-bit multi-channel Wilkinson (single-slope integrating) Analog-to-Digital (ADC). This PWLL was designed and fabricated in a 0.5-um Silicon Germanium (SiGe) BiCMOS process. The PWLL architecture that is comprised of a phase detector, a charge-pump, and a pulse width modulator (PWM), is discussed along with the design details of the primary blocks. Simulation and silicon measurement data are shown that demonstrate a large improvement in the accuracy of the PVT-compensated ADC over the uncompensated ADC.
9

Asic Design of RF Energy Harvester Using 0.13UM CMOS Technology

Zaveri, Jainish K 01 August 2018 (has links) (PDF)
Recent advances in wireless sensor nodes, data acquisition devices, wearable and implantable medical devices have paved way for low power (sub 50uW) devices. These devices generally use small solid state or thin film batteries for power supply which need replacement or need to be removed for charging. RF energy harvesting technology can be used to charge these batteries without the need to remove the battery from the device, thus providing a sustainable power supply. In other cases, a battery can become unnecessary altogether. This enables us to deploy wireless network nodes in places where regular physical access to the nodes is difficult or cumbersome. This thesis proposes a design of an RF energy harvesting device able to charge commercially available thin film or solid-state batteries. The energy harvesting amplifier circuit is designed in Global Foundry 0.13um CMOS technology using Cadence integrated circuit design tools. This Application Specific Integrated Circuit (ASIC) is intended to have as small a footprint as possible so that it can be easily integrated with the above-mentioned devices. While a dedicated RF power source is a direct solution to provide sustainable power to the harvesting circuit, harvesting ambient RF power from TV and UHF cellular frequencies increases the possibilities of where the harvesting device can be placed. The biggest challenge for RF energy harvesting technology is the availability of adequate amount of RF power. This thesis also presents a survey of available RF power at various ultra-high frequencies in San Luis Obispo, CA.The idea is to determine the frequency band which can provide maximum RF power for harvesting and design a harvester for that frequency band.
10

Smart DC Wall Outlet Design with Improved Load Voltage Detection

Granieri, Patrick Donovon 01 June 2019 (has links)
A standard home in the United States has access to the 120V AC power grid for use with home appliances. Many electronics used at home are powered by a DC power supply, which loses energy in the conversion from AC power. The DC House project avoids any conversion between AC and DC by storing energy in batteries as DC power and supplying it directly to DC appliances. While AC systems feature a standardized output voltage, no such standard exists for DC systems. The Smart DC Wall Outlet solves this by automatically adjusting its output voltage to meet any required DC load voltage. A hardware solution was developed using a microcontroller in tandem with a DC to DC Buck converter to monitor trends in the output current and set the output voltage accordingly. The Smart DC Wall Outlet features two 100W output channels that were able to correctly identify the required output voltage of five out of seven test devices. Results indicate that it is possible to generalize the turn on characteristics of DC devices, but that other solutions may find more success.

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