504 research outputs found

    Numerical simulation of a crashworthy rotorcraft substructure including hard and soft surface impacts

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    A design study was performed using Finite Element Analysis. The objective of the study was to demonstrate the feasibility of developing a crashworthy substructure to protect rotorcraft drew in the event of vertical impacts involving landings on both hard surfaces, for example concrete, and water or soft surfaces. Differences in the impacted surfaces behavior can change the mechanisms of loading and render hard surface energy absorption mechanisms ineffective in a water impact. The objective of this thesis was to investigate the possibility of employing a skin to transfer loads from the water to a conventional hard surface energy absorbing structure. Finite element models were developed using the LS-INGRID preprocessor and analyzed with the LS-Dyna commercial FEA code. Use of the LS-Dyna code for simulating water impacts was validated by development of a model and comparison with experimental data in the literature. Models were developed which simulated rotorcraft substructures composed of both composites and aluminum alloys. Rotorcraft model parameters were selected based upon the Bell Helicopter model 412EP, but the model was only intended to be similar to the model 412EP, not an exact copy. Crashworthiness was successfully demonstrated for a 26 feet per second vertical impact onto both hard surfaces and water with both the aluminum alloy and composite structures

    EARLY PERFORMANCE PREDICTION METHODOLOGY FOR MANY-CORES ON CHIP BASED APPLICATIONS

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    Modern high performance computing applications such as personal computing, gaming, numerical simulations require application-specific integrated circuits (ASICs) that comprises of many cores. Performance for these applications depends mainly on latency of interconnects which transfer data between cores that implement applications by distributing tasks. Time-to-market is a critical consideration while designing ASICs for these applications. Therefore, to reduce design cycle time, predicting system performance accurately at an early stage of design is essential. With process technology in nanometer era, physical phenomena such as crosstalk, reflection on the propagating signal have a direct impact on performance. Incorporating these effects provides a better performance estimate at an early stage. This work presents a methodology for better performance prediction at an early stage of design, achieved by mapping system specification to a circuit-level netlist description. At system-level, to simplify description and for efficient simulation, SystemVerilog descriptions are employed. For modeling system performance at this abstraction, queueing theory based bounded queue models are applied. At the circuit level, behavioral Input/Output Buffer Information Specification (IBIS) models can be used for analyzing effects of these physical phenomena on on-chip signal integrity and hence performance. For behavioral circuit-level performance simulation with IBIS models, a netlist must be described consisting of interacting cores and a communication link. Two new netlists, IBIS-ISS and IBIS-AMI-ISS are introduced for this purpose. The cores are represented by a macromodel automatically generated by a developed tool from IBIS models. The generated IBIS models are employed in the new netlists. Early performance prediction methodology maps a system specification to an instance of these netlists to provide a better performance estimate at an early stage of design. The methodology is scalable in nanometer process technology and can be reused in different designs

    IST Austria Thesis

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    The hippocampus is a key brain region for spatial memory and navigation and is needed at all stages of memory, including encoding, consolidation, and recall. Hippocampal place cells selectively discharge at specific locations of the environment to form a cognitive map of the space. During the rest period and sleep following spatial navigation and/or learning, the waking activity of the place cells is reactivated within high synchrony events. This reactivation is thought to be important for memory consolidation and stabilization of the spatial representations. The aim of my thesis was to directly test whether the reactivation content encoded in firing patterns of place cells is important for consolidation of spatial memories. In particular, I aimed to test whether, in cases when multiple spatial memory traces are acquired during learning, the specific disruption of the reactivation of a subset of these memories leads to the selective disruption of the corresponding memory traces or through memory interference the other learned memories are disrupted as well. In this thesis, using a modified cheeseboard paradigm and a closed-loop recording setup with feedback optogenetic stimulation, I examined how the disruption of the reactivation of specific spiking patterns affects consolidation of the corresponding memory traces. To obtain multiple distinctive memories, animals had to perform a spatial task in two distinct cheeseboard environments and the reactivation of spiking patterns associated with one of the environments (target) was disrupted after learning during four hours rest period using a real-time decoding method. This real-time decoding method was capable of selectively affecting the firing rates and cofiring correlations of the target environment-encoding cells. The selective disruption led to behavioural impairment in the memory tests after the rest periods in the target environment but not in the other undisrupted control environment. In addition, the map of the target environment was less stable in the impaired memory tests compared to the learning session before than the map of the control environment. However, when the animal relearned the task, the same map recurred in the target environment that was present during learning before the disruption. Altogether my work demonstrated that the reactivation content is important: assembly-related disruption of reactivation can lead to a selective memory impairment and deficiency in map stability. These findings indeed suggest that reactivated assembly patterns reflect processes associated with the consolidation of memory traces

    Reverse engineering of drug induced DNA damage response signalling pathway reveals dual outcomes of ATM kinase inhibition

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    The DNA Damage Response (DDR) pathway represents a signalling mechanism that is activated in eukaryotic cells following DNA damage and comprises of proteins involved in DNA damage detection, DNA repair, cell cycle arrest and apoptosis. This pathway consists of an intricate network of signalling interactions driving the cellular ability to recognise DNA damage and recruit specialised proteins to take decisions between DNA repair or apoptosis. ATM and ATR are central components of the DDR pathway. The activities of these kinases are vital in DNA damage induced phosphorylational induction of DDR substrates. Here, firstly we have experimentally determined DDR signalling network surrounding the ATM/ATR pathway induced following double stranded DNA damage by monitoring and quantifying time dependent inductions of their phosphorylated forms and their key substrates. We next involved an automated inference of unsupervised predictive models of time series data to generate in silico (molecular) interaction maps. We characterized the complex signalling network through system analysis and gradual utilisation of small time series measurements of key substrates through a novel network inference algorithm. Furthermore, we demonstrate an application of an assumption-free reverse engineering of the intricate signalling network of the activated ATM/ATR pathway. We next studied the consequences of such drug induced inductions as well as of time dependent ATM kinase inhibition on cell survival through further biological experiments. Intermediate and temporal modelling outcomes revealed the distinct signaling profile associated with ATM kinase activity and inhibition and explained the underlying signalling mechanism for dual ATM functionality in cytotoxic and cytoprotective pathways

    Detecting and locating electronic devices using their unintended electromagnetic emissions

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    Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii

    PathEx: a novel multi factors based datasets selector web tool

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    <p>Abstract</p> <p>Background</p> <p>Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information.</p> <p>Description</p> <p>Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach.</p> <p>Conclusion</p> <p>This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.</p

    Distributed Localization of Active Transmitters in a Wireless Sensor Network

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    In today\u27s military environment, emphasis has been placed on bandwidth efficiency and total use of the available spectrum. Current communication standards divide the spectrum into several different frequency bands, all of which are assigned to one or multiple primary users. Cognitive Radio utilizes potential white spaces that exist between currently defined channels or in time. One under-explored dimension of white space exploration is spatial. If a frequency band is being used in one region, it may be underutilized, or not occupied in another. Using an active localization method can allow for the discovery of spatial white; trying to spatially map all of the frequencies in a large area would become very computationally intensive, and may even be impractical using modern centralized methods. Applying a distributed method and the concepts discussed in Wireless Distributed Computing to the problem can be scaled onto many small wireless sensors and could improve the measuring system\u27s effectiveness. For a bandwidth contested environment that must be spectrally mapped, three metrics stand out: Accuracy, Power Consumption, and Latency. All of these metrics must be explored and measured to determine which method could be most effectively applied to the spectral mapping of a spatial environment

    Visual Exploration And Information Analytics Of High-Dimensional Medical Images

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    Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography. In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses. The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders. Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end
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