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An inertial motion capture framework for constructing body sensor networksPascu, Tudor January 2015 (has links)
Motion capture is the process of measuring and subsequently reconstructing the movement of an animated object or being in virtual space. Virtual reconstructions of human motion play an important role in numerous application areas such as animation, medical science, ergonomics, etc. While optical motion capture systems are the industry standard, inertial body sensor networks are becoming viable alternatives due to portability, practicality and cost. This thesis presents an innovative inertial motion capture framework for constructing body sensor networks through software environments, smartphones and web technologies. The first component of the framework is a unique inertial motion capture software environment aimed at providing an improved experimentation environment, accompanied by programming scaffolding and a driver development kit, for users interested in studying or engineering body sensor networks. The software environment provides a bespoke 3D engine for kinematic motion visualisations and a set of tools for hardware integration. The software environment is used to develop the hardware behind a prototype motion capture suit focused on low-power consumption and hardware-centricity. Additional inertial measurement units, which are available commercially, are also integrated to demonstrate the functionality the software environment while providing the framework with additional sources for motion data. The smartphone is the most ubiquitous computing technology and its worldwide uptake has prompted many advances in wearable inertial sensing technologies. Smartphones contain gyroscopes, accelerometers and magnetometers, a combination of sensors that is commonly found in inertial measurement units. This thesis presents a mobile application that investigates whether the smartphone is capable of inertial motion capture by constructing a novel omnidirectional body sensor network. This thesis proposes a novel use for web technologies through the development of the Motion Cloud, a repository and gateway for inertial data. Web technologies have the potential to replace motion capture file formats with online repositories and to set a new standard for how motion data is stored. From a single inertial measurement unit to a more complex body sensor network, the proposed architecture is extendable and facilitates the integration of any inertial hardware configuration. The Motion Cloud's data can be accessed through an application-programming interface or through a web portal that provides users with the functionality for visualising and exporting the motion data.
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Electric Load Forecasting Using Long Short-term Memory AlgorithmYang, Tianshu 01 January 2019 (has links)
Abstract
Power system load forecasting refers to the study or uses a mathematical method to process
past and future loads systematically, taking into account important system operating
characteristics, capacity expansion decisions, natural conditions, and social impacts, to
meet specific accuracy requirements. Dependence of this, determine the load value at a
specific moment in the future. Improving the level of load forecasting technology is
conducive to the planned power management, which is conducive to rationally arranging
the grid operation mode and unit maintenance plan, and is conducive to formulating
reasonable power supply construction plans and facilitating power improvement, and
improve the economic and social benefits of the system.
At present, there are many methods for load forecasting. The newer algorithms mainly
include the neural network method, time series method, regression analysis method,
support vector machine method, and fuzzy prediction method. However, most of them do
not apply to long-term time-series predictions, and as a result, the prediction accuracy for
long-term power grids does not perform well.
This thesis describes the design of an algorithm that is used to predict the load in a long
time-series. Predict the load is significant and necessary for a dynamic electrical network.
Improved the forecasting algorithm can save a ton of the cost of the load. In this paper, we
propose a load forecasting model using long short-term memory(LSTM). The proposed
implementation of LSTM match with the time-series dataset very well, which can improve
the accuracy of convergence of the training process. We experiment with the difference
time-step to expedites the convergence of the training process. It is found that all cases
achieve significant different forecasting accuracy while forecasting the difference timesteps.
Keywords—Load forecasting, long short-term memory, micro-grid
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Secure routing and trust computation in multihop infrastructureless networksGhosh, Tirthankar 02 June 2005 (has links)
Today's wireless networks rely mostly on infrastructural support for their operation. With the concept of ubiquitous computing growing more popular, research on infrastructureless networks have been rapidly growing. However, such types of networks face serious security challenges when deployed. This dissertation focuses on designing a secure routing solution and trust modeling for these infrastructureless networks.
The dissertation presents a trusted routing protocol that is capable of finding a secure end-to-end route in the presence of malicious nodes acting either independently or in collusion, The solution protects the network from active internal attacks, known to be the most severe types of attacks in an ad hoc application. Route discovery is based on trust levels of the nodes, which need to be dynamically computed to reflect the malicious behavior in the network. As such, we have developed a trust computational model in conjunction with the secure routing protocol that analyzes the different malicious behavior and quantifies them in the model itself. Our work is the first step towards protecting an ad hoc network from colluding internal attack. To demonstrate the feasibility of the approach, extensive simulation has been carried out to evaluate the protocol efficiency and scalability with both network size and mobility.
This research has laid the foundation for developing a variety of techniques that will permit people to justifiably trust the use of ad hoc networks to perform critical functions, as well as to process sensitive information without depending on any infrastructural support and hence will enhance the use of ad hoc applications in both military and civilian domains.
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RADIATION EFFECTS ON JUNCTION TRANSISTOR NOISE STUDIED BY A COMPUTERIZED NOISE MEASURING SYSTEMUnknown Date (has links)
Source: Dissertation Abstracts International, Volume: 32-09, Section: B, page: 5185. / Thesis (Ph.D.)--The Florida State University, 1971.
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Practical adaptive numerical integration for finite element electromagneticsWang, Honghou, 1963- January 2000 (has links)
No description available.
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Modifying LPC parameter dynamics to improve speech coder efficiencyPereira, Wesley. January 2001 (has links)
No description available.
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Testing and characterization of a parallel optical interconnect for a scalable routing systemSalzberg, Mitchell January 2002 (has links)
No description available.
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Bayesian multiple sclerosis lesion classification modeling regional and local spatial informationHarmouche, Rola. January 2006 (has links)
No description available.
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On distance measurement methods for turbo codesOuld Cheikh Mouhamedou, Youssouf. January 2005 (has links)
No description available.
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A simplified model for lightning exposure of wind turbinesLo, Pape Momar January 2009 (has links)
No description available.
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